From 4192b5fb446524f9fa695a0495cc519a15826aea Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Wed, 13 Dec 2023 21:56:39 +0100
Subject: [PATCH 01/84] added progress bar for trainer + accept texts args for
 encoder

---
 meshgpt_pytorch/meshgpt_pytorch.py |   1 +
 meshgpt_pytorch/trainer.py         | 105 ++++++++++++++++++++++++++++-
 2 files changed, 104 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/meshgpt_pytorch.py b/meshgpt_pytorch/meshgpt_pytorch.py
index f2228afa..f93847e9 100644
--- a/meshgpt_pytorch/meshgpt_pytorch.py
+++ b/meshgpt_pytorch/meshgpt_pytorch.py
@@ -724,6 +724,7 @@ def forward(
         vertices:       TensorType['b', 'nv', 3, float],
         faces:          TensorType['b', 'nf', 3, int],
         face_edges:     Optional[TensorType['b', 'e', 2, int]] = None,
+        texts: Optional[List[str]] = None,
         return_codes = False,
         return_loss_breakdown = False,
         return_recon_faces = False,
diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index ce1e36ff..415880f4 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -23,7 +23,8 @@
 from meshgpt_pytorch.data import custom_collate
 
 from meshgpt_pytorch.version import __version__
-
+import matplotlib.pyplot as plt
+from tqdm import tqdm
 from meshgpt_pytorch.meshgpt_pytorch import (
     MeshAutoencoder,
     MeshTransformer
@@ -367,7 +368,56 @@ def forward(self):
             self.wait()
 
         self.print('training complete')
+    def train(self, num_epochs, diplay_graph = False):
+        epoch_losses = []  # Initialize a list to store epoch losses
+        self.model.train() 
+        for epoch in range(num_epochs): 
+            total_loss = 0.0
+            num_batches = 0
+
+            progress_bar = tqdm(self.dataloader, desc=f'Epoch {epoch + 1}/{num_epochs}') 
+
+            for data in progress_bar: 
+
+                if isinstance(data, tuple): 
+                    forward_kwargs = dict(zip(self.data_kwargs, data))
 
+                elif isinstance(data, dict): 
+                    forward_kwargs = data 
+                
+
+                with self.accelerator.autocast():
+                    loss = self.model(**forward_kwargs)
+                    self.accelerator.backward(loss / self.grad_accum_every)
+
+                if exists(self.max_grad_norm):
+                    self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
+
+                self.optimizer.step()
+                self.optimizer.zero_grad()
+
+                if not self.accelerator.optimizer_step_was_skipped:
+                    with self.warmup.dampening():
+                        self.scheduler.step()
+ 
+                current_loss = loss.item()
+                total_loss += current_loss
+                num_batches += 1
+                progress_bar.set_postfix(loss=current_loss)
+ 
+            avg_epoch_loss = total_loss / num_batches 
+            epoch_losses.append(avg_epoch_loss)
+            self.print(f'Epoch {epoch + 1} average loss: {avg_epoch_loss}')
+
+        self.print('Training complete') 
+        if diplay_graph:
+            plt.figure(figsize=(10, 5))
+            plt.plot(range(1, num_epochs + 1), epoch_losses, marker='o')
+            plt.title('Training Loss Over Epochs')
+            plt.xlabel('Epoch')
+            plt.ylabel('Average Loss')
+            plt.grid(True)
+            plt.show()
 # mesh transformer trainer
 
 class MeshTransformerTrainer(Module):
@@ -576,4 +626,55 @@ def forward(self):
 
             self.wait()
 
-        self.print('training complete')
\ No newline at end of file
+        self.print('training complete')
+        
+    def train(self, num_epochs, diplay_graph = False):
+        epoch_losses = []  # Initialize a list to store epoch losses
+        self.model.train()
+        for epoch in range(num_epochs): 
+            total_loss = 0.0
+            num_batches = 0
+
+            progress_bar = tqdm(self.dataloader, desc=f'Epoch {epoch + 1}/{num_epochs}') 
+
+            for data in progress_bar: 
+
+                if isinstance(data, tuple): 
+                    forward_kwargs = dict(zip(self.data_kwargs, data))
+
+                elif isinstance(data, dict): 
+                    forward_kwargs = data 
+                
+
+                with self.accelerator.autocast():
+                    loss = self.model(**forward_kwargs)
+                    self.accelerator.backward(loss / self.grad_accum_every)
+
+                if exists(self.max_grad_norm):
+                    self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
+
+                self.optimizer.step()
+                self.optimizer.zero_grad()
+
+                if not self.accelerator.optimizer_step_was_skipped:
+                    with self.warmup.dampening():
+                        self.scheduler.step()
+ 
+                current_loss = loss.item()
+                total_loss += current_loss
+                num_batches += 1
+                progress_bar.set_postfix(loss=current_loss)
+ 
+            avg_epoch_loss = total_loss / num_batches 
+            epoch_losses.append(avg_epoch_loss)
+            self.print(f'Epoch {epoch + 1} average loss: {avg_epoch_loss}')
+
+        self.print('Training complete') 
+        if diplay_graph:
+            plt.figure(figsize=(10, 5))
+            plt.plot(range(1, num_epochs + 1), epoch_losses, marker='o')
+            plt.title('Training Loss Over Epochs')
+            plt.xlabel('Epoch')
+            plt.ylabel('Average Loss')
+            plt.grid(True)
+            plt.show()
\ No newline at end of file

From 0ae54e96758c599829a5c96bf142489b2e6be474 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Wed, 13 Dec 2023 22:13:40 +0100
Subject: [PATCH 02/84] added epoch checkpoint saver for trainer

---
 meshgpt_pytorch/trainer.py | 18 +++++++++++++++++-
 1 file changed, 17 insertions(+), 1 deletion(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 415880f4..f73ac57c 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -120,6 +120,7 @@ def __init__(
         accelerator_kwargs: dict = dict(),
         optimizer_kwargs: dict = dict(),
         checkpoint_every = 1000,
+        checkpoint_every_epoch: Optional[int] = None,
         checkpoint_folder = './checkpoints',
         data_kwargs: Tuple[str, ...] = ['vertices', 'faces', 'face_edges'],
         warmup_steps = 1000,
@@ -198,6 +199,7 @@ def __init__(
         self.num_train_steps = num_train_steps
         self.register_buffer('step', torch.tensor(0))
 
+        self.checkpoint_every_epoch = checkpoint_every_epoch
         self.checkpoint_every = checkpoint_every
         self.checkpoint_folder = Path(checkpoint_folder)
         self.checkpoint_folder.mkdir(exist_ok = True, parents = True)
@@ -404,10 +406,17 @@ def train(self, num_epochs, diplay_graph = False):
                 total_loss += current_loss
                 num_batches += 1
                 progress_bar.set_postfix(loss=current_loss)
+                
+            
  
             avg_epoch_loss = total_loss / num_batches 
             epoch_losses.append(avg_epoch_loss)
             self.print(f'Epoch {epoch + 1} average loss: {avg_epoch_loss}')
+            self.wait()
+
+            if self.checkpoint_every_epoch is not None and epoch % self.checkpoint_every_epoch == 0:
+                self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.{epoch}.pt')
+ 
 
         self.print('Training complete') 
         if diplay_graph:
@@ -437,7 +446,9 @@ def __init__(
         ema_kwargs: dict = dict(),
         accelerator_kwargs: dict = dict(),
         optimizer_kwargs: dict = dict(),
-        checkpoint_every = 1000,
+        
+        checkpoint_every = 1000, 
+        checkpoint_every_epoch: Optional[int] = None,
         checkpoint_folder = './checkpoints',
         data_kwargs: Tuple[str, ...] = ['vertices', 'faces', 'face_edges', 'face_len', 'face_edges_len'],
         warmup_steps = 1000,
@@ -501,6 +512,7 @@ def __init__(
         self.num_train_steps = num_train_steps
         self.register_buffer('step', torch.tensor(0))
 
+        self.checkpoint_every_epoch = checkpoint_every_epoch
         self.checkpoint_every = checkpoint_every
         self.checkpoint_folder = Path(checkpoint_folder)
         self.checkpoint_folder.mkdir(exist_ok = True, parents = True)
@@ -668,6 +680,10 @@ def train(self, num_epochs, diplay_graph = False):
             avg_epoch_loss = total_loss / num_batches 
             epoch_losses.append(avg_epoch_loss)
             self.print(f'Epoch {epoch + 1} average loss: {avg_epoch_loss}')
+            self.wait()
+
+            if self.checkpoint_every_epoch is not None and epoch % self.checkpoint_every_epoch == 0:
+                self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.{epoch}.pt')
 
         self.print('Training complete') 
         if diplay_graph:

From a09b2e8395a19799113718a0883836231044e42a Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Wed, 13 Dec 2023 22:16:28 +0100
Subject: [PATCH 03/84] setup.py forgot comma

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 91c61836..45583ff8 100644
--- a/setup.py
+++ b/setup.py
@@ -30,7 +30,7 @@
     'torch>=2.0',
     'torch_geometric',
     'torchtyping',
-    'tqdm'
+    'tqdm',
     'vector-quantize-pytorch>=1.12.0',
     'x-transformers>=1.26.0',
   ],

From 83baaeecbfce52fd6af7615cef7db061ecd42ade Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Wed, 13 Dec 2023 22:20:42 +0100
Subject: [PATCH 04/84] custom_collate can now accecpt texts

---
 meshgpt_pytorch/data.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/meshgpt_pytorch/data.py b/meshgpt_pytorch/data.py
index 8916640c..d12be703 100644
--- a/meshgpt_pytorch/data.py
+++ b/meshgpt_pytorch/data.py
@@ -5,6 +5,7 @@
 from torch.nn.utils.rnn import pad_sequence
 
 from einops import rearrange, reduce
+from torch import nn, Tensor
 
 from beartype import beartype
 from beartype.typing import Tuple, Union, Optional, Callable, Dict
@@ -105,12 +106,15 @@ def custom_collate(data, pad_id = -1):
     output = []
 
     for datum in zip(*data):
-        padded = pad_sequence(datum, batch_first = True, padding_value = pad_id)
-        output.append(padded)
+        if isinstance(datum[0], Tensor):
+            padded = pad_sequence(datum, batch_first=True, padding_value=pad_id)
+            output.append(padded)
+        else:
+            output.append(list(datum)) 
 
     output = tuple(output)
 
     if is_dict:
         output = dict(zip(keys, output))
 
-    return output
+    return output
\ No newline at end of file

From 195d14ee36a4e1cfe2b307d3d2c523f40e0c89b7 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Wed, 13 Dec 2023 22:31:51 +0100
Subject: [PATCH 05/84] bug fix - save every_epoch

---
 meshgpt_pytorch/trainer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index f73ac57c..623b9ff3 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -414,7 +414,7 @@ def train(self, num_epochs, diplay_graph = False):
             self.print(f'Epoch {epoch + 1} average loss: {avg_epoch_loss}')
             self.wait()
 
-            if self.checkpoint_every_epoch is not None and epoch % self.checkpoint_every_epoch == 0:
+            if self.checkpoint_every_epoch is not None and epoch % self.checkpoint_every_epoch+1 == 0:
                 self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.{epoch}.pt')
  
 
@@ -682,7 +682,7 @@ def train(self, num_epochs, diplay_graph = False):
             self.print(f'Epoch {epoch + 1} average loss: {avg_epoch_loss}')
             self.wait()
 
-            if self.checkpoint_every_epoch is not None and epoch % self.checkpoint_every_epoch == 0:
+            if self.checkpoint_every_epoch is not None and epoch % self.checkpoint_every_epoch +1 == 0:
                 self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.{epoch}.pt')
 
         self.print('Training complete') 

From 1d4b70509079b2d2dd154d79f86fee80e79a25f7 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Wed, 13 Dec 2023 22:32:36 +0100
Subject: [PATCH 06/84] bug fix - save every_epoch

---
 meshgpt_pytorch/trainer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 623b9ff3..7dc668a6 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -415,7 +415,7 @@ def train(self, num_epochs, diplay_graph = False):
             self.wait()
 
             if self.checkpoint_every_epoch is not None and epoch % self.checkpoint_every_epoch+1 == 0:
-                self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.{epoch}.pt')
+                self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.{epoch+1}.pt')
  
 
         self.print('Training complete') 
@@ -683,7 +683,7 @@ def train(self, num_epochs, diplay_graph = False):
             self.wait()
 
             if self.checkpoint_every_epoch is not None and epoch % self.checkpoint_every_epoch +1 == 0:
-                self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.{epoch}.pt')
+                self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.{epoch+1}.pt')
 
         self.print('Training complete') 
         if diplay_graph:

From b5d9b1ef805025d616c6a9fa97b1e04b047c2be4 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Wed, 13 Dec 2023 22:42:36 +0100
Subject: [PATCH 07/84] bug fix - save every_epoch extra info

---
 meshgpt_pytorch/trainer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 7dc668a6..4eb31da4 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -415,7 +415,7 @@ def train(self, num_epochs, diplay_graph = False):
             self.wait()
 
             if self.checkpoint_every_epoch is not None and epoch % self.checkpoint_every_epoch+1 == 0:
-                self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.{epoch+1}.pt')
+                self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.epoch_{(epoch+1)}_avg_loss_{avg_epoch_loss:.3f}.pt')
  
 
         self.print('Training complete') 
@@ -683,7 +683,7 @@ def train(self, num_epochs, diplay_graph = False):
             self.wait()
 
             if self.checkpoint_every_epoch is not None and epoch % self.checkpoint_every_epoch +1 == 0:
-                self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.{epoch+1}.pt')
+                self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.{(epoch+1)}_avg_loss_{avg_epoch_loss:.3f}.pt')
 
         self.print('Training complete') 
         if diplay_graph:

From d20e3d8da1d3723e4d0db6776db7dd37bad2bff4 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Wed, 13 Dec 2023 22:45:19 +0100
Subject: [PATCH 08/84] bug fix - every_epoch

---
 meshgpt_pytorch/trainer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 4eb31da4..e736b504 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -414,7 +414,7 @@ def train(self, num_epochs, diplay_graph = False):
             self.print(f'Epoch {epoch + 1} average loss: {avg_epoch_loss}')
             self.wait()
 
-            if self.checkpoint_every_epoch is not None and epoch % self.checkpoint_every_epoch+1 == 0:
+            if self.checkpoint_every_epoch is not None and (epoch % (self.checkpoint_every_epoch +1)) == 0:
                 self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.epoch_{(epoch+1)}_avg_loss_{avg_epoch_loss:.3f}.pt')
  
 
@@ -682,7 +682,7 @@ def train(self, num_epochs, diplay_graph = False):
             self.print(f'Epoch {epoch + 1} average loss: {avg_epoch_loss}')
             self.wait()
 
-            if self.checkpoint_every_epoch is not None and epoch % self.checkpoint_every_epoch +1 == 0:
+            if self.checkpoint_every_epoch is not None and (epoch % (self.checkpoint_every_epoch +1)) == 0:
                 self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.{(epoch+1)}_avg_loss_{avg_epoch_loss:.3f}.pt')
 
         self.print('Training complete') 

From 4085b70abed37911cd84cf9eab85e2b22a0dab86 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Wed, 13 Dec 2023 22:53:47 +0100
Subject: [PATCH 09/84] final bug fix - every_epoch

---
 meshgpt_pytorch/trainer.py | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index e736b504..dd3b55b7 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -414,8 +414,8 @@ def train(self, num_epochs, diplay_graph = False):
             self.print(f'Epoch {epoch + 1} average loss: {avg_epoch_loss}')
             self.wait()
 
-            if self.checkpoint_every_epoch is not None and (epoch % (self.checkpoint_every_epoch +1)) == 0:
-                self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.epoch_{(epoch+1)}_avg_loss_{avg_epoch_loss:.3f}.pt')
+            if self.checkpoint_every_epoch is not None and epoch != 0 and epoch % self.checkpoint_every_epoch == 0:
+                self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.3f}.pt')
  
 
         self.print('Training complete') 
@@ -680,11 +680,10 @@ def train(self, num_epochs, diplay_graph = False):
             avg_epoch_loss = total_loss / num_batches 
             epoch_losses.append(avg_epoch_loss)
             self.print(f'Epoch {epoch + 1} average loss: {avg_epoch_loss}')
-            self.wait()
-
-            if self.checkpoint_every_epoch is not None and (epoch % (self.checkpoint_every_epoch +1)) == 0:
-                self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.{(epoch+1)}_avg_loss_{avg_epoch_loss:.3f}.pt')
-
+            self.wait() 
+            if self.checkpoint_every_epoch is not None and epoch != 0 and epoch % self.checkpoint_every_epoch == 0:
+                self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.3f}.pt')
+                
         self.print('Training complete') 
         if diplay_graph:
             plt.figure(figsize=(10, 5))

From fa515a6adbde0afede2152e4594954c5185d72c8 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Thu, 14 Dec 2023 04:56:00 +0100
Subject: [PATCH 10/84] removed grad_accum_every

---
 meshgpt_pytorch/meshgpt_pytorch.py | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/meshgpt_pytorch/meshgpt_pytorch.py b/meshgpt_pytorch/meshgpt_pytorch.py
index 64b0fb6d..1df786ac 100644
--- a/meshgpt_pytorch/meshgpt_pytorch.py
+++ b/meshgpt_pytorch/meshgpt_pytorch.py
@@ -421,7 +421,7 @@ def __init__(
             )
 
         self.pad_id = pad_id # for variable lengthed faces, padding quantized ids will be set to this value
-
+        
         self.project_codebook_out = nn.Linear(dim_codebook * 3, dim)
 
         self.decoders = ModuleList([])
@@ -429,7 +429,19 @@ def __init__(
         for _ in range(decoder_depth):
             resnet_block = ResnetBlock(dim)
 
+<<<<<<< Updated upstream
+            self.decoders.append(resnet_block)
+=======
+<<<<<<< HEAD
+            self.decoders.append(ModuleList([
+                attn,
+                resnet_block,
+                nn.Dropout1d(0.1)
+            ]))
+=======
             self.decoders.append(resnet_block)
+>>>>>>> ee440683b4d266e612da90a020bc33d7f26f1f61
+>>>>>>> Stashed changes
 
         self.to_coor_logits = nn.Sequential(
             nn.Linear(dim, num_discrete_coors * 9),

From fc49064bacd3e86b41ad92b5feb20a83413aaeb4 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Thu, 14 Dec 2023 05:04:55 +0100
Subject: [PATCH 11/84] removed grad_accum_every

---
 meshgpt_pytorch/trainer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index dd3b55b7..64095290 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -390,7 +390,7 @@ def train(self, num_epochs, diplay_graph = False):
 
                 with self.accelerator.autocast():
                     loss = self.model(**forward_kwargs)
-                    self.accelerator.backward(loss / self.grad_accum_every)
+                    self.accelerator.backward(loss)
 
                 if exists(self.max_grad_norm):
                     self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
@@ -660,7 +660,7 @@ def train(self, num_epochs, diplay_graph = False):
 
                 with self.accelerator.autocast():
                     loss = self.model(**forward_kwargs)
-                    self.accelerator.backward(loss / self.grad_accum_every)
+                    self.accelerator.backward(loss)
 
                 if exists(self.max_grad_norm):
                     self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)

From 5a6877257f3dde3c5911dc81a187e67d4825c5d1 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Thu, 14 Dec 2023 06:06:35 +0100
Subject: [PATCH 12/84] fix error

---
 meshgpt_pytorch/trainer.py | 1483 ++++++++++++++++++++++++------------
 1 file changed, 993 insertions(+), 490 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 64095290..4e969b1d 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -1,43 +1,55 @@
 from pathlib import Path
 from functools import partial
-from packaging import version
-from contextlib import nullcontext, contextmanager
+from math import ceil, pi
 
 import torch
-from torch import nn, Tensor
-from torch.nn import Module
+from torch import nn, Tensor, einsum
+from torch.nn import Module, ModuleList
 import torch.nn.functional as F
-from torch.optim import AdamW, Adam
-from torch.utils.data import Dataset, DataLoader
-from torch.optim.lr_scheduler import LambdaLR, LRScheduler
-import pytorch_warmup as warmup
+from torch.cuda.amp import autocast
 
-from accelerate import Accelerator
-from accelerate.utils import DistributedDataParallelKwargs
+from torchtyping import TensorType
 
 from beartype import beartype
-from beartype.typing import Optional, Tuple, Type
+from beartype.typing import Tuple, Callable, Optional, List, Dict
 
-from ema_pytorch import EMA
+from einops import rearrange, repeat, reduce, pack, unpack
+from einops.layers.torch import Rearrange
 
-from meshgpt_pytorch.data import custom_collate
+from x_transformers import Decoder
+from x_transformers.attend import Attend
+from x_transformers.x_transformers import RMSNorm, FeedForward, LayerIntermediates
 
-from meshgpt_pytorch.version import __version__
-import matplotlib.pyplot as plt
-from tqdm import tqdm
-from meshgpt_pytorch.meshgpt_pytorch import (
-    MeshAutoencoder,
-    MeshTransformer
+from x_transformers.autoregressive_wrapper import (
+    eval_decorator,
+    top_k,
+    top_p,
 )
 
-# constants
+from local_attention import LocalMHA
+
+from vector_quantize_pytorch import (
+    ResidualVQ,
+    ResidualLFQ
+)
 
-ConstantLRScheduler = partial(LambdaLR, lr_lambda = lambda step: 1.)
+from meshgpt_pytorch.data import derive_face_edges_from_faces
+from meshgpt_pytorch.version import __version__
 
-DEFAULT_DDP_KWARGS = DistributedDataParallelKwargs(
-    find_unused_parameters = True
+from classifier_free_guidance_pytorch import (
+    classifier_free_guidance,
+    TextEmbeddingReturner
 )
 
+from torch_geometric.nn.conv import SAGEConv
+
+from gateloop_transformer import SimpleGateLoopLayer
+
+from tqdm import tqdm
+from packaging import version
+
+import pickle
+
 # helper functions
 
 def exists(v):
@@ -49,647 +61,1138 @@ def default(v, d):
 def divisible_by(num, den):
     return (num % den) == 0
 
-def cycle(dl):
-    while True:
-        for data in dl:
-            yield data
+def is_empty(l):
+    return len(l) == 0
+
+def set_module_requires_grad_(
+    module: Module,
+    requires_grad: bool
+):
+    for param in module.parameters():
+        param.requires_grad = requires_grad
+
+def l1norm(t):
+    return F.normalize(t, dim = -1, p = 1)
 
-# optimizer
+def l2norm(t):
+    return F.normalize(t, dim = -1, p = 2)
 
-def separate_weight_decayable_params(params):
-    wd_params, no_wd_params = [], []
+def pad_at_dim(t, padding, dim = -1, value = 0):
+    ndim = t.ndim
+    right_dims = (ndim - dim - 1) if dim >= 0 else (-dim - 1)
+    zeros = (0, 0) * right_dims
+    return F.pad(t, (*zeros, *padding), value = value)
 
-    for param in params:
-        param_list = no_wd_params if param.ndim < 2 else wd_params
-        param_list.append(param)
+def pad_to_length(t, length, dim = -1, value = 0, right = True):
+    curr_length = t.shape[dim]
+    remainder = length - curr_length
 
-    return wd_params, no_wd_params
+    if remainder <= 0:
+        return t
 
-def get_optimizer(
-    params,
-    lr = 1e-4,
-    wd = 1e-2,
-    betas = (0.9, 0.99),
-    eps = 1e-8,
-    filter_by_requires_grad = False,
-    group_wd_params = True,
-    **kwargs
+    padding = (0, remainder) if right else (remainder, 0)
+    return pad_at_dim(t, padding, dim = dim, value = value)
+
+# continuous embed
+
+def ContinuousEmbed(dim_cont):
+    return nn.Sequential(
+        Rearrange('... -> ... 1'),
+        nn.Linear(1, dim_cont),
+        nn.SiLU(),
+        nn.Linear(dim_cont, dim_cont),
+        nn.LayerNorm(dim_cont)
+    )
+
+# additional encoder features
+# 1. angle (3), 2. area (1), 3. normals (3)
+
+def derive_angle(x, y, eps = 1e-5):
+    z = einsum('... d, ... d -> ...', l2norm(x), l2norm(y))
+    return z.clip(-1 + eps, 1 - eps).arccos()
+
+@torch.no_grad()
+def get_derived_face_features(
+    face_coords: TensorType['b', 'nf', 3, 3, float]  # 3 vertices with 3 coordinates
+):
+    shifted_face_coords = torch.cat((face_coords[:, :, -1:], face_coords[:, :, :-1]), dim = 2)
+
+    angles  = derive_angle(face_coords, shifted_face_coords)
+
+    edge1, edge2, _ = (face_coords - shifted_face_coords).unbind(dim = 2)
+
+    normals = l2norm(torch.cross(edge1, edge2, dim = -1))
+    area = normals.norm(dim = -1, keepdim = True) * 0.5
+
+    return dict(
+        angles = angles,
+        area = area,
+        normals = normals
+    )   
+
+# tensor helper functions
+
+@beartype
+def discretize(
+    t: Tensor,
+    *,
+    continuous_range: Tuple[float, float],
+    num_discrete: int = 128
+) -> Tensor:
+    lo, hi = continuous_range
+    assert hi > lo
+
+    t = (t - lo) / (hi - lo)
+    t *= num_discrete
+    t -= 0.5
+
+    return t.round().long().clamp(min = 0, max = num_discrete - 1)
+
+@beartype
+def undiscretize(
+    t: Tensor,
+    *,
+    continuous_range = Tuple[float, float],
+    num_discrete: int = 128
+) -> Tensor:
+    lo, hi = continuous_range
+    assert hi > lo
+
+    t = t.float()
+
+    t += 0.5
+    t /= num_discrete
+    return t * (hi - lo) + lo
+
+@beartype
+def gaussian_blur_1d(
+    t: Tensor,
+    *,
+    sigma: float = 1.
+) -> Tensor:
+
+    _, channels, _, device = *t.shape, t.device
+
+    width = int(ceil(sigma * 5))
+    width += (width + 1) % 2
+    half_width = width // 2
+
+    distance = torch.arange(-half_width, half_width + 1, dtype = torch.float, device = device)
+
+    gaussian = torch.exp(-(distance ** 2) / (2 * sigma ** 2))
+    gaussian = l1norm(gaussian)
+
+    kernel = repeat(gaussian, 'n -> c 1 n', c = channels)
+    return F.conv1d(t, kernel, padding = half_width, groups = channels)
+
+@beartype
+def scatter_mean(
+    tgt: Tensor,
+    indices: Tensor,
+    src = Tensor,
+    *,
+    dim: int = -1,
+    eps: float = 1e-5
 ):
-    if filter_by_requires_grad:
-        params = [t for t in params if t.requires_grad]
+    """
+    todo: update to pytorch 2.1 and try https://pytorch.org/docs/stable/generated/torch.Tensor.scatter_reduce_.html#torch.Tensor.scatter_reduce_
+    """
+    num = tgt.scatter_add(dim, indices, src)
+    den = torch.zeros_like(tgt).scatter_add(dim, indices, torch.ones_like(src))
+    return num / den.clamp(min = eps)
 
-    opt_kwargs = dict(lr = lr, betas = betas, eps = eps)
+# resnet block
 
-    if wd == 0:
-        return Adam(params, **opt_kwargs)
+class Block(Module):
+    def __init__(self, dim, groups = 8):
+        super().__init__()
+        self.proj = nn.Conv1d(dim, dim, 3, padding = 1)
+        self.norm = nn.GroupNorm(groups, dim)
+        self.act = nn.SiLU()
 
-    opt_kwargs = {'weight_decay': wd, **opt_kwargs}
+    def forward(self, x, mask = None):
+        if exists(mask):
+            x = x.masked_fill(~mask, 0.)
 
-    if not group_wd_params:
-        return AdamW(params, **opt_kwargs)
+        x = self.proj(x)
 
-    wd_params, no_wd_params = separate_weight_decayable_params(params)
+        if exists(mask):
+            x = x.masked_fill(~mask, 0.)
 
-    params = [
-        {'params': wd_params},
-        {'params': no_wd_params, 'weight_decay': 0},
-    ]
+        x = self.norm(x)
+        x = self.act(x)
+        return x
 
-    return AdamW(params, **opt_kwargs)
+class ResnetBlock(Module):
+    def __init__(
+        self,
+        dim,
+        *,
+        groups = 8
+    ):
+        super().__init__()
+        self.block1 = Block(dim, groups = groups)
+        self.block2 = Block(dim, groups = groups)
 
-# autoencoder trainer
+    def forward(
+        self,
+        x,
+        mask = None
+    ):
+        h = self.block1(x, mask = mask)
+        h = self.block2(h, mask = mask)
+        return h + x
+
+# main classes
 
-class MeshAutoencoderTrainer(Module):
+class MeshAutoencoder(Module):
     @beartype
     def __init__(
         self,
-        model: MeshAutoencoder,
-        dataset: Dataset,
-        num_train_steps: int,
-        batch_size: int,
-        grad_accum_every: int,
-        val_dataset: Optional[Dataset] = None,
-        val_every: int = 100,
-        val_num_batches: int = 5,
-        learning_rate: float = 1e-4,
-        weight_decay: float = 0.,
-        max_grad_norm: Optional[float] = None,
-        ema_kwargs: dict = dict(),
-        scheduler: Optional[Type[LRScheduler]] = None,
-        scheduler_kwargs: dict = dict(),
-        accelerator_kwargs: dict = dict(),
-        optimizer_kwargs: dict = dict(),
-        checkpoint_every = 1000,
-        checkpoint_every_epoch: Optional[int] = None,
-        checkpoint_folder = './checkpoints',
-        data_kwargs: Tuple[str, ...] = ['vertices', 'faces', 'face_edges'],
-        warmup_steps = 1000,
-        use_wandb_tracking = False
+        dim,
+        num_discrete_coors = 128,
+        coor_continuous_range: Tuple[float, float] = (-1., 1.),
+        dim_coor_embed = 64,
+        num_discrete_area = 128,
+        dim_area_embed = 16,
+        num_discrete_normals = 128,
+        dim_normal_embed = 64,
+        num_discrete_angle = 128,
+        dim_angle_embed = 16,
+        encoder_depth = 2,
+        decoder_depth = 2,
+        dim_codebook = 192,
+        num_quantizers = 2,           # or 'D' in the paper
+        codebook_size = 16384,        # they use 16k, shared codebook between layers
+        use_residual_lfq = True,      # whether to use the latest lookup-free quantization
+        rq_kwargs: dict = dict(),
+        rvq_stochastic_sample_codes = True,
+        sageconv_kwargs: dict = dict(
+            normalize = True,
+            project = True
+        ),
+        commit_loss_weight = 0.1,
+        bin_smooth_blur_sigma = 0.4,  # they blur the one hot discretized coordinate positions
+        local_attn_depth = 0,
+        local_attn_kwargs: dict = dict(
+            dim_head = 32,
+            heads = 8
+        ),
+        local_attn_window_size = 128,
+        final_encoder_norm = True,
+        pad_id = -1,
+        flash_attn = True
     ):
         super().__init__()
 
-        # experiment tracker
+        # autosaving the config
 
-        self.use_wandb_tracking = use_wandb_tracking
+        _locals = locals()
+        _locals.pop('self', None)
+        _locals.pop('__class__', None)
+        self._config = pickle.dumps(_locals)
 
-        if use_wandb_tracking:
-            accelerator_kwargs['log_with'] = 'wandb'
+        # main face coordinate embedding
 
-        if 'kwargs_handlers' not in accelerator_kwargs:
-            accelerator_kwargs['kwargs_handlers'] = [DEFAULT_DDP_KWARGS]
+        self.num_discrete_coors = num_discrete_coors
+        self.coor_continuous_range = coor_continuous_range
 
-        # accelerator
+        self.discretize_face_coords = partial(discretize, num_discrete = num_discrete_coors, continuous_range = coor_continuous_range)
+        self.coor_embed = nn.Embedding(num_discrete_coors, dim_coor_embed)
 
-        self.accelerator = Accelerator(**accelerator_kwargs)
+        # derived feature embedding
 
-        self.model = model
+        self.discretize_angle = partial(discretize, num_discrete = num_discrete_angle, continuous_range = (0., pi))
+        self.angle_embed = nn.Embedding(num_discrete_angle, dim_angle_embed)
 
-        if self.is_main:
-            self.ema_model = EMA(model, **ema_kwargs)
+        lo, hi = coor_continuous_range
+        self.discretize_area = partial(discretize, num_discrete = num_discrete_area, continuous_range = (0., (hi - lo) ** 2))
+        self.area_embed = nn.Embedding(num_discrete_area, dim_area_embed)
 
-        self.optimizer = get_optimizer(model.parameters(), lr = learning_rate, wd = weight_decay, **optimizer_kwargs)
+        self.discretize_normals = partial(discretize, num_discrete = num_discrete_normals, continuous_range = coor_continuous_range)
+        self.normal_embed = nn.Embedding(num_discrete_normals, dim_normal_embed)
 
-        self.warmup = warmup.LinearWarmup(self.optimizer, warmup_period = warmup_steps)
+        # initial dimension
 
-        if exists(scheduler):
-            self.scheduler = scheduler(self.optimizer, **scheduler_kwargs)
-        else:
-            self.scheduler = ConstantLRScheduler(self.optimizer)
-
-        self.dataloader = DataLoader(
-            dataset,
-            batch_size = batch_size,
-            shuffle = True,
-            drop_last = True,
-            collate_fn = partial(custom_collate, pad_id = model.pad_id)
-        )
+        init_dim = dim_coor_embed * 9 + dim_angle_embed * 3 + dim_normal_embed * 3 + dim_area_embed
+
+        # project into model dimension
+
+        self.project_in = nn.Linear(init_dim, dim)
+
+        self.encoders = ModuleList([])
 
-        self.should_validate = exists(val_dataset)
+        for _ in range(encoder_depth):
+            sage_conv = SAGEConv(dim, dim, **sageconv_kwargs)
 
-        if self.should_validate:
-            assert len(val_dataset) > 0, 'your validation dataset is empty'
+            self.encoders.append(sage_conv)
 
-            self.val_every = val_every
-            self.val_num_batches = val_num_batches
+        self.final_encoder_norm = nn.LayerNorm(dim) if final_encoder_norm else nn.Identity()
 
-            self.val_dataloader = DataLoader(
-                val_dataset,
-                batch_size = batch_size,
-                shuffle = True,
-                drop_last = True,
-                collate_fn = partial(custom_collate, pad_id = model.pad_id)
+        self.codebook_size = codebook_size
+        self.num_quantizers = num_quantizers
+
+        self.project_dim_codebook = nn.Linear(dim, dim_codebook * 3)
+
+        if use_residual_lfq:
+            self.quantizer = ResidualLFQ(
+                dim = dim_codebook,
+                num_quantizers = num_quantizers,
+                codebook_size = codebook_size,
+                commitment_loss_weight = 1.,
+                **rq_kwargs
+            )
+        else:
+            self.quantizer = ResidualVQ(
+                dim = dim_codebook,
+                num_quantizers = num_quantizers,
+                codebook_size = codebook_size,
+                shared_codebook = True,
+                commitment_weight = 1.,
+                stochastic_sample_codes = rvq_stochastic_sample_codes,
+                **rq_kwargs
             )
 
-        self.data_kwargs = data_kwargs
-
-        (
-            self.model,
-            self.optimizer,
-            self.scheduler,
-            self.dataloader
-        ) = self.accelerator.prepare(
-            self.model,
-            self.optimizer,
-            self.scheduler,
-            self.dataloader
-        )
+        self.pad_id = pad_id # for variable lengthed faces, padding quantized ids will be set to this value
 
-        self.grad_accum_every = grad_accum_every
-        self.max_grad_norm = max_grad_norm
-        self.num_train_steps = num_train_steps
-        self.register_buffer('step', torch.tensor(0))
+        self.project_codebook_out = nn.Linear(dim_codebook * 3, dim)
 
-        self.checkpoint_every_epoch = checkpoint_every_epoch
-        self.checkpoint_every = checkpoint_every
-        self.checkpoint_folder = Path(checkpoint_folder)
-        self.checkpoint_folder.mkdir(exist_ok = True, parents = True)
+        self.decoders = ModuleList([])
 
-    @property
-    def ema_tokenizer(self):
-        return self.ema_model.ema_model
+        for _ in range(decoder_depth):
+            resnet_block = ResnetBlock(dim)
 
-    def tokenize(self, *args, **kwargs):
-        return self.ema_tokenizer.tokenize(*args, **kwargs)
+            self.decoders.append(resnet_block)
 
-    @contextmanager
-    @beartype
-    def trackers(
-        self,
-        project_name: str,
-        run_name: Optional[str] = None,
-        hps: Optional[dict] = None
-    ):
-        assert self.use_wandb_tracking
+        self.to_coor_logits = nn.Sequential(
+            nn.Linear(dim, num_discrete_coors * 9),
+            Rearrange('... (v c) -> ... v c', v = 9)
+        )
 
-        self.accelerator.init_trackers(project_name, config = hps)
+        # local attention related
 
-        if exists(run_name):
-            self.accelerator.trackers[0].run.name = run_name
+        self.encoder_local_attn_blocks = ModuleList([])
+        self.decoder_local_attn_blocks = ModuleList([])
 
-        yield
-        self.accelerator.end_training()
+        attn_kwargs = dict(
+            dim = dim,
+            causal = False,
+            prenorm = True,
+            window_size = local_attn_window_size,
+        )
 
-    def log(self, **data_kwargs):
-        self.accelerator.log(data_kwargs, step = self.step.item())
+        for _ in range(local_attn_depth):
+            self.encoder_local_attn_blocks.append(nn.ModuleList([
+                LocalMHA(**attn_kwargs, **local_attn_kwargs),
+                nn.Sequential(RMSNorm(dim), FeedForward(dim, glu = True))
+            ]))
 
-    @property
-    def device(self):
-        return self.unwrapped_model.device
+            self.decoder_local_attn_blocks.append(nn.ModuleList([
+                LocalMHA(**attn_kwargs, **local_attn_kwargs),
+                nn.Sequential(RMSNorm(dim), FeedForward(dim, glu = True))
+            ]))
 
-    @property
-    def is_main(self):
-        return self.accelerator.is_main_process
+        # loss related
 
-    @property
-    def unwrapped_model(self):
-        return self.accelerator.unwrap_model(self.model)
+        self.commit_loss_weight = commit_loss_weight
+        self.bin_smooth_blur_sigma = bin_smooth_blur_sigma
 
-    @property
-    def is_local_main(self):
-        return self.accelerator.is_local_main_process
+    @classmethod
+    def init_and_load_from(cls, path, strict = True):
+        path = Path(path)
+        assert path.exists()
+        pkg = torch.load(str(path), map_location = 'cpu')
+
+        assert 'config' in pkg, 'model configs were not found in this saved checkpoint'
 
-    def wait(self):
-        return self.accelerator.wait_for_everyone()
+        if version.parse(__version__) != version.parse(pkg['version']):
+            self.print(f'loading saved mesh autoencoder at version {pkg["version"]}, but current package version is {__version__}')
 
-    def print(self, msg):
-        return self.accelerator.print(msg)
+        config = pickle.loads(pkg['config'])
+        tokenizer = cls(**config)
+
+        tokenizer.load_state_dict(pkg['model'], strict = strict)
+        return tokenizer
 
     def save(self, path, overwrite = True):
         path = Path(path)
         assert overwrite or not path.exists()
 
         pkg = dict(
-            model = self.unwrapped_model.state_dict(),
-            ema_model = self.ema_model.state_dict(),
-            optimizer = self.optimizer.state_dict(),
-            warmup = self.warmup.state_dict(),
-            scheduler = self.scheduler.state_dict(),
+            model = self.state_dict(),
+            config = self._config,
             version = __version__,
-            step = self.step.item(),
-            config = self.unwrapped_model._config
         )
 
         torch.save(pkg, str(path))
 
-    def load(self, path):
-        path = Path(path)
-        assert path.exists()
+    @beartype
+    def encode(
+        self,
+        *,
+        vertices:         TensorType['b', 'nv', 3, float],
+        faces:            TensorType['b', 'nf', 3, int],
+        face_edges:       TensorType['b', 'e', 2, int],
+        face_mask:        TensorType['b', 'nf', bool],
+        face_edges_mask:  TensorType['b', 'e', bool],
+        return_face_coordinates = False
+    ):
+        """
+        einops:
+        b - batch
+        nf - number of faces
+        nv - number of vertices (3)
+        c - coordinates (3)
+        d - embed dim
+        """
 
-        pkg = torch.load(str(path))
+        batch, num_vertices, num_coors, device = *vertices.shape, vertices.device
+        _, num_faces, _ = faces.shape
 
-        if version.parse(__version__) != version.parse(pkg['version']):
-            self.print(f'loading saved mesh autoencoder at version {pkg["version"]}, but current package version is {__version__}')
+        face_without_pad = faces.masked_fill(~rearrange(face_mask, 'b nf -> b nf 1'), 0)
+
+        faces_vertices = repeat(face_without_pad, 'b nf nv -> b nf nv c', c = num_coors)
+        vertices = repeat(vertices, 'b nv c -> b nf nv c', nf = num_faces)
+
+        # continuous face coords
+
+        face_coords = vertices.gather(-2, faces_vertices)
+
+        # compute derived features and embed
+
+        derived_features = get_derived_face_features(face_coords)
+
+        discrete_angle = self.discretize_angle(derived_features['angles'])
+        angle_embed = self.angle_embed(discrete_angle)
+
+        discrete_area = self.discretize_area(derived_features['area'])
+        area_embed = self.area_embed(discrete_area)
+
+        discrete_normal = self.discretize_normals(derived_features['normals'])
+        normal_embed = self.normal_embed(discrete_normal)
+
+        # discretize vertices for face coordinate embedding
+
+        discrete_face_coords = self.discretize_face_coords(face_coords)
+        discrete_face_coords = rearrange(discrete_face_coords, 'b nf nv c -> b nf (nv c)') # 9 coordinates per face
 
-        self.model.load_state_dict(pkg['model'])
-        self.ema_model.load_state_dict(pkg['ema_model'])
-        self.optimizer.load_state_dict(pkg['optimizer'])
-        self.warmup.load_state_dict(pkg['warmup'])
-        self.scheduler.load_state_dict(pkg['scheduler'])
+        face_coor_embed = self.coor_embed(discrete_face_coords)
+        face_coor_embed = rearrange(face_coor_embed, 'b nf c d -> b nf (c d)')
 
-        self.step.copy_(pkg['step'])
+        # combine all features and project into model dimension
 
-    def next_data_to_forward_kwargs(self, dl_iter) -> dict:
-        data = next(dl_iter)
+        face_embed, _ = pack([face_coor_embed, angle_embed, area_embed, normal_embed], 'b nf *')
+        face_embed = self.project_in(face_embed)
 
-        if isinstance(data, tuple):
-            forward_kwargs = dict(zip(self.data_kwargs, data))
+        # handle variable lengths by using masked_select and masked_scatter
 
-        elif isinstance(data, dict):
-            forward_kwargs = data
+        # first handle edges
+        # needs to be offset by number of faces for each batch
 
-        return forward_kwargs
+        face_index_offsets = reduce(face_mask.long(), 'b nf -> b', 'sum')
+        face_index_offsets = F.pad(face_index_offsets.cumsum(dim = 0), (1, -1), value = 0)
+        face_index_offsets = rearrange(face_index_offsets, 'b -> b 1 1')
 
-    def forward(self):
-        step = self.step.item()
-        dl_iter = cycle(self.dataloader)
+        face_edges = face_edges + face_index_offsets
+        face_edges = face_edges[face_edges_mask]
+        face_edges = rearrange(face_edges, 'be ij -> ij be')
 
-        if self.is_main and self.should_validate:
-            val_dl_iter = cycle(self.val_dataloader)
+        # next prepare the face_mask for using masked_select and masked_scatter
 
-        while step < self.num_train_steps:
+        dtype = face_embed.dtype
+        orig_face_embed_shape = face_embed.shape
 
-            for i in range(self.grad_accum_every):
-                is_last = i == (self.grad_accum_every - 1)
-                maybe_no_sync = partial(self.accelerator.no_sync, self.model) if not is_last else nullcontext
+        face_embed = face_embed[face_mask]
 
-                forward_kwargs = self.next_data_to_forward_kwargs(dl_iter)
+        for conv in self.encoders:
+            face_embed = conv(face_embed, face_edges)
 
-                with self.accelerator.autocast(), maybe_no_sync():
-                    loss = self.model(**forward_kwargs)
+        zeros = torch.zeros(orig_face_embed_shape, device = device, dtype = dtype)
+        face_embed = zeros.masked_scatter(rearrange(face_mask, '... -> ... 1'), face_embed)
 
-                    self.accelerator.backward(loss / self.grad_accum_every)
+        for attn, ff in self.encoder_local_attn_blocks:
+            face_embed = attn(face_embed, mask = face_mask) + face_embed
+            face_embed = ff(face_embed) + face_embed
 
-            self.print(f'loss: {loss.item():.3f}')
+        face_embed = self.final_encoder_norm(face_embed)
 
-            self.log(loss = loss.item())
+        if not return_face_coordinates:
+            return face_embed
 
-            if exists(self.max_grad_norm):
-                self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
+        return face_embed, discrete_face_coords
 
-            self.optimizer.step()
-            self.optimizer.zero_grad()
+    @beartype
+    def quantize(
+        self,
+        *,
+        faces: TensorType['b', 'nf', 3, int],
+        face_mask: TensorType['b', 'n', bool],
+        face_embed: TensorType['b', 'nf', 'd', float],
+        pad_id = None,
+        rvq_sample_codebook_temp = 1.
+    ):
+        pad_id = default(pad_id, self.pad_id)
+        batch, num_faces, device = *faces.shape[:2], faces.device
+
+        max_vertex_index = faces.amax()
+        num_vertices = int(max_vertex_index.item() + 1)
 
-            if not self.accelerator.optimizer_step_was_skipped:
-                with self.warmup.dampening():
-                    self.scheduler.step()
+        face_embed = self.project_dim_codebook(face_embed)
+        face_embed = rearrange(face_embed, 'b nf (nv d) -> b nf nv d', nv = 3)
 
-            step += 1
-            self.step.add_(1)
+        vertex_dim = face_embed.shape[-1]
+        vertices = torch.zeros((batch, num_vertices, vertex_dim), device = device)
 
-            self.wait()
+        # create pad vertex, due to variable lengthed faces
 
-            if self.is_main:
-                self.ema_model.update()
+        pad_vertex_id = num_vertices
+        vertices = F.pad(vertices, (0, 0, 0, 1), value = 0.)
 
-            self.wait()
+        faces = faces.masked_fill(~rearrange(face_mask, 'b n -> b n 1'), pad_vertex_id)
 
-            if self.is_main and self.should_validate and divisible_by(step, self.val_every):
-                total_val_loss = 0.
-                self.ema_model.eval()
+        # prepare for scatter mean
 
-                num_val_batches = self.val_num_batches * self.grad_accum_every
+        faces_with_dim = repeat(faces, 'b nf nv -> b (nf nv) d', d = vertex_dim)
 
-                for _ in range(num_val_batches):
-                    with self.accelerator.autocast(), torch.no_grad():
+        face_embed = rearrange(face_embed, 'b ... d -> b (...) d')
 
-                        forward_kwargs = self.next_data_to_forward_kwargs(val_dl_iter)
+        # scatter mean
 
-                        val_loss = self.ema_model(**forward_kwargs)
+        averaged_vertices = scatter_mean(vertices, faces_with_dim, face_embed, dim = -2)
 
-                        total_val_loss += (val_loss / num_val_batches)
+        # mask out null vertex token
 
-                self.print(f'valid loss: {total_val_loss:.3f}')
-                self.log(val_loss = total_val_loss, step = step)
+        mask = torch.ones((batch, num_vertices + 1), device = device, dtype = torch.bool)
+        mask[:, -1] = False
 
-            self.wait()
+        # rvq specific kwargs
 
-            if self.is_main:
-                checkpoint_num = step // self.checkpoint_every
-                self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.{checkpoint_num}.pt')
+        quantize_kwargs = dict()
 
-            self.wait()
+        if isinstance(self.quantizer, ResidualVQ):
+            quantize_kwargs.update(sample_codebook_temp = rvq_sample_codebook_temp)
 
-        self.print('training complete')
-    def train(self, num_epochs, diplay_graph = False):
-        epoch_losses = []  # Initialize a list to store epoch losses
-        self.model.train() 
-        for epoch in range(num_epochs): 
-            total_loss = 0.0
-            num_batches = 0
+        # residual VQ
 
-            progress_bar = tqdm(self.dataloader, desc=f'Epoch {epoch + 1}/{num_epochs}') 
+        quantized, codes, commit_loss = self.quantizer(averaged_vertices, mask = mask, **quantize_kwargs)
 
-            for data in progress_bar: 
+        # gather quantized vertexes back to faces for decoding
+        # now the faces have quantized vertices
 
-                if isinstance(data, tuple): 
-                    forward_kwargs = dict(zip(self.data_kwargs, data))
+        face_embed_output = quantized.gather(-2, faces_with_dim)
+        face_embed_output = rearrange(face_embed_output, 'b (nf nv) d -> b nf (nv d)', nv = 3)
 
-                elif isinstance(data, dict): 
-                    forward_kwargs = data 
-                
+        face_embed_output = self.project_codebook_out(face_embed_output)
 
-                with self.accelerator.autocast():
-                    loss = self.model(**forward_kwargs)
-                    self.accelerator.backward(loss)
+        # vertex codes also need to be gathered to be organized by face sequence
+        # for autoregressive learning
 
-                if exists(self.max_grad_norm):
-                    self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
+        faces_with_quantized_dim = repeat(faces, 'b nf nv -> b (nf nv) q', q = self.num_quantizers)
+        codes_output = codes.gather(-2, faces_with_quantized_dim)
 
-                self.optimizer.step()
-                self.optimizer.zero_grad()
+        # make sure codes being outputted have this padding
 
-                if not self.accelerator.optimizer_step_was_skipped:
-                    with self.warmup.dampening():
-                        self.scheduler.step()
- 
-                current_loss = loss.item()
-                total_loss += current_loss
-                num_batches += 1
-                progress_bar.set_postfix(loss=current_loss)
-                
-            
- 
-            avg_epoch_loss = total_loss / num_batches 
-            epoch_losses.append(avg_epoch_loss)
-            self.print(f'Epoch {epoch + 1} average loss: {avg_epoch_loss}')
-            self.wait()
+        face_mask = repeat(face_mask, 'b nf -> b (nf nv) 1', nv = 3)
+        codes_output = codes_output.masked_fill(~face_mask, self.pad_id)
 
-            if self.checkpoint_every_epoch is not None and epoch != 0 and epoch % self.checkpoint_every_epoch == 0:
-                self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.3f}.pt')
- 
+        # output quantized, codes, as well as commitment loss
 
-        self.print('Training complete') 
-        if diplay_graph:
-            plt.figure(figsize=(10, 5))
-            plt.plot(range(1, num_epochs + 1), epoch_losses, marker='o')
-            plt.title('Training Loss Over Epochs')
-            plt.xlabel('Epoch')
-            plt.ylabel('Average Loss')
-            plt.grid(True)
-            plt.show()
-# mesh transformer trainer
+        return face_embed_output, codes_output, commit_loss
 
-class MeshTransformerTrainer(Module):
     @beartype
-    def __init__(
+    def decode(
         self,
-        model: MeshTransformer,
-        dataset: Dataset,
-        num_train_steps: int,
-        batch_size: int,
-        grad_accum_every: int,
-        learning_rate: float = 2e-4,
-        weight_decay: float = 0.,
-        max_grad_norm: Optional[float] = 0.5,
-        scheduler: Optional[Type[LRScheduler]] = None,
-        scheduler_kwargs: dict = dict(),
-        ema_kwargs: dict = dict(),
-        accelerator_kwargs: dict = dict(),
-        optimizer_kwargs: dict = dict(),
-        
-        checkpoint_every = 1000, 
-        checkpoint_every_epoch: Optional[int] = None,
-        checkpoint_folder = './checkpoints',
-        data_kwargs: Tuple[str, ...] = ['vertices', 'faces', 'face_edges', 'face_len', 'face_edges_len'],
-        warmup_steps = 1000,
-        use_wandb_tracking = False
+        quantized: TensorType['b', 'n', 'd', float],
+        face_mask:  TensorType['b', 'n', bool]
     ):
-        super().__init__()
+        conv_face_mask = rearrange(face_mask, 'b n -> b 1 n')
+
+        x = quantized
+
+        for attn, ff in self.encoder_local_attn_blocks:
+            x = attn(x, mask = face_mask) + x
+            x = ff(x) + x
+
+        x = rearrange(x, 'b n d -> b d n')
+
+        for resnet_block in self.decoders:
+            x = resnet_block(x, mask = conv_face_mask)
+
+        return rearrange(x, 'b d n -> b n d')
+
+    @beartype
+    @torch.no_grad()
+    def decode_from_codes_to_faces(
+        self,
+        codes: Tensor,
+        face_mask: Optional[TensorType['b', 'n', bool]] = None,
+        return_discrete_codes = False
+    ):
+        codes = rearrange(codes, 'b ... -> b (...)')
 
-        # experiment tracker
+        if not exists(face_mask):
+            face_mask = reduce(codes != self.pad_id, 'b (nf nv q) -> b nf', 'all', nv = 3, q = self.num_quantizers)
 
-        self.use_wandb_tracking = use_wandb_tracking
+        # handle different code shapes
 
-        if use_wandb_tracking:
-            accelerator_kwargs['log_with'] = 'wandb'
+        codes = rearrange(codes, 'b (n q) -> b n q', q = self.num_quantizers)
 
-        if 'kwargs_handlers' not in accelerator_kwargs:
-            accelerator_kwargs['kwargs_handlers'] = [DEFAULT_DDP_KWARGS]
+        # decode
 
-        self.accelerator = Accelerator(**accelerator_kwargs)
+        quantized = self.quantizer.get_output_from_indices(codes)
+        quantized = rearrange(quantized, 'b (nf nv) d -> b nf (nv d)', nv = 3)
 
-        self.model = model
+        quantized = quantized.masked_fill(~face_mask[..., None], 0.)
+        face_embed_output = self.project_codebook_out(quantized)
 
-        self.optimizer = get_optimizer(
-            model.parameters(),
-            lr = learning_rate,
-            wd = weight_decay,
-            filter_by_requires_grad = True,
-            **optimizer_kwargs
+        decoded = self.decode(
+            face_embed_output,
+            face_mask = face_mask
         )
 
-        self.warmup = warmup.LinearWarmup(self.optimizer, warmup_period = warmup_steps)
+        decoded = decoded.masked_fill(~face_mask[..., None], 0.)
+        pred_face_coords = self.to_coor_logits(decoded)
 
-        if exists(scheduler):
-            self.scheduler = scheduler(self.optimizer, **scheduler_kwargs)
-        else:
-            self.scheduler = ConstantLRScheduler(self.optimizer)
-
-        self.dataloader = DataLoader(
-            dataset,
-            batch_size = batch_size,
-            shuffle = True,
-            drop_last = True,
-            collate_fn = partial(custom_collate, pad_id = model.pad_id)
+        pred_face_coords = pred_face_coords.argmax(dim = -1)
+
+        pred_face_coords = rearrange(pred_face_coords, '... (v c) -> ... v c', v = 3)
+
+        # back to continuous space
+
+        continuous_coors = undiscretize(
+            pred_face_coords,
+            num_discrete = self.num_discrete_coors,
+            continuous_range = self.coor_continuous_range
         )
 
-        self.data_kwargs = data_kwargs
-
-        (
-            self.model,
-            self.optimizer,
-            self.scheduler,
-            self.dataloader
-        ) = self.accelerator.prepare(
-            self.model,
-            self.optimizer,
-            self.scheduler,
-            self.dataloader
+        if not return_discrete_codes:
+            return continuous_coors
+
+        return continuous_coors, pred_face_coords
+
+    @torch.no_grad()
+    def tokenize(self, *args, **kwargs):
+        assert 'return_codes' not in kwargs
+        self.eval()
+        return self.forward(*args, return_codes = True, **kwargs)
+
+    @beartype
+    def forward(
+        self,
+        *,
+        vertices:       TensorType['b', 'nv', 3, float],
+        faces:          TensorType['b', 'nf', 3, int],
+        face_edges:     Optional[TensorType['b', 'e', 2, int]] = None,
+        return_codes = False,
+        return_loss_breakdown = False,
+        return_recon_faces = False,
+        only_return_recon_faces = False,
+        rvq_sample_codebook_temp = 1.
+    ):
+        if not exists(face_edges):
+            face_edges = derive_face_edges_from_faces(faces, pad_id = self.pad_id)
+
+        num_faces, num_face_edges, device = faces.shape[1], face_edges.shape[1], faces.device
+
+        face_mask = reduce(faces != self.pad_id, 'b nf c -> b nf', 'all')
+        face_edges_mask = reduce(face_edges != self.pad_id, 'b e ij -> b e', 'all')
+
+        encoded, face_coordinates = self.encode(
+            vertices = vertices,
+            faces = faces,
+            face_edges = face_edges,
+            face_edges_mask = face_edges_mask,
+            face_mask = face_mask,
+            return_face_coordinates = True
         )
 
-        self.grad_accum_every = grad_accum_every
-        self.max_grad_norm = max_grad_norm
-        self.num_train_steps = num_train_steps
-        self.register_buffer('step', torch.tensor(0))
+        quantized, codes, commit_loss = self.quantize(
+            face_embed = encoded,
+            faces = faces,
+            face_mask = face_mask,
+            rvq_sample_codebook_temp = rvq_sample_codebook_temp
+        )
+
+        if return_codes:
+            assert not return_recon_faces, 'cannot return reconstructed faces when just returning raw codes'
+            return codes
+
+        decode = self.decode(
+            quantized,
+            face_mask = face_mask
+        )
+
+        pred_face_coords = self.to_coor_logits(decode)
+
+        # compute reconstructed faces if needed
+
+        if return_recon_faces or only_return_recon_faces:
+
+            recon_faces = undiscretize(
+                pred_face_coords.argmax(dim = -1),
+                num_discrete = self.num_discrete_coors,
+                continuous_range = self.coor_continuous_range,
+            )
+
+            recon_faces = rearrange(recon_faces, 'b nf (nv c) -> b nf nv c', nv = 3)
+            face_mask = rearrange(face_mask, 'b nf -> b nf 1 1')
+            recon_faces = recon_faces.masked_fill(~face_mask, float('nan'))
+
+        if only_return_recon_faces:
+            return recon_faces
+
+        # prepare for recon loss
+
+        pred_face_coords = rearrange(pred_face_coords, 'b ... c -> b c (...)')
+        face_coordinates = rearrange(face_coordinates, 'b ... -> b 1 (...)')
+
+        # reconstruction loss on discretized coordinates on each face
+        # they also smooth (blur) the one hot positions, localized label smoothing basically
+
+        with autocast(enabled = False):
+            pred_log_prob = pred_face_coords.log_softmax(dim = 1)
 
-        self.checkpoint_every_epoch = checkpoint_every_epoch
-        self.checkpoint_every = checkpoint_every
-        self.checkpoint_folder = Path(checkpoint_folder)
-        self.checkpoint_folder.mkdir(exist_ok = True, parents = True)
+            target_one_hot = torch.zeros_like(pred_log_prob).scatter(1, face_coordinates, 1.)
 
-    @contextmanager
+            if self.bin_smooth_blur_sigma >= 0.:
+                target_one_hot = gaussian_blur_1d(target_one_hot, sigma = self.bin_smooth_blur_sigma)
+
+            # cross entropy with localized smoothing
+
+            recon_losses = (-target_one_hot * pred_log_prob).sum(dim = 1)
+
+            face_mask = repeat(face_mask, 'b nf -> b (nf r)', r = 9)
+            recon_loss = recon_losses[face_mask].mean()
+
+        # calculate total loss
+
+        total_loss = recon_loss + \
+                     commit_loss.sum() * self.commit_loss_weight
+
+        # calculate loss breakdown if needed
+
+        loss_breakdown = (recon_loss, commit_loss)
+
+        # some return logic
+
+        if not return_loss_breakdown:
+            if not return_recon_faces:
+                return total_loss
+
+            return recon_faces, total_loss
+
+        if not return_recon_faces:
+            return total_loss, loss_breakdown
+
+        return recon_faces, total_loss, loss_breakdown
+
+class MeshTransformer(Module):
     @beartype
-    def trackers(
+    def __init__(
         self,
-        project_name: str,
-        run_name: Optional[str] = None,
-        hps: Optional[dict] = None
+        autoencoder: MeshAutoencoder,
+        *,
+        dim = 512,
+        max_seq_len = 8192,
+        flash_attn = True,
+        attn_depth = 12,
+        attn_dim_head = 64,
+        attn_heads = 16,
+        attn_kwargs: dict = dict(
+            ff_glu = True,
+            num_mem_kv = 4
+        ),
+        fine_pre_gateloop_depth = 2,
+        fine_attn_depth = 2,
+        fine_attn_dim_head = 32,
+        fine_attn_heads = 8,
+        pad_id = -1,
+        condition_on_text = False,
+        text_condition_model_types = ('t5',),
+        text_condition_cond_drop_prob = 0.25
     ):
-        assert self.use_wandb_tracking
+        super().__init__()
 
-        self.accelerator.init_trackers(project_name, config = hps)
+        self.autoencoder = autoencoder
+        set_module_requires_grad_(autoencoder, False)
 
-        if exists(run_name):
-            self.accelerator.trackers[0].run.name = run_name
+        self.codebook_size = autoencoder.codebook_size
+        self.num_quantizers = autoencoder.num_quantizers
 
-        yield
-        self.accelerator.end_training()
+        self.sos_token = nn.Parameter(torch.randn(dim))
+        self.eos_token_id = self.codebook_size
 
-    def log(self, **data_kwargs):
-        self.accelerator.log(data_kwargs, step = self.step.item())
+        # they use axial positional embeddings
+
+        assert divisible_by(max_seq_len, 3 * self.num_quantizers) # 3 vertices per face, with D codes per vertex
+
+        self.token_embed = nn.Embedding(self.codebook_size + 1, dim)
+
+        self.quantize_level_embed = nn.Parameter(torch.randn(self.num_quantizers, dim))
+        self.vertex_embed = nn.Parameter(torch.randn(3, dim))
+
+        self.abs_pos_emb = nn.Embedding(max_seq_len, dim)
+
+        self.max_seq_len = max_seq_len
+
+        # text condition
+
+        self.condition_on_text = condition_on_text
+        self.conditioner = None
+
+        cross_attn_dim_context = None
+
+        if condition_on_text:
+            self.conditioner = TextEmbeddingReturner(
+                model_types = text_condition_model_types,
+                cond_drop_prob = text_condition_cond_drop_prob
+            )
+            cross_attn_dim_context = self.conditioner.dim_latent
+
+        # for summarizing the vertices of each face
+
+        self.to_face_tokens = nn.Sequential(
+            nn.Linear(self.num_quantizers * 3 * dim, dim),
+            nn.LayerNorm(dim)
+        )
+
+        # main autoregressive attention network
+        # attending to a face token
+
+        self.decoder = Decoder(
+            dim = dim,
+            depth = attn_depth,
+            dim_head = attn_dim_head,
+            heads = attn_heads,
+            flash_attn = flash_attn,
+            cross_attend = condition_on_text,
+            cross_attn_dim_context = cross_attn_dim_context,
+            **attn_kwargs
+        )
+
+        # address a weakness in attention
+
+        self.gateloop_layers = ModuleList([])
+
+        for _ in range(fine_pre_gateloop_depth):
+            self.gateloop_layers.append(SimpleGateLoopLayer(dim))
+
+        # decoding the vertices, 2-stage hierarchy
+
+        self.fine_decoder = Decoder(
+            dim = dim,
+            depth = fine_attn_depth,
+            dim_head = attn_dim_head,
+            heads = attn_heads,
+            flash_attn = flash_attn,
+            **attn_kwargs
+        )
+
+        # to logits
+
+        self.to_logits = nn.Linear(dim, self.codebook_size + 1)
+
+        # padding id
+        # force the autoencoder to use the same pad_id given in transformer
+
+        self.pad_id = pad_id
+        autoencoder.pad_id = pad_id
 
     @property
     def device(self):
-        return self.unwrapped_model.device
+        return next(self.parameters()).device
 
-    @property
-    def is_main(self):
-        return self.accelerator.is_main_process
+    @beartype
+    def embed_texts(self, texts: List[str]):
+        return self.conditioner.embed_texts(texts)
 
-    @property
-    def unwrapped_model(self):
-        return self.accelerator.unwrap_model(self.model)
+    @eval_decorator
+    @torch.no_grad()
+    @beartype
+    def generate(
+        self,
+        prompt: Optional[Tensor] = None,
+        batch_size: Optional[int] = None,
+        filter_logits_fn: Callable = top_k,
+        filter_kwargs: dict = dict(),
+        temperature = 1.,
+        return_codes = False,
+        texts: Optional[List[str]] = None,
+        text_embeds: Optional[Tensor] = None,
+        cond_scale = 1.,
+        cache_kv = False
+    ):
+        if exists(prompt):
+            assert not exists(batch_size)
 
-    @property
-    def is_local_main(self):
-        return self.accelerator.is_local_main_process
+            prompt = rearrange(prompt, 'b ... -> b (...)')
+            assert prompt.shape[-1] <= self.max_seq_len
 
-    def wait(self):
-        return self.accelerator.wait_for_everyone()
+            batch_size = prompt.shape[0]
 
-    def print(self, msg):
-        return self.accelerator.print(msg)
+        if self.condition_on_text:
+            assert exists(texts) ^ exists(text_embeds), '`text` or `text_embeds` must be passed in if `condition_on_text` is set to True'
+            if exists(texts):
+                text_embeds = self.embed_texts(texts)
 
-    def save(self, path, overwrite = True):
-        path = Path(path)
-        assert overwrite or not path.exists()
+            batch_size = default(batch_size, text_embeds.shape[0])
 
-        pkg = dict(
-            model = self.unwrapped_model.state_dict(),
-            optimizer = self.optimizer.state_dict(),
-            warmup = self.warmup.state_dict(),
-            scheduler = self.scheduler.state_dict(),
-            step = self.step.item(),
-            version = __version__
+        batch_size = default(batch_size, 1)
+
+        codes = default(prompt, torch.empty((batch_size, 0), dtype = torch.long, device = self.device))
+
+        curr_length = codes.shape[-1]
+
+        # for now, kv cache disabled when conditioning on text
+
+        assert not cache_kv, 'caching not available yet'
+        can_cache = cache_kv and (not self.condition_on_text or cond_scale == 1.)
+
+        cache = None
+
+        for i in tqdm(range(curr_length, self.max_seq_len)):
+            # [sos] v1([q1] [q2] [q1] [q2] [q1] [q2]) v2([q1] [q2] [q1] [q2] [q1] [q2]) -> 0 1 2 3 4 5 6 7 8 9 10 11 12 -> F v1(F F F F F T) v2(F F F F F T)
+
+            can_eos = i != 0 and divisible_by(i, self.num_quantizers * 3)  # only allow for eos to be decoded at the end of each face, defined as 3 vertices with D residual VQ codes
+
+            logits, new_cache = self.forward_on_codes(
+                codes,
+                cache = cache,
+                text_embeds = text_embeds,
+                return_loss = False,
+                return_cache = True,
+                append_eos = False,
+                cond_scale = cond_scale
+            )
+
+            if can_cache:
+                cache = new_cache
+
+            logits = logits[:, -1]
+
+            if not can_eos:
+                logits[:, -1] = -torch.finfo(logits.dtype).max
+
+            filtered_logits = filter_logits_fn(logits, **filter_kwargs)
+
+            if temperature == 0.:
+                sample = filtered_logits.argmax(dim = -1)
+            else:
+                probs = F.softmax(filtered_logits / temperature, dim = -1)
+                sample = torch.multinomial(probs, 1)
+
+            codes, _ = pack([codes, sample], 'b *')
+
+            # check for all rows to have [eos] to terminate
+
+            is_eos_codes = (codes == self.eos_token_id)
+
+            if not is_eos_codes.any(dim = -1).all():
+                continue
+
+            shifted_is_eos_tokens = F.pad(is_eos_codes, (1, -1))
+            mask = shifted_is_eos_tokens.float().cumsum(dim = -1) >= 1
+            codes = codes.masked_fill(mask, self.pad_id)
+            break
+
+        if return_codes:
+            codes = codes[:, 1:] # remove sos
+            codes = rearrange(codes, 'b (n q) -> b n q', q = self.num_quantizers)
+            return codes
+
+        self.autoencoder.eval()
+        return self.autoencoder.decode_from_codes_to_faces(codes)
+
+    def forward(
+        self,
+        *,
+        vertices:       TensorType['b', 'nv', 3, int],
+        faces:          TensorType['b', 'nf', 3, int],
+        face_edges:     Optional[TensorType['b', 'e', 2, int]] = None,
+        cache:          Optional[LayerIntermediates] = None,
+        **kwargs
+    ):
+        codes = self.autoencoder.tokenize(
+            vertices = vertices,
+            faces = faces,
+            face_edges = face_edges
         )
 
-        torch.save(pkg, str(path))
+        return self.forward_on_codes(codes, cache = cache, **kwargs)
 
-    def load(self, path):
-        path = Path(path)
-        assert path.exists()
+    @classifier_free_guidance
+    def forward_on_codes(
+        self,
+        codes = None,
+        return_loss = True,
+        return_cache = False,
+        append_eos = True,
+        cache = None,
+        texts: Optional[List[str]] = None,
+        text_embeds: Optional[Tensor] = None,
+        cond_drop_prob = 0.
+    ):
+        # handle text conditions
 
-        pkg = torch.load(str(path))
+        attn_context_kwargs = dict()
 
-        if version.parse(__version__) != version.parse(pkg['version']):
-            self.print(f'loading saved mesh transformer at version {pkg["version"]}, but current package version is {__version__}')
+        if self.condition_on_text:
+            assert exists(texts) ^ exists(text_embeds), '`text` or `text_embeds` must be passed in if `condition_on_text` is set to True'
+
+            if exists(texts):
+                text_embeds = self.conditioner.embed_texts(texts)
+
+            if exists(codes):
+                assert text_embeds.shape[0] == codes.shape[0], 'batch size of texts or text embeddings is not equal to the batch size of the mesh codes'
+
+            _, maybe_dropped_text_embeds = self.conditioner(
+                text_embeds = text_embeds,
+                cond_drop_prob = cond_drop_prob
+            )
+
+            attn_context_kwargs = dict(
+                context = maybe_dropped_text_embeds.embed,
+                context_mask = maybe_dropped_text_embeds.mask
+            )
+
+        # take care of codes that may be flattened
+
+        if codes.ndim > 2:
+            codes = rearrange(codes, 'b ... -> b (...)')
 
-        self.model.load_state_dict(pkg['model'])
-        self.optimizer.load_state_dict(pkg['optimizer'])
-        self.scheduler.load_state_dict(pkg['scheduler'])
-        self.warmup.load_state_dict(pkg['warmup'])
-        self.step.copy_(pkg['step'])
+        # get some variable
 
-    def forward(self):
-        step = self.step.item()
-        dl_iter = cycle(self.dataloader)
+        batch, seq_len, device = *codes.shape, codes.device
 
-        while step < self.num_train_steps:
+        assert seq_len <= self.max_seq_len, f'received codes of length {seq_len} but needs to be less than or equal to set max_seq_len {self.max_seq_len}'
 
-            for i in range(self.grad_accum_every):
-                is_last = i == (self.grad_accum_every - 1)
-                maybe_no_sync = partial(self.accelerator.no_sync, self.model) if not is_last else nullcontext
+        # auto append eos token
 
-                data = next(dl_iter)
+        if append_eos:
+            assert exists(codes)
 
-                if isinstance(data, tuple):
-                    forward_kwargs = dict(zip(self.data_kwargs, data))
+            code_lens = ((codes != self.pad_id).cumsum(dim = -1) == 0).sum(dim = -1)
 
-                elif isinstance(data, dict):
-                    forward_kwargs = data
+            codes = F.pad(codes, (0, 1), value = 0)
 
-                with self.accelerator.autocast(), maybe_no_sync():
-                    loss = self.model(**forward_kwargs)
+            batch_arange = torch.arange(batch, device = device)
+            batch_arange = rearrange(batch_arange, '... -> ... 1')
 
-                    self.accelerator.backward(loss / self.grad_accum_every)
+            codes[batch_arange, code_lens] = self.eos_token_id
 
-            self.print(f'loss: {loss.item():.3f}')
+        # if returning loss, save the labels for cross entropy
 
-            self.log(loss = loss.item())
+        if return_loss:
+            assert seq_len > 0
+            codes, labels = codes[:, :-1], codes
 
-            if exists(self.max_grad_norm):
-                self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
+        # token embed (each residual VQ id)
 
-            self.optimizer.step()
-            self.optimizer.zero_grad()
+        codes = codes.masked_fill(codes == self.pad_id, 0)
+        codes = self.token_embed(codes)
 
-            if not self.accelerator.optimizer_step_was_skipped:
-                with self.warmup.dampening():
-                    self.scheduler.step()
+        # codebook embed + absolute positions
 
-            step += 1
-            self.step.add_(1)
+        seq_arange = torch.arange(codes.shape[-2], device = device)
 
-            self.wait()
+        codes = codes + self.abs_pos_emb(seq_arange)
 
-            if self.is_main and divisible_by(step, self.checkpoint_every):
-                checkpoint_num = step // self.checkpoint_every
-                self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.{checkpoint_num}.pt')
+        # embedding for quantizer level
 
-            self.wait()
+        code_len = codes.shape[1]
 
-        self.print('training complete')
-        
-    def train(self, num_epochs, diplay_graph = False):
-        epoch_losses = []  # Initialize a list to store epoch losses
-        self.model.train()
-        for epoch in range(num_epochs): 
-            total_loss = 0.0
-            num_batches = 0
+        level_embed = repeat(self.quantize_level_embed, 'q d -> (r q) d', r = ceil(code_len / self.num_quantizers))
+        codes = codes + level_embed[:code_len]
 
-            progress_bar = tqdm(self.dataloader, desc=f'Epoch {epoch + 1}/{num_epochs}') 
+        # embedding for each vertex
 
-            for data in progress_bar: 
+        vertex_embed = repeat(self.vertex_embed, 'nv d -> (r nv q) d', r = ceil(code_len / (3 * self.num_quantizers)), q = self.num_quantizers)
+        codes = codes + vertex_embed[:code_len]
 
-                if isinstance(data, tuple): 
-                    forward_kwargs = dict(zip(self.data_kwargs, data))
+        # create a token per face, by summarizing the 3 vertices
+        # this is similar in design to the RQ transformer from Lee et al. https://arxiv.org/abs/2203.01941
 
-                elif isinstance(data, dict): 
-                    forward_kwargs = data 
-                
+        num_tokens_per_face = self.num_quantizers * 3
+        code_len_is_multiple_of_face = divisible_by(code_len, num_tokens_per_face)
+        next_multiple_code_len = ceil(code_len / num_tokens_per_face) * num_tokens_per_face
 
-                with self.accelerator.autocast():
-                    loss = self.model(**forward_kwargs)
-                    self.accelerator.backward(loss)
+        codes = pad_to_length(codes, next_multiple_code_len, dim = -2)
 
-                if exists(self.max_grad_norm):
-                    self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
+        # grouped codes will be used for the second stage
 
-                self.optimizer.step()
-                self.optimizer.zero_grad()
+        grouped_codes = rearrange(codes, 'b (nf n) d -> b nf n d', n = num_tokens_per_face)
+
+        # create the coarse tokens for the first attention network
+
+        face_codes = grouped_codes if code_len_is_multiple_of_face else grouped_codes[:, :-1]
+        face_codes = rearrange(face_codes, 'b nf n d -> b nf (n d)')
+        face_codes = self.to_face_tokens(face_codes)
+
+        # caches
+
+        coarse_cache, fine_cache = cache if exists(cache) else (None, None)
+
+        # attention on face codes (coarse)
+
+        attended_face_codes, coarse_cache = self.decoder(
+            face_codes,
+            cache = coarse_cache,
+            return_hiddens = True,
+            **attn_context_kwargs
+        )
+
+        # auto prepend sos token
+
+        sos = repeat(self.sos_token, 'd -> b d', b = batch)
+
+        attended_face_codes, _ = pack([sos, attended_face_codes], 'b * d')
+
+        grouped_codes = pad_to_length(grouped_codes, attended_face_codes.shape[-2], dim = 1)
+        fine_vertex_codes, _ = pack([attended_face_codes, grouped_codes], 'b n * d')
+
+        fine_vertex_codes = fine_vertex_codes[..., :-1, :]
+
+        # gateloop layers
+
+        if not is_empty(self.gateloop_layers):
+            fine_vertex_codes = rearrange(fine_vertex_codes, 'b nf n d -> b (nf n) d')
+
+            for gateloop in self.gateloop_layers:
+                fine_vertex_codes = gateloop(fine_vertex_codes) + fine_vertex_codes
+
+            fine_vertex_codes = rearrange(fine_vertex_codes, 'b (nf n) d -> b nf n d', n = num_tokens_per_face)
+
+        # fine attention - 2nd stage
+
+        fine_vertex_codes = rearrange(fine_vertex_codes, 'b nf n d -> (b nf) n d')
+
+        attended_vertex_codes, fine_cache = self.fine_decoder(
+            fine_vertex_codes,
+            cache = fine_cache,
+            return_hiddens = True
+        )
+
+        # reconstitute original sequence
+
+        attended_vertex_codes = rearrange(attended_vertex_codes, '(b nf) n d -> b (nf n) d', b = batch)
+        attended_vertex_codes = attended_vertex_codes[:, :(code_len + 1)]
+
+        # logits
+
+        logits = self.to_logits(attended_vertex_codes)
+
+        if not return_loss:
+            if not return_cache:
+                return logits
+
+            return logits, (coarse_cache, fine_cache)
+
+        # loss
+
+        ce_loss = F.cross_entropy(
+            rearrange(logits, 'b n c -> b c n'),
+            labels,
+            ignore_index = self.pad_id
+        )
 
-                if not self.accelerator.optimizer_step_was_skipped:
-                    with self.warmup.dampening():
-                        self.scheduler.step()
- 
-                current_loss = loss.item()
-                total_loss += current_loss
-                num_batches += 1
-                progress_bar.set_postfix(loss=current_loss)
- 
-            avg_epoch_loss = total_loss / num_batches 
-            epoch_losses.append(avg_epoch_loss)
-            self.print(f'Epoch {epoch + 1} average loss: {avg_epoch_loss}')
-            self.wait() 
-            if self.checkpoint_every_epoch is not None and epoch != 0 and epoch % self.checkpoint_every_epoch == 0:
-                self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.3f}.pt')
-                
-        self.print('Training complete') 
-        if diplay_graph:
-            plt.figure(figsize=(10, 5))
-            plt.plot(range(1, num_epochs + 1), epoch_losses, marker='o')
-            plt.title('Training Loss Over Epochs')
-            plt.xlabel('Epoch')
-            plt.ylabel('Average Loss')
-            plt.grid(True)
-            plt.show()
\ No newline at end of file
+        return ce_loss

From cdb8b5268ee5556e0b1ea7037f17d01999870e3f Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Thu, 14 Dec 2023 06:10:17 +0100
Subject: [PATCH 13/84] Revert "fix error"

This reverts commit 5a6877257f3dde3c5911dc81a187e67d4825c5d1.
---
 meshgpt_pytorch/trainer.py | 1483 ++++++++++++------------------------
 1 file changed, 490 insertions(+), 993 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 4e969b1d..64095290 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -1,54 +1,42 @@
 from pathlib import Path
 from functools import partial
-from math import ceil, pi
+from packaging import version
+from contextlib import nullcontext, contextmanager
 
 import torch
-from torch import nn, Tensor, einsum
-from torch.nn import Module, ModuleList
+from torch import nn, Tensor
+from torch.nn import Module
 import torch.nn.functional as F
-from torch.cuda.amp import autocast
+from torch.optim import AdamW, Adam
+from torch.utils.data import Dataset, DataLoader
+from torch.optim.lr_scheduler import LambdaLR, LRScheduler
+import pytorch_warmup as warmup
 
-from torchtyping import TensorType
+from accelerate import Accelerator
+from accelerate.utils import DistributedDataParallelKwargs
 
 from beartype import beartype
-from beartype.typing import Tuple, Callable, Optional, List, Dict
-
-from einops import rearrange, repeat, reduce, pack, unpack
-from einops.layers.torch import Rearrange
-
-from x_transformers import Decoder
-from x_transformers.attend import Attend
-from x_transformers.x_transformers import RMSNorm, FeedForward, LayerIntermediates
-
-from x_transformers.autoregressive_wrapper import (
-    eval_decorator,
-    top_k,
-    top_p,
-)
+from beartype.typing import Optional, Tuple, Type
 
-from local_attention import LocalMHA
+from ema_pytorch import EMA
 
-from vector_quantize_pytorch import (
-    ResidualVQ,
-    ResidualLFQ
-)
+from meshgpt_pytorch.data import custom_collate
 
-from meshgpt_pytorch.data import derive_face_edges_from_faces
 from meshgpt_pytorch.version import __version__
-
-from classifier_free_guidance_pytorch import (
-    classifier_free_guidance,
-    TextEmbeddingReturner
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+from meshgpt_pytorch.meshgpt_pytorch import (
+    MeshAutoencoder,
+    MeshTransformer
 )
 
-from torch_geometric.nn.conv import SAGEConv
+# constants
 
-from gateloop_transformer import SimpleGateLoopLayer
+ConstantLRScheduler = partial(LambdaLR, lr_lambda = lambda step: 1.)
 
-from tqdm import tqdm
-from packaging import version
-
-import pickle
+DEFAULT_DDP_KWARGS = DistributedDataParallelKwargs(
+    find_unused_parameters = True
+)
 
 # helper functions
 
@@ -61,1138 +49,647 @@ def default(v, d):
 def divisible_by(num, den):
     return (num % den) == 0
 
-def is_empty(l):
-    return len(l) == 0
-
-def set_module_requires_grad_(
-    module: Module,
-    requires_grad: bool
-):
-    for param in module.parameters():
-        param.requires_grad = requires_grad
-
-def l1norm(t):
-    return F.normalize(t, dim = -1, p = 1)
+def cycle(dl):
+    while True:
+        for data in dl:
+            yield data
 
-def l2norm(t):
-    return F.normalize(t, dim = -1, p = 2)
+# optimizer
 
-def pad_at_dim(t, padding, dim = -1, value = 0):
-    ndim = t.ndim
-    right_dims = (ndim - dim - 1) if dim >= 0 else (-dim - 1)
-    zeros = (0, 0) * right_dims
-    return F.pad(t, (*zeros, *padding), value = value)
+def separate_weight_decayable_params(params):
+    wd_params, no_wd_params = [], []
 
-def pad_to_length(t, length, dim = -1, value = 0, right = True):
-    curr_length = t.shape[dim]
-    remainder = length - curr_length
+    for param in params:
+        param_list = no_wd_params if param.ndim < 2 else wd_params
+        param_list.append(param)
 
-    if remainder <= 0:
-        return t
+    return wd_params, no_wd_params
 
-    padding = (0, remainder) if right else (remainder, 0)
-    return pad_at_dim(t, padding, dim = dim, value = value)
-
-# continuous embed
-
-def ContinuousEmbed(dim_cont):
-    return nn.Sequential(
-        Rearrange('... -> ... 1'),
-        nn.Linear(1, dim_cont),
-        nn.SiLU(),
-        nn.Linear(dim_cont, dim_cont),
-        nn.LayerNorm(dim_cont)
-    )
-
-# additional encoder features
-# 1. angle (3), 2. area (1), 3. normals (3)
-
-def derive_angle(x, y, eps = 1e-5):
-    z = einsum('... d, ... d -> ...', l2norm(x), l2norm(y))
-    return z.clip(-1 + eps, 1 - eps).arccos()
-
-@torch.no_grad()
-def get_derived_face_features(
-    face_coords: TensorType['b', 'nf', 3, 3, float]  # 3 vertices with 3 coordinates
-):
-    shifted_face_coords = torch.cat((face_coords[:, :, -1:], face_coords[:, :, :-1]), dim = 2)
-
-    angles  = derive_angle(face_coords, shifted_face_coords)
-
-    edge1, edge2, _ = (face_coords - shifted_face_coords).unbind(dim = 2)
-
-    normals = l2norm(torch.cross(edge1, edge2, dim = -1))
-    area = normals.norm(dim = -1, keepdim = True) * 0.5
-
-    return dict(
-        angles = angles,
-        area = area,
-        normals = normals
-    )   
-
-# tensor helper functions
-
-@beartype
-def discretize(
-    t: Tensor,
-    *,
-    continuous_range: Tuple[float, float],
-    num_discrete: int = 128
-) -> Tensor:
-    lo, hi = continuous_range
-    assert hi > lo
-
-    t = (t - lo) / (hi - lo)
-    t *= num_discrete
-    t -= 0.5
-
-    return t.round().long().clamp(min = 0, max = num_discrete - 1)
-
-@beartype
-def undiscretize(
-    t: Tensor,
-    *,
-    continuous_range = Tuple[float, float],
-    num_discrete: int = 128
-) -> Tensor:
-    lo, hi = continuous_range
-    assert hi > lo
-
-    t = t.float()
-
-    t += 0.5
-    t /= num_discrete
-    return t * (hi - lo) + lo
-
-@beartype
-def gaussian_blur_1d(
-    t: Tensor,
-    *,
-    sigma: float = 1.
-) -> Tensor:
-
-    _, channels, _, device = *t.shape, t.device
-
-    width = int(ceil(sigma * 5))
-    width += (width + 1) % 2
-    half_width = width // 2
-
-    distance = torch.arange(-half_width, half_width + 1, dtype = torch.float, device = device)
-
-    gaussian = torch.exp(-(distance ** 2) / (2 * sigma ** 2))
-    gaussian = l1norm(gaussian)
-
-    kernel = repeat(gaussian, 'n -> c 1 n', c = channels)
-    return F.conv1d(t, kernel, padding = half_width, groups = channels)
-
-@beartype
-def scatter_mean(
-    tgt: Tensor,
-    indices: Tensor,
-    src = Tensor,
-    *,
-    dim: int = -1,
-    eps: float = 1e-5
+def get_optimizer(
+    params,
+    lr = 1e-4,
+    wd = 1e-2,
+    betas = (0.9, 0.99),
+    eps = 1e-8,
+    filter_by_requires_grad = False,
+    group_wd_params = True,
+    **kwargs
 ):
-    """
-    todo: update to pytorch 2.1 and try https://pytorch.org/docs/stable/generated/torch.Tensor.scatter_reduce_.html#torch.Tensor.scatter_reduce_
-    """
-    num = tgt.scatter_add(dim, indices, src)
-    den = torch.zeros_like(tgt).scatter_add(dim, indices, torch.ones_like(src))
-    return num / den.clamp(min = eps)
+    if filter_by_requires_grad:
+        params = [t for t in params if t.requires_grad]
 
-# resnet block
+    opt_kwargs = dict(lr = lr, betas = betas, eps = eps)
 
-class Block(Module):
-    def __init__(self, dim, groups = 8):
-        super().__init__()
-        self.proj = nn.Conv1d(dim, dim, 3, padding = 1)
-        self.norm = nn.GroupNorm(groups, dim)
-        self.act = nn.SiLU()
+    if wd == 0:
+        return Adam(params, **opt_kwargs)
 
-    def forward(self, x, mask = None):
-        if exists(mask):
-            x = x.masked_fill(~mask, 0.)
+    opt_kwargs = {'weight_decay': wd, **opt_kwargs}
 
-        x = self.proj(x)
+    if not group_wd_params:
+        return AdamW(params, **opt_kwargs)
 
-        if exists(mask):
-            x = x.masked_fill(~mask, 0.)
+    wd_params, no_wd_params = separate_weight_decayable_params(params)
 
-        x = self.norm(x)
-        x = self.act(x)
-        return x
+    params = [
+        {'params': wd_params},
+        {'params': no_wd_params, 'weight_decay': 0},
+    ]
 
-class ResnetBlock(Module):
-    def __init__(
-        self,
-        dim,
-        *,
-        groups = 8
-    ):
-        super().__init__()
-        self.block1 = Block(dim, groups = groups)
-        self.block2 = Block(dim, groups = groups)
+    return AdamW(params, **opt_kwargs)
 
-    def forward(
-        self,
-        x,
-        mask = None
-    ):
-        h = self.block1(x, mask = mask)
-        h = self.block2(h, mask = mask)
-        return h + x
-
-# main classes
+# autoencoder trainer
 
-class MeshAutoencoder(Module):
+class MeshAutoencoderTrainer(Module):
     @beartype
     def __init__(
         self,
-        dim,
-        num_discrete_coors = 128,
-        coor_continuous_range: Tuple[float, float] = (-1., 1.),
-        dim_coor_embed = 64,
-        num_discrete_area = 128,
-        dim_area_embed = 16,
-        num_discrete_normals = 128,
-        dim_normal_embed = 64,
-        num_discrete_angle = 128,
-        dim_angle_embed = 16,
-        encoder_depth = 2,
-        decoder_depth = 2,
-        dim_codebook = 192,
-        num_quantizers = 2,           # or 'D' in the paper
-        codebook_size = 16384,        # they use 16k, shared codebook between layers
-        use_residual_lfq = True,      # whether to use the latest lookup-free quantization
-        rq_kwargs: dict = dict(),
-        rvq_stochastic_sample_codes = True,
-        sageconv_kwargs: dict = dict(
-            normalize = True,
-            project = True
-        ),
-        commit_loss_weight = 0.1,
-        bin_smooth_blur_sigma = 0.4,  # they blur the one hot discretized coordinate positions
-        local_attn_depth = 0,
-        local_attn_kwargs: dict = dict(
-            dim_head = 32,
-            heads = 8
-        ),
-        local_attn_window_size = 128,
-        final_encoder_norm = True,
-        pad_id = -1,
-        flash_attn = True
+        model: MeshAutoencoder,
+        dataset: Dataset,
+        num_train_steps: int,
+        batch_size: int,
+        grad_accum_every: int,
+        val_dataset: Optional[Dataset] = None,
+        val_every: int = 100,
+        val_num_batches: int = 5,
+        learning_rate: float = 1e-4,
+        weight_decay: float = 0.,
+        max_grad_norm: Optional[float] = None,
+        ema_kwargs: dict = dict(),
+        scheduler: Optional[Type[LRScheduler]] = None,
+        scheduler_kwargs: dict = dict(),
+        accelerator_kwargs: dict = dict(),
+        optimizer_kwargs: dict = dict(),
+        checkpoint_every = 1000,
+        checkpoint_every_epoch: Optional[int] = None,
+        checkpoint_folder = './checkpoints',
+        data_kwargs: Tuple[str, ...] = ['vertices', 'faces', 'face_edges'],
+        warmup_steps = 1000,
+        use_wandb_tracking = False
     ):
         super().__init__()
 
-        # autosaving the config
-
-        _locals = locals()
-        _locals.pop('self', None)
-        _locals.pop('__class__', None)
-        self._config = pickle.dumps(_locals)
+        # experiment tracker
 
-        # main face coordinate embedding
+        self.use_wandb_tracking = use_wandb_tracking
 
-        self.num_discrete_coors = num_discrete_coors
-        self.coor_continuous_range = coor_continuous_range
+        if use_wandb_tracking:
+            accelerator_kwargs['log_with'] = 'wandb'
 
-        self.discretize_face_coords = partial(discretize, num_discrete = num_discrete_coors, continuous_range = coor_continuous_range)
-        self.coor_embed = nn.Embedding(num_discrete_coors, dim_coor_embed)
+        if 'kwargs_handlers' not in accelerator_kwargs:
+            accelerator_kwargs['kwargs_handlers'] = [DEFAULT_DDP_KWARGS]
 
-        # derived feature embedding
+        # accelerator
 
-        self.discretize_angle = partial(discretize, num_discrete = num_discrete_angle, continuous_range = (0., pi))
-        self.angle_embed = nn.Embedding(num_discrete_angle, dim_angle_embed)
+        self.accelerator = Accelerator(**accelerator_kwargs)
 
-        lo, hi = coor_continuous_range
-        self.discretize_area = partial(discretize, num_discrete = num_discrete_area, continuous_range = (0., (hi - lo) ** 2))
-        self.area_embed = nn.Embedding(num_discrete_area, dim_area_embed)
+        self.model = model
 
-        self.discretize_normals = partial(discretize, num_discrete = num_discrete_normals, continuous_range = coor_continuous_range)
-        self.normal_embed = nn.Embedding(num_discrete_normals, dim_normal_embed)
+        if self.is_main:
+            self.ema_model = EMA(model, **ema_kwargs)
 
-        # initial dimension
+        self.optimizer = get_optimizer(model.parameters(), lr = learning_rate, wd = weight_decay, **optimizer_kwargs)
 
-        init_dim = dim_coor_embed * 9 + dim_angle_embed * 3 + dim_normal_embed * 3 + dim_area_embed
+        self.warmup = warmup.LinearWarmup(self.optimizer, warmup_period = warmup_steps)
 
-        # project into model dimension
-
-        self.project_in = nn.Linear(init_dim, dim)
-
-        self.encoders = ModuleList([])
-
-        for _ in range(encoder_depth):
-            sage_conv = SAGEConv(dim, dim, **sageconv_kwargs)
-
-            self.encoders.append(sage_conv)
+        if exists(scheduler):
+            self.scheduler = scheduler(self.optimizer, **scheduler_kwargs)
+        else:
+            self.scheduler = ConstantLRScheduler(self.optimizer)
+
+        self.dataloader = DataLoader(
+            dataset,
+            batch_size = batch_size,
+            shuffle = True,
+            drop_last = True,
+            collate_fn = partial(custom_collate, pad_id = model.pad_id)
+        )
 
-        self.final_encoder_norm = nn.LayerNorm(dim) if final_encoder_norm else nn.Identity()
+        self.should_validate = exists(val_dataset)
 
-        self.codebook_size = codebook_size
-        self.num_quantizers = num_quantizers
+        if self.should_validate:
+            assert len(val_dataset) > 0, 'your validation dataset is empty'
 
-        self.project_dim_codebook = nn.Linear(dim, dim_codebook * 3)
+            self.val_every = val_every
+            self.val_num_batches = val_num_batches
 
-        if use_residual_lfq:
-            self.quantizer = ResidualLFQ(
-                dim = dim_codebook,
-                num_quantizers = num_quantizers,
-                codebook_size = codebook_size,
-                commitment_loss_weight = 1.,
-                **rq_kwargs
-            )
-        else:
-            self.quantizer = ResidualVQ(
-                dim = dim_codebook,
-                num_quantizers = num_quantizers,
-                codebook_size = codebook_size,
-                shared_codebook = True,
-                commitment_weight = 1.,
-                stochastic_sample_codes = rvq_stochastic_sample_codes,
-                **rq_kwargs
+            self.val_dataloader = DataLoader(
+                val_dataset,
+                batch_size = batch_size,
+                shuffle = True,
+                drop_last = True,
+                collate_fn = partial(custom_collate, pad_id = model.pad_id)
             )
 
-        self.pad_id = pad_id # for variable lengthed faces, padding quantized ids will be set to this value
-
-        self.project_codebook_out = nn.Linear(dim_codebook * 3, dim)
-
-        self.decoders = ModuleList([])
+        self.data_kwargs = data_kwargs
+
+        (
+            self.model,
+            self.optimizer,
+            self.scheduler,
+            self.dataloader
+        ) = self.accelerator.prepare(
+            self.model,
+            self.optimizer,
+            self.scheduler,
+            self.dataloader
+        )
 
-        for _ in range(decoder_depth):
-            resnet_block = ResnetBlock(dim)
+        self.grad_accum_every = grad_accum_every
+        self.max_grad_norm = max_grad_norm
+        self.num_train_steps = num_train_steps
+        self.register_buffer('step', torch.tensor(0))
 
-            self.decoders.append(resnet_block)
+        self.checkpoint_every_epoch = checkpoint_every_epoch
+        self.checkpoint_every = checkpoint_every
+        self.checkpoint_folder = Path(checkpoint_folder)
+        self.checkpoint_folder.mkdir(exist_ok = True, parents = True)
 
-        self.to_coor_logits = nn.Sequential(
-            nn.Linear(dim, num_discrete_coors * 9),
-            Rearrange('... (v c) -> ... v c', v = 9)
-        )
+    @property
+    def ema_tokenizer(self):
+        return self.ema_model.ema_model
 
-        # local attention related
+    def tokenize(self, *args, **kwargs):
+        return self.ema_tokenizer.tokenize(*args, **kwargs)
 
-        self.encoder_local_attn_blocks = ModuleList([])
-        self.decoder_local_attn_blocks = ModuleList([])
+    @contextmanager
+    @beartype
+    def trackers(
+        self,
+        project_name: str,
+        run_name: Optional[str] = None,
+        hps: Optional[dict] = None
+    ):
+        assert self.use_wandb_tracking
 
-        attn_kwargs = dict(
-            dim = dim,
-            causal = False,
-            prenorm = True,
-            window_size = local_attn_window_size,
-        )
+        self.accelerator.init_trackers(project_name, config = hps)
 
-        for _ in range(local_attn_depth):
-            self.encoder_local_attn_blocks.append(nn.ModuleList([
-                LocalMHA(**attn_kwargs, **local_attn_kwargs),
-                nn.Sequential(RMSNorm(dim), FeedForward(dim, glu = True))
-            ]))
+        if exists(run_name):
+            self.accelerator.trackers[0].run.name = run_name
 
-            self.decoder_local_attn_blocks.append(nn.ModuleList([
-                LocalMHA(**attn_kwargs, **local_attn_kwargs),
-                nn.Sequential(RMSNorm(dim), FeedForward(dim, glu = True))
-            ]))
+        yield
+        self.accelerator.end_training()
 
-        # loss related
+    def log(self, **data_kwargs):
+        self.accelerator.log(data_kwargs, step = self.step.item())
 
-        self.commit_loss_weight = commit_loss_weight
-        self.bin_smooth_blur_sigma = bin_smooth_blur_sigma
+    @property
+    def device(self):
+        return self.unwrapped_model.device
 
-    @classmethod
-    def init_and_load_from(cls, path, strict = True):
-        path = Path(path)
-        assert path.exists()
-        pkg = torch.load(str(path), map_location = 'cpu')
+    @property
+    def is_main(self):
+        return self.accelerator.is_main_process
 
-        assert 'config' in pkg, 'model configs were not found in this saved checkpoint'
+    @property
+    def unwrapped_model(self):
+        return self.accelerator.unwrap_model(self.model)
 
-        if version.parse(__version__) != version.parse(pkg['version']):
-            self.print(f'loading saved mesh autoencoder at version {pkg["version"]}, but current package version is {__version__}')
+    @property
+    def is_local_main(self):
+        return self.accelerator.is_local_main_process
 
-        config = pickle.loads(pkg['config'])
-        tokenizer = cls(**config)
+    def wait(self):
+        return self.accelerator.wait_for_everyone()
 
-        tokenizer.load_state_dict(pkg['model'], strict = strict)
-        return tokenizer
+    def print(self, msg):
+        return self.accelerator.print(msg)
 
     def save(self, path, overwrite = True):
         path = Path(path)
         assert overwrite or not path.exists()
 
         pkg = dict(
-            model = self.state_dict(),
-            config = self._config,
+            model = self.unwrapped_model.state_dict(),
+            ema_model = self.ema_model.state_dict(),
+            optimizer = self.optimizer.state_dict(),
+            warmup = self.warmup.state_dict(),
+            scheduler = self.scheduler.state_dict(),
             version = __version__,
+            step = self.step.item(),
+            config = self.unwrapped_model._config
         )
 
         torch.save(pkg, str(path))
 
-    @beartype
-    def encode(
-        self,
-        *,
-        vertices:         TensorType['b', 'nv', 3, float],
-        faces:            TensorType['b', 'nf', 3, int],
-        face_edges:       TensorType['b', 'e', 2, int],
-        face_mask:        TensorType['b', 'nf', bool],
-        face_edges_mask:  TensorType['b', 'e', bool],
-        return_face_coordinates = False
-    ):
-        """
-        einops:
-        b - batch
-        nf - number of faces
-        nv - number of vertices (3)
-        c - coordinates (3)
-        d - embed dim
-        """
-
-        batch, num_vertices, num_coors, device = *vertices.shape, vertices.device
-        _, num_faces, _ = faces.shape
-
-        face_without_pad = faces.masked_fill(~rearrange(face_mask, 'b nf -> b nf 1'), 0)
-
-        faces_vertices = repeat(face_without_pad, 'b nf nv -> b nf nv c', c = num_coors)
-        vertices = repeat(vertices, 'b nv c -> b nf nv c', nf = num_faces)
-
-        # continuous face coords
-
-        face_coords = vertices.gather(-2, faces_vertices)
-
-        # compute derived features and embed
-
-        derived_features = get_derived_face_features(face_coords)
-
-        discrete_angle = self.discretize_angle(derived_features['angles'])
-        angle_embed = self.angle_embed(discrete_angle)
-
-        discrete_area = self.discretize_area(derived_features['area'])
-        area_embed = self.area_embed(discrete_area)
-
-        discrete_normal = self.discretize_normals(derived_features['normals'])
-        normal_embed = self.normal_embed(discrete_normal)
-
-        # discretize vertices for face coordinate embedding
-
-        discrete_face_coords = self.discretize_face_coords(face_coords)
-        discrete_face_coords = rearrange(discrete_face_coords, 'b nf nv c -> b nf (nv c)') # 9 coordinates per face
-
-        face_coor_embed = self.coor_embed(discrete_face_coords)
-        face_coor_embed = rearrange(face_coor_embed, 'b nf c d -> b nf (c d)')
-
-        # combine all features and project into model dimension
+    def load(self, path):
+        path = Path(path)
+        assert path.exists()
 
-        face_embed, _ = pack([face_coor_embed, angle_embed, area_embed, normal_embed], 'b nf *')
-        face_embed = self.project_in(face_embed)
+        pkg = torch.load(str(path))
 
-        # handle variable lengths by using masked_select and masked_scatter
+        if version.parse(__version__) != version.parse(pkg['version']):
+            self.print(f'loading saved mesh autoencoder at version {pkg["version"]}, but current package version is {__version__}')
 
-        # first handle edges
-        # needs to be offset by number of faces for each batch
+        self.model.load_state_dict(pkg['model'])
+        self.ema_model.load_state_dict(pkg['ema_model'])
+        self.optimizer.load_state_dict(pkg['optimizer'])
+        self.warmup.load_state_dict(pkg['warmup'])
+        self.scheduler.load_state_dict(pkg['scheduler'])
 
-        face_index_offsets = reduce(face_mask.long(), 'b nf -> b', 'sum')
-        face_index_offsets = F.pad(face_index_offsets.cumsum(dim = 0), (1, -1), value = 0)
-        face_index_offsets = rearrange(face_index_offsets, 'b -> b 1 1')
+        self.step.copy_(pkg['step'])
 
-        face_edges = face_edges + face_index_offsets
-        face_edges = face_edges[face_edges_mask]
-        face_edges = rearrange(face_edges, 'be ij -> ij be')
+    def next_data_to_forward_kwargs(self, dl_iter) -> dict:
+        data = next(dl_iter)
 
-        # next prepare the face_mask for using masked_select and masked_scatter
+        if isinstance(data, tuple):
+            forward_kwargs = dict(zip(self.data_kwargs, data))
 
-        dtype = face_embed.dtype
-        orig_face_embed_shape = face_embed.shape
+        elif isinstance(data, dict):
+            forward_kwargs = data
 
-        face_embed = face_embed[face_mask]
+        return forward_kwargs
 
-        for conv in self.encoders:
-            face_embed = conv(face_embed, face_edges)
+    def forward(self):
+        step = self.step.item()
+        dl_iter = cycle(self.dataloader)
 
-        zeros = torch.zeros(orig_face_embed_shape, device = device, dtype = dtype)
-        face_embed = zeros.masked_scatter(rearrange(face_mask, '... -> ... 1'), face_embed)
+        if self.is_main and self.should_validate:
+            val_dl_iter = cycle(self.val_dataloader)
 
-        for attn, ff in self.encoder_local_attn_blocks:
-            face_embed = attn(face_embed, mask = face_mask) + face_embed
-            face_embed = ff(face_embed) + face_embed
+        while step < self.num_train_steps:
 
-        face_embed = self.final_encoder_norm(face_embed)
+            for i in range(self.grad_accum_every):
+                is_last = i == (self.grad_accum_every - 1)
+                maybe_no_sync = partial(self.accelerator.no_sync, self.model) if not is_last else nullcontext
 
-        if not return_face_coordinates:
-            return face_embed
+                forward_kwargs = self.next_data_to_forward_kwargs(dl_iter)
 
-        return face_embed, discrete_face_coords
+                with self.accelerator.autocast(), maybe_no_sync():
+                    loss = self.model(**forward_kwargs)
 
-    @beartype
-    def quantize(
-        self,
-        *,
-        faces: TensorType['b', 'nf', 3, int],
-        face_mask: TensorType['b', 'n', bool],
-        face_embed: TensorType['b', 'nf', 'd', float],
-        pad_id = None,
-        rvq_sample_codebook_temp = 1.
-    ):
-        pad_id = default(pad_id, self.pad_id)
-        batch, num_faces, device = *faces.shape[:2], faces.device
+                    self.accelerator.backward(loss / self.grad_accum_every)
 
-        max_vertex_index = faces.amax()
-        num_vertices = int(max_vertex_index.item() + 1)
+            self.print(f'loss: {loss.item():.3f}')
 
-        face_embed = self.project_dim_codebook(face_embed)
-        face_embed = rearrange(face_embed, 'b nf (nv d) -> b nf nv d', nv = 3)
+            self.log(loss = loss.item())
 
-        vertex_dim = face_embed.shape[-1]
-        vertices = torch.zeros((batch, num_vertices, vertex_dim), device = device)
+            if exists(self.max_grad_norm):
+                self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
 
-        # create pad vertex, due to variable lengthed faces
+            self.optimizer.step()
+            self.optimizer.zero_grad()
 
-        pad_vertex_id = num_vertices
-        vertices = F.pad(vertices, (0, 0, 0, 1), value = 0.)
+            if not self.accelerator.optimizer_step_was_skipped:
+                with self.warmup.dampening():
+                    self.scheduler.step()
 
-        faces = faces.masked_fill(~rearrange(face_mask, 'b n -> b n 1'), pad_vertex_id)
+            step += 1
+            self.step.add_(1)
 
-        # prepare for scatter mean
+            self.wait()
 
-        faces_with_dim = repeat(faces, 'b nf nv -> b (nf nv) d', d = vertex_dim)
+            if self.is_main:
+                self.ema_model.update()
 
-        face_embed = rearrange(face_embed, 'b ... d -> b (...) d')
+            self.wait()
 
-        # scatter mean
+            if self.is_main and self.should_validate and divisible_by(step, self.val_every):
+                total_val_loss = 0.
+                self.ema_model.eval()
 
-        averaged_vertices = scatter_mean(vertices, faces_with_dim, face_embed, dim = -2)
+                num_val_batches = self.val_num_batches * self.grad_accum_every
 
-        # mask out null vertex token
+                for _ in range(num_val_batches):
+                    with self.accelerator.autocast(), torch.no_grad():
 
-        mask = torch.ones((batch, num_vertices + 1), device = device, dtype = torch.bool)
-        mask[:, -1] = False
+                        forward_kwargs = self.next_data_to_forward_kwargs(val_dl_iter)
 
-        # rvq specific kwargs
+                        val_loss = self.ema_model(**forward_kwargs)
 
-        quantize_kwargs = dict()
+                        total_val_loss += (val_loss / num_val_batches)
 
-        if isinstance(self.quantizer, ResidualVQ):
-            quantize_kwargs.update(sample_codebook_temp = rvq_sample_codebook_temp)
+                self.print(f'valid loss: {total_val_loss:.3f}')
+                self.log(val_loss = total_val_loss, step = step)
 
-        # residual VQ
+            self.wait()
 
-        quantized, codes, commit_loss = self.quantizer(averaged_vertices, mask = mask, **quantize_kwargs)
+            if self.is_main:
+                checkpoint_num = step // self.checkpoint_every
+                self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.{checkpoint_num}.pt')
 
-        # gather quantized vertexes back to faces for decoding
-        # now the faces have quantized vertices
+            self.wait()
 
-        face_embed_output = quantized.gather(-2, faces_with_dim)
-        face_embed_output = rearrange(face_embed_output, 'b (nf nv) d -> b nf (nv d)', nv = 3)
+        self.print('training complete')
+    def train(self, num_epochs, diplay_graph = False):
+        epoch_losses = []  # Initialize a list to store epoch losses
+        self.model.train() 
+        for epoch in range(num_epochs): 
+            total_loss = 0.0
+            num_batches = 0
 
-        face_embed_output = self.project_codebook_out(face_embed_output)
+            progress_bar = tqdm(self.dataloader, desc=f'Epoch {epoch + 1}/{num_epochs}') 
 
-        # vertex codes also need to be gathered to be organized by face sequence
-        # for autoregressive learning
+            for data in progress_bar: 
 
-        faces_with_quantized_dim = repeat(faces, 'b nf nv -> b (nf nv) q', q = self.num_quantizers)
-        codes_output = codes.gather(-2, faces_with_quantized_dim)
+                if isinstance(data, tuple): 
+                    forward_kwargs = dict(zip(self.data_kwargs, data))
 
-        # make sure codes being outputted have this padding
+                elif isinstance(data, dict): 
+                    forward_kwargs = data 
+                
 
-        face_mask = repeat(face_mask, 'b nf -> b (nf nv) 1', nv = 3)
-        codes_output = codes_output.masked_fill(~face_mask, self.pad_id)
+                with self.accelerator.autocast():
+                    loss = self.model(**forward_kwargs)
+                    self.accelerator.backward(loss)
 
-        # output quantized, codes, as well as commitment loss
+                if exists(self.max_grad_norm):
+                    self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
 
-        return face_embed_output, codes_output, commit_loss
+                self.optimizer.step()
+                self.optimizer.zero_grad()
 
-    @beartype
-    def decode(
-        self,
-        quantized: TensorType['b', 'n', 'd', float],
-        face_mask:  TensorType['b', 'n', bool]
-    ):
-        conv_face_mask = rearrange(face_mask, 'b n -> b 1 n')
-
-        x = quantized
-
-        for attn, ff in self.encoder_local_attn_blocks:
-            x = attn(x, mask = face_mask) + x
-            x = ff(x) + x
+                if not self.accelerator.optimizer_step_was_skipped:
+                    with self.warmup.dampening():
+                        self.scheduler.step()
+ 
+                current_loss = loss.item()
+                total_loss += current_loss
+                num_batches += 1
+                progress_bar.set_postfix(loss=current_loss)
+                
+            
+ 
+            avg_epoch_loss = total_loss / num_batches 
+            epoch_losses.append(avg_epoch_loss)
+            self.print(f'Epoch {epoch + 1} average loss: {avg_epoch_loss}')
+            self.wait()
 
-        x = rearrange(x, 'b n d -> b d n')
+            if self.checkpoint_every_epoch is not None and epoch != 0 and epoch % self.checkpoint_every_epoch == 0:
+                self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.3f}.pt')
+ 
 
-        for resnet_block in self.decoders:
-            x = resnet_block(x, mask = conv_face_mask)
-
-        return rearrange(x, 'b d n -> b n d')
+        self.print('Training complete') 
+        if diplay_graph:
+            plt.figure(figsize=(10, 5))
+            plt.plot(range(1, num_epochs + 1), epoch_losses, marker='o')
+            plt.title('Training Loss Over Epochs')
+            plt.xlabel('Epoch')
+            plt.ylabel('Average Loss')
+            plt.grid(True)
+            plt.show()
+# mesh transformer trainer
 
+class MeshTransformerTrainer(Module):
     @beartype
-    @torch.no_grad()
-    def decode_from_codes_to_faces(
+    def __init__(
         self,
-        codes: Tensor,
-        face_mask: Optional[TensorType['b', 'n', bool]] = None,
-        return_discrete_codes = False
+        model: MeshTransformer,
+        dataset: Dataset,
+        num_train_steps: int,
+        batch_size: int,
+        grad_accum_every: int,
+        learning_rate: float = 2e-4,
+        weight_decay: float = 0.,
+        max_grad_norm: Optional[float] = 0.5,
+        scheduler: Optional[Type[LRScheduler]] = None,
+        scheduler_kwargs: dict = dict(),
+        ema_kwargs: dict = dict(),
+        accelerator_kwargs: dict = dict(),
+        optimizer_kwargs: dict = dict(),
+        
+        checkpoint_every = 1000, 
+        checkpoint_every_epoch: Optional[int] = None,
+        checkpoint_folder = './checkpoints',
+        data_kwargs: Tuple[str, ...] = ['vertices', 'faces', 'face_edges', 'face_len', 'face_edges_len'],
+        warmup_steps = 1000,
+        use_wandb_tracking = False
     ):
-        codes = rearrange(codes, 'b ... -> b (...)')
-
-        if not exists(face_mask):
-            face_mask = reduce(codes != self.pad_id, 'b (nf nv q) -> b nf', 'all', nv = 3, q = self.num_quantizers)
-
-        # handle different code shapes
-
-        codes = rearrange(codes, 'b (n q) -> b n q', q = self.num_quantizers)
-
-        # decode
-
-        quantized = self.quantizer.get_output_from_indices(codes)
-        quantized = rearrange(quantized, 'b (nf nv) d -> b nf (nv d)', nv = 3)
+        super().__init__()
 
-        quantized = quantized.masked_fill(~face_mask[..., None], 0.)
-        face_embed_output = self.project_codebook_out(quantized)
+        # experiment tracker
 
-        decoded = self.decode(
-            face_embed_output,
-            face_mask = face_mask
-        )
+        self.use_wandb_tracking = use_wandb_tracking
 
-        decoded = decoded.masked_fill(~face_mask[..., None], 0.)
-        pred_face_coords = self.to_coor_logits(decoded)
+        if use_wandb_tracking:
+            accelerator_kwargs['log_with'] = 'wandb'
 
-        pred_face_coords = pred_face_coords.argmax(dim = -1)
+        if 'kwargs_handlers' not in accelerator_kwargs:
+            accelerator_kwargs['kwargs_handlers'] = [DEFAULT_DDP_KWARGS]
 
-        pred_face_coords = rearrange(pred_face_coords, '... (v c) -> ... v c', v = 3)
+        self.accelerator = Accelerator(**accelerator_kwargs)
 
-        # back to continuous space
+        self.model = model
 
-        continuous_coors = undiscretize(
-            pred_face_coords,
-            num_discrete = self.num_discrete_coors,
-            continuous_range = self.coor_continuous_range
+        self.optimizer = get_optimizer(
+            model.parameters(),
+            lr = learning_rate,
+            wd = weight_decay,
+            filter_by_requires_grad = True,
+            **optimizer_kwargs
         )
 
-        if not return_discrete_codes:
-            return continuous_coors
-
-        return continuous_coors, pred_face_coords
-
-    @torch.no_grad()
-    def tokenize(self, *args, **kwargs):
-        assert 'return_codes' not in kwargs
-        self.eval()
-        return self.forward(*args, return_codes = True, **kwargs)
-
-    @beartype
-    def forward(
-        self,
-        *,
-        vertices:       TensorType['b', 'nv', 3, float],
-        faces:          TensorType['b', 'nf', 3, int],
-        face_edges:     Optional[TensorType['b', 'e', 2, int]] = None,
-        return_codes = False,
-        return_loss_breakdown = False,
-        return_recon_faces = False,
-        only_return_recon_faces = False,
-        rvq_sample_codebook_temp = 1.
-    ):
-        if not exists(face_edges):
-            face_edges = derive_face_edges_from_faces(faces, pad_id = self.pad_id)
-
-        num_faces, num_face_edges, device = faces.shape[1], face_edges.shape[1], faces.device
-
-        face_mask = reduce(faces != self.pad_id, 'b nf c -> b nf', 'all')
-        face_edges_mask = reduce(face_edges != self.pad_id, 'b e ij -> b e', 'all')
+        self.warmup = warmup.LinearWarmup(self.optimizer, warmup_period = warmup_steps)
 
-        encoded, face_coordinates = self.encode(
-            vertices = vertices,
-            faces = faces,
-            face_edges = face_edges,
-            face_edges_mask = face_edges_mask,
-            face_mask = face_mask,
-            return_face_coordinates = True
-        )
-
-        quantized, codes, commit_loss = self.quantize(
-            face_embed = encoded,
-            faces = faces,
-            face_mask = face_mask,
-            rvq_sample_codebook_temp = rvq_sample_codebook_temp
+        if exists(scheduler):
+            self.scheduler = scheduler(self.optimizer, **scheduler_kwargs)
+        else:
+            self.scheduler = ConstantLRScheduler(self.optimizer)
+
+        self.dataloader = DataLoader(
+            dataset,
+            batch_size = batch_size,
+            shuffle = True,
+            drop_last = True,
+            collate_fn = partial(custom_collate, pad_id = model.pad_id)
         )
 
-        if return_codes:
-            assert not return_recon_faces, 'cannot return reconstructed faces when just returning raw codes'
-            return codes
-
-        decode = self.decode(
-            quantized,
-            face_mask = face_mask
+        self.data_kwargs = data_kwargs
+
+        (
+            self.model,
+            self.optimizer,
+            self.scheduler,
+            self.dataloader
+        ) = self.accelerator.prepare(
+            self.model,
+            self.optimizer,
+            self.scheduler,
+            self.dataloader
         )
 
-        pred_face_coords = self.to_coor_logits(decode)
-
-        # compute reconstructed faces if needed
-
-        if return_recon_faces or only_return_recon_faces:
-
-            recon_faces = undiscretize(
-                pred_face_coords.argmax(dim = -1),
-                num_discrete = self.num_discrete_coors,
-                continuous_range = self.coor_continuous_range,
-            )
-
-            recon_faces = rearrange(recon_faces, 'b nf (nv c) -> b nf nv c', nv = 3)
-            face_mask = rearrange(face_mask, 'b nf -> b nf 1 1')
-            recon_faces = recon_faces.masked_fill(~face_mask, float('nan'))
-
-        if only_return_recon_faces:
-            return recon_faces
-
-        # prepare for recon loss
-
-        pred_face_coords = rearrange(pred_face_coords, 'b ... c -> b c (...)')
-        face_coordinates = rearrange(face_coordinates, 'b ... -> b 1 (...)')
-
-        # reconstruction loss on discretized coordinates on each face
-        # they also smooth (blur) the one hot positions, localized label smoothing basically
-
-        with autocast(enabled = False):
-            pred_log_prob = pred_face_coords.log_softmax(dim = 1)
+        self.grad_accum_every = grad_accum_every
+        self.max_grad_norm = max_grad_norm
+        self.num_train_steps = num_train_steps
+        self.register_buffer('step', torch.tensor(0))
 
-            target_one_hot = torch.zeros_like(pred_log_prob).scatter(1, face_coordinates, 1.)
+        self.checkpoint_every_epoch = checkpoint_every_epoch
+        self.checkpoint_every = checkpoint_every
+        self.checkpoint_folder = Path(checkpoint_folder)
+        self.checkpoint_folder.mkdir(exist_ok = True, parents = True)
 
-            if self.bin_smooth_blur_sigma >= 0.:
-                target_one_hot = gaussian_blur_1d(target_one_hot, sigma = self.bin_smooth_blur_sigma)
-
-            # cross entropy with localized smoothing
-
-            recon_losses = (-target_one_hot * pred_log_prob).sum(dim = 1)
-
-            face_mask = repeat(face_mask, 'b nf -> b (nf r)', r = 9)
-            recon_loss = recon_losses[face_mask].mean()
-
-        # calculate total loss
-
-        total_loss = recon_loss + \
-                     commit_loss.sum() * self.commit_loss_weight
-
-        # calculate loss breakdown if needed
-
-        loss_breakdown = (recon_loss, commit_loss)
-
-        # some return logic
-
-        if not return_loss_breakdown:
-            if not return_recon_faces:
-                return total_loss
-
-            return recon_faces, total_loss
-
-        if not return_recon_faces:
-            return total_loss, loss_breakdown
-
-        return recon_faces, total_loss, loss_breakdown
-
-class MeshTransformer(Module):
+    @contextmanager
     @beartype
-    def __init__(
+    def trackers(
         self,
-        autoencoder: MeshAutoencoder,
-        *,
-        dim = 512,
-        max_seq_len = 8192,
-        flash_attn = True,
-        attn_depth = 12,
-        attn_dim_head = 64,
-        attn_heads = 16,
-        attn_kwargs: dict = dict(
-            ff_glu = True,
-            num_mem_kv = 4
-        ),
-        fine_pre_gateloop_depth = 2,
-        fine_attn_depth = 2,
-        fine_attn_dim_head = 32,
-        fine_attn_heads = 8,
-        pad_id = -1,
-        condition_on_text = False,
-        text_condition_model_types = ('t5',),
-        text_condition_cond_drop_prob = 0.25
+        project_name: str,
+        run_name: Optional[str] = None,
+        hps: Optional[dict] = None
     ):
-        super().__init__()
+        assert self.use_wandb_tracking
 
-        self.autoencoder = autoencoder
-        set_module_requires_grad_(autoencoder, False)
+        self.accelerator.init_trackers(project_name, config = hps)
 
-        self.codebook_size = autoencoder.codebook_size
-        self.num_quantizers = autoencoder.num_quantizers
+        if exists(run_name):
+            self.accelerator.trackers[0].run.name = run_name
 
-        self.sos_token = nn.Parameter(torch.randn(dim))
-        self.eos_token_id = self.codebook_size
+        yield
+        self.accelerator.end_training()
 
-        # they use axial positional embeddings
-
-        assert divisible_by(max_seq_len, 3 * self.num_quantizers) # 3 vertices per face, with D codes per vertex
-
-        self.token_embed = nn.Embedding(self.codebook_size + 1, dim)
-
-        self.quantize_level_embed = nn.Parameter(torch.randn(self.num_quantizers, dim))
-        self.vertex_embed = nn.Parameter(torch.randn(3, dim))
-
-        self.abs_pos_emb = nn.Embedding(max_seq_len, dim)
-
-        self.max_seq_len = max_seq_len
-
-        # text condition
-
-        self.condition_on_text = condition_on_text
-        self.conditioner = None
-
-        cross_attn_dim_context = None
-
-        if condition_on_text:
-            self.conditioner = TextEmbeddingReturner(
-                model_types = text_condition_model_types,
-                cond_drop_prob = text_condition_cond_drop_prob
-            )
-            cross_attn_dim_context = self.conditioner.dim_latent
-
-        # for summarizing the vertices of each face
-
-        self.to_face_tokens = nn.Sequential(
-            nn.Linear(self.num_quantizers * 3 * dim, dim),
-            nn.LayerNorm(dim)
-        )
-
-        # main autoregressive attention network
-        # attending to a face token
-
-        self.decoder = Decoder(
-            dim = dim,
-            depth = attn_depth,
-            dim_head = attn_dim_head,
-            heads = attn_heads,
-            flash_attn = flash_attn,
-            cross_attend = condition_on_text,
-            cross_attn_dim_context = cross_attn_dim_context,
-            **attn_kwargs
-        )
-
-        # address a weakness in attention
-
-        self.gateloop_layers = ModuleList([])
-
-        for _ in range(fine_pre_gateloop_depth):
-            self.gateloop_layers.append(SimpleGateLoopLayer(dim))
-
-        # decoding the vertices, 2-stage hierarchy
-
-        self.fine_decoder = Decoder(
-            dim = dim,
-            depth = fine_attn_depth,
-            dim_head = attn_dim_head,
-            heads = attn_heads,
-            flash_attn = flash_attn,
-            **attn_kwargs
-        )
-
-        # to logits
-
-        self.to_logits = nn.Linear(dim, self.codebook_size + 1)
-
-        # padding id
-        # force the autoencoder to use the same pad_id given in transformer
-
-        self.pad_id = pad_id
-        autoencoder.pad_id = pad_id
+    def log(self, **data_kwargs):
+        self.accelerator.log(data_kwargs, step = self.step.item())
 
     @property
     def device(self):
-        return next(self.parameters()).device
-
-    @beartype
-    def embed_texts(self, texts: List[str]):
-        return self.conditioner.embed_texts(texts)
-
-    @eval_decorator
-    @torch.no_grad()
-    @beartype
-    def generate(
-        self,
-        prompt: Optional[Tensor] = None,
-        batch_size: Optional[int] = None,
-        filter_logits_fn: Callable = top_k,
-        filter_kwargs: dict = dict(),
-        temperature = 1.,
-        return_codes = False,
-        texts: Optional[List[str]] = None,
-        text_embeds: Optional[Tensor] = None,
-        cond_scale = 1.,
-        cache_kv = False
-    ):
-        if exists(prompt):
-            assert not exists(batch_size)
-
-            prompt = rearrange(prompt, 'b ... -> b (...)')
-            assert prompt.shape[-1] <= self.max_seq_len
-
-            batch_size = prompt.shape[0]
-
-        if self.condition_on_text:
-            assert exists(texts) ^ exists(text_embeds), '`text` or `text_embeds` must be passed in if `condition_on_text` is set to True'
-            if exists(texts):
-                text_embeds = self.embed_texts(texts)
-
-            batch_size = default(batch_size, text_embeds.shape[0])
-
-        batch_size = default(batch_size, 1)
-
-        codes = default(prompt, torch.empty((batch_size, 0), dtype = torch.long, device = self.device))
-
-        curr_length = codes.shape[-1]
-
-        # for now, kv cache disabled when conditioning on text
-
-        assert not cache_kv, 'caching not available yet'
-        can_cache = cache_kv and (not self.condition_on_text or cond_scale == 1.)
-
-        cache = None
-
-        for i in tqdm(range(curr_length, self.max_seq_len)):
-            # [sos] v1([q1] [q2] [q1] [q2] [q1] [q2]) v2([q1] [q2] [q1] [q2] [q1] [q2]) -> 0 1 2 3 4 5 6 7 8 9 10 11 12 -> F v1(F F F F F T) v2(F F F F F T)
-
-            can_eos = i != 0 and divisible_by(i, self.num_quantizers * 3)  # only allow for eos to be decoded at the end of each face, defined as 3 vertices with D residual VQ codes
-
-            logits, new_cache = self.forward_on_codes(
-                codes,
-                cache = cache,
-                text_embeds = text_embeds,
-                return_loss = False,
-                return_cache = True,
-                append_eos = False,
-                cond_scale = cond_scale
-            )
-
-            if can_cache:
-                cache = new_cache
-
-            logits = logits[:, -1]
-
-            if not can_eos:
-                logits[:, -1] = -torch.finfo(logits.dtype).max
+        return self.unwrapped_model.device
 
-            filtered_logits = filter_logits_fn(logits, **filter_kwargs)
-
-            if temperature == 0.:
-                sample = filtered_logits.argmax(dim = -1)
-            else:
-                probs = F.softmax(filtered_logits / temperature, dim = -1)
-                sample = torch.multinomial(probs, 1)
-
-            codes, _ = pack([codes, sample], 'b *')
-
-            # check for all rows to have [eos] to terminate
+    @property
+    def is_main(self):
+        return self.accelerator.is_main_process
 
-            is_eos_codes = (codes == self.eos_token_id)
+    @property
+    def unwrapped_model(self):
+        return self.accelerator.unwrap_model(self.model)
 
-            if not is_eos_codes.any(dim = -1).all():
-                continue
+    @property
+    def is_local_main(self):
+        return self.accelerator.is_local_main_process
 
-            shifted_is_eos_tokens = F.pad(is_eos_codes, (1, -1))
-            mask = shifted_is_eos_tokens.float().cumsum(dim = -1) >= 1
-            codes = codes.masked_fill(mask, self.pad_id)
-            break
+    def wait(self):
+        return self.accelerator.wait_for_everyone()
 
-        if return_codes:
-            codes = codes[:, 1:] # remove sos
-            codes = rearrange(codes, 'b (n q) -> b n q', q = self.num_quantizers)
-            return codes
+    def print(self, msg):
+        return self.accelerator.print(msg)
 
-        self.autoencoder.eval()
-        return self.autoencoder.decode_from_codes_to_faces(codes)
+    def save(self, path, overwrite = True):
+        path = Path(path)
+        assert overwrite or not path.exists()
 
-    def forward(
-        self,
-        *,
-        vertices:       TensorType['b', 'nv', 3, int],
-        faces:          TensorType['b', 'nf', 3, int],
-        face_edges:     Optional[TensorType['b', 'e', 2, int]] = None,
-        cache:          Optional[LayerIntermediates] = None,
-        **kwargs
-    ):
-        codes = self.autoencoder.tokenize(
-            vertices = vertices,
-            faces = faces,
-            face_edges = face_edges
+        pkg = dict(
+            model = self.unwrapped_model.state_dict(),
+            optimizer = self.optimizer.state_dict(),
+            warmup = self.warmup.state_dict(),
+            scheduler = self.scheduler.state_dict(),
+            step = self.step.item(),
+            version = __version__
         )
 
-        return self.forward_on_codes(codes, cache = cache, **kwargs)
-
-    @classifier_free_guidance
-    def forward_on_codes(
-        self,
-        codes = None,
-        return_loss = True,
-        return_cache = False,
-        append_eos = True,
-        cache = None,
-        texts: Optional[List[str]] = None,
-        text_embeds: Optional[Tensor] = None,
-        cond_drop_prob = 0.
-    ):
-        # handle text conditions
-
-        attn_context_kwargs = dict()
-
-        if self.condition_on_text:
-            assert exists(texts) ^ exists(text_embeds), '`text` or `text_embeds` must be passed in if `condition_on_text` is set to True'
-
-            if exists(texts):
-                text_embeds = self.conditioner.embed_texts(texts)
-
-            if exists(codes):
-                assert text_embeds.shape[0] == codes.shape[0], 'batch size of texts or text embeddings is not equal to the batch size of the mesh codes'
-
-            _, maybe_dropped_text_embeds = self.conditioner(
-                text_embeds = text_embeds,
-                cond_drop_prob = cond_drop_prob
-            )
-
-            attn_context_kwargs = dict(
-                context = maybe_dropped_text_embeds.embed,
-                context_mask = maybe_dropped_text_embeds.mask
-            )
-
-        # take care of codes that may be flattened
-
-        if codes.ndim > 2:
-            codes = rearrange(codes, 'b ... -> b (...)')
-
-        # get some variable
-
-        batch, seq_len, device = *codes.shape, codes.device
-
-        assert seq_len <= self.max_seq_len, f'received codes of length {seq_len} but needs to be less than or equal to set max_seq_len {self.max_seq_len}'
-
-        # auto append eos token
-
-        if append_eos:
-            assert exists(codes)
-
-            code_lens = ((codes != self.pad_id).cumsum(dim = -1) == 0).sum(dim = -1)
-
-            codes = F.pad(codes, (0, 1), value = 0)
-
-            batch_arange = torch.arange(batch, device = device)
-            batch_arange = rearrange(batch_arange, '... -> ... 1')
-
-            codes[batch_arange, code_lens] = self.eos_token_id
-
-        # if returning loss, save the labels for cross entropy
-
-        if return_loss:
-            assert seq_len > 0
-            codes, labels = codes[:, :-1], codes
-
-        # token embed (each residual VQ id)
-
-        codes = codes.masked_fill(codes == self.pad_id, 0)
-        codes = self.token_embed(codes)
-
-        # codebook embed + absolute positions
-
-        seq_arange = torch.arange(codes.shape[-2], device = device)
-
-        codes = codes + self.abs_pos_emb(seq_arange)
-
-        # embedding for quantizer level
-
-        code_len = codes.shape[1]
-
-        level_embed = repeat(self.quantize_level_embed, 'q d -> (r q) d', r = ceil(code_len / self.num_quantizers))
-        codes = codes + level_embed[:code_len]
-
-        # embedding for each vertex
-
-        vertex_embed = repeat(self.vertex_embed, 'nv d -> (r nv q) d', r = ceil(code_len / (3 * self.num_quantizers)), q = self.num_quantizers)
-        codes = codes + vertex_embed[:code_len]
-
-        # create a token per face, by summarizing the 3 vertices
-        # this is similar in design to the RQ transformer from Lee et al. https://arxiv.org/abs/2203.01941
-
-        num_tokens_per_face = self.num_quantizers * 3
-        code_len_is_multiple_of_face = divisible_by(code_len, num_tokens_per_face)
-        next_multiple_code_len = ceil(code_len / num_tokens_per_face) * num_tokens_per_face
+        torch.save(pkg, str(path))
 
-        codes = pad_to_length(codes, next_multiple_code_len, dim = -2)
+    def load(self, path):
+        path = Path(path)
+        assert path.exists()
 
-        # grouped codes will be used for the second stage
+        pkg = torch.load(str(path))
 
-        grouped_codes = rearrange(codes, 'b (nf n) d -> b nf n d', n = num_tokens_per_face)
+        if version.parse(__version__) != version.parse(pkg['version']):
+            self.print(f'loading saved mesh transformer at version {pkg["version"]}, but current package version is {__version__}')
 
-        # create the coarse tokens for the first attention network
+        self.model.load_state_dict(pkg['model'])
+        self.optimizer.load_state_dict(pkg['optimizer'])
+        self.scheduler.load_state_dict(pkg['scheduler'])
+        self.warmup.load_state_dict(pkg['warmup'])
+        self.step.copy_(pkg['step'])
 
-        face_codes = grouped_codes if code_len_is_multiple_of_face else grouped_codes[:, :-1]
-        face_codes = rearrange(face_codes, 'b nf n d -> b nf (n d)')
-        face_codes = self.to_face_tokens(face_codes)
+    def forward(self):
+        step = self.step.item()
+        dl_iter = cycle(self.dataloader)
 
-        # caches
+        while step < self.num_train_steps:
 
-        coarse_cache, fine_cache = cache if exists(cache) else (None, None)
+            for i in range(self.grad_accum_every):
+                is_last = i == (self.grad_accum_every - 1)
+                maybe_no_sync = partial(self.accelerator.no_sync, self.model) if not is_last else nullcontext
 
-        # attention on face codes (coarse)
+                data = next(dl_iter)
 
-        attended_face_codes, coarse_cache = self.decoder(
-            face_codes,
-            cache = coarse_cache,
-            return_hiddens = True,
-            **attn_context_kwargs
-        )
+                if isinstance(data, tuple):
+                    forward_kwargs = dict(zip(self.data_kwargs, data))
 
-        # auto prepend sos token
+                elif isinstance(data, dict):
+                    forward_kwargs = data
 
-        sos = repeat(self.sos_token, 'd -> b d', b = batch)
+                with self.accelerator.autocast(), maybe_no_sync():
+                    loss = self.model(**forward_kwargs)
 
-        attended_face_codes, _ = pack([sos, attended_face_codes], 'b * d')
+                    self.accelerator.backward(loss / self.grad_accum_every)
 
-        grouped_codes = pad_to_length(grouped_codes, attended_face_codes.shape[-2], dim = 1)
-        fine_vertex_codes, _ = pack([attended_face_codes, grouped_codes], 'b n * d')
+            self.print(f'loss: {loss.item():.3f}')
 
-        fine_vertex_codes = fine_vertex_codes[..., :-1, :]
+            self.log(loss = loss.item())
 
-        # gateloop layers
+            if exists(self.max_grad_norm):
+                self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
 
-        if not is_empty(self.gateloop_layers):
-            fine_vertex_codes = rearrange(fine_vertex_codes, 'b nf n d -> b (nf n) d')
+            self.optimizer.step()
+            self.optimizer.zero_grad()
 
-            for gateloop in self.gateloop_layers:
-                fine_vertex_codes = gateloop(fine_vertex_codes) + fine_vertex_codes
+            if not self.accelerator.optimizer_step_was_skipped:
+                with self.warmup.dampening():
+                    self.scheduler.step()
 
-            fine_vertex_codes = rearrange(fine_vertex_codes, 'b (nf n) d -> b nf n d', n = num_tokens_per_face)
+            step += 1
+            self.step.add_(1)
 
-        # fine attention - 2nd stage
+            self.wait()
 
-        fine_vertex_codes = rearrange(fine_vertex_codes, 'b nf n d -> (b nf) n d')
+            if self.is_main and divisible_by(step, self.checkpoint_every):
+                checkpoint_num = step // self.checkpoint_every
+                self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.{checkpoint_num}.pt')
 
-        attended_vertex_codes, fine_cache = self.fine_decoder(
-            fine_vertex_codes,
-            cache = fine_cache,
-            return_hiddens = True
-        )
+            self.wait()
 
-        # reconstitute original sequence
+        self.print('training complete')
+        
+    def train(self, num_epochs, diplay_graph = False):
+        epoch_losses = []  # Initialize a list to store epoch losses
+        self.model.train()
+        for epoch in range(num_epochs): 
+            total_loss = 0.0
+            num_batches = 0
 
-        attended_vertex_codes = rearrange(attended_vertex_codes, '(b nf) n d -> b (nf n) d', b = batch)
-        attended_vertex_codes = attended_vertex_codes[:, :(code_len + 1)]
+            progress_bar = tqdm(self.dataloader, desc=f'Epoch {epoch + 1}/{num_epochs}') 
 
-        # logits
+            for data in progress_bar: 
 
-        logits = self.to_logits(attended_vertex_codes)
+                if isinstance(data, tuple): 
+                    forward_kwargs = dict(zip(self.data_kwargs, data))
 
-        if not return_loss:
-            if not return_cache:
-                return logits
+                elif isinstance(data, dict): 
+                    forward_kwargs = data 
+                
 
-            return logits, (coarse_cache, fine_cache)
+                with self.accelerator.autocast():
+                    loss = self.model(**forward_kwargs)
+                    self.accelerator.backward(loss)
 
-        # loss
+                if exists(self.max_grad_norm):
+                    self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
 
-        ce_loss = F.cross_entropy(
-            rearrange(logits, 'b n c -> b c n'),
-            labels,
-            ignore_index = self.pad_id
-        )
+                self.optimizer.step()
+                self.optimizer.zero_grad()
 
-        return ce_loss
+                if not self.accelerator.optimizer_step_was_skipped:
+                    with self.warmup.dampening():
+                        self.scheduler.step()
+ 
+                current_loss = loss.item()
+                total_loss += current_loss
+                num_batches += 1
+                progress_bar.set_postfix(loss=current_loss)
+ 
+            avg_epoch_loss = total_loss / num_batches 
+            epoch_losses.append(avg_epoch_loss)
+            self.print(f'Epoch {epoch + 1} average loss: {avg_epoch_loss}')
+            self.wait() 
+            if self.checkpoint_every_epoch is not None and epoch != 0 and epoch % self.checkpoint_every_epoch == 0:
+                self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.3f}.pt')
+                
+        self.print('Training complete') 
+        if diplay_graph:
+            plt.figure(figsize=(10, 5))
+            plt.plot(range(1, num_epochs + 1), epoch_losses, marker='o')
+            plt.title('Training Loss Over Epochs')
+            plt.xlabel('Epoch')
+            plt.ylabel('Average Loss')
+            plt.grid(True)
+            plt.show()
\ No newline at end of file

From 3a4645595a3765cc3f2fb6be9c521f383a0bf0d6 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Thu, 14 Dec 2023 06:10:46 +0100
Subject: [PATCH 14/84] Revert "removed grad_accum_every"

This reverts commit fc49064bacd3e86b41ad92b5feb20a83413aaeb4.
---
 meshgpt_pytorch/trainer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 64095290..dd3b55b7 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -390,7 +390,7 @@ def train(self, num_epochs, diplay_graph = False):
 
                 with self.accelerator.autocast():
                     loss = self.model(**forward_kwargs)
-                    self.accelerator.backward(loss)
+                    self.accelerator.backward(loss / self.grad_accum_every)
 
                 if exists(self.max_grad_norm):
                     self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
@@ -660,7 +660,7 @@ def train(self, num_epochs, diplay_graph = False):
 
                 with self.accelerator.autocast():
                     loss = self.model(**forward_kwargs)
-                    self.accelerator.backward(loss)
+                    self.accelerator.backward(loss / self.grad_accum_every)
 
                 if exists(self.max_grad_norm):
                     self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)

From 7833b5b7431d9365e89b2d15b8c12be058f436bd Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Thu, 14 Dec 2023 06:23:42 +0100
Subject: [PATCH 15/84] Revert "removed grad_accum_every"

This reverts commit fa515a6adbde0afede2152e4594954c5185d72c8.
---
 meshgpt_pytorch/meshgpt_pytorch.py | 14 +-------------
 1 file changed, 1 insertion(+), 13 deletions(-)

diff --git a/meshgpt_pytorch/meshgpt_pytorch.py b/meshgpt_pytorch/meshgpt_pytorch.py
index dd61cf91..f9b39d28 100644
--- a/meshgpt_pytorch/meshgpt_pytorch.py
+++ b/meshgpt_pytorch/meshgpt_pytorch.py
@@ -354,7 +354,7 @@ def __init__(
             )
 
         self.pad_id = pad_id # for variable lengthed faces, padding quantized ids will be set to this value
-        
+
         self.project_codebook_out = nn.Linear(dim_codebook * 3, dim)
 
         self.decoders = ModuleList([])
@@ -362,19 +362,7 @@ def __init__(
         for _ in range(decoder_depth):
             resnet_block = ResnetBlock(dim)
 
-<<<<<<< Updated upstream
-            self.decoders.append(resnet_block)
-=======
-<<<<<<< HEAD
-            self.decoders.append(ModuleList([
-                attn,
-                resnet_block,
-                nn.Dropout1d(0.1)
-            ]))
-=======
             self.decoders.append(resnet_block)
->>>>>>> ee440683b4d266e612da90a020bc33d7f26f1f61
->>>>>>> Stashed changes
 
         self.to_coor_logits = nn.Sequential(
             nn.Linear(dim, num_discrete_coors * 9),

From ea4017038b4ad16014619ceb0f109f928653433e Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Thu, 14 Dec 2023 19:17:34 +0100
Subject: [PATCH 16/84] return avg loss

---
 meshgpt_pytorch/trainer.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 80a316cf..6dcb0d30 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -435,6 +435,7 @@ def train(self, num_epochs, diplay_graph = False):
             plt.ylabel('Average Loss')
             plt.grid(True)
             plt.show()
+        return epoch_losses[-1]
 # mesh transformer trainer
 
 class MeshTransformerTrainer(Module):
@@ -700,4 +701,5 @@ def train(self, num_epochs, diplay_graph = False):
             plt.xlabel('Epoch')
             plt.ylabel('Average Loss')
             plt.grid(True)
-            plt.show()
\ No newline at end of file
+            plt.show()
+        return epoch_losses[-1]
\ No newline at end of file

From b9204f62d937fdccada978b4a862f371937c55d0 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Thu, 14 Dec 2023 23:24:27 +0100
Subject: [PATCH 17/84] forced model input

---
 meshgpt_pytorch/trainer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 6dcb0d30..27ac6f53 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -397,8 +397,8 @@ def train(self, num_epochs, diplay_graph = False):
                 
 
                 with self.accelerator.autocast():
-                    loss = self.model(**forward_kwargs)
-                    self.accelerator.backward(loss / self.grad_accum_every)
+                    loss = self.model(vertices = forward_kwargs['vertices'], faces= forward_kwargs['faces'])
+                    self.accelerator.backward(loss)
 
                 if exists(self.max_grad_norm):
                     self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)

From 628d756017c04af3c1b571d9f1a0039cac7af245 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Fri, 15 Dec 2023 01:47:26 +0100
Subject: [PATCH 18/84] custom_collate fixed

---
 meshgpt_pytorch/data.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/meshgpt_pytorch/data.py b/meshgpt_pytorch/data.py
index d91f16c9..6122d587 100644
--- a/meshgpt_pytorch/data.py
+++ b/meshgpt_pytorch/data.py
@@ -116,6 +116,7 @@ def custom_collate(data, pad_id = -1):
             output.append(padded)
         else:
             datum = list(datum)
+            output.append(datum)
 
     output = tuple(output)
 

From e57e6f14991ca73a4e919b320eb3393c06d2c85b Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Sat, 16 Dec 2023 20:50:58 +0100
Subject: [PATCH 19/84] added stop_at_loss

---
 meshgpt_pytorch/trainer.py | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 44eca34b..ee32739b 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -391,7 +391,7 @@ def forward(self):
             self.wait()
 
         self.print('training complete')
-    def train(self, num_epochs, diplay_graph = False):
+    def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
         epoch_losses = []  # Initialize a list to store epoch losses
         self.model.train() 
         for epoch in range(num_epochs): 
@@ -437,6 +437,12 @@ def train(self, num_epochs, diplay_graph = False):
 
             if self.checkpoint_every_epoch is not None and epoch != 0 and epoch % self.checkpoint_every_epoch == 0:
                 self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.3f}.pt')
+                
+            if stop_at_loss is not None and avg_epoch_loss < stop_at_loss: 
+                self.print(f'Stopping training at epoch {epoch} with average loss {avg_epoch_loss}')
+                if self.checkpoint_every_epoch is not None:
+                    self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.stop_at_loss_avg_loss_{avg_epoch_loss:.3f}.pt') 
+                break   
  
 
         self.print('Training complete') 
@@ -662,7 +668,7 @@ def forward(self):
 
         self.print('training complete')
         
-    def train(self, num_epochs, diplay_graph = False):
+    def train(self, num_epochs, stop_at_loss = None,  diplay_graph = False):
         epoch_losses = []  # Initialize a list to store epoch losses
         self.model.train()
         for epoch in range(num_epochs): 
@@ -706,6 +712,12 @@ def train(self, num_epochs, diplay_graph = False):
             if self.checkpoint_every_epoch is not None and epoch != 0 and epoch % self.checkpoint_every_epoch == 0:
                 self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.3f}.pt')
                 
+            if stop_at_loss is not None and avg_epoch_loss < stop_at_loss: 
+                self.print(f'Stopping training at epoch {epoch} with average loss {avg_epoch_loss}')
+                if self.checkpoint_every_epoch is not None:
+                    self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.stop_at_loss_avg_loss_{avg_epoch_loss:.3f}.pt') 
+                break   
+                
         self.print('Training complete') 
         if diplay_graph:
             plt.figure(figsize=(10, 5))

From f012e9501f64f1e558ca0e8061cc06e1efb89681 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Tue, 19 Dec 2023 16:56:51 +0100
Subject: [PATCH 20/84] added notebook

---
 MeshGPT Testing.ipynb | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 MeshGPT Testing.ipynb

diff --git a/MeshGPT Testing.ipynb b/MeshGPT Testing.ipynb
new file mode 100644
index 00000000..6f43ee09
--- /dev/null
+++ b/MeshGPT Testing.ipynb	
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:12.944690Z","iopub.status.busy":"2023-12-19T01:39:12.944345Z","iopub.status.idle":"2023-12-19T01:39:38.152713Z","shell.execute_reply":"2023-12-19T01:39:38.151433Z","shell.execute_reply.started":"2023-12-19T01:39:12.944662Z"},"trusted":true},"outputs":[],"source":["!pip install -q git+https://github.com/MarcusLoppe/meshgpt-pytorch.git trimesh"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:38.155749Z","iopub.status.busy":"2023-12-19T01:39:38.155103Z","iopub.status.idle":"2023-12-19T01:39:44.048426Z","shell.execute_reply":"2023-12-19T01:39:44.047605Z","shell.execute_reply.started":"2023-12-19T01:39:38.155692Z"},"trusted":true},"outputs":[],"source":["import torch\n","import trimesh\n","import numpy as np\n","import os\n","import csv \n","import math \n","\n","from meshgpt_pytorch import (\n","    MeshTransformerTrainer,\n","    MeshAutoencoderTrainer,\n","    MeshAutoencoder,\n","    MeshTransformer\n",")\n","\n","def get_3d_data(file_path): \n","    mesh = trimesh.load(file_path, force='mesh')\n","    \n","    # Extract vertices and faces\n","    vertices = mesh.vertices\n","    faces = mesh.faces\n","       \n","    centered_vertices = vertices - np.mean(vertices, axis=0)\n"," \n","    max_abs = np.max(np.abs(centered_vertices))\n","    vertices_normalized = centered_vertices / (max_abs / 0.95)  \n","     \n","    \n","    # Sort vertices in specified order: y, x, z\n","    vertices_sorted_indices = np.lexsort((vertices_normalized[:, 1], vertices_normalized[:, 0], vertices_normalized[:, 2]))\n","    vertices_normalized_sorted = vertices_normalized[vertices_sorted_indices]\n","    \n","    # Convert indices to tuples for creating Look-Up Table (LUT)\n","    tuples_sorted_indices = [tuple([index]) for index in vertices_sorted_indices.tolist()]\n","    \n","    # Create Look-Up Table (LUT)\n","    lut = {old_index[0]: new_index for new_index, old_index in enumerate(tuples_sorted_indices)}\n","    \n","    # Reindex faces using LUT\n","    faces_reindexed = np.vectorize(lut.get, otypes=[int])(faces) \n","    # Sort faces based on their lowest vertex index\n","    faces_sorted = faces_reindexed[np.lexsort(faces_reindexed.T)]\n","    \n","    #print(f\"{file_path} vertices {len(vertices)} faces {len(faces)}\")\n","    \n","    return vertices_normalized_sorted, faces_sorted \n","\n","def augment_mesh_scalar(vertices, scale_factor):\n","    # Apply a scalar factor to XYZ coordinates\n","    transformed_vertices = vertices * scale_factor\n","    return transformed_vertices\n","\n","def generate_scale_factors(num_examples, lower_limit=0.75, upper_limit=1.25): \n","    scale_factors = np.random.uniform(lower_limit, upper_limit, size=num_examples)\n","    return scale_factors\n","\n","def jitter_mesh(vertices, jitter_factor=0.01): \n","    offsets = np.random.uniform(-jitter_factor, jitter_factor, size=vertices.shape)\n"," \n","    jittered_vertices = vertices + offsets \n","    return jittered_vertices \n","\n","def augment_mesh(vertices, scale_factor):\n","    #vertices = jitter_mesh(vertices)\n","    transformed_vertices = vertices * scale_factor\n","    \n","    return transformed_vertices\n"," \n","\n","def load_models(directory, num_examples, variations):\n","    obj_datas = []  \n","    \n","    print(f\"num_examples: {num_examples}\")\n","    for filename in os.listdir(directory):  \n","        if (filename.endswith(\".obj\") or  filename.endswith(\".glb\") or  filename.endswith(\".off\")):\n","            file_path = os.path.join(directory, filename)\n","\n","            scale_factors = generate_scale_factors(variations, 0.7, 0.9) \n","            vertices, faces = get_3d_data(file_path) \n","\n","            for scale_factor in scale_factors: \n","                aug_vertices = augment_mesh(vertices.copy(), scale_factor) \n","                \n","                for _ in range(num_examples):\n","                    obj_data = {\"vertices\": torch.tensor(aug_vertices, dtype=torch.float), \"faces\":  torch.tensor(faces, dtype=torch.long),\"texts\": filename[:-4]}\n","                    obj_datas.append(obj_data)   \n","    return obj_datas\n","  \n","\n","def load_json(file,num_examples):\n","    obj_datas = []\n","    with open(file, \"r\") as json_file:\n","        loaded_data = json.load(json_file) \n","        for item in loaded_data:\n","            for _ in range(num_examples):\n","                obj_data = {\"vertices\": torch.tensor(item[\"vertices\"], dtype=torch.float), \"faces\":  torch.tensor(item[\"faces\"], dtype=torch.long),\"texts\": item[\"texts\"] } \n","                obj_datas.append(obj_data)\n","    return obj_datas\n","  \n","                        \n","         "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:44.050017Z","iopub.status.busy":"2023-12-19T01:39:44.049685Z","iopub.status.idle":"2023-12-19T01:39:44.063671Z","shell.execute_reply":"2023-12-19T01:39:44.062641Z","shell.execute_reply.started":"2023-12-19T01:39:44.049992Z"},"trusted":true},"outputs":[],"source":["import torch\n","from torch.utils.data import Dataset, DataLoader \n","from tqdm import tqdm\n","import numpy as np\n","import torch\n"," \n","class MeshDataset(Dataset): \n","    \n","    def __init__(self, data): \n","        self.data = data\n","        print(f\"Got {len(data)} data\")\n","\n","    def __len__(self):\n","        return len(self.data)\n","\n","    def __getitem__(self, idx): \n","        return self.data[idx]\n","    \n","    def embed_texts(self,transformer): \n","        unique_texts = set(item['texts'] for item in self.data)\n"," \n","        text_embeddings = transformer.embed_texts(list(unique_texts))\n","        print(f\"Got text_embeddings: {len(text_embeddings)}\") \n","        text_embedding_dict = dict(zip(unique_texts, text_embeddings))\n"," \n","        for item in self.data:\n","            text_value = item['texts']\n","            item['text_embeds'] = text_embedding_dict.get(text_value, None)\n","            del item['texts']\n"," \n","        \n","    def sample_obj(self):\n","        all_vertices = []\n","        all_faces = []\n","        vertex_offset = 0 \n","\n","\n","        translation_distance = 0.5  # Adjust as needed \n","        vertex_offset = len(all_vertices)\n","        \n","        for r, faces_coordinates in enumerate(self.data):    \n","            if r > 30:\n","                break\n","            for vertex in faces_coordinates[\"vertices\"]: \n","                all_vertices.append(f\"v {vertex[0]+translation_distance * (r / 0.2 - 1)} {vertex[1]} {vertex[2]}\\n\")\n","                #all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","            for face in faces_coordinates[\"faces\"]:\n","                all_faces.append(f\"f {face[0]+1+vertex_offset} {face[1]+1+vertex_offset} {face[2]+1+vertex_offset}\\n\") \n","                try:\n","                    all_vertices[face[0]+vertex_offset]\n","                    all_vertices[face[1]+vertex_offset]\n","                    all_vertices[face[2]+vertex_offset]\n","                except Exception  as e :\n","                    print(e)\n","                    print(face[0]+vertex_offset)\n","                    print(face[1]+vertex_offset)\n","                    print(face[2]+vertex_offset)\n","                    print(len(all_vertices))\n","                \n","            vertex_offset = len(all_vertices) \n","\n","\n","        obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","        # Save to a single file\n","        obj_file_path = \"./combined_3d_models.obj\"\n","        with open(obj_file_path, \"w\") as file:\n","            file.write(obj_file_content)\n","\n","        print(obj_file_path)\n","         "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:44.066146Z","iopub.status.busy":"2023-12-19T01:39:44.065812Z","iopub.status.idle":"2023-12-19T01:39:52.313349Z","shell.execute_reply":"2023-12-19T01:39:52.312381Z","shell.execute_reply.started":"2023-12-19T01:39:44.066116Z"},"trusted":true},"outputs":[],"source":["import json\n","#tables = load_models(r\" filtered\",5,5)  \n","#with open(\"data.json\", \"w\") as json_file:\n","#    json.dump(tables, json_file)\n","\n","\n","tables = load_json(\"/kaggle/input/shapenet/data.json\",2)\n","dataset = MeshDataset(tables) \n","\n","unique_values = set(item[\"texts\"] for item in dataset.data)\n","\n","print(len(unique_values))  \n","print(unique_values)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["autoencoder = MeshAutoencoder( \n","    num_discrete_coors = 128  , \n",") \n","total_params = sum(p.numel() for p in autoencoder.encoders.parameters())\n","print(f\"encoders Total parameters: {total_params}\")\n","total_params = sum(p.numel() for p in autoencoder.decoders.parameters())\n","print(f\"decoders Total parameters: {total_params}\")  \n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:44:00.715656Z","iopub.status.busy":"2023-12-19T01:44:00.714727Z","iopub.status.idle":"2023-12-19T12:00:40.840982Z","shell.execute_reply":"2023-12-19T12:00:40.840110Z","shell.execute_reply.started":"2023-12-19T01:44:00.715620Z"},"trusted":true},"outputs":[],"source":["total_params = sum(p.numel() for p in autoencoder.encoders.parameters())\n","print(f\"Total parameters: {total_params}\")\n","print(autoencoder.encoders)\n","\n","autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder,learning_rate = 1e-3, \n","                                             warmup_steps = 10,\n","                                             dataset = dataset,   \n","                                             num_train_steps=100,\n","                                             batch_size=16,\n","                                             grad_accum_every=1)\n","\n","loss = autoencoder_trainer.train(40,stop_at_loss = 0.25)   \n","autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder,learning_rate = 1e-4, \n","                                             warmup_steps = 10,\n","                                             dataset = dataset,\n","                                             checkpoint_every_epoch = 20,  \n","                                             num_train_steps=100,\n","                                             batch_size=16,\n","                                             grad_accum_every=1)\n","\n","loss = autoencoder_trainer.train(180,stop_at_loss = 0.25)   \n","autoencoder_trainer.save(f'./mesh-encoder_2_loss_{loss:.3f}.pt') "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n","max_seq =  max_length * 6  \n","print(max_length)\n","print(max_seq)\n","transformer = MeshTransformer(\n","    autoencoder,\n","    dim = 768,\n","    max_seq_len = max_seq,\n","    condition_on_text = True\n",")\n","total_params = sum(p.numel() for p in transformer.parameters())\n","print(f\"Total parameters: {total_params}\") "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[" \n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  , dataset = dataset,\n","                                 learning_rate = 1e-1, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)   \n","\n"," \n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  , dataset = dataset,\n","                                 learning_rate = 1e-2, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)    \n","\n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  , dataset = dataset,\n","                                 learning_rate = 1e-4, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)   \n","\n","trainer.save(f'./mesh-transformer_2_{loss:.3f}.pt')    "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["unique_values = set(item[\"texts\"] for item in dataset.data)\n","print(len(unique_values))  \n","coords = []\n","for text in unique_values: \n","    print(f\"doing {text}\")\n","    faces_coordinates = transformer.generate(texts = [text]) \n","    coords.append(faces_coordinates)\n","    tensor_data = faces_coordinates[0].cpu()\n","    \n","    numpy_data = tensor_data.numpy().reshape(-1, 3)\n","    \n","    obj_file_content = \"\"\n","    \n","    for vertex in numpy_data:\n","        obj_file_content += f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\"\n","\n","    for i in range(1, len(numpy_data), 3):\n","        obj_file_content += f\"f {i} {i + 1} {i + 2}\\n\"\n","\n","    # Save to a file\n","    obj_file_path = f'./tests/3d_output_{text}.obj'\n","    with open(obj_file_path, \"w\") as file:\n","        file.write(obj_file_content)\n","\n","    print(obj_file_path) \n","    \n","    \n","all_vertices = []\n","all_faces = []\n","vertex_offset = 0\n"," \n","translation_distance = 0.3  \n","\n","for r, faces_coordinates in enumerate(coords): \n","    tensor_data = faces_coordinates[0].cpu()\n","\n","    numpy_data = tensor_data.numpy().reshape(-1, 3)\n","\n","    # Translate the model to avoid overlapping\n","    numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  # Adjust X coordinate\n","\n","    # Accumulate vertices\n","    for vertex in numpy_data:\n","        all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","    # Accumulate faces with adjusted indices\n","    for i in range(1, len(numpy_data), 3):\n","        all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n","\n","    # Update the vertex offset for the next model\n","    vertex_offset += len(numpy_data)\n","\n","# Combine vertices and faces\n","obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","# Save to a single file\n","obj_file_path = f\"./tests/3d_models_all.obj\"\n","with open(obj_file_path, \"w\") as file:\n","    file.write(obj_file_content)\n","\n","print(obj_file_path)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","coords_all = []\n","for text in set(item[\"texts\"] for item in dataset.data): \n","    print(f\"Doing {text}\")\n","    coords = []\n","    for r in np.arange(0, 1.0, 0.1):\n","        faces_coordinates = transformer.generate(temperature=r, texts = [text]) \n","        coords.append(faces_coordinates)\n","    coords_all.append(coords)\n","    \n","    all_vertices = []\n","    all_faces = []\n","    vertex_offset = 0\n","\n","    # Translation distance for each model\n","    translation_distance = 0.3  # Adjust as needed\n","\n","    for r, faces_coordinates in enumerate(coords): \n","        tensor_data = faces_coordinates[0].cpu()\n","\n","        numpy_data = tensor_data.numpy().reshape(-1, 3)\n","\n","        # Translate the model to avoid overlapping\n","        numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  # Adjust X coordinate\n","\n","        # Accumulate vertices\n","        for vertex in numpy_data:\n","            all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","        # Accumulate faces with adjusted indices\n","        for i in range(1, len(numpy_data), 3):\n","            all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n","\n","        # Update the vertex offset for the next model\n","        vertex_offset += len(numpy_data)\n","\n","    # Combine vertices and faces\n","    obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","    # Save to a single file\n","    obj_file_path = f\"./results/3d_models_{text}_temps.obj\"\n","    with open(obj_file_path, \"w\") as file:\n","        file.write(obj_file_content)\n","\n","    print(obj_file_path)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","def loadModels():\n","    autoencoder = MeshAutoencoder(\n","        dim = 576,\n","        encoder_depth = 6,\n","        decoder_depth = 6,\n","        num_discrete_coors = 128  ,\n","        local_attn_depth =0, \n","        \n","    )\n","    autoencoder_trainer = MeshAutoencoderTrainer(model = autoencoder,\n","                                    learning_rate = 1e-1, \n","                                                checkpoint_every_epoch= 5,\n","                                                warmup_steps = 10,\n","                                                dataset = dataset,  \n","                                                num_train_steps=100,\n","                                                batch_size=2,\n","                                                grad_accum_every=1)\n","\n","    autoencoder_trainer.load(r\"mesh-encoder_last.pt\")\n","    encoder = autoencoder_trainer.model\n","    max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n","    max_seq =  max_length * 6  \n","    \n","    transformer = MeshTransformer(\n","        autoencoder,\n","        dim = 768,\n","        max_seq_len = max_seq,\n","        condition_on_text = True)\n","     \n","    trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100, checkpoint_folder = r\"F:\\MachineLearning\\Mesh\\MeshGPT\\checkpoints\" , dataset = dataset,\n","                                    learning_rate = 1e-3, batch_size=2) \n","    trainer.load(r\"mesh-transformer.pt\")\n","    transformer = trainer.model\n","    return transformer, encoder\n","\n","#transformer, autoencoder =  loadModels() "]}],"metadata":{"kaggle":{"accelerator":"gpu","dataSources":[{"datasetId":4189032,"sourceId":7234116,"sourceType":"datasetVersion"}],"dockerImageVersionId":30627,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"}},"nbformat":4,"nbformat_minor":4}

From 49dcaefc3e54b37cacd034de52472d5ee47793ce Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Tue, 19 Dec 2023 17:00:33 +0100
Subject: [PATCH 21/84] added notebook & training estimation time

---
 meshgpt_pytorch/trainer.py | 28 ++++++++++++++++++++++++++--
 1 file changed, 26 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 5aa0d055..154a27f7 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -374,7 +374,18 @@ def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
  
             avg_epoch_loss = total_loss / num_batches 
             epoch_losses.append(avg_epoch_loss)
-            self.print(f'Epoch {epoch + 1} average loss: {avg_epoch_loss}')
+            if len(epoch_losses) >= 4 and avg_epoch_loss > 0:
+                avg_loss_improvement = sum(epoch_losses[-4:-1]) / 3 - avg_epoch_loss
+                outStr = f'Epoch {epoch + 1} average loss: {avg_epoch_loss}           avg loss speed: {avg_loss_improvement}'
+                 
+                if avg_loss_improvement > 0 and avg_loss_improvement < 0.2:
+                    epochs_until_0_3 = max(0, abs(avg_epoch_loss-0.3) / avg_loss_improvement)
+                    if epochs_until_0_3> 0:
+                       outStr += f' epochs left: {epochs_until_0_3:.2f}'
+                       
+                self.print(outStr)
+            else:
+                self.print(f'Epoch {epoch + 1} average loss: {avg_epoch_loss}')
             self.wait()
 
             if self.checkpoint_every_epoch is not None and epoch != 0 and epoch % self.checkpoint_every_epoch == 0:
@@ -685,7 +696,20 @@ def train(self, num_epochs, stop_at_loss = None,  diplay_graph = False):
  
             avg_epoch_loss = total_loss / num_batches 
             epoch_losses.append(avg_epoch_loss)
-            self.print(f'Epoch {epoch + 1} average loss: {avg_epoch_loss}')
+            
+            if len(epoch_losses) >= 4 and avg_epoch_loss > 0:
+                avg_loss_improvement = sum(epoch_losses[-4:-1]) / 3 - avg_epoch_loss
+                outStr = f'Epoch {epoch + 1} average loss: {avg_epoch_loss}           avg loss speed: {avg_loss_improvement}'
+                 
+                if avg_loss_improvement > 0 and avg_loss_improvement < 0.2:
+                    epochs_until_0_3 = max(0, abs(avg_epoch_loss-0.001) / avg_loss_improvement)
+                    if epochs_until_0_3> 0:
+                       outStr += f' epochs left: {epochs_until_0_3:.2f}'
+                       
+                self.print(outStr)
+            else:
+                self.print(f'Epoch {epoch + 1} average loss: {avg_epoch_loss}')
+                
             self.wait() 
             if self.checkpoint_every_epoch is not None and epoch != 0 and epoch % self.checkpoint_every_epoch == 0:
                 self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.3f}.pt')

From 0bd6c0c7154f051c5c8be316b888b12fc37fdb71 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Tue, 19 Dec 2023 17:03:50 +0100
Subject: [PATCH 22/84] notebook fixs

---
 MeshGPT Testing.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/MeshGPT Testing.ipynb b/MeshGPT Testing.ipynb
index 6f43ee09..37e9e96f 100644
--- a/MeshGPT Testing.ipynb	
+++ b/MeshGPT Testing.ipynb	
@@ -1 +1 @@
-{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:12.944690Z","iopub.status.busy":"2023-12-19T01:39:12.944345Z","iopub.status.idle":"2023-12-19T01:39:38.152713Z","shell.execute_reply":"2023-12-19T01:39:38.151433Z","shell.execute_reply.started":"2023-12-19T01:39:12.944662Z"},"trusted":true},"outputs":[],"source":["!pip install -q git+https://github.com/MarcusLoppe/meshgpt-pytorch.git trimesh"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:38.155749Z","iopub.status.busy":"2023-12-19T01:39:38.155103Z","iopub.status.idle":"2023-12-19T01:39:44.048426Z","shell.execute_reply":"2023-12-19T01:39:44.047605Z","shell.execute_reply.started":"2023-12-19T01:39:38.155692Z"},"trusted":true},"outputs":[],"source":["import torch\n","import trimesh\n","import numpy as np\n","import os\n","import csv \n","import math \n","\n","from meshgpt_pytorch import (\n","    MeshTransformerTrainer,\n","    MeshAutoencoderTrainer,\n","    MeshAutoencoder,\n","    MeshTransformer\n",")\n","\n","def get_3d_data(file_path): \n","    mesh = trimesh.load(file_path, force='mesh')\n","    \n","    # Extract vertices and faces\n","    vertices = mesh.vertices\n","    faces = mesh.faces\n","       \n","    centered_vertices = vertices - np.mean(vertices, axis=0)\n"," \n","    max_abs = np.max(np.abs(centered_vertices))\n","    vertices_normalized = centered_vertices / (max_abs / 0.95)  \n","     \n","    \n","    # Sort vertices in specified order: y, x, z\n","    vertices_sorted_indices = np.lexsort((vertices_normalized[:, 1], vertices_normalized[:, 0], vertices_normalized[:, 2]))\n","    vertices_normalized_sorted = vertices_normalized[vertices_sorted_indices]\n","    \n","    # Convert indices to tuples for creating Look-Up Table (LUT)\n","    tuples_sorted_indices = [tuple([index]) for index in vertices_sorted_indices.tolist()]\n","    \n","    # Create Look-Up Table (LUT)\n","    lut = {old_index[0]: new_index for new_index, old_index in enumerate(tuples_sorted_indices)}\n","    \n","    # Reindex faces using LUT\n","    faces_reindexed = np.vectorize(lut.get, otypes=[int])(faces) \n","    # Sort faces based on their lowest vertex index\n","    faces_sorted = faces_reindexed[np.lexsort(faces_reindexed.T)]\n","    \n","    #print(f\"{file_path} vertices {len(vertices)} faces {len(faces)}\")\n","    \n","    return vertices_normalized_sorted, faces_sorted \n","\n","def augment_mesh_scalar(vertices, scale_factor):\n","    # Apply a scalar factor to XYZ coordinates\n","    transformed_vertices = vertices * scale_factor\n","    return transformed_vertices\n","\n","def generate_scale_factors(num_examples, lower_limit=0.75, upper_limit=1.25): \n","    scale_factors = np.random.uniform(lower_limit, upper_limit, size=num_examples)\n","    return scale_factors\n","\n","def jitter_mesh(vertices, jitter_factor=0.01): \n","    offsets = np.random.uniform(-jitter_factor, jitter_factor, size=vertices.shape)\n"," \n","    jittered_vertices = vertices + offsets \n","    return jittered_vertices \n","\n","def augment_mesh(vertices, scale_factor):\n","    #vertices = jitter_mesh(vertices)\n","    transformed_vertices = vertices * scale_factor\n","    \n","    return transformed_vertices\n"," \n","\n","def load_models(directory, num_examples, variations):\n","    obj_datas = []  \n","    \n","    print(f\"num_examples: {num_examples}\")\n","    for filename in os.listdir(directory):  \n","        if (filename.endswith(\".obj\") or  filename.endswith(\".glb\") or  filename.endswith(\".off\")):\n","            file_path = os.path.join(directory, filename)\n","\n","            scale_factors = generate_scale_factors(variations, 0.7, 0.9) \n","            vertices, faces = get_3d_data(file_path) \n","\n","            for scale_factor in scale_factors: \n","                aug_vertices = augment_mesh(vertices.copy(), scale_factor) \n","                \n","                for _ in range(num_examples):\n","                    obj_data = {\"vertices\": torch.tensor(aug_vertices, dtype=torch.float), \"faces\":  torch.tensor(faces, dtype=torch.long),\"texts\": filename[:-4]}\n","                    obj_datas.append(obj_data)   \n","    return obj_datas\n","  \n","\n","def load_json(file,num_examples):\n","    obj_datas = []\n","    with open(file, \"r\") as json_file:\n","        loaded_data = json.load(json_file) \n","        for item in loaded_data:\n","            for _ in range(num_examples):\n","                obj_data = {\"vertices\": torch.tensor(item[\"vertices\"], dtype=torch.float), \"faces\":  torch.tensor(item[\"faces\"], dtype=torch.long),\"texts\": item[\"texts\"] } \n","                obj_datas.append(obj_data)\n","    return obj_datas\n","  \n","                        \n","         "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:44.050017Z","iopub.status.busy":"2023-12-19T01:39:44.049685Z","iopub.status.idle":"2023-12-19T01:39:44.063671Z","shell.execute_reply":"2023-12-19T01:39:44.062641Z","shell.execute_reply.started":"2023-12-19T01:39:44.049992Z"},"trusted":true},"outputs":[],"source":["import torch\n","from torch.utils.data import Dataset, DataLoader \n","from tqdm import tqdm\n","import numpy as np\n","import torch\n"," \n","class MeshDataset(Dataset): \n","    \n","    def __init__(self, data): \n","        self.data = data\n","        print(f\"Got {len(data)} data\")\n","\n","    def __len__(self):\n","        return len(self.data)\n","\n","    def __getitem__(self, idx): \n","        return self.data[idx]\n","    \n","    def embed_texts(self,transformer): \n","        unique_texts = set(item['texts'] for item in self.data)\n"," \n","        text_embeddings = transformer.embed_texts(list(unique_texts))\n","        print(f\"Got text_embeddings: {len(text_embeddings)}\") \n","        text_embedding_dict = dict(zip(unique_texts, text_embeddings))\n"," \n","        for item in self.data:\n","            text_value = item['texts']\n","            item['text_embeds'] = text_embedding_dict.get(text_value, None)\n","            del item['texts']\n"," \n","        \n","    def sample_obj(self):\n","        all_vertices = []\n","        all_faces = []\n","        vertex_offset = 0 \n","\n","\n","        translation_distance = 0.5  # Adjust as needed \n","        vertex_offset = len(all_vertices)\n","        \n","        for r, faces_coordinates in enumerate(self.data):    \n","            if r > 30:\n","                break\n","            for vertex in faces_coordinates[\"vertices\"]: \n","                all_vertices.append(f\"v {vertex[0]+translation_distance * (r / 0.2 - 1)} {vertex[1]} {vertex[2]}\\n\")\n","                #all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","            for face in faces_coordinates[\"faces\"]:\n","                all_faces.append(f\"f {face[0]+1+vertex_offset} {face[1]+1+vertex_offset} {face[2]+1+vertex_offset}\\n\") \n","                try:\n","                    all_vertices[face[0]+vertex_offset]\n","                    all_vertices[face[1]+vertex_offset]\n","                    all_vertices[face[2]+vertex_offset]\n","                except Exception  as e :\n","                    print(e)\n","                    print(face[0]+vertex_offset)\n","                    print(face[1]+vertex_offset)\n","                    print(face[2]+vertex_offset)\n","                    print(len(all_vertices))\n","                \n","            vertex_offset = len(all_vertices) \n","\n","\n","        obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","        # Save to a single file\n","        obj_file_path = \"./combined_3d_models.obj\"\n","        with open(obj_file_path, \"w\") as file:\n","            file.write(obj_file_content)\n","\n","        print(obj_file_path)\n","         "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:44.066146Z","iopub.status.busy":"2023-12-19T01:39:44.065812Z","iopub.status.idle":"2023-12-19T01:39:52.313349Z","shell.execute_reply":"2023-12-19T01:39:52.312381Z","shell.execute_reply.started":"2023-12-19T01:39:44.066116Z"},"trusted":true},"outputs":[],"source":["import json\n","#tables = load_models(r\" filtered\",5,5)  \n","#with open(\"data.json\", \"w\") as json_file:\n","#    json.dump(tables, json_file)\n","\n","\n","tables = load_json(\"/kaggle/input/shapenet/data.json\",2)\n","dataset = MeshDataset(tables) \n","\n","unique_values = set(item[\"texts\"] for item in dataset.data)\n","\n","print(len(unique_values))  \n","print(unique_values)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["autoencoder = MeshAutoencoder( \n","    num_discrete_coors = 128  , \n",") \n","total_params = sum(p.numel() for p in autoencoder.encoders.parameters())\n","print(f\"encoders Total parameters: {total_params}\")\n","total_params = sum(p.numel() for p in autoencoder.decoders.parameters())\n","print(f\"decoders Total parameters: {total_params}\")  \n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:44:00.715656Z","iopub.status.busy":"2023-12-19T01:44:00.714727Z","iopub.status.idle":"2023-12-19T12:00:40.840982Z","shell.execute_reply":"2023-12-19T12:00:40.840110Z","shell.execute_reply.started":"2023-12-19T01:44:00.715620Z"},"trusted":true},"outputs":[],"source":["total_params = sum(p.numel() for p in autoencoder.encoders.parameters())\n","print(f\"Total parameters: {total_params}\")\n","print(autoencoder.encoders)\n","\n","autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder,learning_rate = 1e-3, \n","                                             warmup_steps = 10,\n","                                             dataset = dataset,   \n","                                             num_train_steps=100,\n","                                             batch_size=16,\n","                                             grad_accum_every=1)\n","\n","loss = autoencoder_trainer.train(40,stop_at_loss = 0.25)   \n","autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder,learning_rate = 1e-4, \n","                                             warmup_steps = 10,\n","                                             dataset = dataset,\n","                                             checkpoint_every_epoch = 20,  \n","                                             num_train_steps=100,\n","                                             batch_size=16,\n","                                             grad_accum_every=1)\n","\n","loss = autoencoder_trainer.train(180,stop_at_loss = 0.25)   \n","autoencoder_trainer.save(f'./mesh-encoder_2_loss_{loss:.3f}.pt') "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n","max_seq =  max_length * 6  \n","print(max_length)\n","print(max_seq)\n","transformer = MeshTransformer(\n","    autoencoder,\n","    dim = 768,\n","    max_seq_len = max_seq,\n","    condition_on_text = True\n",")\n","total_params = sum(p.numel() for p in transformer.parameters())\n","print(f\"Total parameters: {total_params}\") "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[" \n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  , dataset = dataset,\n","                                 learning_rate = 1e-1, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)   \n","\n"," \n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  , dataset = dataset,\n","                                 learning_rate = 1e-2, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)    \n","\n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  , dataset = dataset,\n","                                 learning_rate = 1e-4, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)   \n","\n","trainer.save(f'./mesh-transformer_2_{loss:.3f}.pt')    "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["unique_values = set(item[\"texts\"] for item in dataset.data)\n","print(len(unique_values))  \n","coords = []\n","for text in unique_values: \n","    print(f\"doing {text}\")\n","    faces_coordinates = transformer.generate(texts = [text]) \n","    coords.append(faces_coordinates)\n","    tensor_data = faces_coordinates[0].cpu()\n","    \n","    numpy_data = tensor_data.numpy().reshape(-1, 3)\n","    \n","    obj_file_content = \"\"\n","    \n","    for vertex in numpy_data:\n","        obj_file_content += f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\"\n","\n","    for i in range(1, len(numpy_data), 3):\n","        obj_file_content += f\"f {i} {i + 1} {i + 2}\\n\"\n","\n","    # Save to a file\n","    obj_file_path = f'./tests/3d_output_{text}.obj'\n","    with open(obj_file_path, \"w\") as file:\n","        file.write(obj_file_content)\n","\n","    print(obj_file_path) \n","    \n","    \n","all_vertices = []\n","all_faces = []\n","vertex_offset = 0\n"," \n","translation_distance = 0.3  \n","\n","for r, faces_coordinates in enumerate(coords): \n","    tensor_data = faces_coordinates[0].cpu()\n","\n","    numpy_data = tensor_data.numpy().reshape(-1, 3)\n","\n","    # Translate the model to avoid overlapping\n","    numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  # Adjust X coordinate\n","\n","    # Accumulate vertices\n","    for vertex in numpy_data:\n","        all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","    # Accumulate faces with adjusted indices\n","    for i in range(1, len(numpy_data), 3):\n","        all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n","\n","    # Update the vertex offset for the next model\n","    vertex_offset += len(numpy_data)\n","\n","# Combine vertices and faces\n","obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","# Save to a single file\n","obj_file_path = f\"./tests/3d_models_all.obj\"\n","with open(obj_file_path, \"w\") as file:\n","    file.write(obj_file_content)\n","\n","print(obj_file_path)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","coords_all = []\n","for text in set(item[\"texts\"] for item in dataset.data): \n","    print(f\"Doing {text}\")\n","    coords = []\n","    for r in np.arange(0, 1.0, 0.1):\n","        faces_coordinates = transformer.generate(temperature=r, texts = [text]) \n","        coords.append(faces_coordinates)\n","    coords_all.append(coords)\n","    \n","    all_vertices = []\n","    all_faces = []\n","    vertex_offset = 0\n","\n","    # Translation distance for each model\n","    translation_distance = 0.3  # Adjust as needed\n","\n","    for r, faces_coordinates in enumerate(coords): \n","        tensor_data = faces_coordinates[0].cpu()\n","\n","        numpy_data = tensor_data.numpy().reshape(-1, 3)\n","\n","        # Translate the model to avoid overlapping\n","        numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  # Adjust X coordinate\n","\n","        # Accumulate vertices\n","        for vertex in numpy_data:\n","            all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","        # Accumulate faces with adjusted indices\n","        for i in range(1, len(numpy_data), 3):\n","            all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n","\n","        # Update the vertex offset for the next model\n","        vertex_offset += len(numpy_data)\n","\n","    # Combine vertices and faces\n","    obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","    # Save to a single file\n","    obj_file_path = f\"./results/3d_models_{text}_temps.obj\"\n","    with open(obj_file_path, \"w\") as file:\n","        file.write(obj_file_content)\n","\n","    print(obj_file_path)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","def loadModels():\n","    autoencoder = MeshAutoencoder(\n","        dim = 576,\n","        encoder_depth = 6,\n","        decoder_depth = 6,\n","        num_discrete_coors = 128  ,\n","        local_attn_depth =0, \n","        \n","    )\n","    autoencoder_trainer = MeshAutoencoderTrainer(model = autoencoder,\n","                                    learning_rate = 1e-1, \n","                                                checkpoint_every_epoch= 5,\n","                                                warmup_steps = 10,\n","                                                dataset = dataset,  \n","                                                num_train_steps=100,\n","                                                batch_size=2,\n","                                                grad_accum_every=1)\n","\n","    autoencoder_trainer.load(r\"mesh-encoder_last.pt\")\n","    encoder = autoencoder_trainer.model\n","    max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n","    max_seq =  max_length * 6  \n","    \n","    transformer = MeshTransformer(\n","        autoencoder,\n","        dim = 768,\n","        max_seq_len = max_seq,\n","        condition_on_text = True)\n","     \n","    trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100, checkpoint_folder = r\"F:\\MachineLearning\\Mesh\\MeshGPT\\checkpoints\" , dataset = dataset,\n","                                    learning_rate = 1e-3, batch_size=2) \n","    trainer.load(r\"mesh-transformer.pt\")\n","    transformer = trainer.model\n","    return transformer, encoder\n","\n","#transformer, autoencoder =  loadModels() "]}],"metadata":{"kaggle":{"accelerator":"gpu","dataSources":[{"datasetId":4189032,"sourceId":7234116,"sourceType":"datasetVersion"}],"dockerImageVersionId":30627,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"}},"nbformat":4,"nbformat_minor":4}
+{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:12.944690Z","iopub.status.busy":"2023-12-19T01:39:12.944345Z","iopub.status.idle":"2023-12-19T01:39:38.152713Z","shell.execute_reply":"2023-12-19T01:39:38.151433Z","shell.execute_reply.started":"2023-12-19T01:39:12.944662Z"},"trusted":true},"outputs":[],"source":["!pip install -q git+https://github.com/MarcusLoppe/meshgpt-pytorch.git trimesh"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:38.155749Z","iopub.status.busy":"2023-12-19T01:39:38.155103Z","iopub.status.idle":"2023-12-19T01:39:44.048426Z","shell.execute_reply":"2023-12-19T01:39:44.047605Z","shell.execute_reply.started":"2023-12-19T01:39:38.155692Z"},"trusted":true},"outputs":[],"source":["import torch\n","import trimesh\n","import numpy as np\n","import os\n","import csv \n","import math \n","\n","from meshgpt_pytorch import (\n","    MeshTransformerTrainer,\n","    MeshAutoencoderTrainer,\n","    MeshAutoencoder,\n","    MeshTransformer\n",")\n","\n","def get_3d_data(file_path): \n","    mesh = trimesh.load(file_path, force='mesh')\n","    \n","    # Extract vertices and faces\n","    vertices = mesh.vertices\n","    faces = mesh.faces\n","       \n","    centered_vertices = vertices - np.mean(vertices, axis=0)\n"," \n","    max_abs = np.max(np.abs(centered_vertices))\n","    vertices_normalized = centered_vertices / (max_abs / 0.95)  \n","     \n","    \n","    # Sort vertices in specified order: y, x, z\n","    vertices_sorted_indices = np.lexsort((vertices_normalized[:, 1], vertices_normalized[:, 0], vertices_normalized[:, 2]))\n","    vertices_normalized_sorted = vertices_normalized[vertices_sorted_indices]\n","    \n","    # Convert indices to tuples for creating Look-Up Table (LUT)\n","    tuples_sorted_indices = [tuple([index]) for index in vertices_sorted_indices.tolist()]\n","    \n","    # Create Look-Up Table (LUT)\n","    lut = {old_index[0]: new_index for new_index, old_index in enumerate(tuples_sorted_indices)}\n","    \n","    # Reindex faces using LUT\n","    faces_reindexed = np.vectorize(lut.get, otypes=[int])(faces) \n","    # Sort faces based on their lowest vertex index\n","    faces_sorted = faces_reindexed[np.lexsort(faces_reindexed.T)]\n","    \n","    #print(f\"{file_path} vertices {len(vertices)} faces {len(faces)}\")\n","    \n","    return vertices_normalized_sorted, faces_sorted \n","\n","def augment_mesh_scalar(vertices, scale_factor):\n","    # Apply a scalar factor to XYZ coordinates\n","    transformed_vertices = vertices * scale_factor\n","    return transformed_vertices\n","\n","def generate_scale_factors(num_examples, lower_limit=0.75, upper_limit=1.25): \n","    scale_factors = np.random.uniform(lower_limit, upper_limit, size=num_examples)\n","    return scale_factors\n","\n","def jitter_mesh(vertices, jitter_factor=0.01): \n","    offsets = np.random.uniform(-jitter_factor, jitter_factor, size=vertices.shape)\n"," \n","    jittered_vertices = vertices + offsets \n","    return jittered_vertices \n","\n","def augment_mesh(vertices, scale_factor):\n","    #vertices = jitter_mesh(vertices)\n","    transformed_vertices = vertices * scale_factor\n","    \n","    return transformed_vertices\n"," \n","\n","def load_models(directory, num_examples, variations):\n","    obj_datas = []  \n","    \n","    print(f\"num_examples: {num_examples}\")\n","    for filename in os.listdir(directory):  \n","        if (filename.endswith(\".obj\") or  filename.endswith(\".glb\") or  filename.endswith(\".off\")):\n","            file_path = os.path.join(directory, filename)\n","\n","            scale_factors = generate_scale_factors(variations, 0.7, 0.9) \n","            vertices, faces = get_3d_data(file_path) \n","\n","            for scale_factor in scale_factors: \n","                aug_vertices = augment_mesh(vertices.copy(), scale_factor) \n","                \n","                for _ in range(num_examples):\n","                    obj_data = {\"vertices\": torch.tensor(aug_vertices, dtype=torch.float), \"faces\":  torch.tensor(faces, dtype=torch.long),\"texts\": filename[:-4]}\n","                    obj_datas.append(obj_data)   \n","    return obj_datas\n","  \n","\n","def load_json(file,num_examples):\n","    obj_datas = []\n","    with open(file, \"r\") as json_file:\n","        loaded_data = json.load(json_file) \n","        for item in loaded_data:\n","            for _ in range(num_examples):\n","                obj_data = {\"vertices\": torch.tensor(item[\"vertices\"], dtype=torch.float), \"faces\":  torch.tensor(item[\"faces\"], dtype=torch.long),\"texts\": item[\"texts\"] } \n","                obj_datas.append(obj_data)\n","    return obj_datas\n","  \n","                        \n","         "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:44.050017Z","iopub.status.busy":"2023-12-19T01:39:44.049685Z","iopub.status.idle":"2023-12-19T01:39:44.063671Z","shell.execute_reply":"2023-12-19T01:39:44.062641Z","shell.execute_reply.started":"2023-12-19T01:39:44.049992Z"},"trusted":true},"outputs":[],"source":["import torch\n","from torch.utils.data import Dataset, DataLoader \n","from tqdm import tqdm\n","import numpy as np\n","import torch\n"," \n","class MeshDataset(Dataset): \n","    \n","    def __init__(self, data): \n","        self.data = data\n","        print(f\"Got {len(data)} data\")\n","\n","    def __len__(self):\n","        return len(self.data)\n","\n","    def __getitem__(self, idx): \n","        return self.data[idx]\n","    \n","    def embed_texts(self,transformer): \n","        unique_texts = set(item['texts'] for item in self.data)\n"," \n","        text_embeddings = transformer.embed_texts(list(unique_texts))\n","        print(f\"Got text_embeddings: {len(text_embeddings)}\") \n","        text_embedding_dict = dict(zip(unique_texts, text_embeddings))\n"," \n","        for item in self.data:\n","            text_value = item['texts']\n","            item['text_embeds'] = text_embedding_dict.get(text_value, None)\n","            del item['texts']\n"," \n","        \n","    def sample_obj(self):\n","        all_vertices = []\n","        all_faces = []\n","        vertex_offset = 0 \n","\n","\n","        translation_distance = 0.5  # Adjust as needed \n","        vertex_offset = len(all_vertices)\n","        \n","        for r, faces_coordinates in enumerate(self.data):    \n","            if r > 30:\n","                break\n","            for vertex in faces_coordinates[\"vertices\"]: \n","                all_vertices.append(f\"v {vertex[0]+translation_distance * (r / 0.2 - 1)} {vertex[1]} {vertex[2]}\\n\")\n","                #all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","            for face in faces_coordinates[\"faces\"]:\n","                all_faces.append(f\"f {face[0]+1+vertex_offset} {face[1]+1+vertex_offset} {face[2]+1+vertex_offset}\\n\") \n","                try:\n","                    all_vertices[face[0]+vertex_offset]\n","                    all_vertices[face[1]+vertex_offset]\n","                    all_vertices[face[2]+vertex_offset]\n","                except Exception  as e :\n","                    print(e)\n","                    print(face[0]+vertex_offset)\n","                    print(face[1]+vertex_offset)\n","                    print(face[2]+vertex_offset)\n","                    print(len(all_vertices))\n","                \n","            vertex_offset = len(all_vertices) \n","\n","\n","        obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","        # Save to a single file\n","        obj_file_path = \"./combined_3d_models.obj\"\n","        with open(obj_file_path, \"w\") as file:\n","            file.write(obj_file_content)\n","\n","        print(obj_file_path)\n","         "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:44.066146Z","iopub.status.busy":"2023-12-19T01:39:44.065812Z","iopub.status.idle":"2023-12-19T01:39:52.313349Z","shell.execute_reply":"2023-12-19T01:39:52.312381Z","shell.execute_reply.started":"2023-12-19T01:39:44.066116Z"},"trusted":true},"outputs":[],"source":["import json\n","#tables = load_models(r\" filtered\",5,5)  \n","#with open(\"data.json\", \"w\") as json_file:\n","#    json.dump(tables, json_file)\n","\n","\n","tables = load_json(\"/kaggle/input/shapenet/data.json\",2)\n","dataset = MeshDataset(tables) \n","\n","unique_values = set(item[\"texts\"] for item in dataset.data)\n","\n","print(len(unique_values))  \n","print(unique_values)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["autoencoder = MeshAutoencoder( \n","    num_discrete_coors = 128  , \n",") \n","total_params = sum(p.numel() for p in autoencoder.encoders.parameters())\n","print(f\"encoders Total parameters: {total_params}\")\n","total_params = sum(p.numel() for p in autoencoder.decoders.parameters())\n","print(f\"decoders Total parameters: {total_params}\")  \n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:44:00.715656Z","iopub.status.busy":"2023-12-19T01:44:00.714727Z","iopub.status.idle":"2023-12-19T12:00:40.840982Z","shell.execute_reply":"2023-12-19T12:00:40.840110Z","shell.execute_reply.started":"2023-12-19T01:44:00.715620Z"},"trusted":true},"outputs":[],"source":["total_params = sum(p.numel() for p in autoencoder.encoders.parameters())\n","print(f\"Total parameters: {total_params}\")\n","print(autoencoder.encoders)\n","\n","autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder,learning_rate = 1e-3, \n","                                             warmup_steps = 10,\n","                                             dataset = dataset,   \n","                                             num_train_steps=100,\n","                                             batch_size=16,\n","                                             grad_accum_every=1)\n","\n","loss = autoencoder_trainer.train(40,stop_at_loss = 0.25)   \n","autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder,learning_rate = 1e-4, \n","                                             warmup_steps = 10,\n","                                             dataset = dataset,\n","                                             checkpoint_every_epoch = 20,  \n","                                             num_train_steps=100,\n","                                             batch_size=16,\n","                                             grad_accum_every=1)\n","\n","loss = autoencoder_trainer.train(180,stop_at_loss = 0.25)   \n","autoencoder_trainer.save(f'./mesh-encoder_2_loss_{loss:.3f}.pt') "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n","max_seq =  max_length * 6  \n","print(max_length)\n","print(max_seq)\n","transformer = MeshTransformer(\n","    autoencoder,\n","    dim = 768,\n","    max_seq_len = max_seq,\n","    condition_on_text = True\n",")\n","total_params = sum(p.numel() for p in transformer.parameters())\n","print(f\"Total parameters: {total_params}\") "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Untested, but should work (?)\n","\n","#dataset.embed_texts(transformer)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[" \n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,   dataset = dataset,\n","                                 learning_rate = 1e-1, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)   \n","\n"," \n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  dataset = dataset,\n","                                 learning_rate = 1e-2, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)    \n","\n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  dataset = dataset,\n","                                 learning_rate = 1e-4, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)   \n","\n","trainer.save(f'./mesh-transformer_2_{loss:.3f}.pt')    "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["unique_values = set(item[\"texts\"] for item in dataset.data)\n","print(len(unique_values))  \n","coords = []\n","for text in unique_values: \n","    print(f\"doing {text}\")\n","    faces_coordinates = transformer.generate(texts = [text]) \n","    coords.append(faces_coordinates)\n","    tensor_data = faces_coordinates[0].cpu()\n","    \n","    numpy_data = tensor_data.numpy().reshape(-1, 3)\n","    \n","    obj_file_content = \"\"\n","    \n","    for vertex in numpy_data:\n","        obj_file_content += f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\"\n","\n","    for i in range(1, len(numpy_data), 3):\n","        obj_file_content += f\"f {i} {i + 1} {i + 2}\\n\"\n","\n","    # Save to a file\n","    obj_file_path = f'./tests/3d_output_{text}.obj'\n","    with open(obj_file_path, \"w\") as file:\n","        file.write(obj_file_content)\n","\n","    print(obj_file_path) \n","    \n","    \n","all_vertices = []\n","all_faces = []\n","vertex_offset = 0\n"," \n","translation_distance = 0.3  \n","\n","for r, faces_coordinates in enumerate(coords): \n","    tensor_data = faces_coordinates[0].cpu()\n","\n","    numpy_data = tensor_data.numpy().reshape(-1, 3)\n","\n","    # Translate the model to avoid overlapping\n","    numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  # Adjust X coordinate\n","\n","    # Accumulate vertices\n","    for vertex in numpy_data:\n","        all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","    # Accumulate faces with adjusted indices\n","    for i in range(1, len(numpy_data), 3):\n","        all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n","\n","    # Update the vertex offset for the next model\n","    vertex_offset += len(numpy_data)\n","\n","# Combine vertices and faces\n","obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","# Save to a single file\n","obj_file_path = f\"./tests/3d_models_all.obj\"\n","with open(obj_file_path, \"w\") as file:\n","    file.write(obj_file_content)\n","\n","print(obj_file_path)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","coords_all = []\n","for text in set(item[\"texts\"] for item in dataset.data): \n","    print(f\"Doing {text}\")\n","    coords = []\n","    for r in np.arange(0, 1.0, 0.1):\n","        faces_coordinates = transformer.generate(temperature=r, texts = [text]) \n","        coords.append(faces_coordinates)\n","    coords_all.append(coords)\n","    \n","    all_vertices = []\n","    all_faces = []\n","    vertex_offset = 0\n","\n","    # Translation distance for each model\n","    translation_distance = 0.3  # Adjust as needed\n","\n","    for r, faces_coordinates in enumerate(coords): \n","        tensor_data = faces_coordinates[0].cpu()\n","\n","        numpy_data = tensor_data.numpy().reshape(-1, 3)\n","\n","        # Translate the model to avoid overlapping\n","        numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  # Adjust X coordinate\n","\n","        # Accumulate vertices\n","        for vertex in numpy_data:\n","            all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","        # Accumulate faces with adjusted indices\n","        for i in range(1, len(numpy_data), 3):\n","            all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n","\n","        # Update the vertex offset for the next model\n","        vertex_offset += len(numpy_data)\n","\n","    # Combine vertices and faces\n","    obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","    # Save to a single file\n","    obj_file_path = f\"./results/3d_models_{text}_temps.obj\"\n","    with open(obj_file_path, \"w\") as file:\n","        file.write(obj_file_content)\n","\n","    print(obj_file_path)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","def loadModels():\n","    autoencoder = MeshAutoencoder(\n","        dim = 576,\n","        encoder_depth = 6,\n","        decoder_depth = 6,\n","        num_discrete_coors = 128  ,\n","        local_attn_depth =0, \n","        \n","    )\n","    autoencoder_trainer = MeshAutoencoderTrainer(model = autoencoder,\n","                                    learning_rate = 1e-1, \n","                                                checkpoint_every_epoch= 5,\n","                                                warmup_steps = 10,\n","                                                dataset = dataset,  \n","                                                num_train_steps=100,\n","                                                batch_size=2,\n","                                                grad_accum_every=1)\n","\n","    autoencoder_trainer.load(r\"mesh-encoder_last.pt\")\n","    encoder = autoencoder_trainer.model\n","    max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n","    max_seq =  max_length * 6  \n","    \n","    transformer = MeshTransformer(\n","        autoencoder,\n","        dim = 768,\n","        max_seq_len = max_seq,\n","        condition_on_text = True)\n","     \n","    trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100, checkpoint_folder = r\"F:\\MachineLearning\\Mesh\\MeshGPT\\checkpoints\" , dataset = dataset,\n","                                    learning_rate = 1e-3, batch_size=2) \n","    trainer.load(r\"mesh-transformer.pt\")\n","    transformer = trainer.model\n","    return transformer, encoder\n","\n","#transformer, autoencoder =  loadModels() "]}],"metadata":{"kaggle":{"accelerator":"gpu","dataSources":[{"datasetId":4189032,"sourceId":7234116,"sourceType":"datasetVersion"}],"dockerImageVersionId":30627,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"}},"nbformat":4,"nbformat_minor":4}

From 31c8e78a2a509070ec51c38877e16589a854590d Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Tue, 19 Dec 2023 17:06:01 +0100
Subject: [PATCH 23/84] notebook fixs 2

---
 MeshGPT Testing.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/MeshGPT Testing.ipynb b/MeshGPT Testing.ipynb
index 37e9e96f..1587c792 100644
--- a/MeshGPT Testing.ipynb	
+++ b/MeshGPT Testing.ipynb	
@@ -1 +1 @@
-{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:12.944690Z","iopub.status.busy":"2023-12-19T01:39:12.944345Z","iopub.status.idle":"2023-12-19T01:39:38.152713Z","shell.execute_reply":"2023-12-19T01:39:38.151433Z","shell.execute_reply.started":"2023-12-19T01:39:12.944662Z"},"trusted":true},"outputs":[],"source":["!pip install -q git+https://github.com/MarcusLoppe/meshgpt-pytorch.git trimesh"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:38.155749Z","iopub.status.busy":"2023-12-19T01:39:38.155103Z","iopub.status.idle":"2023-12-19T01:39:44.048426Z","shell.execute_reply":"2023-12-19T01:39:44.047605Z","shell.execute_reply.started":"2023-12-19T01:39:38.155692Z"},"trusted":true},"outputs":[],"source":["import torch\n","import trimesh\n","import numpy as np\n","import os\n","import csv \n","import math \n","\n","from meshgpt_pytorch import (\n","    MeshTransformerTrainer,\n","    MeshAutoencoderTrainer,\n","    MeshAutoencoder,\n","    MeshTransformer\n",")\n","\n","def get_3d_data(file_path): \n","    mesh = trimesh.load(file_path, force='mesh')\n","    \n","    # Extract vertices and faces\n","    vertices = mesh.vertices\n","    faces = mesh.faces\n","       \n","    centered_vertices = vertices - np.mean(vertices, axis=0)\n"," \n","    max_abs = np.max(np.abs(centered_vertices))\n","    vertices_normalized = centered_vertices / (max_abs / 0.95)  \n","     \n","    \n","    # Sort vertices in specified order: y, x, z\n","    vertices_sorted_indices = np.lexsort((vertices_normalized[:, 1], vertices_normalized[:, 0], vertices_normalized[:, 2]))\n","    vertices_normalized_sorted = vertices_normalized[vertices_sorted_indices]\n","    \n","    # Convert indices to tuples for creating Look-Up Table (LUT)\n","    tuples_sorted_indices = [tuple([index]) for index in vertices_sorted_indices.tolist()]\n","    \n","    # Create Look-Up Table (LUT)\n","    lut = {old_index[0]: new_index for new_index, old_index in enumerate(tuples_sorted_indices)}\n","    \n","    # Reindex faces using LUT\n","    faces_reindexed = np.vectorize(lut.get, otypes=[int])(faces) \n","    # Sort faces based on their lowest vertex index\n","    faces_sorted = faces_reindexed[np.lexsort(faces_reindexed.T)]\n","    \n","    #print(f\"{file_path} vertices {len(vertices)} faces {len(faces)}\")\n","    \n","    return vertices_normalized_sorted, faces_sorted \n","\n","def augment_mesh_scalar(vertices, scale_factor):\n","    # Apply a scalar factor to XYZ coordinates\n","    transformed_vertices = vertices * scale_factor\n","    return transformed_vertices\n","\n","def generate_scale_factors(num_examples, lower_limit=0.75, upper_limit=1.25): \n","    scale_factors = np.random.uniform(lower_limit, upper_limit, size=num_examples)\n","    return scale_factors\n","\n","def jitter_mesh(vertices, jitter_factor=0.01): \n","    offsets = np.random.uniform(-jitter_factor, jitter_factor, size=vertices.shape)\n"," \n","    jittered_vertices = vertices + offsets \n","    return jittered_vertices \n","\n","def augment_mesh(vertices, scale_factor):\n","    #vertices = jitter_mesh(vertices)\n","    transformed_vertices = vertices * scale_factor\n","    \n","    return transformed_vertices\n"," \n","\n","def load_models(directory, num_examples, variations):\n","    obj_datas = []  \n","    \n","    print(f\"num_examples: {num_examples}\")\n","    for filename in os.listdir(directory):  \n","        if (filename.endswith(\".obj\") or  filename.endswith(\".glb\") or  filename.endswith(\".off\")):\n","            file_path = os.path.join(directory, filename)\n","\n","            scale_factors = generate_scale_factors(variations, 0.7, 0.9) \n","            vertices, faces = get_3d_data(file_path) \n","\n","            for scale_factor in scale_factors: \n","                aug_vertices = augment_mesh(vertices.copy(), scale_factor) \n","                \n","                for _ in range(num_examples):\n","                    obj_data = {\"vertices\": torch.tensor(aug_vertices, dtype=torch.float), \"faces\":  torch.tensor(faces, dtype=torch.long),\"texts\": filename[:-4]}\n","                    obj_datas.append(obj_data)   \n","    return obj_datas\n","  \n","\n","def load_json(file,num_examples):\n","    obj_datas = []\n","    with open(file, \"r\") as json_file:\n","        loaded_data = json.load(json_file) \n","        for item in loaded_data:\n","            for _ in range(num_examples):\n","                obj_data = {\"vertices\": torch.tensor(item[\"vertices\"], dtype=torch.float), \"faces\":  torch.tensor(item[\"faces\"], dtype=torch.long),\"texts\": item[\"texts\"] } \n","                obj_datas.append(obj_data)\n","    return obj_datas\n","  \n","                        \n","         "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:44.050017Z","iopub.status.busy":"2023-12-19T01:39:44.049685Z","iopub.status.idle":"2023-12-19T01:39:44.063671Z","shell.execute_reply":"2023-12-19T01:39:44.062641Z","shell.execute_reply.started":"2023-12-19T01:39:44.049992Z"},"trusted":true},"outputs":[],"source":["import torch\n","from torch.utils.data import Dataset, DataLoader \n","from tqdm import tqdm\n","import numpy as np\n","import torch\n"," \n","class MeshDataset(Dataset): \n","    \n","    def __init__(self, data): \n","        self.data = data\n","        print(f\"Got {len(data)} data\")\n","\n","    def __len__(self):\n","        return len(self.data)\n","\n","    def __getitem__(self, idx): \n","        return self.data[idx]\n","    \n","    def embed_texts(self,transformer): \n","        unique_texts = set(item['texts'] for item in self.data)\n"," \n","        text_embeddings = transformer.embed_texts(list(unique_texts))\n","        print(f\"Got text_embeddings: {len(text_embeddings)}\") \n","        text_embedding_dict = dict(zip(unique_texts, text_embeddings))\n"," \n","        for item in self.data:\n","            text_value = item['texts']\n","            item['text_embeds'] = text_embedding_dict.get(text_value, None)\n","            del item['texts']\n"," \n","        \n","    def sample_obj(self):\n","        all_vertices = []\n","        all_faces = []\n","        vertex_offset = 0 \n","\n","\n","        translation_distance = 0.5  # Adjust as needed \n","        vertex_offset = len(all_vertices)\n","        \n","        for r, faces_coordinates in enumerate(self.data):    \n","            if r > 30:\n","                break\n","            for vertex in faces_coordinates[\"vertices\"]: \n","                all_vertices.append(f\"v {vertex[0]+translation_distance * (r / 0.2 - 1)} {vertex[1]} {vertex[2]}\\n\")\n","                #all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","            for face in faces_coordinates[\"faces\"]:\n","                all_faces.append(f\"f {face[0]+1+vertex_offset} {face[1]+1+vertex_offset} {face[2]+1+vertex_offset}\\n\") \n","                try:\n","                    all_vertices[face[0]+vertex_offset]\n","                    all_vertices[face[1]+vertex_offset]\n","                    all_vertices[face[2]+vertex_offset]\n","                except Exception  as e :\n","                    print(e)\n","                    print(face[0]+vertex_offset)\n","                    print(face[1]+vertex_offset)\n","                    print(face[2]+vertex_offset)\n","                    print(len(all_vertices))\n","                \n","            vertex_offset = len(all_vertices) \n","\n","\n","        obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","        # Save to a single file\n","        obj_file_path = \"./combined_3d_models.obj\"\n","        with open(obj_file_path, \"w\") as file:\n","            file.write(obj_file_content)\n","\n","        print(obj_file_path)\n","         "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:44.066146Z","iopub.status.busy":"2023-12-19T01:39:44.065812Z","iopub.status.idle":"2023-12-19T01:39:52.313349Z","shell.execute_reply":"2023-12-19T01:39:52.312381Z","shell.execute_reply.started":"2023-12-19T01:39:44.066116Z"},"trusted":true},"outputs":[],"source":["import json\n","#tables = load_models(r\" filtered\",5,5)  \n","#with open(\"data.json\", \"w\") as json_file:\n","#    json.dump(tables, json_file)\n","\n","\n","tables = load_json(\"/kaggle/input/shapenet/data.json\",2)\n","dataset = MeshDataset(tables) \n","\n","unique_values = set(item[\"texts\"] for item in dataset.data)\n","\n","print(len(unique_values))  \n","print(unique_values)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["autoencoder = MeshAutoencoder( \n","    num_discrete_coors = 128  , \n",") \n","total_params = sum(p.numel() for p in autoencoder.encoders.parameters())\n","print(f\"encoders Total parameters: {total_params}\")\n","total_params = sum(p.numel() for p in autoencoder.decoders.parameters())\n","print(f\"decoders Total parameters: {total_params}\")  \n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:44:00.715656Z","iopub.status.busy":"2023-12-19T01:44:00.714727Z","iopub.status.idle":"2023-12-19T12:00:40.840982Z","shell.execute_reply":"2023-12-19T12:00:40.840110Z","shell.execute_reply.started":"2023-12-19T01:44:00.715620Z"},"trusted":true},"outputs":[],"source":["total_params = sum(p.numel() for p in autoencoder.encoders.parameters())\n","print(f\"Total parameters: {total_params}\")\n","print(autoencoder.encoders)\n","\n","autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder,learning_rate = 1e-3, \n","                                             warmup_steps = 10,\n","                                             dataset = dataset,   \n","                                             num_train_steps=100,\n","                                             batch_size=16,\n","                                             grad_accum_every=1)\n","\n","loss = autoencoder_trainer.train(40,stop_at_loss = 0.25)   \n","autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder,learning_rate = 1e-4, \n","                                             warmup_steps = 10,\n","                                             dataset = dataset,\n","                                             checkpoint_every_epoch = 20,  \n","                                             num_train_steps=100,\n","                                             batch_size=16,\n","                                             grad_accum_every=1)\n","\n","loss = autoencoder_trainer.train(180,stop_at_loss = 0.25)   \n","autoencoder_trainer.save(f'./mesh-encoder_2_loss_{loss:.3f}.pt') "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n","max_seq =  max_length * 6  \n","print(max_length)\n","print(max_seq)\n","transformer = MeshTransformer(\n","    autoencoder,\n","    dim = 768,\n","    max_seq_len = max_seq,\n","    condition_on_text = True\n",")\n","total_params = sum(p.numel() for p in transformer.parameters())\n","print(f\"Total parameters: {total_params}\") "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Untested, but should work (?)\n","\n","#dataset.embed_texts(transformer)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[" \n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,   dataset = dataset,\n","                                 learning_rate = 1e-1, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)   \n","\n"," \n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  dataset = dataset,\n","                                 learning_rate = 1e-2, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)    \n","\n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  dataset = dataset,\n","                                 learning_rate = 1e-4, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)   \n","\n","trainer.save(f'./mesh-transformer_2_{loss:.3f}.pt')    "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["unique_values = set(item[\"texts\"] for item in dataset.data)\n","print(len(unique_values))  \n","coords = []\n","for text in unique_values: \n","    print(f\"doing {text}\")\n","    faces_coordinates = transformer.generate(texts = [text]) \n","    coords.append(faces_coordinates)\n","    tensor_data = faces_coordinates[0].cpu()\n","    \n","    numpy_data = tensor_data.numpy().reshape(-1, 3)\n","    \n","    obj_file_content = \"\"\n","    \n","    for vertex in numpy_data:\n","        obj_file_content += f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\"\n","\n","    for i in range(1, len(numpy_data), 3):\n","        obj_file_content += f\"f {i} {i + 1} {i + 2}\\n\"\n","\n","    # Save to a file\n","    obj_file_path = f'./tests/3d_output_{text}.obj'\n","    with open(obj_file_path, \"w\") as file:\n","        file.write(obj_file_content)\n","\n","    print(obj_file_path) \n","    \n","    \n","all_vertices = []\n","all_faces = []\n","vertex_offset = 0\n"," \n","translation_distance = 0.3  \n","\n","for r, faces_coordinates in enumerate(coords): \n","    tensor_data = faces_coordinates[0].cpu()\n","\n","    numpy_data = tensor_data.numpy().reshape(-1, 3)\n","\n","    # Translate the model to avoid overlapping\n","    numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  # Adjust X coordinate\n","\n","    # Accumulate vertices\n","    for vertex in numpy_data:\n","        all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","    # Accumulate faces with adjusted indices\n","    for i in range(1, len(numpy_data), 3):\n","        all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n","\n","    # Update the vertex offset for the next model\n","    vertex_offset += len(numpy_data)\n","\n","# Combine vertices and faces\n","obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","# Save to a single file\n","obj_file_path = f\"./tests/3d_models_all.obj\"\n","with open(obj_file_path, \"w\") as file:\n","    file.write(obj_file_content)\n","\n","print(obj_file_path)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","coords_all = []\n","for text in set(item[\"texts\"] for item in dataset.data): \n","    print(f\"Doing {text}\")\n","    coords = []\n","    for r in np.arange(0, 1.0, 0.1):\n","        faces_coordinates = transformer.generate(temperature=r, texts = [text]) \n","        coords.append(faces_coordinates)\n","    coords_all.append(coords)\n","    \n","    all_vertices = []\n","    all_faces = []\n","    vertex_offset = 0\n","\n","    # Translation distance for each model\n","    translation_distance = 0.3  # Adjust as needed\n","\n","    for r, faces_coordinates in enumerate(coords): \n","        tensor_data = faces_coordinates[0].cpu()\n","\n","        numpy_data = tensor_data.numpy().reshape(-1, 3)\n","\n","        # Translate the model to avoid overlapping\n","        numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  # Adjust X coordinate\n","\n","        # Accumulate vertices\n","        for vertex in numpy_data:\n","            all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","        # Accumulate faces with adjusted indices\n","        for i in range(1, len(numpy_data), 3):\n","            all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n","\n","        # Update the vertex offset for the next model\n","        vertex_offset += len(numpy_data)\n","\n","    # Combine vertices and faces\n","    obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","    # Save to a single file\n","    obj_file_path = f\"./results/3d_models_{text}_temps.obj\"\n","    with open(obj_file_path, \"w\") as file:\n","        file.write(obj_file_content)\n","\n","    print(obj_file_path)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","def loadModels():\n","    autoencoder = MeshAutoencoder(\n","        dim = 576,\n","        encoder_depth = 6,\n","        decoder_depth = 6,\n","        num_discrete_coors = 128  ,\n","        local_attn_depth =0, \n","        \n","    )\n","    autoencoder_trainer = MeshAutoencoderTrainer(model = autoencoder,\n","                                    learning_rate = 1e-1, \n","                                                checkpoint_every_epoch= 5,\n","                                                warmup_steps = 10,\n","                                                dataset = dataset,  \n","                                                num_train_steps=100,\n","                                                batch_size=2,\n","                                                grad_accum_every=1)\n","\n","    autoencoder_trainer.load(r\"mesh-encoder_last.pt\")\n","    encoder = autoencoder_trainer.model\n","    max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n","    max_seq =  max_length * 6  \n","    \n","    transformer = MeshTransformer(\n","        autoencoder,\n","        dim = 768,\n","        max_seq_len = max_seq,\n","        condition_on_text = True)\n","     \n","    trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100, checkpoint_folder = r\"F:\\MachineLearning\\Mesh\\MeshGPT\\checkpoints\" , dataset = dataset,\n","                                    learning_rate = 1e-3, batch_size=2) \n","    trainer.load(r\"mesh-transformer.pt\")\n","    transformer = trainer.model\n","    return transformer, encoder\n","\n","#transformer, autoencoder =  loadModels() "]}],"metadata":{"kaggle":{"accelerator":"gpu","dataSources":[{"datasetId":4189032,"sourceId":7234116,"sourceType":"datasetVersion"}],"dockerImageVersionId":30627,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"}},"nbformat":4,"nbformat_minor":4}
+{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:12.944690Z","iopub.status.busy":"2023-12-19T01:39:12.944345Z","iopub.status.idle":"2023-12-19T01:39:38.152713Z","shell.execute_reply":"2023-12-19T01:39:38.151433Z","shell.execute_reply.started":"2023-12-19T01:39:12.944662Z"},"trusted":true},"outputs":[],"source":["!pip install -q git+https://github.com/MarcusLoppe/meshgpt-pytorch.git trimesh"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:38.155749Z","iopub.status.busy":"2023-12-19T01:39:38.155103Z","iopub.status.idle":"2023-12-19T01:39:44.048426Z","shell.execute_reply":"2023-12-19T01:39:44.047605Z","shell.execute_reply.started":"2023-12-19T01:39:38.155692Z"},"trusted":true},"outputs":[],"source":["import torch\n","import trimesh\n","import numpy as np\n","import os\n","import csv \n","import json\n","import math \n","\n","from meshgpt_pytorch import (\n","    MeshTransformerTrainer,\n","    MeshAutoencoderTrainer,\n","    MeshAutoencoder,\n","    MeshTransformer\n",")\n","\n","def get_3d_data(file_path): \n","    mesh = trimesh.load(file_path, force='mesh')\n","    \n","    # Extract vertices and faces\n","    vertices = mesh.vertices\n","    faces = mesh.faces\n","       \n","    centered_vertices = vertices - np.mean(vertices, axis=0)\n"," \n","    max_abs = np.max(np.abs(centered_vertices))\n","    vertices_normalized = centered_vertices / (max_abs / 0.95)  \n","     \n","    \n","    # Sort vertices in specified order: y, x, z\n","    vertices_sorted_indices = np.lexsort((vertices_normalized[:, 1], vertices_normalized[:, 0], vertices_normalized[:, 2]))\n","    vertices_normalized_sorted = vertices_normalized[vertices_sorted_indices]\n","    \n","    # Convert indices to tuples for creating Look-Up Table (LUT)\n","    tuples_sorted_indices = [tuple([index]) for index in vertices_sorted_indices.tolist()]\n","    \n","    # Create Look-Up Table (LUT)\n","    lut = {old_index[0]: new_index for new_index, old_index in enumerate(tuples_sorted_indices)}\n","    \n","    # Reindex faces using LUT\n","    faces_reindexed = np.vectorize(lut.get, otypes=[int])(faces) \n","    # Sort faces based on their lowest vertex index\n","    faces_sorted = faces_reindexed[np.lexsort(faces_reindexed.T)]\n","    \n","    #print(f\"{file_path} vertices {len(vertices)} faces {len(faces)}\")\n","    \n","    return vertices_normalized_sorted, faces_sorted \n","\n","def augment_mesh_scalar(vertices, scale_factor):\n","    # Apply a scalar factor to XYZ coordinates\n","    transformed_vertices = vertices * scale_factor\n","    return transformed_vertices\n","\n","def generate_scale_factors(num_examples, lower_limit=0.75, upper_limit=1.25): \n","    scale_factors = np.random.uniform(lower_limit, upper_limit, size=num_examples)\n","    return scale_factors\n","\n","def jitter_mesh(vertices, jitter_factor=0.01): \n","    offsets = np.random.uniform(-jitter_factor, jitter_factor, size=vertices.shape)\n"," \n","    jittered_vertices = vertices + offsets \n","    return jittered_vertices \n","\n","def augment_mesh(vertices, scale_factor):\n","    #vertices = jitter_mesh(vertices)\n","    transformed_vertices = vertices * scale_factor\n","    \n","    return transformed_vertices\n"," \n","\n","def load_models(directory, num_examples, variations):\n","    obj_datas = []  \n","    \n","    print(f\"num_examples: {num_examples}\")\n","    for filename in os.listdir(directory):  \n","        if (filename.endswith(\".obj\") or  filename.endswith(\".glb\") or  filename.endswith(\".off\")):\n","            file_path = os.path.join(directory, filename)\n","\n","            scale_factors = generate_scale_factors(variations, 0.7, 0.9) \n","            vertices, faces = get_3d_data(file_path) \n","\n","            for scale_factor in scale_factors: \n","                aug_vertices = augment_mesh(vertices.copy(), scale_factor) \n","                \n","                for _ in range(num_examples):\n","                    obj_data = {\"vertices\": aug_vertices.tolist(), \"faces\":  faces.tolist(), \"texts\": filename[:-4]}\n","                    obj_datas.append(obj_data)   \n","    return obj_datas\n","  \n","\n","def load_json(file,num_examples):\n","    obj_datas = []\n","    with open(file, \"r\") as json_file:\n","        loaded_data = json.load(json_file) \n","        for item in loaded_data:\n","            for _ in range(num_examples):\n","                obj_data = {\"vertices\": torch.tensor(item[\"vertices\"], dtype=torch.float), \"faces\":  torch.tensor(item[\"faces\"], dtype=torch.long),\"texts\": item[\"texts\"] } \n","                obj_datas.append(obj_data)\n","    return obj_datas\n","  \n","                        \n","         "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:44.050017Z","iopub.status.busy":"2023-12-19T01:39:44.049685Z","iopub.status.idle":"2023-12-19T01:39:44.063671Z","shell.execute_reply":"2023-12-19T01:39:44.062641Z","shell.execute_reply.started":"2023-12-19T01:39:44.049992Z"},"trusted":true},"outputs":[],"source":["import torch\n","from torch.utils.data import Dataset, DataLoader \n","from tqdm import tqdm\n","import numpy as np\n","import torch\n"," \n","class MeshDataset(Dataset): \n","    \n","    def __init__(self, data): \n","        self.data = data\n","        print(f\"Got {len(data)} data\")\n","\n","    def __len__(self):\n","        return len(self.data)\n","\n","    def __getitem__(self, idx): \n","        return self.data[idx]\n","    \n","    def embed_texts(self,transformer): \n","        unique_texts = set(item['texts'] for item in self.data)\n"," \n","        text_embeddings = transformer.embed_texts(list(unique_texts))\n","        print(f\"Got text_embeddings: {len(text_embeddings)}\") \n","        text_embedding_dict = dict(zip(unique_texts, text_embeddings))\n"," \n","        for item in self.data:\n","            text_value = item['texts']\n","            item['text_embeds'] = text_embedding_dict.get(text_value, None)\n","            del item['texts']\n"," \n","        \n","    def sample_obj(self):\n","        all_vertices = []\n","        all_faces = []\n","        vertex_offset = 0 \n","\n","\n","        translation_distance = 0.5  # Adjust as needed \n","        vertex_offset = len(all_vertices)\n","        \n","        for r, faces_coordinates in enumerate(self.data):    \n","            if r > 30:\n","                break\n","            for vertex in faces_coordinates[\"vertices\"]: \n","                all_vertices.append(f\"v {vertex[0]+translation_distance * (r / 0.2 - 1)} {vertex[1]} {vertex[2]}\\n\")\n","                #all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","            for face in faces_coordinates[\"faces\"]:\n","                all_faces.append(f\"f {face[0]+1+vertex_offset} {face[1]+1+vertex_offset} {face[2]+1+vertex_offset}\\n\") \n","                try:\n","                    all_vertices[face[0]+vertex_offset]\n","                    all_vertices[face[1]+vertex_offset]\n","                    all_vertices[face[2]+vertex_offset]\n","                except Exception  as e :\n","                    print(e)\n","                    print(face[0]+vertex_offset)\n","                    print(face[1]+vertex_offset)\n","                    print(face[2]+vertex_offset)\n","                    print(len(all_vertices))\n","                \n","            vertex_offset = len(all_vertices) \n","\n","\n","        obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","        # Save to a single file\n","        obj_file_path = \"./combined_3d_models.obj\"\n","        with open(obj_file_path, \"w\") as file:\n","            file.write(obj_file_content)\n","\n","        print(obj_file_path)\n","         "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:44.066146Z","iopub.status.busy":"2023-12-19T01:39:44.065812Z","iopub.status.idle":"2023-12-19T01:39:52.313349Z","shell.execute_reply":"2023-12-19T01:39:52.312381Z","shell.execute_reply.started":"2023-12-19T01:39:44.066116Z"},"trusted":true},"outputs":[],"source":["import json\n","#tables = load_models(r\" filtered\",5,5)  \n","#with open(\"data.json\", \"w\") as json_file:\n","#    json.dump(tables, json_file)\n","\n","\n","tables = load_json(\"/kaggle/input/shapenet/data.json\",2)\n","dataset = MeshDataset(tables) \n","\n","unique_values = set(item[\"texts\"] for item in dataset.data)\n","\n","print(len(unique_values))  \n","print(unique_values)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["autoencoder = MeshAutoencoder( \n","    num_discrete_coors = 128  , \n",") \n","total_params = sum(p.numel() for p in autoencoder.encoders.parameters())\n","print(f\"encoders Total parameters: {total_params}\")\n","total_params = sum(p.numel() for p in autoencoder.decoders.parameters())\n","print(f\"decoders Total parameters: {total_params}\")  \n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:44:00.715656Z","iopub.status.busy":"2023-12-19T01:44:00.714727Z","iopub.status.idle":"2023-12-19T12:00:40.840982Z","shell.execute_reply":"2023-12-19T12:00:40.840110Z","shell.execute_reply.started":"2023-12-19T01:44:00.715620Z"},"trusted":true},"outputs":[],"source":["total_params = sum(p.numel() for p in autoencoder.encoders.parameters())\n","print(f\"Total parameters: {total_params}\")\n","print(autoencoder.encoders)\n","\n","autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder,learning_rate = 1e-3, \n","                                             warmup_steps = 10,\n","                                             dataset = dataset,   \n","                                             num_train_steps=100,\n","                                             batch_size=16,\n","                                             grad_accum_every=1)\n","\n","loss = autoencoder_trainer.train(40,stop_at_loss = 0.25)   \n","autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder,learning_rate = 1e-4, \n","                                             warmup_steps = 10,\n","                                             dataset = dataset,\n","                                             checkpoint_every_epoch = 20,  \n","                                             num_train_steps=100,\n","                                             batch_size=16,\n","                                             grad_accum_every=1)\n","\n","loss = autoencoder_trainer.train(180,stop_at_loss = 0.25)   \n","autoencoder_trainer.save(f'./mesh-encoder_2_loss_{loss:.3f}.pt') "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n","max_seq =  max_length * 6  \n","print(max_length)\n","print(max_seq)\n","transformer = MeshTransformer(\n","    autoencoder,\n","    dim = 768,\n","    max_seq_len = max_seq,\n","    condition_on_text = True\n",")\n","total_params = sum(p.numel() for p in transformer.parameters())\n","print(f\"Total parameters: {total_params}\") "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Untested, but should work (?)\n","\n","#dataset.embed_texts(transformer)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[" \n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,   dataset = dataset,\n","                                 learning_rate = 1e-1, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)   \n","\n"," \n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  dataset = dataset,\n","                                 learning_rate = 1e-2, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)    \n","\n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  dataset = dataset,\n","                                 learning_rate = 1e-4, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)   \n","\n","trainer.save(f'./mesh-transformer_2_{loss:.3f}.pt')    "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["unique_values = set(item[\"texts\"] for item in dataset.data)\n","print(len(unique_values))  \n","coords = []\n","for text in unique_values: \n","    print(f\"doing {text}\")\n","    faces_coordinates = transformer.generate(texts = [text]) \n","    coords.append(faces_coordinates)\n","    tensor_data = faces_coordinates[0].cpu()\n","    \n","    numpy_data = tensor_data.numpy().reshape(-1, 3)\n","    \n","    obj_file_content = \"\"\n","    \n","    for vertex in numpy_data:\n","        obj_file_content += f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\"\n","\n","    for i in range(1, len(numpy_data), 3):\n","        obj_file_content += f\"f {i} {i + 1} {i + 2}\\n\"\n","\n","    # Save to a file\n","    obj_file_path = f'./tests/3d_output_{text}.obj'\n","    with open(obj_file_path, \"w\") as file:\n","        file.write(obj_file_content)\n","\n","    print(obj_file_path) \n","    \n","    \n","all_vertices = []\n","all_faces = []\n","vertex_offset = 0\n"," \n","translation_distance = 0.3  \n","\n","for r, faces_coordinates in enumerate(coords): \n","    tensor_data = faces_coordinates[0].cpu()\n","\n","    numpy_data = tensor_data.numpy().reshape(-1, 3)\n","\n","    # Translate the model to avoid overlapping\n","    numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  # Adjust X coordinate\n","\n","    # Accumulate vertices\n","    for vertex in numpy_data:\n","        all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","    # Accumulate faces with adjusted indices\n","    for i in range(1, len(numpy_data), 3):\n","        all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n","\n","    # Update the vertex offset for the next model\n","    vertex_offset += len(numpy_data)\n","\n","# Combine vertices and faces\n","obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","# Save to a single file\n","obj_file_path = f\"./tests/3d_models_all.obj\"\n","with open(obj_file_path, \"w\") as file:\n","    file.write(obj_file_content)\n","\n","print(obj_file_path)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","coords_all = []\n","for text in set(item[\"texts\"] for item in dataset.data): \n","    print(f\"Doing {text}\")\n","    coords = []\n","    for r in np.arange(0, 1.0, 0.1):\n","        faces_coordinates = transformer.generate(temperature=r, texts = [text]) \n","        coords.append(faces_coordinates)\n","    coords_all.append(coords)\n","    \n","    all_vertices = []\n","    all_faces = []\n","    vertex_offset = 0\n","\n","    # Translation distance for each model\n","    translation_distance = 0.3  # Adjust as needed\n","\n","    for r, faces_coordinates in enumerate(coords): \n","        tensor_data = faces_coordinates[0].cpu()\n","\n","        numpy_data = tensor_data.numpy().reshape(-1, 3)\n","\n","        # Translate the model to avoid overlapping\n","        numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  # Adjust X coordinate\n","\n","        # Accumulate vertices\n","        for vertex in numpy_data:\n","            all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","        # Accumulate faces with adjusted indices\n","        for i in range(1, len(numpy_data), 3):\n","            all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n","\n","        # Update the vertex offset for the next model\n","        vertex_offset += len(numpy_data)\n","\n","    # Combine vertices and faces\n","    obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","    # Save to a single file\n","    obj_file_path = f\"./results/3d_models_{text}_temps.obj\"\n","    with open(obj_file_path, \"w\") as file:\n","        file.write(obj_file_content)\n","\n","    print(obj_file_path)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","def loadModels():\n","    autoencoder = MeshAutoencoder(\n","        dim = 576,\n","        encoder_depth = 6,\n","        decoder_depth = 6,\n","        num_discrete_coors = 128  ,\n","        local_attn_depth =0, \n","        \n","    )\n","    autoencoder_trainer = MeshAutoencoderTrainer(model = autoencoder,\n","                                    learning_rate = 1e-1, \n","                                                checkpoint_every_epoch= 5,\n","                                                warmup_steps = 10,\n","                                                dataset = dataset,  \n","                                                num_train_steps=100,\n","                                                batch_size=2,\n","                                                grad_accum_every=1)\n","\n","    autoencoder_trainer.load(r\"mesh-encoder_last.pt\")\n","    encoder = autoencoder_trainer.model\n","    max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n","    max_seq =  max_length * 6  \n","    \n","    transformer = MeshTransformer(\n","        autoencoder,\n","        dim = 768,\n","        max_seq_len = max_seq,\n","        condition_on_text = True)\n","     \n","    trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100, checkpoint_folder = r\"F:\\MachineLearning\\Mesh\\MeshGPT\\checkpoints\" , dataset = dataset,\n","                                    learning_rate = 1e-3, batch_size=2) \n","    trainer.load(r\"mesh-transformer.pt\")\n","    transformer = trainer.model\n","    return transformer, encoder\n","\n","#transformer, autoencoder =  loadModels() "]}],"metadata":{"kaggle":{"accelerator":"gpu","dataSources":[{"datasetId":4189032,"sourceId":7234116,"sourceType":"datasetVersion"}],"dockerImageVersionId":30627,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"}},"nbformat":4,"nbformat_minor":4}

From 68cd53366d3e04b83fd483c3ce78f2cc0e25ef65 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Tue, 19 Dec 2023 17:49:08 +0100
Subject: [PATCH 24/84] load fix

---
 meshgpt_pytorch/trainer.py | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 154a27f7..1f1b429b 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -240,7 +240,7 @@ def load(self, path):
 
         self.model.load_state_dict(pkg['model'])
         self.ema_model.load_state_dict(pkg['ema_model'])
-        self.optimizer.load_state_dict(pkg['optimizer'])
+        self.optimizer.load_state_dict(pkg)
 
         self.step.copy_(pkg['step'])
 
@@ -353,10 +353,7 @@ def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
 
                 with self.accelerator.autocast():
                     loss = self.model(vertices = forward_kwargs['vertices'], faces= forward_kwargs['faces'])
-                    self.accelerator.backward(loss)
-
-                if exists(self.max_grad_norm):
-                    self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
+                    self.accelerator.backward(loss) 
 
                 self.optimizer.step()
                 self.optimizer.zero_grad()

From 22b2b9180ef82a6c9e52181fd27402dd8dd7f39f Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Tue, 19 Dec 2023 17:50:08 +0100
Subject: [PATCH 25/84] trainer load fix

---
 meshgpt_pytorch/trainer.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 1f1b429b..61477f40 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -588,7 +588,7 @@ def load(self, path):
             self.print(f'loading saved mesh transformer at version {pkg["version"]}, but current package version is {__version__}')
 
         self.model.load_state_dict(pkg['model'])
-        self.optimizer.load_state_dict(pkg['optimizer'])
+        self.optimizer.load_state_dict(pkg)
         self.step.copy_(pkg['step'])
 
     def forward(self):
@@ -676,8 +676,6 @@ def train(self, num_epochs, stop_at_loss = None,  diplay_graph = False):
                     loss = self.model(**forward_kwargs)
                     self.accelerator.backward(loss / self.grad_accum_every)
 
-                if exists(self.max_grad_norm):
-                    self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
 
                 self.optimizer.step()
                 self.optimizer.zero_grad()

From aab84af93886bfc4f50340d48dfeb916e111b4fb Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Tue, 19 Dec 2023 17:52:55 +0100
Subject: [PATCH 26/84] trainer warmup -ix

---
 meshgpt_pytorch/trainer.py | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 61477f40..ed55e076 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -358,10 +358,6 @@ def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
                 self.optimizer.step()
                 self.optimizer.zero_grad()
 
-                if not self.accelerator.optimizer_step_was_skipped:
-                    with self.warmup.dampening():
-                        self.scheduler.step()
- 
                 current_loss = loss.item()
                 total_loss += current_loss
                 num_batches += 1
@@ -680,9 +676,6 @@ def train(self, num_epochs, stop_at_loss = None,  diplay_graph = False):
                 self.optimizer.step()
                 self.optimizer.zero_grad()
 
-                if not self.accelerator.optimizer_step_was_skipped:
-                    with self.warmup.dampening():
-                        self.scheduler.step()
  
                 current_loss = loss.item()
                 total_loss += current_loss

From 17a65e8414f48a03bb4d122cf871abfac1b4c2c2 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Tue, 19 Dec 2023 22:42:02 +0100
Subject: [PATCH 27/84] trainer revert fix

---
 meshgpt_pytorch/trainer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index ed55e076..36077ab4 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -240,7 +240,7 @@ def load(self, path):
 
         self.model.load_state_dict(pkg['model'])
         self.ema_model.load_state_dict(pkg['ema_model'])
-        self.optimizer.load_state_dict(pkg)
+        self.optimizer.load_state_dict(pkg['optimizer'])
 
         self.step.copy_(pkg['step'])
 
@@ -584,7 +584,7 @@ def load(self, path):
             self.print(f'loading saved mesh transformer at version {pkg["version"]}, but current package version is {__version__}')
 
         self.model.load_state_dict(pkg['model'])
-        self.optimizer.load_state_dict(pkg)
+        self.optimizer.load_state_dict(pkg['optimizer'])
         self.step.copy_(pkg['step'])
 
     def forward(self):

From b87e051246ba539c06248aaeee1f254d8604d5ff Mon Sep 17 00:00:00 2001
From: MarcusLoppe <65302107+MarcusLoppe@users.noreply.github.com>
Date: Sun, 24 Dec 2023 14:00:02 +0100
Subject: [PATCH 28/84] use **forward args

---
 meshgpt_pytorch/trainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 3e88005e..4f28e65a 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -341,7 +341,7 @@ def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
                 
 
                 with self.accelerator.autocast():
-                    loss = self.model(vertices = forward_kwargs['vertices'], faces= forward_kwargs['faces'])
+                    loss = self.model(**forward_kwargs)
                     self.accelerator.backward(loss) 
 
                 self.optimizer.step()

From 9078a534354c042723ff6b7fb95504a00aec8bbb Mon Sep 17 00:00:00 2001
From: MarcusLoppe <65302107+MarcusLoppe@users.noreply.github.com>
Date: Sun, 24 Dec 2023 14:11:48 +0100
Subject: [PATCH 29/84] skip data kwargs

---
 meshgpt_pytorch/trainer.py | 10 ++--------
 1 file changed, 2 insertions(+), 8 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 4f28e65a..0ccf48e2 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -332,16 +332,10 @@ def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
             progress_bar = tqdm(self.dataloader, desc=f'Epoch {epoch + 1}/{num_epochs}') 
 
             for data in progress_bar: 
-
-                if isinstance(data, tuple): 
-                    forward_kwargs = dict(zip(self.data_kwargs, data))
-
-                elif isinstance(data, dict): 
-                    forward_kwargs = data 
-                
+ 
 
                 with self.accelerator.autocast():
-                    loss = self.model(**forward_kwargs)
+                    loss = self.model(**data)
                     self.accelerator.backward(loss) 
 
                 self.optimizer.step()

From 0163070a491e3a916edf4e39d22f4b8db634546a Mon Sep 17 00:00:00 2001
From: MarcusLoppe <65302107+MarcusLoppe@users.noreply.github.com>
Date: Sun, 24 Dec 2023 14:49:50 +0100
Subject: [PATCH 30/84] readded data kwargs

---
 meshgpt_pytorch/trainer.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 0ccf48e2..4f28e65a 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -332,10 +332,16 @@ def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
             progress_bar = tqdm(self.dataloader, desc=f'Epoch {epoch + 1}/{num_epochs}') 
 
             for data in progress_bar: 
- 
+
+                if isinstance(data, tuple): 
+                    forward_kwargs = dict(zip(self.data_kwargs, data))
+
+                elif isinstance(data, dict): 
+                    forward_kwargs = data 
+                
 
                 with self.accelerator.autocast():
-                    loss = self.model(**data)
+                    loss = self.model(**forward_kwargs)
                     self.accelerator.backward(loss) 
 
                 self.optimizer.step()

From 673379b7dc1ae607510937a9ec5fddae47c207d0 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Tue, 26 Dec 2023 19:42:04 +0100
Subject: [PATCH 31/84] implemented grad_accum_every

---
 meshgpt_pytorch/trainer.py | 76 +++++++++++++++++++++++---------------
 1 file changed, 47 insertions(+), 29 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 36077ab4..fab81041 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -334,52 +334,68 @@ def forward(self):
 
         self.print('training complete')
     def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
-        epoch_losses = []  # Initialize a list to store epoch losses
+        epoch_losses, epoch_recon_losses, epoch_commit_losses = [] , [],[]
+        dl_iter = cycle(self.dataloader)
+        epoch_size = len(self.dataloader)
         self.model.train() 
+        
         for epoch in range(num_epochs): 
-            total_loss = 0.0
-            num_batches = 0
+            total_epoch_loss, total_epoch_recon_loss, total_epoch_commit_loss = 0.0, 0.0, 0.0 
+            num_batches = 0 
+            progress_bar = tqdm(total=epoch_size, desc=f'Epoch {epoch + 1}/{num_epochs}') 
+            
+            while num_batches < epoch_size:
+                for i in range(self.grad_accum_every):
+                    is_last = i == (self.grad_accum_every - 1)
+                    maybe_no_sync = partial(self.accelerator.no_sync, self.model) if not is_last else nullcontext
 
-            progress_bar = tqdm(self.dataloader, desc=f'Epoch {epoch + 1}/{num_epochs}') 
+                    forward_kwargs = self.next_data_to_forward_kwargs(dl_iter)
 
-            for data in progress_bar: 
+                    with self.accelerator.autocast(), maybe_no_sync():
 
-                if isinstance(data, tuple): 
-                    forward_kwargs = dict(zip(self.data_kwargs, data))
-
-                elif isinstance(data, dict): 
-                    forward_kwargs = data 
-                
+                        total_loss, (recon_loss, commit_loss) = self.model(
+                            **forward_kwargs,
+                            return_loss_breakdown = True
+                        )
 
-                with self.accelerator.autocast():
-                    loss = self.model(vertices = forward_kwargs['vertices'], faces= forward_kwargs['faces'])
-                    self.accelerator.backward(loss) 
+                        self.accelerator.backward(total_loss / self.grad_accum_every)
 
+        
+                    current_loss = total_loss.item()
+                    total_epoch_loss += current_loss
+                    total_epoch_recon_loss += recon_loss.item()
+                    total_epoch_commit_loss += commit_loss.sum().item() 
+                    num_batches += 1
+                    
+                progress_bar.update(self.grad_accum_every)
+                progress_bar.set_postfix(loss=current_loss, recon_loss = round(recon_loss.item(),3), commit_loss = round(commit_loss.sum().item(),4))
+                    
                 self.optimizer.step()
                 self.optimizer.zero_grad()
-
-                current_loss = loss.item()
-                total_loss += current_loss
-                num_batches += 1
-                progress_bar.set_postfix(loss=current_loss)
-                
-            
  
-            avg_epoch_loss = total_loss / num_batches 
+             
+            avg_recon_loss = total_epoch_recon_loss / num_batches
+            avg_commit_loss = total_epoch_commit_loss / num_batches
+            avg_epoch_loss = total_epoch_loss / num_batches 
+            epochOut = f'Epoch {epoch + 1} average loss: {avg_epoch_loss} recon loss: {avg_recon_loss:.4f}: commit_loss {avg_commit_loss:.4f}'
+            
+    
             epoch_losses.append(avg_epoch_loss)
+            epoch_recon_losses.append(avg_recon_loss)
+            epoch_commit_losses.append(avg_commit_loss)
+
             if len(epoch_losses) >= 4 and avg_epoch_loss > 0:
                 avg_loss_improvement = sum(epoch_losses[-4:-1]) / 3 - avg_epoch_loss
-                outStr = f'Epoch {epoch + 1} average loss: {avg_epoch_loss}           avg loss speed: {avg_loss_improvement}'
-                 
+                epochOut += f'          avg loss speed: {avg_loss_improvement}' 
                 if avg_loss_improvement > 0 and avg_loss_improvement < 0.2:
                     epochs_until_0_3 = max(0, abs(avg_epoch_loss-0.3) / avg_loss_improvement)
                     if epochs_until_0_3> 0:
-                       outStr += f' epochs left: {epochs_until_0_3:.2f}'
+                       epochOut += f' epochs left: {epochs_until_0_3:.2f}'  
                        
-                self.print(outStr)
-            else:
-                self.print(f'Epoch {epoch + 1} average loss: {avg_epoch_loss}')
             self.wait()
+            
+            progress_bar.close()
+            self.print(epochOut)
 
             if self.checkpoint_every_epoch is not None and epoch != 0 and epoch % self.checkpoint_every_epoch == 0:
                 self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.3f}.pt')
@@ -394,7 +410,9 @@ def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
         self.print('Training complete') 
         if diplay_graph:
             plt.figure(figsize=(10, 5))
-            plt.plot(range(1, num_epochs + 1), epoch_losses, marker='o')
+            plt.plot(range(1, num_epochs + 1), epoch_losses, marker='o', label='Total Loss')
+            plt.plot(range(1, num_epochs + 1), epoch_recon_losses, marker='o', label='Recon Loss')
+            plt.plot(range(1, num_epochs + 1), epoch_commit_losses, marker='o', label='Commit Loss') 
             plt.title('Training Loss Over Epochs')
             plt.xlabel('Epoch')
             plt.ylabel('Average Loss')

From 5c6f8325805913cfdbac55cd093c95f389f76039 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Tue, 26 Dec 2023 22:40:11 +0100
Subject: [PATCH 32/84] modifyed notebook

---
 MeshGPT Testing.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/MeshGPT Testing.ipynb b/MeshGPT Testing.ipynb
index 1587c792..a76245e4 100644
--- a/MeshGPT Testing.ipynb	
+++ b/MeshGPT Testing.ipynb	
@@ -1 +1 @@
-{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:12.944690Z","iopub.status.busy":"2023-12-19T01:39:12.944345Z","iopub.status.idle":"2023-12-19T01:39:38.152713Z","shell.execute_reply":"2023-12-19T01:39:38.151433Z","shell.execute_reply.started":"2023-12-19T01:39:12.944662Z"},"trusted":true},"outputs":[],"source":["!pip install -q git+https://github.com/MarcusLoppe/meshgpt-pytorch.git trimesh"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:38.155749Z","iopub.status.busy":"2023-12-19T01:39:38.155103Z","iopub.status.idle":"2023-12-19T01:39:44.048426Z","shell.execute_reply":"2023-12-19T01:39:44.047605Z","shell.execute_reply.started":"2023-12-19T01:39:38.155692Z"},"trusted":true},"outputs":[],"source":["import torch\n","import trimesh\n","import numpy as np\n","import os\n","import csv \n","import json\n","import math \n","\n","from meshgpt_pytorch import (\n","    MeshTransformerTrainer,\n","    MeshAutoencoderTrainer,\n","    MeshAutoencoder,\n","    MeshTransformer\n",")\n","\n","def get_3d_data(file_path): \n","    mesh = trimesh.load(file_path, force='mesh')\n","    \n","    # Extract vertices and faces\n","    vertices = mesh.vertices\n","    faces = mesh.faces\n","       \n","    centered_vertices = vertices - np.mean(vertices, axis=0)\n"," \n","    max_abs = np.max(np.abs(centered_vertices))\n","    vertices_normalized = centered_vertices / (max_abs / 0.95)  \n","     \n","    \n","    # Sort vertices in specified order: y, x, z\n","    vertices_sorted_indices = np.lexsort((vertices_normalized[:, 1], vertices_normalized[:, 0], vertices_normalized[:, 2]))\n","    vertices_normalized_sorted = vertices_normalized[vertices_sorted_indices]\n","    \n","    # Convert indices to tuples for creating Look-Up Table (LUT)\n","    tuples_sorted_indices = [tuple([index]) for index in vertices_sorted_indices.tolist()]\n","    \n","    # Create Look-Up Table (LUT)\n","    lut = {old_index[0]: new_index for new_index, old_index in enumerate(tuples_sorted_indices)}\n","    \n","    # Reindex faces using LUT\n","    faces_reindexed = np.vectorize(lut.get, otypes=[int])(faces) \n","    # Sort faces based on their lowest vertex index\n","    faces_sorted = faces_reindexed[np.lexsort(faces_reindexed.T)]\n","    \n","    #print(f\"{file_path} vertices {len(vertices)} faces {len(faces)}\")\n","    \n","    return vertices_normalized_sorted, faces_sorted \n","\n","def augment_mesh_scalar(vertices, scale_factor):\n","    # Apply a scalar factor to XYZ coordinates\n","    transformed_vertices = vertices * scale_factor\n","    return transformed_vertices\n","\n","def generate_scale_factors(num_examples, lower_limit=0.75, upper_limit=1.25): \n","    scale_factors = np.random.uniform(lower_limit, upper_limit, size=num_examples)\n","    return scale_factors\n","\n","def jitter_mesh(vertices, jitter_factor=0.01): \n","    offsets = np.random.uniform(-jitter_factor, jitter_factor, size=vertices.shape)\n"," \n","    jittered_vertices = vertices + offsets \n","    return jittered_vertices \n","\n","def augment_mesh(vertices, scale_factor):\n","    #vertices = jitter_mesh(vertices)\n","    transformed_vertices = vertices * scale_factor\n","    \n","    return transformed_vertices\n"," \n","\n","def load_models(directory, num_examples, variations):\n","    obj_datas = []  \n","    \n","    print(f\"num_examples: {num_examples}\")\n","    for filename in os.listdir(directory):  \n","        if (filename.endswith(\".obj\") or  filename.endswith(\".glb\") or  filename.endswith(\".off\")):\n","            file_path = os.path.join(directory, filename)\n","\n","            scale_factors = generate_scale_factors(variations, 0.7, 0.9) \n","            vertices, faces = get_3d_data(file_path) \n","\n","            for scale_factor in scale_factors: \n","                aug_vertices = augment_mesh(vertices.copy(), scale_factor) \n","                \n","                for _ in range(num_examples):\n","                    obj_data = {\"vertices\": aug_vertices.tolist(), \"faces\":  faces.tolist(), \"texts\": filename[:-4]}\n","                    obj_datas.append(obj_data)   \n","    return obj_datas\n","  \n","\n","def load_json(file,num_examples):\n","    obj_datas = []\n","    with open(file, \"r\") as json_file:\n","        loaded_data = json.load(json_file) \n","        for item in loaded_data:\n","            for _ in range(num_examples):\n","                obj_data = {\"vertices\": torch.tensor(item[\"vertices\"], dtype=torch.float), \"faces\":  torch.tensor(item[\"faces\"], dtype=torch.long),\"texts\": item[\"texts\"] } \n","                obj_datas.append(obj_data)\n","    return obj_datas\n","  \n","                        \n","         "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:44.050017Z","iopub.status.busy":"2023-12-19T01:39:44.049685Z","iopub.status.idle":"2023-12-19T01:39:44.063671Z","shell.execute_reply":"2023-12-19T01:39:44.062641Z","shell.execute_reply.started":"2023-12-19T01:39:44.049992Z"},"trusted":true},"outputs":[],"source":["import torch\n","from torch.utils.data import Dataset, DataLoader \n","from tqdm import tqdm\n","import numpy as np\n","import torch\n"," \n","class MeshDataset(Dataset): \n","    \n","    def __init__(self, data): \n","        self.data = data\n","        print(f\"Got {len(data)} data\")\n","\n","    def __len__(self):\n","        return len(self.data)\n","\n","    def __getitem__(self, idx): \n","        return self.data[idx]\n","    \n","    def embed_texts(self,transformer): \n","        unique_texts = set(item['texts'] for item in self.data)\n"," \n","        text_embeddings = transformer.embed_texts(list(unique_texts))\n","        print(f\"Got text_embeddings: {len(text_embeddings)}\") \n","        text_embedding_dict = dict(zip(unique_texts, text_embeddings))\n"," \n","        for item in self.data:\n","            text_value = item['texts']\n","            item['text_embeds'] = text_embedding_dict.get(text_value, None)\n","            del item['texts']\n"," \n","        \n","    def sample_obj(self):\n","        all_vertices = []\n","        all_faces = []\n","        vertex_offset = 0 \n","\n","\n","        translation_distance = 0.5  # Adjust as needed \n","        vertex_offset = len(all_vertices)\n","        \n","        for r, faces_coordinates in enumerate(self.data):    \n","            if r > 30:\n","                break\n","            for vertex in faces_coordinates[\"vertices\"]: \n","                all_vertices.append(f\"v {vertex[0]+translation_distance * (r / 0.2 - 1)} {vertex[1]} {vertex[2]}\\n\")\n","                #all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","            for face in faces_coordinates[\"faces\"]:\n","                all_faces.append(f\"f {face[0]+1+vertex_offset} {face[1]+1+vertex_offset} {face[2]+1+vertex_offset}\\n\") \n","                try:\n","                    all_vertices[face[0]+vertex_offset]\n","                    all_vertices[face[1]+vertex_offset]\n","                    all_vertices[face[2]+vertex_offset]\n","                except Exception  as e :\n","                    print(e)\n","                    print(face[0]+vertex_offset)\n","                    print(face[1]+vertex_offset)\n","                    print(face[2]+vertex_offset)\n","                    print(len(all_vertices))\n","                \n","            vertex_offset = len(all_vertices) \n","\n","\n","        obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","        # Save to a single file\n","        obj_file_path = \"./combined_3d_models.obj\"\n","        with open(obj_file_path, \"w\") as file:\n","            file.write(obj_file_content)\n","\n","        print(obj_file_path)\n","         "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:44.066146Z","iopub.status.busy":"2023-12-19T01:39:44.065812Z","iopub.status.idle":"2023-12-19T01:39:52.313349Z","shell.execute_reply":"2023-12-19T01:39:52.312381Z","shell.execute_reply.started":"2023-12-19T01:39:44.066116Z"},"trusted":true},"outputs":[],"source":["import json\n","#tables = load_models(r\" filtered\",5,5)  \n","#with open(\"data.json\", \"w\") as json_file:\n","#    json.dump(tables, json_file)\n","\n","\n","tables = load_json(\"/kaggle/input/shapenet/data.json\",2)\n","dataset = MeshDataset(tables) \n","\n","unique_values = set(item[\"texts\"] for item in dataset.data)\n","\n","print(len(unique_values))  \n","print(unique_values)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["autoencoder = MeshAutoencoder( \n","    num_discrete_coors = 128  , \n",") \n","total_params = sum(p.numel() for p in autoencoder.encoders.parameters())\n","print(f\"encoders Total parameters: {total_params}\")\n","total_params = sum(p.numel() for p in autoencoder.decoders.parameters())\n","print(f\"decoders Total parameters: {total_params}\")  \n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:44:00.715656Z","iopub.status.busy":"2023-12-19T01:44:00.714727Z","iopub.status.idle":"2023-12-19T12:00:40.840982Z","shell.execute_reply":"2023-12-19T12:00:40.840110Z","shell.execute_reply.started":"2023-12-19T01:44:00.715620Z"},"trusted":true},"outputs":[],"source":["total_params = sum(p.numel() for p in autoencoder.encoders.parameters())\n","print(f\"Total parameters: {total_params}\")\n","print(autoencoder.encoders)\n","\n","autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder,learning_rate = 1e-3, \n","                                             warmup_steps = 10,\n","                                             dataset = dataset,   \n","                                             num_train_steps=100,\n","                                             batch_size=16,\n","                                             grad_accum_every=1)\n","\n","loss = autoencoder_trainer.train(40,stop_at_loss = 0.25)   \n","autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder,learning_rate = 1e-4, \n","                                             warmup_steps = 10,\n","                                             dataset = dataset,\n","                                             checkpoint_every_epoch = 20,  \n","                                             num_train_steps=100,\n","                                             batch_size=16,\n","                                             grad_accum_every=1)\n","\n","loss = autoencoder_trainer.train(180,stop_at_loss = 0.25)   \n","autoencoder_trainer.save(f'./mesh-encoder_2_loss_{loss:.3f}.pt') "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n","max_seq =  max_length * 6  \n","print(max_length)\n","print(max_seq)\n","transformer = MeshTransformer(\n","    autoencoder,\n","    dim = 768,\n","    max_seq_len = max_seq,\n","    condition_on_text = True\n",")\n","total_params = sum(p.numel() for p in transformer.parameters())\n","print(f\"Total parameters: {total_params}\") "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Untested, but should work (?)\n","\n","#dataset.embed_texts(transformer)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[" \n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,   dataset = dataset,\n","                                 learning_rate = 1e-1, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)   \n","\n"," \n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  dataset = dataset,\n","                                 learning_rate = 1e-2, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)    \n","\n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  dataset = dataset,\n","                                 learning_rate = 1e-4, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)   \n","\n","trainer.save(f'./mesh-transformer_2_{loss:.3f}.pt')    "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["unique_values = set(item[\"texts\"] for item in dataset.data)\n","print(len(unique_values))  \n","coords = []\n","for text in unique_values: \n","    print(f\"doing {text}\")\n","    faces_coordinates = transformer.generate(texts = [text]) \n","    coords.append(faces_coordinates)\n","    tensor_data = faces_coordinates[0].cpu()\n","    \n","    numpy_data = tensor_data.numpy().reshape(-1, 3)\n","    \n","    obj_file_content = \"\"\n","    \n","    for vertex in numpy_data:\n","        obj_file_content += f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\"\n","\n","    for i in range(1, len(numpy_data), 3):\n","        obj_file_content += f\"f {i} {i + 1} {i + 2}\\n\"\n","\n","    # Save to a file\n","    obj_file_path = f'./tests/3d_output_{text}.obj'\n","    with open(obj_file_path, \"w\") as file:\n","        file.write(obj_file_content)\n","\n","    print(obj_file_path) \n","    \n","    \n","all_vertices = []\n","all_faces = []\n","vertex_offset = 0\n"," \n","translation_distance = 0.3  \n","\n","for r, faces_coordinates in enumerate(coords): \n","    tensor_data = faces_coordinates[0].cpu()\n","\n","    numpy_data = tensor_data.numpy().reshape(-1, 3)\n","\n","    # Translate the model to avoid overlapping\n","    numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  # Adjust X coordinate\n","\n","    # Accumulate vertices\n","    for vertex in numpy_data:\n","        all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","    # Accumulate faces with adjusted indices\n","    for i in range(1, len(numpy_data), 3):\n","        all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n","\n","    # Update the vertex offset for the next model\n","    vertex_offset += len(numpy_data)\n","\n","# Combine vertices and faces\n","obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","# Save to a single file\n","obj_file_path = f\"./tests/3d_models_all.obj\"\n","with open(obj_file_path, \"w\") as file:\n","    file.write(obj_file_content)\n","\n","print(obj_file_path)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","coords_all = []\n","for text in set(item[\"texts\"] for item in dataset.data): \n","    print(f\"Doing {text}\")\n","    coords = []\n","    for r in np.arange(0, 1.0, 0.1):\n","        faces_coordinates = transformer.generate(temperature=r, texts = [text]) \n","        coords.append(faces_coordinates)\n","    coords_all.append(coords)\n","    \n","    all_vertices = []\n","    all_faces = []\n","    vertex_offset = 0\n","\n","    # Translation distance for each model\n","    translation_distance = 0.3  # Adjust as needed\n","\n","    for r, faces_coordinates in enumerate(coords): \n","        tensor_data = faces_coordinates[0].cpu()\n","\n","        numpy_data = tensor_data.numpy().reshape(-1, 3)\n","\n","        # Translate the model to avoid overlapping\n","        numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  # Adjust X coordinate\n","\n","        # Accumulate vertices\n","        for vertex in numpy_data:\n","            all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","        # Accumulate faces with adjusted indices\n","        for i in range(1, len(numpy_data), 3):\n","            all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n","\n","        # Update the vertex offset for the next model\n","        vertex_offset += len(numpy_data)\n","\n","    # Combine vertices and faces\n","    obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","    # Save to a single file\n","    obj_file_path = f\"./results/3d_models_{text}_temps.obj\"\n","    with open(obj_file_path, \"w\") as file:\n","        file.write(obj_file_content)\n","\n","    print(obj_file_path)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","def loadModels():\n","    autoencoder = MeshAutoencoder(\n","        dim = 576,\n","        encoder_depth = 6,\n","        decoder_depth = 6,\n","        num_discrete_coors = 128  ,\n","        local_attn_depth =0, \n","        \n","    )\n","    autoencoder_trainer = MeshAutoencoderTrainer(model = autoencoder,\n","                                    learning_rate = 1e-1, \n","                                                checkpoint_every_epoch= 5,\n","                                                warmup_steps = 10,\n","                                                dataset = dataset,  \n","                                                num_train_steps=100,\n","                                                batch_size=2,\n","                                                grad_accum_every=1)\n","\n","    autoencoder_trainer.load(r\"mesh-encoder_last.pt\")\n","    encoder = autoencoder_trainer.model\n","    max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n","    max_seq =  max_length * 6  \n","    \n","    transformer = MeshTransformer(\n","        autoencoder,\n","        dim = 768,\n","        max_seq_len = max_seq,\n","        condition_on_text = True)\n","     \n","    trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100, checkpoint_folder = r\"F:\\MachineLearning\\Mesh\\MeshGPT\\checkpoints\" , dataset = dataset,\n","                                    learning_rate = 1e-3, batch_size=2) \n","    trainer.load(r\"mesh-transformer.pt\")\n","    transformer = trainer.model\n","    return transformer, encoder\n","\n","#transformer, autoencoder =  loadModels() "]}],"metadata":{"kaggle":{"accelerator":"gpu","dataSources":[{"datasetId":4189032,"sourceId":7234116,"sourceType":"datasetVersion"}],"dockerImageVersionId":30627,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"}},"nbformat":4,"nbformat_minor":4}
+{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:12.944690Z","iopub.status.busy":"2023-12-19T01:39:12.944345Z","iopub.status.idle":"2023-12-19T01:39:38.152713Z","shell.execute_reply":"2023-12-19T01:39:38.151433Z","shell.execute_reply.started":"2023-12-19T01:39:12.944662Z"},"trusted":true},"outputs":[],"source":["!pip install -q git+https://github.com/MarcusLoppe/meshgpt-pytorch.git trimesh"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:38.155749Z","iopub.status.busy":"2023-12-19T01:39:38.155103Z","iopub.status.idle":"2023-12-19T01:39:44.048426Z","shell.execute_reply":"2023-12-19T01:39:44.047605Z","shell.execute_reply.started":"2023-12-19T01:39:38.155692Z"},"trusted":true},"outputs":[],"source":["import torch\n","import trimesh\n","import numpy as np\n","import os\n","import csv \n","import json\n","import math \n","from collections import OrderedDict\n","\n","from meshgpt_pytorch import (\n","    MeshTransformerTrainer,\n","    MeshAutoencoderTrainer,\n","    MeshAutoencoder,\n","    MeshTransformer\n",")\n","\n","def get_3d_data(file_path): \n","    mesh = trimesh.load(file_path, force='mesh')\n","    \n","    # Extract vertices and faces\n","    vertices = mesh.vertices.tolist()\n","    faces = mesh.faces.tolist()\n","    centered_vertices = vertices - np.mean(vertices, axis=0)\n"," \n","    max_abs = np.max(np.abs(centered_vertices))\n","    vertices = centered_vertices / (max_abs / 0.95)  \n","      \n","      \n","    def sort_vertices(vertex): # Sort by Y , X, Z.  Y is vertical\n","        return vertex[1], vertex[0], vertex[2]   \n"," \n","    seen = OrderedDict()\n","    for point in vertices: \n","      key = tuple(point)\n","      if key not in seen:\n","        seen[key] = point\n","    unique_vertices =  list(seen.values()) \n","    sorted_vertices = sorted(unique_vertices, key=sort_vertices)\n","     \n","    vertices_as_tuples = [tuple(v) for v in vertices]\n","    sorted_vertices_as_tuples = [tuple(v) for v in sorted_vertices]\n","\n","    vertex_map = {old_index: new_index for old_index, vertex_tuple in enumerate(vertices_as_tuples) for new_index, sorted_vertex_tuple in enumerate(sorted_vertices_as_tuples) if vertex_tuple == sorted_vertex_tuple}\n"," \n","\n","    reindexed_faces = [[vertex_map[face[0]], vertex_map[face[1]], vertex_map[face[2]]] for face in faces] \n","    return np.array(sorted_vertices), np.array(reindexed_faces)\n","\n","def augment_mesh_scalar(vertices, scale_factor):\n","    # Apply a scalar factor to XYZ coordinates\n","    transformed_vertices = vertices * scale_factor\n","    return transformed_vertices\n","\n","def generate_scale_factors(num_examples, lower_limit=0.75, upper_limit=1.25): \n","    scale_factors = np.random.uniform(lower_limit, upper_limit, size=num_examples)\n","    return scale_factors\n","\n","def jitter_mesh(vertices, jitter_factor=0.01): \n","    offsets = np.random.uniform(-jitter_factor, jitter_factor, size=vertices.shape)\n"," \n","    jittered_vertices = vertices + offsets \n","    return jittered_vertices \n","\n","def augment_mesh(vertices, scale_factor):\n","    #vertices = jitter_mesh(vertices)\n","    transformed_vertices = vertices * scale_factor\n","    \n","    return transformed_vertices\n"," \n","\n","def load_models(directory, num_examples, variations):\n","    obj_datas = []  \n","    \n","    print(f\"num_examples: {num_examples}\")\n","    for filename in os.listdir(directory):  \n","        if (filename.endswith(\".obj\") or  filename.endswith(\".glb\") or  filename.endswith(\".off\")):\n","            file_path = os.path.join(directory, filename)\n","\n","            scale_factors = generate_scale_factors(variations, 0.7, 0.9) \n","            vertices, faces = get_3d_data(file_path) \n","\n","            for scale_factor in scale_factors: \n","                aug_vertices = augment_mesh(vertices.copy(), scale_factor) \n","                \n","                for _ in range(num_examples):\n","                    obj_data = {\"vertices\": aug_vertices.tolist(), \"faces\":  faces.tolist(), \"texts\": filename[:-4]}\n","                    obj_datas.append(obj_data)   \n","    return obj_datas\n","  \n","\n","\n","  \n","def load_json(file,num_examples):\n","    obj_datas = []\n","    with open(file, \"r\") as json_file:\n","        loaded_data = json.load(json_file) \n","        for item in loaded_data:\n","            for _ in range(num_examples): \n","                obj_data = {\"vertices\": torch.tensor(item[\"vertices\"], dtype=torch.float).to(\"cuda\"), \"faces\":  torch.tensor(item[\"faces\"], dtype=torch.long).to(\"cuda\"),\"texts\": item[\"texts\"] } \n","                obj_datas.append(obj_data)\n","    return obj_datas\n","                        \n","         "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:44.050017Z","iopub.status.busy":"2023-12-19T01:39:44.049685Z","iopub.status.idle":"2023-12-19T01:39:44.063671Z","shell.execute_reply":"2023-12-19T01:39:44.062641Z","shell.execute_reply.started":"2023-12-19T01:39:44.049992Z"},"trusted":true},"outputs":[],"source":["import torch\n","from torch import Tensor, tensor\n","from torch.utils.data import Dataset, DataLoader \n","from tqdm import tqdm\n","import numpy as np \n","import gc\n","from torch.nn.utils.rnn import pad_sequence\n","from meshgpt_pytorch.data import ( \n","    derive_face_edges_from_faces\n",") \n"," \n","class MeshDataset(Dataset): \n","    \n","    def __init__(self, data): \n","        self.data = data\n","        print(f\"Got {len(data)} data\")\n","\n","    def __len__(self):\n","        return len(self.data)\n","\n","    def __getitem__(self, idx): \n","        data = self.data[idx] \n","        return data  \n","    \n","    def save(self, path):\n","        np.savez_compressed(path, *self.data)\n","\n","    \n","    @classmethod\n","    def load(cls, path): \n","        loaded_data = np.load(path, allow_pickle=True)\n"," \n","        data = []\n","        for i in range(len(loaded_data.files)):\n","            data_item = {}\n","            for key in loaded_data[f\"arr_{i}\"].item():\n","                data_item[key] = loaded_data[f\"arr_{i}\"].item()[key]\n","            data.append(data_item)\n","\n","        return cls(data)\n","    \n","    def embed_texts(self,transformer): \n","        unique_texts = set(item['texts'] for item in self.data)\n"," \n","        text_embeddings = transformer.embed_texts(list(unique_texts))\n","        print(f\"Got text_embeddings: {len(text_embeddings)}\") \n","        text_embedding_dict = dict(zip(unique_texts, text_embeddings))\n"," \n","        for item in self.data:\n","            text_value = item['texts']\n","            item['text_embeds'] = text_embedding_dict.get(text_value, None)\n","            del item['texts']\n","            \n","    def generate_face_edges(self):\n","        n = 0\n","        for i in range(0, len(self.data)):  \n","            item = self.data[i]\n","            item['face_edges'] =  derive_face_edges_from_faces(item['faces'])\n","            n += 1  \n","        print(f\"done {n}/{len(self.data)}\")\n","\n","    def generate_codes(self, autoencoder : MeshAutoencoder):\n","        n = 0\n","        for i in range(0, len(self.data)):  \n","            item = self.data[i]\n","             \n","            codes = autoencoder.tokenize(\n","                vertices = item['vertices'],\n","                faces = item['faces'],\n","                face_edges = item['face_edges']\n","            ) \n","            item['codes'] = codes \n","            n += 1  \n","\n","        print(f\"[generate_codes] done {n}/{len(self.data)}\") \n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:44.066146Z","iopub.status.busy":"2023-12-19T01:39:44.065812Z","iopub.status.idle":"2023-12-19T01:39:52.313349Z","shell.execute_reply":"2023-12-19T01:39:52.312381Z","shell.execute_reply.started":"2023-12-19T01:39:44.066116Z"},"trusted":true},"outputs":[],"source":["import json\n","#tables = load_models(r\" filtered\",5,5)  \n","#with open(\"data.json\", \"w\") as json_file:\n","#    json.dump(tables, json_file) \n","\n","tables = load_json(\"/kaggle/input/shapenet/data.json\",2)\n","dataset = MeshDataset(tables) \n","dataset.generate_face_edges()\n","dataset.data[0].keys()\n","\n","desired_order = ['vertices', 'faces', 'face_edges', 'texts']\n","\n","dataset.data = [\n","    {key: d[key] for key in desired_order} for d in dataset.data\n","]\n","\n","unique_values = set(item[\"texts\"] for item in dataset.data)\n","\n","print(len(unique_values))  \n","print(unique_values)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["autoencoder = MeshAutoencoder( \n","    num_discrete_coors = 128  , \n",") \n","total_params = sum(p.numel() for p in autoencoder.encoders.parameters())\n","print(f\"encoders Total parameters: {total_params}\")\n","total_params = sum(p.numel() for p in autoencoder.decoders.parameters())\n","print(f\"decoders Total parameters: {total_params}\")  \n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:44:00.715656Z","iopub.status.busy":"2023-12-19T01:44:00.714727Z","iopub.status.idle":"2023-12-19T12:00:40.840982Z","shell.execute_reply":"2023-12-19T12:00:40.840110Z","shell.execute_reply.started":"2023-12-19T01:44:00.715620Z"},"trusted":true},"outputs":[],"source":["total_params = sum(p.numel() for p in autoencoder.encoders.parameters())\n","print(f\"Total parameters: {total_params}\")\n","print(autoencoder.encoders)\n","\n","autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder,learning_rate = 1e-3, \n","                                             warmup_steps = 10,\n","                                             dataset = dataset,   \n","                                             num_train_steps=100,\n","                                             batch_size=16,\n","                                             grad_accum_every=1)\n","\n","loss = autoencoder_trainer.train(40,stop_at_loss = 0.25)   \n","autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder,learning_rate = 1e-4, \n","                                             warmup_steps = 10,\n","                                             dataset = dataset,\n","                                             checkpoint_every_epoch = 20,  \n","                                             num_train_steps=100,\n","                                             batch_size=16,\n","                                             grad_accum_every=1)\n","\n","loss = autoencoder_trainer.train(180,stop_at_loss = 0.25)   \n","autoencoder_trainer.save(f'./mesh-encoder_2_loss_{loss:.3f}.pt') "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n","max_seq =  max_length * 6  \n","print(max_length)\n","print(max_seq)\n","transformer = MeshTransformer(\n","    autoencoder,\n","    dim = 512,\n","    max_seq_len = max_seq,\n","    coarse_pre_gateloop_depth = 6,\n","    fine_pre_gateloop_depth= 4, \n","    condition_on_text = True\n",")\n","total_params = sum(p.numel() for p in transformer.parameters())\n","print(f\"Total parameters: {total_params}\") "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["dataset.embed_texts(transformer)\n","dataset.generate_codes(autoencoder,1)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[" \n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,   dataset = dataset,\n","                                 learning_rate = 1e-1, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)   \n","\n"," \n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  dataset = dataset,\n","                                 learning_rate = 1e-2, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)    \n","\n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  dataset = dataset,\n","                                 learning_rate = 1e-4, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)   \n","\n","trainer.save(f'./mesh-transformer_2_{loss:.3f}.pt')    "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["unique_values = set(item[\"texts\"] for item in dataset.data)\n","print(len(unique_values))  \n","coords = []\n","for text in unique_values: \n","    print(f\"doing {text}\")\n","    faces_coordinates = transformer.generate(texts = [text]) \n","    coords.append(faces_coordinates)\n","    tensor_data = faces_coordinates[0].cpu()\n","    \n","    numpy_data = tensor_data.numpy().reshape(-1, 3)\n","    \n","    obj_file_content = \"\"\n","    \n","    for vertex in numpy_data:\n","        obj_file_content += f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\"\n","\n","    for i in range(1, len(numpy_data), 3):\n","        obj_file_content += f\"f {i} {i + 1} {i + 2}\\n\"\n","\n","    # Save to a file\n","    obj_file_path = f'./tests/3d_output_{text}.obj'\n","    with open(obj_file_path, \"w\") as file:\n","        file.write(obj_file_content)\n","\n","    print(obj_file_path) \n","    \n","    \n","all_vertices = []\n","all_faces = []\n","vertex_offset = 0\n"," \n","translation_distance = 0.3  \n","\n","for r, faces_coordinates in enumerate(coords): \n","    tensor_data = faces_coordinates[0].cpu()\n","\n","    numpy_data = tensor_data.numpy().reshape(-1, 3)\n","\n","    # Translate the model to avoid overlapping\n","    numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  # Adjust X coordinate\n","\n","    # Accumulate vertices\n","    for vertex in numpy_data:\n","        all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","    # Accumulate faces with adjusted indices\n","    for i in range(1, len(numpy_data), 3):\n","        all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n","\n","    # Update the vertex offset for the next model\n","    vertex_offset += len(numpy_data)\n","\n","# Combine vertices and faces\n","obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","# Save to a single file\n","obj_file_path = f\"./tests/3d_models_all.obj\"\n","with open(obj_file_path, \"w\") as file:\n","    file.write(obj_file_content)\n","\n","print(obj_file_path)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","coords_all = []\n","for text in set(item[\"texts\"] for item in dataset.data): \n","    print(f\"Doing {text}\")\n","    coords = []\n","    for r in np.arange(0, 1.0, 0.1):\n","        faces_coordinates = transformer.generate(temperature=r, texts = [text]) \n","        coords.append(faces_coordinates)\n","    coords_all.append(coords)\n","    \n","    all_vertices = []\n","    all_faces = []\n","    vertex_offset = 0\n","\n","    # Translation distance for each model\n","    translation_distance = 0.3  # Adjust as needed\n","\n","    for r, faces_coordinates in enumerate(coords): \n","        tensor_data = faces_coordinates[0].cpu()\n","\n","        numpy_data = tensor_data.numpy().reshape(-1, 3)\n","\n","        # Translate the model to avoid overlapping\n","        numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  # Adjust X coordinate\n","\n","        # Accumulate vertices\n","        for vertex in numpy_data:\n","            all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","        # Accumulate faces with adjusted indices\n","        for i in range(1, len(numpy_data), 3):\n","            all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n","\n","        # Update the vertex offset for the next model\n","        vertex_offset += len(numpy_data)\n","\n","    # Combine vertices and faces\n","    obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","    # Save to a single file\n","    obj_file_path = f\"./results/3d_models_{text}_temps.obj\"\n","    with open(obj_file_path, \"w\") as file:\n","        file.write(obj_file_content)\n","\n","    print(obj_file_path)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","def loadModels():\n","    autoencoder = MeshAutoencoder(\n","        dim = 576,\n","        encoder_depth = 6,\n","        decoder_depth = 6,\n","        num_discrete_coors = 128  ,\n","        local_attn_depth =0, \n","        \n","    )\n","    autoencoder_trainer = MeshAutoencoderTrainer(model = autoencoder,\n","                                    learning_rate = 1e-1, \n","                                                checkpoint_every_epoch= 5,\n","                                                warmup_steps = 10,\n","                                                dataset = dataset,  \n","                                                num_train_steps=100,\n","                                                batch_size=2,\n","                                                grad_accum_every=1)\n","\n","    autoencoder_trainer.load(r\"mesh-encoder_last.pt\")\n","    encoder = autoencoder_trainer.model\n","    max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n","    max_seq =  max_length * 6  \n","    \n","    transformer = MeshTransformer(\n","        autoencoder,\n","        dim = 768,\n","        max_seq_len = max_seq,\n","        condition_on_text = True)\n","     \n","    trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100, checkpoint_folder = r\"F:\\MachineLearning\\Mesh\\MeshGPT\\checkpoints\" , dataset = dataset,\n","                                    learning_rate = 1e-3, batch_size=2) \n","    trainer.load(r\"mesh-transformer.pt\")\n","    transformer = trainer.model\n","    return transformer, encoder\n","\n","#transformer, autoencoder =  loadModels() "]}],"metadata":{"kaggle":{"accelerator":"gpu","dataSources":[{"datasetId":4189032,"sourceId":7234116,"sourceType":"datasetVersion"}],"dockerImageVersionId":30627,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"}},"nbformat":4,"nbformat_minor":4}

From 9766d41999c0dccdeb15d45a123f73710d4c51ad Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Wed, 27 Dec 2023 01:41:21 +0100
Subject: [PATCH 33/84] added mesh_dataset

---
 meshgpt_pytorch/mesh_dataset.py | 107 ++++++++++++++++++++++++++++++++
 1 file changed, 107 insertions(+)
 create mode 100644 meshgpt_pytorch/mesh_dataset.py

diff --git a/meshgpt_pytorch/mesh_dataset.py b/meshgpt_pytorch/mesh_dataset.py
new file mode 100644
index 00000000..9ce58ee0
--- /dev/null
+++ b/meshgpt_pytorch/mesh_dataset.py
@@ -0,0 +1,107 @@
+from torch.utils.data import Dataset
+import numpy as np  
+from meshgpt_pytorch import ( 
+    MeshAutoencoder,
+    MeshTransformer
+)
+
+from meshgpt_pytorch.data import ( 
+    derive_face_edges_from_faces
+) 
+ 
+class MeshDataset(Dataset): 
+    """
+    A PyTorch Dataset to load and process mesh data. 
+    The `MeshDataset` provides functions to load mesh data from a file, embed text information, generate face edges, and generate codes.
+
+    Attributes:
+        data (list): A list of mesh data entries. Each entry is a dictionary containing the following keys:
+            vertices (torch.Tensor): A tensor of vertices with shape (num_vertices, 3).
+            faces (torch.Tensor): A tensor of faces with shape (num_faces, 3).
+            text (str): A string containing the associated text information for the mesh.
+            text_embeds (torch.Tensor): A tensor of text embeddings for the mesh.
+            face_edges (torch.Tensor): A tensor of face edges with shape (num_faces, num_edges).
+            codes (torch.Tensor): A tensor of codes generated from the mesh data.
+
+    Example usage:
+
+    ``` 
+    data = [
+        {'vertices': torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.float32), 'faces': torch.tensor([[0, 1, 2]], dtype=torch.long), 'text': 'table'},
+        {'vertices': torch.tensor([[10, 20, 30], [40, 50, 60]], dtype=torch.float32), 'faces': torch.tensor([[1, 2, 0]], dtype=torch.long), "text": "chair"},
+    ]
+
+    # Create a MeshDataset instance
+    mesh_dataset = MeshDataset(data)
+
+    # Save the MeshDataset to disk
+    mesh_dataset.save('mesh_dataset.npz')
+
+    # Load the MeshDataset from disk
+    loaded_mesh_dataset = MeshDataset.load('mesh_dataset.npz')
+    
+    # Generate face edges so it doesn't need to be done every time during training
+    dataset.generate_face_edges()
+    ```
+    """
+    def __init__(self, data): 
+        self.data = data 
+        print(f"[MeshDataset] Created from {len(self.data)} entrys")
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx): 
+        data = self.data[idx] 
+        return data  
+    
+    def save(self, path):  
+        np.savez_compressed(path, self.data, allow_pickle=True) 
+        print(f"[MeshDataset] Saved {len(self.data)} entrys at {path}")
+        
+    @classmethod
+    def load(cls, path):   
+        loaded_data = np.load(path, allow_pickle=True)  
+        data = []
+        for item in loaded_data["arr_0"]:
+            data.append(item)  
+        print(f"[MeshDataset] Loaded {len(data)} entrys")
+        return cls(data)
+     
+
+            
+    def generate_face_edges(self): 
+        for i in range(0, len(self.data)):  
+            item = self.data[i]
+            item['face_edges'] =  derive_face_edges_from_faces(item['faces']) 
+            
+        desired_order = ['vertices', 'faces', 'face_edges', 'texts'] 
+        self.data = [
+            {key: d[key] for key in desired_order} for d in self.data
+        ]
+        print(f"[MeshDataset] Generated face_edges for {len(self.data)} entrys")
+
+    def generate_codes(self, autoencoder : MeshAutoencoder): 
+        for i in range(0, len(self.data)):  
+            item = self.data[i]  
+             
+            codes = autoencoder.tokenize(
+                vertices = item['vertices'],
+                faces = item['faces'],
+                face_edges = item['face_edges']
+            ) 
+            item['codes'] = codes  
+ 
+        print(f"[MeshDataset] Generated codes for {len(self.data)} entrys")
+    
+    def embed_texts(self, transformer : MeshTransformer): 
+        unique_texts = set(item['texts'] for item in self.data)
+ 
+        text_embeddings = transformer.embed_texts(list(unique_texts))
+        print(f"[MeshDataset] Generated {len(text_embeddings)} text_embeddings") 
+        text_embedding_dict = dict(zip(unique_texts, text_embeddings))
+ 
+        for item in self.data:
+            text_value = item['texts']
+            item['text_embeds'] = text_embedding_dict.get(text_value, None)
+            del item['texts']
\ No newline at end of file

From bf08333afe80cc82843cc00f865b16df93c7a83a Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Thu, 28 Dec 2023 01:33:59 +0100
Subject: [PATCH 34/84] fixed tdqm bug

---
 MeshGPT Testing.ipynb      |   2 +-
 meshgpt_pytorch/trainer.py | 170 +++++++++++++++++++------------------
 2 files changed, 89 insertions(+), 83 deletions(-)

diff --git a/MeshGPT Testing.ipynb b/MeshGPT Testing.ipynb
index a76245e4..62168f86 100644
--- a/MeshGPT Testing.ipynb	
+++ b/MeshGPT Testing.ipynb	
@@ -1 +1 @@
-{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:12.944690Z","iopub.status.busy":"2023-12-19T01:39:12.944345Z","iopub.status.idle":"2023-12-19T01:39:38.152713Z","shell.execute_reply":"2023-12-19T01:39:38.151433Z","shell.execute_reply.started":"2023-12-19T01:39:12.944662Z"},"trusted":true},"outputs":[],"source":["!pip install -q git+https://github.com/MarcusLoppe/meshgpt-pytorch.git trimesh"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:38.155749Z","iopub.status.busy":"2023-12-19T01:39:38.155103Z","iopub.status.idle":"2023-12-19T01:39:44.048426Z","shell.execute_reply":"2023-12-19T01:39:44.047605Z","shell.execute_reply.started":"2023-12-19T01:39:38.155692Z"},"trusted":true},"outputs":[],"source":["import torch\n","import trimesh\n","import numpy as np\n","import os\n","import csv \n","import json\n","import math \n","from collections import OrderedDict\n","\n","from meshgpt_pytorch import (\n","    MeshTransformerTrainer,\n","    MeshAutoencoderTrainer,\n","    MeshAutoencoder,\n","    MeshTransformer\n",")\n","\n","def get_3d_data(file_path): \n","    mesh = trimesh.load(file_path, force='mesh')\n","    \n","    # Extract vertices and faces\n","    vertices = mesh.vertices.tolist()\n","    faces = mesh.faces.tolist()\n","    centered_vertices = vertices - np.mean(vertices, axis=0)\n"," \n","    max_abs = np.max(np.abs(centered_vertices))\n","    vertices = centered_vertices / (max_abs / 0.95)  \n","      \n","      \n","    def sort_vertices(vertex): # Sort by Y , X, Z.  Y is vertical\n","        return vertex[1], vertex[0], vertex[2]   \n"," \n","    seen = OrderedDict()\n","    for point in vertices: \n","      key = tuple(point)\n","      if key not in seen:\n","        seen[key] = point\n","    unique_vertices =  list(seen.values()) \n","    sorted_vertices = sorted(unique_vertices, key=sort_vertices)\n","     \n","    vertices_as_tuples = [tuple(v) for v in vertices]\n","    sorted_vertices_as_tuples = [tuple(v) for v in sorted_vertices]\n","\n","    vertex_map = {old_index: new_index for old_index, vertex_tuple in enumerate(vertices_as_tuples) for new_index, sorted_vertex_tuple in enumerate(sorted_vertices_as_tuples) if vertex_tuple == sorted_vertex_tuple}\n"," \n","\n","    reindexed_faces = [[vertex_map[face[0]], vertex_map[face[1]], vertex_map[face[2]]] for face in faces] \n","    return np.array(sorted_vertices), np.array(reindexed_faces)\n","\n","def augment_mesh_scalar(vertices, scale_factor):\n","    # Apply a scalar factor to XYZ coordinates\n","    transformed_vertices = vertices * scale_factor\n","    return transformed_vertices\n","\n","def generate_scale_factors(num_examples, lower_limit=0.75, upper_limit=1.25): \n","    scale_factors = np.random.uniform(lower_limit, upper_limit, size=num_examples)\n","    return scale_factors\n","\n","def jitter_mesh(vertices, jitter_factor=0.01): \n","    offsets = np.random.uniform(-jitter_factor, jitter_factor, size=vertices.shape)\n"," \n","    jittered_vertices = vertices + offsets \n","    return jittered_vertices \n","\n","def augment_mesh(vertices, scale_factor):\n","    #vertices = jitter_mesh(vertices)\n","    transformed_vertices = vertices * scale_factor\n","    \n","    return transformed_vertices\n"," \n","\n","def load_models(directory, num_examples, variations):\n","    obj_datas = []  \n","    \n","    print(f\"num_examples: {num_examples}\")\n","    for filename in os.listdir(directory):  \n","        if (filename.endswith(\".obj\") or  filename.endswith(\".glb\") or  filename.endswith(\".off\")):\n","            file_path = os.path.join(directory, filename)\n","\n","            scale_factors = generate_scale_factors(variations, 0.7, 0.9) \n","            vertices, faces = get_3d_data(file_path) \n","\n","            for scale_factor in scale_factors: \n","                aug_vertices = augment_mesh(vertices.copy(), scale_factor) \n","                \n","                for _ in range(num_examples):\n","                    obj_data = {\"vertices\": aug_vertices.tolist(), \"faces\":  faces.tolist(), \"texts\": filename[:-4]}\n","                    obj_datas.append(obj_data)   \n","    return obj_datas\n","  \n","\n","\n","  \n","def load_json(file,num_examples):\n","    obj_datas = []\n","    with open(file, \"r\") as json_file:\n","        loaded_data = json.load(json_file) \n","        for item in loaded_data:\n","            for _ in range(num_examples): \n","                obj_data = {\"vertices\": torch.tensor(item[\"vertices\"], dtype=torch.float).to(\"cuda\"), \"faces\":  torch.tensor(item[\"faces\"], dtype=torch.long).to(\"cuda\"),\"texts\": item[\"texts\"] } \n","                obj_datas.append(obj_data)\n","    return obj_datas\n","                        \n","         "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:44.050017Z","iopub.status.busy":"2023-12-19T01:39:44.049685Z","iopub.status.idle":"2023-12-19T01:39:44.063671Z","shell.execute_reply":"2023-12-19T01:39:44.062641Z","shell.execute_reply.started":"2023-12-19T01:39:44.049992Z"},"trusted":true},"outputs":[],"source":["import torch\n","from torch import Tensor, tensor\n","from torch.utils.data import Dataset, DataLoader \n","from tqdm import tqdm\n","import numpy as np \n","import gc\n","from torch.nn.utils.rnn import pad_sequence\n","from meshgpt_pytorch.data import ( \n","    derive_face_edges_from_faces\n",") \n"," \n","class MeshDataset(Dataset): \n","    \n","    def __init__(self, data): \n","        self.data = data\n","        print(f\"Got {len(data)} data\")\n","\n","    def __len__(self):\n","        return len(self.data)\n","\n","    def __getitem__(self, idx): \n","        data = self.data[idx] \n","        return data  \n","    \n","    def save(self, path):\n","        np.savez_compressed(path, *self.data)\n","\n","    \n","    @classmethod\n","    def load(cls, path): \n","        loaded_data = np.load(path, allow_pickle=True)\n"," \n","        data = []\n","        for i in range(len(loaded_data.files)):\n","            data_item = {}\n","            for key in loaded_data[f\"arr_{i}\"].item():\n","                data_item[key] = loaded_data[f\"arr_{i}\"].item()[key]\n","            data.append(data_item)\n","\n","        return cls(data)\n","    \n","    def embed_texts(self,transformer): \n","        unique_texts = set(item['texts'] for item in self.data)\n"," \n","        text_embeddings = transformer.embed_texts(list(unique_texts))\n","        print(f\"Got text_embeddings: {len(text_embeddings)}\") \n","        text_embedding_dict = dict(zip(unique_texts, text_embeddings))\n"," \n","        for item in self.data:\n","            text_value = item['texts']\n","            item['text_embeds'] = text_embedding_dict.get(text_value, None)\n","            del item['texts']\n","            \n","    def generate_face_edges(self):\n","        n = 0\n","        for i in range(0, len(self.data)):  \n","            item = self.data[i]\n","            item['face_edges'] =  derive_face_edges_from_faces(item['faces'])\n","            n += 1  \n","        print(f\"done {n}/{len(self.data)}\")\n","\n","    def generate_codes(self, autoencoder : MeshAutoencoder):\n","        n = 0\n","        for i in range(0, len(self.data)):  \n","            item = self.data[i]\n","             \n","            codes = autoencoder.tokenize(\n","                vertices = item['vertices'],\n","                faces = item['faces'],\n","                face_edges = item['face_edges']\n","            ) \n","            item['codes'] = codes \n","            n += 1  \n","\n","        print(f\"[generate_codes] done {n}/{len(self.data)}\") \n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:44.066146Z","iopub.status.busy":"2023-12-19T01:39:44.065812Z","iopub.status.idle":"2023-12-19T01:39:52.313349Z","shell.execute_reply":"2023-12-19T01:39:52.312381Z","shell.execute_reply.started":"2023-12-19T01:39:44.066116Z"},"trusted":true},"outputs":[],"source":["import json\n","#tables = load_models(r\" filtered\",5,5)  \n","#with open(\"data.json\", \"w\") as json_file:\n","#    json.dump(tables, json_file) \n","\n","tables = load_json(\"/kaggle/input/shapenet/data.json\",2)\n","dataset = MeshDataset(tables) \n","dataset.generate_face_edges()\n","dataset.data[0].keys()\n","\n","desired_order = ['vertices', 'faces', 'face_edges', 'texts']\n","\n","dataset.data = [\n","    {key: d[key] for key in desired_order} for d in dataset.data\n","]\n","\n","unique_values = set(item[\"texts\"] for item in dataset.data)\n","\n","print(len(unique_values))  \n","print(unique_values)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["autoencoder = MeshAutoencoder( \n","    num_discrete_coors = 128  , \n",") \n","total_params = sum(p.numel() for p in autoencoder.encoders.parameters())\n","print(f\"encoders Total parameters: {total_params}\")\n","total_params = sum(p.numel() for p in autoencoder.decoders.parameters())\n","print(f\"decoders Total parameters: {total_params}\")  \n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:44:00.715656Z","iopub.status.busy":"2023-12-19T01:44:00.714727Z","iopub.status.idle":"2023-12-19T12:00:40.840982Z","shell.execute_reply":"2023-12-19T12:00:40.840110Z","shell.execute_reply.started":"2023-12-19T01:44:00.715620Z"},"trusted":true},"outputs":[],"source":["total_params = sum(p.numel() for p in autoencoder.encoders.parameters())\n","print(f\"Total parameters: {total_params}\")\n","print(autoencoder.encoders)\n","\n","autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder,learning_rate = 1e-3, \n","                                             warmup_steps = 10,\n","                                             dataset = dataset,   \n","                                             num_train_steps=100,\n","                                             batch_size=16,\n","                                             grad_accum_every=1)\n","\n","loss = autoencoder_trainer.train(40,stop_at_loss = 0.25)   \n","autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder,learning_rate = 1e-4, \n","                                             warmup_steps = 10,\n","                                             dataset = dataset,\n","                                             checkpoint_every_epoch = 20,  \n","                                             num_train_steps=100,\n","                                             batch_size=16,\n","                                             grad_accum_every=1)\n","\n","loss = autoencoder_trainer.train(180,stop_at_loss = 0.25)   \n","autoencoder_trainer.save(f'./mesh-encoder_2_loss_{loss:.3f}.pt') "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n","max_seq =  max_length * 6  \n","print(max_length)\n","print(max_seq)\n","transformer = MeshTransformer(\n","    autoencoder,\n","    dim = 512,\n","    max_seq_len = max_seq,\n","    coarse_pre_gateloop_depth = 6,\n","    fine_pre_gateloop_depth= 4, \n","    condition_on_text = True\n",")\n","total_params = sum(p.numel() for p in transformer.parameters())\n","print(f\"Total parameters: {total_params}\") "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["dataset.embed_texts(transformer)\n","dataset.generate_codes(autoencoder,1)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[" \n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,   dataset = dataset,\n","                                 learning_rate = 1e-1, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)   \n","\n"," \n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  dataset = dataset,\n","                                 learning_rate = 1e-2, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)    \n","\n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  dataset = dataset,\n","                                 learning_rate = 1e-4, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)   \n","\n","trainer.save(f'./mesh-transformer_2_{loss:.3f}.pt')    "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["unique_values = set(item[\"texts\"] for item in dataset.data)\n","print(len(unique_values))  \n","coords = []\n","for text in unique_values: \n","    print(f\"doing {text}\")\n","    faces_coordinates = transformer.generate(texts = [text]) \n","    coords.append(faces_coordinates)\n","    tensor_data = faces_coordinates[0].cpu()\n","    \n","    numpy_data = tensor_data.numpy().reshape(-1, 3)\n","    \n","    obj_file_content = \"\"\n","    \n","    for vertex in numpy_data:\n","        obj_file_content += f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\"\n","\n","    for i in range(1, len(numpy_data), 3):\n","        obj_file_content += f\"f {i} {i + 1} {i + 2}\\n\"\n","\n","    # Save to a file\n","    obj_file_path = f'./tests/3d_output_{text}.obj'\n","    with open(obj_file_path, \"w\") as file:\n","        file.write(obj_file_content)\n","\n","    print(obj_file_path) \n","    \n","    \n","all_vertices = []\n","all_faces = []\n","vertex_offset = 0\n"," \n","translation_distance = 0.3  \n","\n","for r, faces_coordinates in enumerate(coords): \n","    tensor_data = faces_coordinates[0].cpu()\n","\n","    numpy_data = tensor_data.numpy().reshape(-1, 3)\n","\n","    # Translate the model to avoid overlapping\n","    numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  # Adjust X coordinate\n","\n","    # Accumulate vertices\n","    for vertex in numpy_data:\n","        all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","    # Accumulate faces with adjusted indices\n","    for i in range(1, len(numpy_data), 3):\n","        all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n","\n","    # Update the vertex offset for the next model\n","    vertex_offset += len(numpy_data)\n","\n","# Combine vertices and faces\n","obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","# Save to a single file\n","obj_file_path = f\"./tests/3d_models_all.obj\"\n","with open(obj_file_path, \"w\") as file:\n","    file.write(obj_file_content)\n","\n","print(obj_file_path)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","coords_all = []\n","for text in set(item[\"texts\"] for item in dataset.data): \n","    print(f\"Doing {text}\")\n","    coords = []\n","    for r in np.arange(0, 1.0, 0.1):\n","        faces_coordinates = transformer.generate(temperature=r, texts = [text]) \n","        coords.append(faces_coordinates)\n","    coords_all.append(coords)\n","    \n","    all_vertices = []\n","    all_faces = []\n","    vertex_offset = 0\n","\n","    # Translation distance for each model\n","    translation_distance = 0.3  # Adjust as needed\n","\n","    for r, faces_coordinates in enumerate(coords): \n","        tensor_data = faces_coordinates[0].cpu()\n","\n","        numpy_data = tensor_data.numpy().reshape(-1, 3)\n","\n","        # Translate the model to avoid overlapping\n","        numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  # Adjust X coordinate\n","\n","        # Accumulate vertices\n","        for vertex in numpy_data:\n","            all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","        # Accumulate faces with adjusted indices\n","        for i in range(1, len(numpy_data), 3):\n","            all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n","\n","        # Update the vertex offset for the next model\n","        vertex_offset += len(numpy_data)\n","\n","    # Combine vertices and faces\n","    obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","    # Save to a single file\n","    obj_file_path = f\"./results/3d_models_{text}_temps.obj\"\n","    with open(obj_file_path, \"w\") as file:\n","        file.write(obj_file_content)\n","\n","    print(obj_file_path)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","def loadModels():\n","    autoencoder = MeshAutoencoder(\n","        dim = 576,\n","        encoder_depth = 6,\n","        decoder_depth = 6,\n","        num_discrete_coors = 128  ,\n","        local_attn_depth =0, \n","        \n","    )\n","    autoencoder_trainer = MeshAutoencoderTrainer(model = autoencoder,\n","                                    learning_rate = 1e-1, \n","                                                checkpoint_every_epoch= 5,\n","                                                warmup_steps = 10,\n","                                                dataset = dataset,  \n","                                                num_train_steps=100,\n","                                                batch_size=2,\n","                                                grad_accum_every=1)\n","\n","    autoencoder_trainer.load(r\"mesh-encoder_last.pt\")\n","    encoder = autoencoder_trainer.model\n","    max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n","    max_seq =  max_length * 6  \n","    \n","    transformer = MeshTransformer(\n","        autoencoder,\n","        dim = 768,\n","        max_seq_len = max_seq,\n","        condition_on_text = True)\n","     \n","    trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100, checkpoint_folder = r\"F:\\MachineLearning\\Mesh\\MeshGPT\\checkpoints\" , dataset = dataset,\n","                                    learning_rate = 1e-3, batch_size=2) \n","    trainer.load(r\"mesh-transformer.pt\")\n","    transformer = trainer.model\n","    return transformer, encoder\n","\n","#transformer, autoencoder =  loadModels() "]}],"metadata":{"kaggle":{"accelerator":"gpu","dataSources":[{"datasetId":4189032,"sourceId":7234116,"sourceType":"datasetVersion"}],"dockerImageVersionId":30627,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"}},"nbformat":4,"nbformat_minor":4}
+{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Use this if you running on a Kaggle GPU/ Cuda 11.8\n","#!pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --index-url https://download.pytorch.org/whl/cu118"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:12.944690Z","iopub.status.busy":"2023-12-19T01:39:12.944345Z","iopub.status.idle":"2023-12-19T01:39:38.152713Z","shell.execute_reply":"2023-12-19T01:39:38.151433Z","shell.execute_reply.started":"2023-12-19T01:39:12.944662Z"},"trusted":true},"outputs":[],"source":["!pip install -q git+https://github.com/MarcusLoppe/meshgpt-pytorch.git trimesh"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:38.155749Z","iopub.status.busy":"2023-12-19T01:39:38.155103Z","iopub.status.idle":"2023-12-19T01:39:44.048426Z","shell.execute_reply":"2023-12-19T01:39:44.047605Z","shell.execute_reply.started":"2023-12-19T01:39:38.155692Z"},"trusted":true},"outputs":[],"source":["import torch\n","import trimesh\n","import numpy as np\n","import os\n","import csv \n","import json\n","import math \n","from collections import OrderedDict\n","\n","from meshgpt_pytorch import (\n","    MeshTransformerTrainer,\n","    MeshAutoencoderTrainer,\n","    MeshAutoencoder,\n","    MeshTransformer\n",")\n","\n","def get_3d_data(file_path): \n","    mesh = trimesh.load(file_path, force='mesh')\n","    \n","    # Extract vertices and faces\n","    vertices = mesh.vertices.tolist()\n","    faces = mesh.faces.tolist()\n","    centered_vertices = vertices - np.mean(vertices, axis=0)\n"," \n","    max_abs = np.max(np.abs(centered_vertices))\n","    vertices = centered_vertices / (max_abs / 0.95)  \n","      \n","      \n","    def sort_vertices(vertex): # Sort by Y , X, Z.  Y is vertical\n","        return vertex[1], vertex[0], vertex[2]   \n"," \n","    seen = OrderedDict()\n","    for point in vertices: \n","      key = tuple(point)\n","      if key not in seen:\n","        seen[key] = point\n","    unique_vertices =  list(seen.values()) \n","    sorted_vertices = sorted(unique_vertices, key=sort_vertices)\n","     \n","    vertices_as_tuples = [tuple(v) for v in vertices]\n","    sorted_vertices_as_tuples = [tuple(v) for v in sorted_vertices]\n","\n","    vertex_map = {old_index: new_index for old_index, vertex_tuple in enumerate(vertices_as_tuples) for new_index, sorted_vertex_tuple in enumerate(sorted_vertices_as_tuples) if vertex_tuple == sorted_vertex_tuple}\n"," \n","\n","    reindexed_faces = [[vertex_map[face[0]], vertex_map[face[1]], vertex_map[face[2]]] for face in faces] \n","    return np.array(sorted_vertices), np.array(reindexed_faces)\n","\n","def augment_mesh_scalar(vertices, scale_factor):\n","    # Apply a scalar factor to XYZ coordinates\n","    transformed_vertices = vertices * scale_factor\n","    return transformed_vertices\n","\n","def generate_scale_factors(num_examples, lower_limit=0.75, upper_limit=1.25): \n","    scale_factors = np.random.uniform(lower_limit, upper_limit, size=num_examples)\n","    return scale_factors\n","\n","def jitter_mesh(vertices, jitter_factor=0.01): \n","    offsets = np.random.uniform(-jitter_factor, jitter_factor, size=vertices.shape)\n"," \n","    jittered_vertices = vertices + offsets \n","    return jittered_vertices \n","\n","def augment_mesh(vertices, scale_factor):\n","    #vertices = jitter_mesh(vertices)\n","    transformed_vertices = vertices * scale_factor\n","    \n","    return transformed_vertices\n"," \n","\n","def load_models(directory, num_examples, variations):\n","    obj_datas = []  \n","    \n","    print(f\"num_examples: {num_examples}\")\n","    for filename in os.listdir(directory):  \n","        if (filename.endswith(\".obj\") or  filename.endswith(\".glb\") or  filename.endswith(\".off\")):\n","            file_path = os.path.join(directory, filename)\n","\n","            scale_factors = generate_scale_factors(variations, 0.7, 0.9) \n","            vertices, faces = get_3d_data(file_path) \n","\n","            for scale_factor in scale_factors: \n","                aug_vertices = augment_mesh(vertices.copy(), scale_factor) \n","                \n","                for _ in range(num_examples):\n","                    obj_data = {\"vertices\": aug_vertices.tolist(), \"faces\":  faces.tolist(), \"texts\": filename[:-4]}\n","                    obj_datas.append(obj_data)   \n","    return obj_datas\n","  \n","\n","\n","  \n","def load_json(file,num_examples):\n","    obj_datas = []\n","    with open(file, \"r\") as json_file:\n","        loaded_data = json.load(json_file) \n","        for item in loaded_data:\n","            for _ in range(num_examples): \n","                obj_data = {\"vertices\": torch.tensor(item[\"vertices\"], dtype=torch.float).to(\"cuda\"), \"faces\":  torch.tensor(item[\"faces\"], dtype=torch.long).to(\"cuda\"),\"texts\": item[\"texts\"] } \n","                obj_datas.append(obj_data)\n","    return obj_datas\n","                        \n","         "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:44.050017Z","iopub.status.busy":"2023-12-19T01:39:44.049685Z","iopub.status.idle":"2023-12-19T01:39:44.063671Z","shell.execute_reply":"2023-12-19T01:39:44.062641Z","shell.execute_reply.started":"2023-12-19T01:39:44.049992Z"},"trusted":true},"outputs":[],"source":["import torch\n","from torch import Tensor, tensor\n","from torch.utils.data import Dataset, DataLoader \n","from tqdm import tqdm\n","import numpy as np \n","import gc\n","from torch.nn.utils.rnn import pad_sequence\n","from meshgpt_pytorch.data import ( \n","    derive_face_edges_from_faces\n",") \n"," \n","class MeshDataset(Dataset): \n","    \n","    def __init__(self, data): \n","        self.data = data\n","        print(f\"Got {len(data)} data\")\n","\n","    def __len__(self):\n","        return len(self.data)\n","\n","    def __getitem__(self, idx): \n","        data = self.data[idx] \n","        return data  \n","    \n","    def save(self, path):\n","        np.savez_compressed(path, *self.data)\n","\n","    \n","    @classmethod\n","    def load(cls, path): \n","        loaded_data = np.load(path, allow_pickle=True)\n"," \n","        data = []\n","        for i in range(len(loaded_data.files)):\n","            data_item = {}\n","            for key in loaded_data[f\"arr_{i}\"].item():\n","                data_item[key] = loaded_data[f\"arr_{i}\"].item()[key]\n","            data.append(data_item)\n","\n","        return cls(data)\n","    \n","    def embed_texts(self,transformer): \n","        unique_texts = set(item['texts'] for item in self.data)\n"," \n","        text_embeddings = transformer.embed_texts(list(unique_texts))\n","        print(f\"Got text_embeddings: {len(text_embeddings)}\") \n","        text_embedding_dict = dict(zip(unique_texts, text_embeddings))\n"," \n","        for item in self.data:\n","            text_value = item['texts']\n","            item['text_embeds'] = text_embedding_dict.get(text_value, None)\n","            del item['texts']\n","            \n","    def generate_face_edges(self):\n","        n = 0\n","        for i in range(0, len(self.data)):  \n","            item = self.data[i]\n","            item['face_edges'] =  derive_face_edges_from_faces(item['faces'])\n","            n += 1  \n","        print(f\"done {n}/{len(self.data)}\")\n","\n","    def generate_codes(self, autoencoder : MeshAutoencoder):\n","        n = 0\n","        for i in range(0, len(self.data)):  \n","            item = self.data[i]\n","             \n","            codes = autoencoder.tokenize(\n","                vertices = item['vertices'],\n","                faces = item['faces'],\n","                face_edges = item['face_edges']\n","            ) \n","            item['codes'] = codes \n","            n += 1  \n","\n","        print(f\"[generate_codes] done {n}/{len(self.data)}\") \n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:44.066146Z","iopub.status.busy":"2023-12-19T01:39:44.065812Z","iopub.status.idle":"2023-12-19T01:39:52.313349Z","shell.execute_reply":"2023-12-19T01:39:52.312381Z","shell.execute_reply.started":"2023-12-19T01:39:44.066116Z"},"trusted":true},"outputs":[],"source":["import json\n","#tables = load_models(r\" filtered\",5,5)  \n","#with open(\"data.json\", \"w\") as json_file:\n","#    json.dump(tables, json_file) \n","\n","tables = load_json(\"/kaggle/input/shapenet/data.json\",2)\n","dataset = MeshDataset(tables) \n","dataset.generate_face_edges()\n","dataset.data[0].keys()\n","\n","desired_order = ['vertices', 'faces', 'face_edges', 'texts']\n","\n","dataset.data = [\n","    {key: d[key] for key in desired_order} for d in dataset.data\n","]\n","\n","unique_values = set(item[\"texts\"] for item in dataset.data)\n","\n","print(len(unique_values))  \n","print(unique_values)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["autoencoder = MeshAutoencoder( \n","    num_discrete_coors = 128  , \n",") \n","total_params = sum(p.numel() for p in autoencoder.encoders.parameters())\n","print(f\"encoders Total parameters: {total_params}\")\n","total_params = sum(p.numel() for p in autoencoder.decoders.parameters())\n","print(f\"decoders Total parameters: {total_params}\")  \n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:44:00.715656Z","iopub.status.busy":"2023-12-19T01:44:00.714727Z","iopub.status.idle":"2023-12-19T12:00:40.840982Z","shell.execute_reply":"2023-12-19T12:00:40.840110Z","shell.execute_reply.started":"2023-12-19T01:44:00.715620Z"},"trusted":true},"outputs":[],"source":["total_params = sum(p.numel() for p in autoencoder.encoders.parameters())\n","print(f\"Total parameters: {total_params}\")\n","print(autoencoder.encoders)\n","\n","autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder,learning_rate = 1e-3, \n","                                             warmup_steps = 10,\n","                                             dataset = dataset,   \n","                                             num_train_steps=100,\n","                                             batch_size=16,\n","                                             grad_accum_every=1)\n","\n","loss = autoencoder_trainer.train(40,stop_at_loss = 0.25)   \n","autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder,learning_rate = 1e-4, \n","                                             warmup_steps = 10,\n","                                             dataset = dataset,\n","                                             checkpoint_every_epoch = 20,  \n","                                             num_train_steps=100,\n","                                             batch_size=16,\n","                                             grad_accum_every=1)\n","\n","loss = autoencoder_trainer.train(180,stop_at_loss = 0.25)   \n","autoencoder_trainer.save(f'./mesh-encoder_2_loss_{loss:.3f}.pt') "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n","max_seq =  max_length * 6  \n","print(max_length)\n","print(max_seq)\n","transformer = MeshTransformer(\n","    autoencoder,\n","    dim = 512,\n","    max_seq_len = max_seq,\n","    coarse_pre_gateloop_depth = 6,\n","    fine_pre_gateloop_depth= 4, \n","    condition_on_text = True\n",")\n","total_params = sum(p.numel() for p in transformer.parameters())\n","print(f\"Total parameters: {total_params}\") "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["dataset.embed_texts(transformer)\n","dataset.generate_codes(autoencoder,1)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[" \n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,   dataset = dataset,\n","                                 learning_rate = 1e-1, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)   \n","\n"," \n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  dataset = dataset,\n","                                 learning_rate = 1e-2, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)    \n","\n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  dataset = dataset,\n","                                 learning_rate = 1e-4, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)   \n","\n","trainer.save(f'./mesh-transformer_2_{loss:.3f}.pt')    "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["unique_values = set(item[\"texts\"] for item in dataset.data)\n","print(len(unique_values))  \n","coords = []\n","for text in unique_values: \n","    print(f\"doing {text}\")\n","    faces_coordinates = transformer.generate(texts = [text]) \n","    coords.append(faces_coordinates)\n","    tensor_data = faces_coordinates[0].cpu()\n","    \n","    numpy_data = tensor_data.numpy().reshape(-1, 3)\n","    \n","    obj_file_content = \"\"\n","    \n","    for vertex in numpy_data:\n","        obj_file_content += f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\"\n","\n","    for i in range(1, len(numpy_data), 3):\n","        obj_file_content += f\"f {i} {i + 1} {i + 2}\\n\"\n","\n","    # Save to a file\n","    obj_file_path = f'./tests/3d_output_{text}.obj'\n","    with open(obj_file_path, \"w\") as file:\n","        file.write(obj_file_content)\n","\n","    print(obj_file_path) \n","    \n","    \n","all_vertices = []\n","all_faces = []\n","vertex_offset = 0\n"," \n","translation_distance = 0.3  \n","\n","for r, faces_coordinates in enumerate(coords): \n","    tensor_data = faces_coordinates[0].cpu()\n","\n","    numpy_data = tensor_data.numpy().reshape(-1, 3)\n","\n","    # Translate the model to avoid overlapping\n","    numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  # Adjust X coordinate\n","\n","    # Accumulate vertices\n","    for vertex in numpy_data:\n","        all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","    # Accumulate faces with adjusted indices\n","    for i in range(1, len(numpy_data), 3):\n","        all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n","\n","    # Update the vertex offset for the next model\n","    vertex_offset += len(numpy_data)\n","\n","# Combine vertices and faces\n","obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","# Save to a single file\n","obj_file_path = f\"./tests/3d_models_all.obj\"\n","with open(obj_file_path, \"w\") as file:\n","    file.write(obj_file_content)\n","\n","print(obj_file_path)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","coords_all = []\n","for text in set(item[\"texts\"] for item in dataset.data): \n","    print(f\"Doing {text}\")\n","    coords = []\n","    for r in np.arange(0, 1.0, 0.1):\n","        faces_coordinates = transformer.generate(temperature=r, texts = [text]) \n","        coords.append(faces_coordinates)\n","    coords_all.append(coords)\n","    \n","    all_vertices = []\n","    all_faces = []\n","    vertex_offset = 0\n","\n","    # Translation distance for each model\n","    translation_distance = 0.3  # Adjust as needed\n","\n","    for r, faces_coordinates in enumerate(coords): \n","        tensor_data = faces_coordinates[0].cpu()\n","\n","        numpy_data = tensor_data.numpy().reshape(-1, 3)\n","\n","        # Translate the model to avoid overlapping\n","        numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  # Adjust X coordinate\n","\n","        # Accumulate vertices\n","        for vertex in numpy_data:\n","            all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","        # Accumulate faces with adjusted indices\n","        for i in range(1, len(numpy_data), 3):\n","            all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n","\n","        # Update the vertex offset for the next model\n","        vertex_offset += len(numpy_data)\n","\n","    # Combine vertices and faces\n","    obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","    # Save to a single file\n","    obj_file_path = f\"./results/3d_models_{text}_temps.obj\"\n","    with open(obj_file_path, \"w\") as file:\n","        file.write(obj_file_content)\n","\n","    print(obj_file_path)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","def loadModels():\n","    autoencoder = MeshAutoencoder(\n","        dim = 576,\n","        encoder_depth = 6,\n","        decoder_depth = 6,\n","        num_discrete_coors = 128  ,\n","        local_attn_depth =0, \n","        \n","    )\n","    autoencoder_trainer = MeshAutoencoderTrainer(model = autoencoder,\n","                                    learning_rate = 1e-1, \n","                                                checkpoint_every_epoch= 5,\n","                                                warmup_steps = 10,\n","                                                dataset = dataset,  \n","                                                num_train_steps=100,\n","                                                batch_size=2,\n","                                                grad_accum_every=1)\n","\n","    autoencoder_trainer.load(r\"mesh-encoder_last.pt\")\n","    encoder = autoencoder_trainer.model\n","    max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n","    max_seq =  max_length * 6  \n","    \n","    transformer = MeshTransformer(\n","        autoencoder,\n","        dim = 768,\n","        max_seq_len = max_seq,\n","        condition_on_text = True)\n","     \n","    trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100, checkpoint_folder = r\"F:\\MachineLearning\\Mesh\\MeshGPT\\checkpoints\" , dataset = dataset,\n","                                    learning_rate = 1e-3, batch_size=2) \n","    trainer.load(r\"mesh-transformer.pt\")\n","    transformer = trainer.model\n","    return transformer, encoder\n","\n","#transformer, autoencoder =  loadModels() "]}],"metadata":{"kaggle":{"accelerator":"gpu","dataSources":[{"datasetId":4189032,"sourceId":7234116,"sourceType":"datasetVersion"}],"dockerImageVersionId":30627,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"}},"nbformat":4,"nbformat_minor":4}
diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index fab81041..33fb8738 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -342,60 +342,63 @@ def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
         for epoch in range(num_epochs): 
             total_epoch_loss, total_epoch_recon_loss, total_epoch_commit_loss = 0.0, 0.0, 0.0 
             num_batches = 0 
-            progress_bar = tqdm(total=epoch_size, desc=f'Epoch {epoch + 1}/{num_epochs}') 
-            
-            while num_batches < epoch_size:
-                for i in range(self.grad_accum_every):
-                    is_last = i == (self.grad_accum_every - 1)
-                    maybe_no_sync = partial(self.accelerator.no_sync, self.model) if not is_last else nullcontext
+            try:
+                progress_bar = tqdm(total=epoch_size, desc=f'Epoch {epoch + 1}/{num_epochs}') 
+                
+                while num_batches < epoch_size:
+                    for i in range(self.grad_accum_every):
+                        is_last = i == (self.grad_accum_every - 1)
+                        maybe_no_sync = partial(self.accelerator.no_sync, self.model) if not is_last else nullcontext
 
-                    forward_kwargs = self.next_data_to_forward_kwargs(dl_iter)
+                        forward_kwargs = self.next_data_to_forward_kwargs(dl_iter)
 
-                    with self.accelerator.autocast(), maybe_no_sync():
+                        with self.accelerator.autocast(), maybe_no_sync():
 
-                        total_loss, (recon_loss, commit_loss) = self.model(
-                            **forward_kwargs,
-                            return_loss_breakdown = True
-                        )
+                            total_loss, (recon_loss, commit_loss) = self.model(
+                                **forward_kwargs,
+                                return_loss_breakdown = True
+                            )
 
-                        self.accelerator.backward(total_loss / self.grad_accum_every)
+                            self.accelerator.backward(total_loss / self.grad_accum_every)
 
-        
-                    current_loss = total_loss.item()
-                    total_epoch_loss += current_loss
-                    total_epoch_recon_loss += recon_loss.item()
-                    total_epoch_commit_loss += commit_loss.sum().item() 
-                    num_batches += 1
-                    
-                progress_bar.update(self.grad_accum_every)
-                progress_bar.set_postfix(loss=current_loss, recon_loss = round(recon_loss.item(),3), commit_loss = round(commit_loss.sum().item(),4))
-                    
-                self.optimizer.step()
-                self.optimizer.zero_grad()
- 
-             
-            avg_recon_loss = total_epoch_recon_loss / num_batches
-            avg_commit_loss = total_epoch_commit_loss / num_batches
-            avg_epoch_loss = total_epoch_loss / num_batches 
-            epochOut = f'Epoch {epoch + 1} average loss: {avg_epoch_loss} recon loss: {avg_recon_loss:.4f}: commit_loss {avg_commit_loss:.4f}'
             
+                        current_loss = total_loss.item()
+                        total_epoch_loss += current_loss
+                        total_epoch_recon_loss += recon_loss.item()
+                        total_epoch_commit_loss += commit_loss.sum().item() 
+                        num_batches += 1
+                        
+                    progress_bar.update(min(self.grad_accum_every,  progress_bar.total - progress_bar.n))    
+                    progress_bar.set_postfix(loss=current_loss, recon_loss = round(recon_loss.item(),3), commit_loss = round(commit_loss.sum().item(),4))
+                        
+                    self.optimizer.step()
+                    self.optimizer.zero_grad()
     
-            epoch_losses.append(avg_epoch_loss)
-            epoch_recon_losses.append(avg_recon_loss)
-            epoch_commit_losses.append(avg_commit_loss)
-
-            if len(epoch_losses) >= 4 and avg_epoch_loss > 0:
-                avg_loss_improvement = sum(epoch_losses[-4:-1]) / 3 - avg_epoch_loss
-                epochOut += f'          avg loss speed: {avg_loss_improvement}' 
-                if avg_loss_improvement > 0 and avg_loss_improvement < 0.2:
-                    epochs_until_0_3 = max(0, abs(avg_epoch_loss-0.3) / avg_loss_improvement)
-                    if epochs_until_0_3> 0:
-                       epochOut += f' epochs left: {epochs_until_0_3:.2f}'  
-                       
-            self.wait()
-            
-            progress_bar.close()
-            self.print(epochOut)
+                
+                avg_recon_loss = total_epoch_recon_loss / num_batches
+                avg_commit_loss = total_epoch_commit_loss / num_batches
+                avg_epoch_loss = total_epoch_loss / num_batches 
+                epochOut = f'Epoch {epoch + 1} average loss: {avg_epoch_loss} recon loss: {avg_recon_loss:.4f}: commit_loss {avg_commit_loss:.4f}'
+                
+        
+                epoch_losses.append(avg_epoch_loss)
+                epoch_recon_losses.append(avg_recon_loss)
+                epoch_commit_losses.append(avg_commit_loss)
+
+                if len(epoch_losses) >= 4 and avg_epoch_loss > 0:
+                    avg_loss_improvement = sum(epoch_losses[-4:-1]) / 3 - avg_epoch_loss
+                    epochOut += f'          avg loss speed: {avg_loss_improvement}' 
+                    if avg_loss_improvement > 0 and avg_loss_improvement < 0.2:
+                        epochs_until_0_3 = max(0, abs(avg_epoch_loss-0.3) / avg_loss_improvement)
+                        if epochs_until_0_3> 0:
+                            epochOut += f' epochs left: {epochs_until_0_3:.2f}'  
+                        
+                self.wait()
+                
+                progress_bar.close()
+                progress_bar.write(epochOut)
+            finally:
+                progress_bar.close()
 
             if self.checkpoint_every_epoch is not None and epoch != 0 and epoch % self.checkpoint_every_epoch == 0:
                 self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.3f}.pt')
@@ -669,54 +672,57 @@ def forward(self):
  
         
     def train(self, num_epochs, stop_at_loss = None,  diplay_graph = False):
-        epoch_losses = []  # Initialize a list to store epoch losses
+        epoch_losses = []
         self.model.train()
         for epoch in range(num_epochs): 
             total_loss = 0.0
             num_batches = 0
 
-            progress_bar = tqdm(self.dataloader, desc=f'Epoch {epoch + 1}/{num_epochs}') 
-
-            for data in progress_bar: 
+            try:
+                progress_bar = tqdm(self.dataloader, desc=f'Epoch {epoch + 1}/{num_epochs}') 
+                for data in progress_bar: 
 
-                if isinstance(data, tuple): 
-                    forward_kwargs = dict(zip(self.data_kwargs, data))
+                    if isinstance(data, tuple): 
+                        forward_kwargs = dict(zip(self.data_kwargs, data))
 
-                elif isinstance(data, dict): 
-                    forward_kwargs = data 
-                
+                    elif isinstance(data, dict): 
+                        forward_kwargs = data 
+                    
 
-                with self.accelerator.autocast():
-                    loss = self.model(**forward_kwargs)
-                    self.accelerator.backward(loss / self.grad_accum_every)
+                    with self.accelerator.autocast():
+                        loss = self.model(**forward_kwargs)
+                        self.accelerator.backward(loss / self.grad_accum_every)
 
 
-                self.optimizer.step()
-                self.optimizer.zero_grad()
+                    self.optimizer.step()
+                    self.optimizer.zero_grad()
 
- 
-                current_loss = loss.item()
-                total_loss += current_loss
-                num_batches += 1
-                progress_bar.set_postfix(loss=current_loss)
- 
-            avg_epoch_loss = total_loss / num_batches 
-            epoch_losses.append(avg_epoch_loss)
-            
-            if len(epoch_losses) >= 4 and avg_epoch_loss > 0:
-                avg_loss_improvement = sum(epoch_losses[-4:-1]) / 3 - avg_epoch_loss
-                outStr = f'Epoch {epoch + 1} average loss: {avg_epoch_loss}           avg loss speed: {avg_loss_improvement}'
-                 
-                if avg_loss_improvement > 0 and avg_loss_improvement < 0.2:
-                    epochs_until_0_3 = max(0, abs(avg_epoch_loss-0.001) / avg_loss_improvement)
-                    if epochs_until_0_3> 0:
-                       outStr += f' epochs left: {epochs_until_0_3:.2f}'
-                       
-                self.print(outStr)
-            else:
-                self.print(f'Epoch {epoch + 1} average loss: {avg_epoch_loss}')
+    
+                    current_loss = loss.item()
+                    total_loss += current_loss
+                    num_batches += 1
+                    progress_bar.set_postfix(loss=current_loss)
+    
+                avg_epoch_loss = total_loss / num_batches 
+                epoch_losses.append(avg_epoch_loss)
+                epochOut = f'Epoch {epoch + 1} average loss: {avg_epoch_loss}'
                 
+                if len(epoch_losses) >= 4 and avg_epoch_loss > 0:
+                    avg_loss_improvement = sum(epoch_losses[-4:-1]) / 3 - avg_epoch_loss
+                    epochOut += f'           avg loss speed: {avg_loss_improvement}'
+                    
+                    if avg_loss_improvement > 0 and avg_loss_improvement < 0.2:
+                        epochs_until_0_3 = max(0, abs(avg_epoch_loss-0.001) / avg_loss_improvement)
+                        if epochs_until_0_3> 0:
+                            epochOut += f' epochs left: {epochs_until_0_3:.2f}'
+                            
+                    
+                progress_bar.write(epochOut)
+                progress_bar.close()
+            finally:
+                progress_bar.close()
             self.wait() 
+            
             if self.checkpoint_every_epoch is not None and epoch != 0 and epoch % self.checkpoint_every_epoch == 0:
                 self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.3f}.pt')
                 

From 60face85f10954c579b2ad5ccdf11ae2460cb613 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Sun, 31 Dec 2023 17:27:47 +0100
Subject: [PATCH 35/84] flash attention mispelling

---
 meshgpt_pytorch/meshgpt_pytorch.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/meshgpt_pytorch.py b/meshgpt_pytorch/meshgpt_pytorch.py
index 83f210d8..03c709bf 100644
--- a/meshgpt_pytorch/meshgpt_pytorch.py
+++ b/meshgpt_pytorch/meshgpt_pytorch.py
@@ -1132,7 +1132,7 @@ def __init__(
             depth = attn_depth,
             dim_head = attn_dim_head,
             heads = attn_heads,
-            flash_attn = flash_attn,
+            attn_flash = flash_attn,
             attn_dropout = dropout,
             ff_dropout = dropout,
             cross_attend = condition_on_text,
@@ -1155,7 +1155,7 @@ def __init__(
             depth = fine_attn_depth,
             dim_head = attn_dim_head,
             heads = attn_heads,
-            flash_attn = flash_attn,
+            attn_flash = flash_attn,
             attn_dropout = dropout,
             ff_dropout = dropout,
             **attn_kwargs

From f936da83f3e92556467186f89b811e593a6dc2c2 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Sun, 31 Dec 2023 17:28:31 +0100
Subject: [PATCH 36/84] flash attention mispelling

---
 meshgpt_pytorch/meshgpt_pytorch.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/meshgpt_pytorch.py b/meshgpt_pytorch/meshgpt_pytorch.py
index 03c709bf..83f210d8 100644
--- a/meshgpt_pytorch/meshgpt_pytorch.py
+++ b/meshgpt_pytorch/meshgpt_pytorch.py
@@ -1132,7 +1132,7 @@ def __init__(
             depth = attn_depth,
             dim_head = attn_dim_head,
             heads = attn_heads,
-            attn_flash = flash_attn,
+            flash_attn = flash_attn,
             attn_dropout = dropout,
             ff_dropout = dropout,
             cross_attend = condition_on_text,
@@ -1155,7 +1155,7 @@ def __init__(
             depth = fine_attn_depth,
             dim_head = attn_dim_head,
             heads = attn_heads,
-            attn_flash = flash_attn,
+            flash_attn = flash_attn,
             attn_dropout = dropout,
             ff_dropout = dropout,
             **attn_kwargs

From a0f98554aa209f706caba32f3d763577e4b09378 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Sun, 31 Dec 2023 17:28:56 +0100
Subject: [PATCH 37/84] flash attention mispelling

---
 meshgpt_pytorch/meshgpt_pytorch.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/meshgpt_pytorch.py b/meshgpt_pytorch/meshgpt_pytorch.py
index 23c3c4d9..972b499e 100644
--- a/meshgpt_pytorch/meshgpt_pytorch.py
+++ b/meshgpt_pytorch/meshgpt_pytorch.py
@@ -1133,7 +1133,7 @@ def __init__(
             depth = attn_depth,
             dim_head = attn_dim_head,
             heads = attn_heads,
-            flash_attn = flash_attn,
+            attn_flash = flash_attn,
             attn_dropout = dropout,
             ff_dropout = dropout,
             cross_attend = condition_on_text,
@@ -1156,7 +1156,7 @@ def __init__(
             depth = fine_attn_depth,
             dim_head = attn_dim_head,
             heads = attn_heads,
-            flash_attn = flash_attn,
+            attn_flash = flash_attn,
             attn_dropout = dropout,
             ff_dropout = dropout,
             **attn_kwargs

From 70ac14e671d3885cc6215756a591ad16bf248313 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Wed, 3 Jan 2024 00:49:34 +0100
Subject: [PATCH 38/84] reimplemented grad_accum in transformer trainer

---
 meshgpt_pytorch/trainer.py | 82 +++++++++++++++++++++-----------------
 1 file changed, 46 insertions(+), 36 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 33fb8738..eabae5d5 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -671,58 +671,67 @@ def forward(self):
         self.print('training complete')
  
         
-    def train(self, num_epochs, stop_at_loss = None,  diplay_graph = False):
+    def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
         epoch_losses = []
-        self.model.train()
+        dl_iter = cycle(self.dataloader)
+        epoch_size = len(self.dataloader)
+        self.model.train() 
+        
         for epoch in range(num_epochs): 
-            total_loss = 0.0
-            num_batches = 0
-
+            total_epoch_loss = 0.0 
+            num_batches = 0 
             try:
-                progress_bar = tqdm(self.dataloader, desc=f'Epoch {epoch + 1}/{num_epochs}') 
-                for data in progress_bar: 
-
-                    if isinstance(data, tuple): 
-                        forward_kwargs = dict(zip(self.data_kwargs, data))
-
-                    elif isinstance(data, dict): 
-                        forward_kwargs = data 
-                    
+                progress_bar = tqdm(total=epoch_size, desc=f'Epoch {epoch + 1}/{num_epochs}') 
+                n = 0
+                while num_batches < epoch_size:
+                    for i in range(self.grad_accum_every):
+                        is_last = i == (self.grad_accum_every - 1)
+                        maybe_no_sync = partial(self.accelerator.no_sync, self.model) if not is_last else nullcontext
 
-                    with self.accelerator.autocast():
-                        loss = self.model(**forward_kwargs)
-                        self.accelerator.backward(loss / self.grad_accum_every)
+                        forward_kwargs = self.next_data_to_forward_kwargs(dl_iter)
 
+                        with self.accelerator.autocast(), maybe_no_sync(): 
+                            total_loss = self.model(**forward_kwargs) 
+                            self.accelerator.backward(total_loss / self.grad_accum_every)
 
+            
+                        current_loss = total_loss.item()
+                        total_epoch_loss += current_loss 
+                        num_batches += 1
+                        
+                        if num_batches >= epoch_size:
+                            break
+                    
+                    if epoch_size - n > self.grad_accum_every:
+                        progress_bar.update(min(self.grad_accum_every,  epoch_size - n))    
+                        
+                    progress_bar.set_postfix(loss=current_loss)
+                        
                     self.optimizer.step()
                     self.optimizer.zero_grad()
-
-    
-                    current_loss = loss.item()
-                    total_loss += current_loss
-                    num_batches += 1
-                    progress_bar.set_postfix(loss=current_loss)
-    
-                avg_epoch_loss = total_loss / num_batches 
-                epoch_losses.append(avg_epoch_loss)
+                  
+                avg_epoch_loss = total_epoch_loss / num_batches 
                 epochOut = f'Epoch {epoch + 1} average loss: {avg_epoch_loss}'
                 
+        
+                epoch_losses.append(avg_epoch_loss) 
+
                 if len(epoch_losses) >= 4 and avg_epoch_loss > 0:
                     avg_loss_improvement = sum(epoch_losses[-4:-1]) / 3 - avg_epoch_loss
-                    epochOut += f'           avg loss speed: {avg_loss_improvement}'
-                    
+                    epochOut += f'          avg loss speed: {avg_loss_improvement}' 
                     if avg_loss_improvement > 0 and avg_loss_improvement < 0.2:
-                        epochs_until_0_3 = max(0, abs(avg_epoch_loss-0.001) / avg_loss_improvement)
+                        epochs_until_0_3 = max(0, abs(avg_epoch_loss-0.3) / avg_loss_improvement)
                         if epochs_until_0_3> 0:
-                            epochOut += f' epochs left: {epochs_until_0_3:.2f}'
-                            
-                    
+                            epochOut += f' epochs left: {epochs_until_0_3:.2f}'  
+                        
+                self.wait()
+                 
                 progress_bar.write(epochOut)
                 progress_bar.close()
+
             finally:
                 progress_bar.close()
-            self.wait() 
-            
+                
             if self.checkpoint_every_epoch is not None and epoch != 0 and epoch % self.checkpoint_every_epoch == 0:
                 self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.3f}.pt')
                 
@@ -731,11 +740,12 @@ def train(self, num_epochs, stop_at_loss = None,  diplay_graph = False):
                 if self.checkpoint_every_epoch is not None:
                     self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.stop_at_loss_avg_loss_{avg_epoch_loss:.3f}.pt') 
                 break   
-                
+ 
+
         self.print('Training complete') 
         if diplay_graph:
             plt.figure(figsize=(10, 5))
-            plt.plot(range(1, num_epochs + 1), epoch_losses, marker='o')
+            plt.plot(range(1, num_epochs + 1), epoch_losses, marker='o', label='Total Loss') 
             plt.title('Training Loss Over Epochs')
             plt.xlabel('Epoch')
             plt.ylabel('Average Loss')

From 7c86e3623257928b3d90395e0928ad1e70ca4177 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Wed, 3 Jan 2024 18:55:21 +0100
Subject: [PATCH 39/84] cleaned up the trainer

---
 meshgpt_pytorch/trainer.py | 205 ++++++++++++++++---------------------
 1 file changed, 90 insertions(+), 115 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index eabae5d5..2e72c8c9 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -334,72 +334,65 @@ def forward(self):
 
         self.print('training complete')
     def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
-        epoch_losses, epoch_recon_losses, epoch_commit_losses = [] , [],[]
-        dl_iter = cycle(self.dataloader)
-        epoch_size = len(self.dataloader)
+        epoch_losses, epoch_recon_losses, epoch_commit_losses = [] , [],[] 
         self.model.train() 
         
         for epoch in range(num_epochs): 
-            total_epoch_loss, total_epoch_recon_loss, total_epoch_commit_loss = 0.0, 0.0, 0.0 
-            num_batches = 0 
-            try:
-                progress_bar = tqdm(total=epoch_size, desc=f'Epoch {epoch + 1}/{num_epochs}') 
-                
-                while num_batches < epoch_size:
-                    for i in range(self.grad_accum_every):
-                        is_last = i == (self.grad_accum_every - 1)
-                        maybe_no_sync = partial(self.accelerator.no_sync, self.model) if not is_last else nullcontext
-
-                        forward_kwargs = self.next_data_to_forward_kwargs(dl_iter)
-
-                        with self.accelerator.autocast(), maybe_no_sync():
-
-                            total_loss, (recon_loss, commit_loss) = self.model(
-                                **forward_kwargs,
-                                return_loss_breakdown = True
-                            )
-
-                            self.accelerator.backward(total_loss / self.grad_accum_every)
-
+            total_epoch_loss, total_epoch_recon_loss, total_epoch_commit_loss = 0.0, 0.0, 0.0
             
-                        current_loss = total_loss.item()
-                        total_epoch_loss += current_loss
-                        total_epoch_recon_loss += recon_loss.item()
-                        total_epoch_commit_loss += commit_loss.sum().item() 
-                        num_batches += 1
-                        
-                    progress_bar.update(min(self.grad_accum_every,  progress_bar.total - progress_bar.n))    
-                    progress_bar.set_postfix(loss=current_loss, recon_loss = round(recon_loss.item(),3), commit_loss = round(commit_loss.sum().item(),4))
-                        
-                    self.optimizer.step()
-                    self.optimizer.zero_grad()
-    
+            progress_bar = tqdm(enumerate(self.dataloader), desc=f'Epoch {epoch + 1}/{num_epochs}', total=len(self.dataloader))
+            for batch_idx, batch in progress_bar:
+                is_last = (batch_idx+1) % self.grad_accum_every == 0
+                maybe_no_sync = partial(self.accelerator.no_sync, self.model) if not is_last else nullcontext
                 
-                avg_recon_loss = total_epoch_recon_loss / num_batches
-                avg_commit_loss = total_epoch_commit_loss / num_batches
-                avg_epoch_loss = total_epoch_loss / num_batches 
-                epochOut = f'Epoch {epoch + 1} average loss: {avg_epoch_loss} recon loss: {avg_recon_loss:.4f}: commit_loss {avg_commit_loss:.4f}'
+                if isinstance(batch, tuple):
+                    forward_kwargs = dict(zip(self.data_kwargs, batch)) 
+                elif isinstance(batch, dict):
+                    forward_kwargs = batch 
+                maybe_del(forward_kwargs, 'texts', 'text_embeds')
                 
-        
-                epoch_losses.append(avg_epoch_loss)
-                epoch_recon_losses.append(avg_recon_loss)
-                epoch_commit_losses.append(avg_commit_loss)
-
-                if len(epoch_losses) >= 4 and avg_epoch_loss > 0:
-                    avg_loss_improvement = sum(epoch_losses[-4:-1]) / 3 - avg_epoch_loss
-                    epochOut += f'          avg loss speed: {avg_loss_improvement}' 
-                    if avg_loss_improvement > 0 and avg_loss_improvement < 0.2:
-                        epochs_until_0_3 = max(0, abs(avg_epoch_loss-0.3) / avg_loss_improvement)
-                        if epochs_until_0_3> 0:
-                            epochOut += f' epochs left: {epochs_until_0_3:.2f}'  
-                        
-                self.wait()
+                with self.accelerator.autocast(), maybe_no_sync():  
+                    total_loss, (recon_loss, commit_loss) = self.model(
+                        **forward_kwargs,
+                        return_loss_breakdown = True
+                    ) 
+                    self.accelerator.backward(total_loss / self.grad_accum_every)
+
+                current_loss = total_loss.item()
+                total_epoch_loss += current_loss
+                total_epoch_recon_loss += recon_loss.item()
+                total_epoch_commit_loss += commit_loss.sum().item()  
+                
+                progress_bar.set_postfix(loss=current_loss, recon_loss = round(recon_loss.item(),3), commit_loss = round(commit_loss.sum().item(),4)) 
+                    
+                if is_last or (batch_idx + 1 == len(self.dataloader)): 
+                    self.optimizer.step()
+                    self.optimizer.zero_grad() 
+                    
+                    
                 
-                progress_bar.close()
-                progress_bar.write(epochOut)
-            finally:
-                progress_bar.close()
+            avg_recon_loss = total_epoch_recon_loss / len(self.dataloader)
+            avg_commit_loss = total_epoch_commit_loss / len(self.dataloader)
+            avg_epoch_loss = total_epoch_loss / len(self.dataloader)  
+            
+            epoch_losses.append(avg_epoch_loss)
+            epoch_recon_losses.append(avg_recon_loss)
+            epoch_commit_losses.append(avg_commit_loss)
+            
+            epochOut = f'Epoch {epoch + 1} average loss: {avg_epoch_loss} recon loss: {avg_recon_loss:.4f}: commit_loss {avg_commit_loss:.4f}'
+
+            if len(epoch_losses) >= 4 and avg_epoch_loss > 0:
+                avg_loss_improvement = sum(epoch_losses[-4:-1]) / 3 - avg_epoch_loss
+                epochOut += f'          avg loss speed: {avg_loss_improvement}' 
+                if avg_loss_improvement > 0 and avg_loss_improvement < 0.2:
+                    epochs_until_0_3 = max(0, abs(avg_epoch_loss-0.3) / avg_loss_improvement)
+                    if epochs_until_0_3> 0:
+                        epochOut += f' epochs left: {epochs_until_0_3:.2f}'  
+                    
+            self.wait() 
+            self.print(epochOut) 
 
+         
             if self.checkpoint_every_epoch is not None and epoch != 0 and epoch % self.checkpoint_every_epoch == 0:
                 self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.3f}.pt')
                 
@@ -408,14 +401,13 @@ def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
                 if self.checkpoint_every_epoch is not None:
                     self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.stop_at_loss_avg_loss_{avg_epoch_loss:.3f}.pt') 
                 break   
- 
-
+  
         self.print('Training complete') 
         if diplay_graph:
             plt.figure(figsize=(10, 5))
-            plt.plot(range(1, num_epochs + 1), epoch_losses, marker='o', label='Total Loss')
-            plt.plot(range(1, num_epochs + 1), epoch_recon_losses, marker='o', label='Recon Loss')
-            plt.plot(range(1, num_epochs + 1), epoch_commit_losses, marker='o', label='Commit Loss') 
+            plt.plot(range(1, len(epoch_losses)+1), epoch_losses, marker='o', label='Total Loss')
+            plt.plot(range(1, len(epoch_losses)+1), epoch_recon_losses, marker='o', label='Recon Loss')
+            plt.plot(range(1, len(epoch_losses)+1), epoch_commit_losses, marker='o', label='Commit Loss') 
             plt.title('Training Loss Over Epochs')
             plt.xlabel('Epoch')
             plt.ylabel('Average Loss')
@@ -671,67 +663,50 @@ def forward(self):
         self.print('training complete')
  
         
-    def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
-        epoch_losses = []
-        dl_iter = cycle(self.dataloader)
+def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
+        epoch_losses = [] 
         epoch_size = len(self.dataloader)
         self.model.train() 
         
         for epoch in range(num_epochs): 
             total_epoch_loss = 0.0 
-            num_batches = 0 
-            try:
-                progress_bar = tqdm(total=epoch_size, desc=f'Epoch {epoch + 1}/{num_epochs}') 
-                n = 0
-                while num_batches < epoch_size:
-                    for i in range(self.grad_accum_every):
-                        is_last = i == (self.grad_accum_every - 1)
-                        maybe_no_sync = partial(self.accelerator.no_sync, self.model) if not is_last else nullcontext
-
-                        forward_kwargs = self.next_data_to_forward_kwargs(dl_iter)
+            
+            progress_bar = tqdm(enumerate(self.dataloader), desc=f'Epoch {epoch + 1}/{num_epochs}', total=len(self.dataloader))
+            for batch_idx, batch in progress_bar: 
+                    
+                is_last = (batch_idx+1) % self.grad_accum_every == 0
+                maybe_no_sync = partial(self.accelerator.no_sync, self.model) if not is_last else nullcontext
 
-                        with self.accelerator.autocast(), maybe_no_sync(): 
-                            total_loss = self.model(**forward_kwargs) 
-                            self.accelerator.backward(total_loss / self.grad_accum_every)
+                with self.accelerator.autocast(), maybe_no_sync(): 
+                    total_loss = self.model(**batch) 
+                    self.accelerator.backward(total_loss / self.grad_accum_every)
 
-            
-                        current_loss = total_loss.item()
-                        total_epoch_loss += current_loss 
-                        num_batches += 1
-                        
-                        if num_batches >= epoch_size:
-                            break
+                current_loss = total_loss.item()
+                total_epoch_loss += current_loss  
+                
+                progress_bar.set_postfix(loss=current_loss) 
                     
-                    if epoch_size - n > self.grad_accum_every:
-                        progress_bar.update(min(self.grad_accum_every,  epoch_size - n))    
-                        
-                    progress_bar.set_postfix(loss=current_loss)
-                        
+                if is_last or (batch_idx + 1 == len(self.dataloader)): 
                     self.optimizer.step()
-                    self.optimizer.zero_grad()
-                  
-                avg_epoch_loss = total_epoch_loss / num_batches 
-                epochOut = f'Epoch {epoch + 1} average loss: {avg_epoch_loss}'
-                
+                    self.optimizer.zero_grad() 
+                    
+            avg_epoch_loss = total_epoch_loss / epoch_size 
+            epochOut = f'Epoch {epoch + 1} average loss: {avg_epoch_loss}' 
         
-                epoch_losses.append(avg_epoch_loss) 
-
-                if len(epoch_losses) >= 4 and avg_epoch_loss > 0:
-                    avg_loss_improvement = sum(epoch_losses[-4:-1]) / 3 - avg_epoch_loss
-                    epochOut += f'          avg loss speed: {avg_loss_improvement}' 
-                    if avg_loss_improvement > 0 and avg_loss_improvement < 0.2:
-                        epochs_until_0_3 = max(0, abs(avg_epoch_loss-0.3) / avg_loss_improvement)
-                        if epochs_until_0_3> 0:
-                            epochOut += f' epochs left: {epochs_until_0_3:.2f}'  
-                        
-                self.wait()
-                 
-                progress_bar.write(epochOut)
-                progress_bar.close()
-
-            finally:
-                progress_bar.close()
-                
+        
+            epoch_losses.append(avg_epoch_loss) 
+
+            if len(epoch_losses) >= 4 and avg_epoch_loss > 0:
+                avg_loss_improvement = sum(epoch_losses[-4:-1]) / 3 - avg_epoch_loss
+                epochOut += f'          avg loss speed: {avg_loss_improvement}' 
+                if avg_loss_improvement > 0 and avg_loss_improvement < 0.2:
+                    epochs_until_0_3 = max(0, abs(avg_epoch_loss-0.3) / avg_loss_improvement)
+                    if epochs_until_0_3> 0:
+                        epochOut += f' epochs left: {epochs_until_0_3:.2f}'  
+                    
+            self.wait()
+            self.print(epochOut)
+            
             if self.checkpoint_every_epoch is not None and epoch != 0 and epoch % self.checkpoint_every_epoch == 0:
                 self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.3f}.pt')
                 
@@ -745,7 +720,7 @@ def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
         self.print('Training complete') 
         if diplay_graph:
             plt.figure(figsize=(10, 5))
-            plt.plot(range(1, num_epochs + 1), epoch_losses, marker='o', label='Total Loss') 
+            plt.plot(range(1, len(epoch_losses) + 1), epoch_losses, marker='o', label='Total Loss') 
             plt.title('Training Loss Over Epochs')
             plt.xlabel('Epoch')
             plt.ylabel('Average Loss')

From ae057bb354dfe2fd17bbb73e52d0d4146a620845 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Wed, 3 Jan 2024 19:03:00 +0100
Subject: [PATCH 40/84] cleaned up the trainer

---
 meshgpt_pytorch/trainer.py | 122 ++++++++++++++++++-------------------
 1 file changed, 61 insertions(+), 61 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 2e72c8c9..cdf0874c 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -662,69 +662,69 @@ def forward(self):
 
         self.print('training complete')
  
-        
-def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
-        epoch_losses = [] 
-        epoch_size = len(self.dataloader)
-        self.model.train() 
-        
-        for epoch in range(num_epochs): 
-            total_epoch_loss = 0.0 
             
-            progress_bar = tqdm(enumerate(self.dataloader), desc=f'Epoch {epoch + 1}/{num_epochs}', total=len(self.dataloader))
-            for batch_idx, batch in progress_bar: 
-                    
-                is_last = (batch_idx+1) % self.grad_accum_every == 0
-                maybe_no_sync = partial(self.accelerator.no_sync, self.model) if not is_last else nullcontext
-
-                with self.accelerator.autocast(), maybe_no_sync(): 
-                    total_loss = self.model(**batch) 
-                    self.accelerator.backward(total_loss / self.grad_accum_every)
-
-                current_loss = total_loss.item()
-                total_epoch_loss += current_loss  
+    def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
+            epoch_losses = [] 
+            epoch_size = len(self.dataloader)
+            self.model.train() 
+            
+            for epoch in range(num_epochs): 
+                total_epoch_loss = 0.0 
                 
-                progress_bar.set_postfix(loss=current_loss) 
-                    
-                if is_last or (batch_idx + 1 == len(self.dataloader)): 
-                    self.optimizer.step()
-                    self.optimizer.zero_grad() 
-                    
-            avg_epoch_loss = total_epoch_loss / epoch_size 
-            epochOut = f'Epoch {epoch + 1} average loss: {avg_epoch_loss}' 
-        
-        
-            epoch_losses.append(avg_epoch_loss) 
-
-            if len(epoch_losses) >= 4 and avg_epoch_loss > 0:
-                avg_loss_improvement = sum(epoch_losses[-4:-1]) / 3 - avg_epoch_loss
-                epochOut += f'          avg loss speed: {avg_loss_improvement}' 
-                if avg_loss_improvement > 0 and avg_loss_improvement < 0.2:
-                    epochs_until_0_3 = max(0, abs(avg_epoch_loss-0.3) / avg_loss_improvement)
-                    if epochs_until_0_3> 0:
-                        epochOut += f' epochs left: {epochs_until_0_3:.2f}'  
+                progress_bar = tqdm(enumerate(self.dataloader), desc=f'Epoch {epoch + 1}/{num_epochs}', total=len(self.dataloader))
+                for batch_idx, batch in progress_bar: 
+                        
+                    is_last = (batch_idx+1) % self.grad_accum_every == 0
+                    maybe_no_sync = partial(self.accelerator.no_sync, self.model) if not is_last else nullcontext
+
+                    with self.accelerator.autocast(), maybe_no_sync(): 
+                        total_loss = self.model(**batch) 
+                        self.accelerator.backward(total_loss / self.grad_accum_every)
+
+                    current_loss = total_loss.item()
+                    total_epoch_loss += current_loss  
                     
-            self.wait()
-            self.print(epochOut)
+                    progress_bar.set_postfix(loss=current_loss) 
+                        
+                    if is_last or (batch_idx + 1 == len(self.dataloader)): 
+                        self.optimizer.step()
+                        self.optimizer.zero_grad() 
+                        
+                avg_epoch_loss = total_epoch_loss / epoch_size 
+                epochOut = f'Epoch {epoch + 1} average loss: {avg_epoch_loss}' 
             
-            if self.checkpoint_every_epoch is not None and epoch != 0 and epoch % self.checkpoint_every_epoch == 0:
-                self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.3f}.pt')
+            
+                epoch_losses.append(avg_epoch_loss) 
+
+                if len(epoch_losses) >= 4 and avg_epoch_loss > 0:
+                    avg_loss_improvement = sum(epoch_losses[-4:-1]) / 3 - avg_epoch_loss
+                    epochOut += f'          avg loss speed: {avg_loss_improvement}' 
+                    if avg_loss_improvement > 0 and avg_loss_improvement < 0.2:
+                        epochs_until_0_3 = max(0, abs(avg_epoch_loss-0.3) / avg_loss_improvement)
+                        if epochs_until_0_3> 0:
+                            epochOut += f' epochs left: {epochs_until_0_3:.2f}'  
+                        
+                self.wait()
+                self.print(epochOut)
                 
-            if stop_at_loss is not None and avg_epoch_loss < stop_at_loss: 
-                self.print(f'Stopping training at epoch {epoch} with average loss {avg_epoch_loss}')
-                if self.checkpoint_every_epoch is not None:
-                    self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.stop_at_loss_avg_loss_{avg_epoch_loss:.3f}.pt') 
-                break   
- 
-
-        self.print('Training complete') 
-        if diplay_graph:
-            plt.figure(figsize=(10, 5))
-            plt.plot(range(1, len(epoch_losses) + 1), epoch_losses, marker='o', label='Total Loss') 
-            plt.title('Training Loss Over Epochs')
-            plt.xlabel('Epoch')
-            plt.ylabel('Average Loss')
-            plt.grid(True)
-            plt.show()
-        return epoch_losses[-1]
- 
\ No newline at end of file
+                if self.checkpoint_every_epoch is not None and epoch != 0 and epoch % self.checkpoint_every_epoch == 0:
+                    self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.3f}.pt')
+                    
+                if stop_at_loss is not None and avg_epoch_loss < stop_at_loss: 
+                    self.print(f'Stopping training at epoch {epoch} with average loss {avg_epoch_loss}')
+                    if self.checkpoint_every_epoch is not None:
+                        self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.stop_at_loss_avg_loss_{avg_epoch_loss:.3f}.pt') 
+                    break   
+    
+
+            self.print('Training complete') 
+            if diplay_graph:
+                plt.figure(figsize=(10, 5))
+                plt.plot(range(1, len(epoch_losses) + 1), epoch_losses, marker='o', label='Total Loss') 
+                plt.title('Training Loss Over Epochs')
+                plt.xlabel('Epoch')
+                plt.ylabel('Average Loss')
+                plt.grid(True)
+                plt.show()
+            return epoch_losses[-1]
+    
\ No newline at end of file

From 45e628eab26806136af8e2f602a2c59fbe509018 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Fri, 5 Jan 2024 20:22:36 +0100
Subject: [PATCH 41/84] demo + update of meshdataset

---
 MeshGPT Testing.ipynb           |   1 -
 MeshGPT_demo.ipynb              | 866 ++++++++++++++++++++++++++++++++
 demo_mesh/cone.obj              |  66 +++
 demo_mesh/cube.obj              |  14 +
 demo_mesh/cylinder.obj          |  98 ++++
 demo_mesh/icosphere.obj         | 122 +++++
 meshgpt_pytorch/__init__.py     |   5 +-
 meshgpt_pytorch/mesh_dataset.py |  38 +-
 8 files changed, 1190 insertions(+), 20 deletions(-)
 delete mode 100644 MeshGPT Testing.ipynb
 create mode 100644 MeshGPT_demo.ipynb
 create mode 100644 demo_mesh/cone.obj
 create mode 100644 demo_mesh/cube.obj
 create mode 100644 demo_mesh/cylinder.obj
 create mode 100644 demo_mesh/icosphere.obj

diff --git a/MeshGPT Testing.ipynb b/MeshGPT Testing.ipynb
deleted file mode 100644
index 62168f86..00000000
--- a/MeshGPT Testing.ipynb	
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Use this if you running on a Kaggle GPU/ Cuda 11.8\n","#!pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --index-url https://download.pytorch.org/whl/cu118"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:12.944690Z","iopub.status.busy":"2023-12-19T01:39:12.944345Z","iopub.status.idle":"2023-12-19T01:39:38.152713Z","shell.execute_reply":"2023-12-19T01:39:38.151433Z","shell.execute_reply.started":"2023-12-19T01:39:12.944662Z"},"trusted":true},"outputs":[],"source":["!pip install -q git+https://github.com/MarcusLoppe/meshgpt-pytorch.git trimesh"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:38.155749Z","iopub.status.busy":"2023-12-19T01:39:38.155103Z","iopub.status.idle":"2023-12-19T01:39:44.048426Z","shell.execute_reply":"2023-12-19T01:39:44.047605Z","shell.execute_reply.started":"2023-12-19T01:39:38.155692Z"},"trusted":true},"outputs":[],"source":["import torch\n","import trimesh\n","import numpy as np\n","import os\n","import csv \n","import json\n","import math \n","from collections import OrderedDict\n","\n","from meshgpt_pytorch import (\n","    MeshTransformerTrainer,\n","    MeshAutoencoderTrainer,\n","    MeshAutoencoder,\n","    MeshTransformer\n",")\n","\n","def get_3d_data(file_path): \n","    mesh = trimesh.load(file_path, force='mesh')\n","    \n","    # Extract vertices and faces\n","    vertices = mesh.vertices.tolist()\n","    faces = mesh.faces.tolist()\n","    centered_vertices = vertices - np.mean(vertices, axis=0)\n"," \n","    max_abs = np.max(np.abs(centered_vertices))\n","    vertices = centered_vertices / (max_abs / 0.95)  \n","      \n","      \n","    def sort_vertices(vertex): # Sort by Y , X, Z.  Y is vertical\n","        return vertex[1], vertex[0], vertex[2]   \n"," \n","    seen = OrderedDict()\n","    for point in vertices: \n","      key = tuple(point)\n","      if key not in seen:\n","        seen[key] = point\n","    unique_vertices =  list(seen.values()) \n","    sorted_vertices = sorted(unique_vertices, key=sort_vertices)\n","     \n","    vertices_as_tuples = [tuple(v) for v in vertices]\n","    sorted_vertices_as_tuples = [tuple(v) for v in sorted_vertices]\n","\n","    vertex_map = {old_index: new_index for old_index, vertex_tuple in enumerate(vertices_as_tuples) for new_index, sorted_vertex_tuple in enumerate(sorted_vertices_as_tuples) if vertex_tuple == sorted_vertex_tuple}\n"," \n","\n","    reindexed_faces = [[vertex_map[face[0]], vertex_map[face[1]], vertex_map[face[2]]] for face in faces] \n","    return np.array(sorted_vertices), np.array(reindexed_faces)\n","\n","def augment_mesh_scalar(vertices, scale_factor):\n","    # Apply a scalar factor to XYZ coordinates\n","    transformed_vertices = vertices * scale_factor\n","    return transformed_vertices\n","\n","def generate_scale_factors(num_examples, lower_limit=0.75, upper_limit=1.25): \n","    scale_factors = np.random.uniform(lower_limit, upper_limit, size=num_examples)\n","    return scale_factors\n","\n","def jitter_mesh(vertices, jitter_factor=0.01): \n","    offsets = np.random.uniform(-jitter_factor, jitter_factor, size=vertices.shape)\n"," \n","    jittered_vertices = vertices + offsets \n","    return jittered_vertices \n","\n","def augment_mesh(vertices, scale_factor):\n","    #vertices = jitter_mesh(vertices)\n","    transformed_vertices = vertices * scale_factor\n","    \n","    return transformed_vertices\n"," \n","\n","def load_models(directory, num_examples, variations):\n","    obj_datas = []  \n","    \n","    print(f\"num_examples: {num_examples}\")\n","    for filename in os.listdir(directory):  \n","        if (filename.endswith(\".obj\") or  filename.endswith(\".glb\") or  filename.endswith(\".off\")):\n","            file_path = os.path.join(directory, filename)\n","\n","            scale_factors = generate_scale_factors(variations, 0.7, 0.9) \n","            vertices, faces = get_3d_data(file_path) \n","\n","            for scale_factor in scale_factors: \n","                aug_vertices = augment_mesh(vertices.copy(), scale_factor) \n","                \n","                for _ in range(num_examples):\n","                    obj_data = {\"vertices\": aug_vertices.tolist(), \"faces\":  faces.tolist(), \"texts\": filename[:-4]}\n","                    obj_datas.append(obj_data)   \n","    return obj_datas\n","  \n","\n","\n","  \n","def load_json(file,num_examples):\n","    obj_datas = []\n","    with open(file, \"r\") as json_file:\n","        loaded_data = json.load(json_file) \n","        for item in loaded_data:\n","            for _ in range(num_examples): \n","                obj_data = {\"vertices\": torch.tensor(item[\"vertices\"], dtype=torch.float).to(\"cuda\"), \"faces\":  torch.tensor(item[\"faces\"], dtype=torch.long).to(\"cuda\"),\"texts\": item[\"texts\"] } \n","                obj_datas.append(obj_data)\n","    return obj_datas\n","                        \n","         "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:44.050017Z","iopub.status.busy":"2023-12-19T01:39:44.049685Z","iopub.status.idle":"2023-12-19T01:39:44.063671Z","shell.execute_reply":"2023-12-19T01:39:44.062641Z","shell.execute_reply.started":"2023-12-19T01:39:44.049992Z"},"trusted":true},"outputs":[],"source":["import torch\n","from torch import Tensor, tensor\n","from torch.utils.data import Dataset, DataLoader \n","from tqdm import tqdm\n","import numpy as np \n","import gc\n","from torch.nn.utils.rnn import pad_sequence\n","from meshgpt_pytorch.data import ( \n","    derive_face_edges_from_faces\n",") \n"," \n","class MeshDataset(Dataset): \n","    \n","    def __init__(self, data): \n","        self.data = data\n","        print(f\"Got {len(data)} data\")\n","\n","    def __len__(self):\n","        return len(self.data)\n","\n","    def __getitem__(self, idx): \n","        data = self.data[idx] \n","        return data  \n","    \n","    def save(self, path):\n","        np.savez_compressed(path, *self.data)\n","\n","    \n","    @classmethod\n","    def load(cls, path): \n","        loaded_data = np.load(path, allow_pickle=True)\n"," \n","        data = []\n","        for i in range(len(loaded_data.files)):\n","            data_item = {}\n","            for key in loaded_data[f\"arr_{i}\"].item():\n","                data_item[key] = loaded_data[f\"arr_{i}\"].item()[key]\n","            data.append(data_item)\n","\n","        return cls(data)\n","    \n","    def embed_texts(self,transformer): \n","        unique_texts = set(item['texts'] for item in self.data)\n"," \n","        text_embeddings = transformer.embed_texts(list(unique_texts))\n","        print(f\"Got text_embeddings: {len(text_embeddings)}\") \n","        text_embedding_dict = dict(zip(unique_texts, text_embeddings))\n"," \n","        for item in self.data:\n","            text_value = item['texts']\n","            item['text_embeds'] = text_embedding_dict.get(text_value, None)\n","            del item['texts']\n","            \n","    def generate_face_edges(self):\n","        n = 0\n","        for i in range(0, len(self.data)):  \n","            item = self.data[i]\n","            item['face_edges'] =  derive_face_edges_from_faces(item['faces'])\n","            n += 1  \n","        print(f\"done {n}/{len(self.data)}\")\n","\n","    def generate_codes(self, autoencoder : MeshAutoencoder):\n","        n = 0\n","        for i in range(0, len(self.data)):  \n","            item = self.data[i]\n","             \n","            codes = autoencoder.tokenize(\n","                vertices = item['vertices'],\n","                faces = item['faces'],\n","                face_edges = item['face_edges']\n","            ) \n","            item['codes'] = codes \n","            n += 1  \n","\n","        print(f\"[generate_codes] done {n}/{len(self.data)}\") \n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:39:44.066146Z","iopub.status.busy":"2023-12-19T01:39:44.065812Z","iopub.status.idle":"2023-12-19T01:39:52.313349Z","shell.execute_reply":"2023-12-19T01:39:52.312381Z","shell.execute_reply.started":"2023-12-19T01:39:44.066116Z"},"trusted":true},"outputs":[],"source":["import json\n","#tables = load_models(r\" filtered\",5,5)  \n","#with open(\"data.json\", \"w\") as json_file:\n","#    json.dump(tables, json_file) \n","\n","tables = load_json(\"/kaggle/input/shapenet/data.json\",2)\n","dataset = MeshDataset(tables) \n","dataset.generate_face_edges()\n","dataset.data[0].keys()\n","\n","desired_order = ['vertices', 'faces', 'face_edges', 'texts']\n","\n","dataset.data = [\n","    {key: d[key] for key in desired_order} for d in dataset.data\n","]\n","\n","unique_values = set(item[\"texts\"] for item in dataset.data)\n","\n","print(len(unique_values))  \n","print(unique_values)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["autoencoder = MeshAutoencoder( \n","    num_discrete_coors = 128  , \n",") \n","total_params = sum(p.numel() for p in autoencoder.encoders.parameters())\n","print(f\"encoders Total parameters: {total_params}\")\n","total_params = sum(p.numel() for p in autoencoder.decoders.parameters())\n","print(f\"decoders Total parameters: {total_params}\")  \n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-19T01:44:00.715656Z","iopub.status.busy":"2023-12-19T01:44:00.714727Z","iopub.status.idle":"2023-12-19T12:00:40.840982Z","shell.execute_reply":"2023-12-19T12:00:40.840110Z","shell.execute_reply.started":"2023-12-19T01:44:00.715620Z"},"trusted":true},"outputs":[],"source":["total_params = sum(p.numel() for p in autoencoder.encoders.parameters())\n","print(f\"Total parameters: {total_params}\")\n","print(autoencoder.encoders)\n","\n","autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder,learning_rate = 1e-3, \n","                                             warmup_steps = 10,\n","                                             dataset = dataset,   \n","                                             num_train_steps=100,\n","                                             batch_size=16,\n","                                             grad_accum_every=1)\n","\n","loss = autoencoder_trainer.train(40,stop_at_loss = 0.25)   \n","autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder,learning_rate = 1e-4, \n","                                             warmup_steps = 10,\n","                                             dataset = dataset,\n","                                             checkpoint_every_epoch = 20,  \n","                                             num_train_steps=100,\n","                                             batch_size=16,\n","                                             grad_accum_every=1)\n","\n","loss = autoencoder_trainer.train(180,stop_at_loss = 0.25)   \n","autoencoder_trainer.save(f'./mesh-encoder_2_loss_{loss:.3f}.pt') "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n","max_seq =  max_length * 6  \n","print(max_length)\n","print(max_seq)\n","transformer = MeshTransformer(\n","    autoencoder,\n","    dim = 512,\n","    max_seq_len = max_seq,\n","    coarse_pre_gateloop_depth = 6,\n","    fine_pre_gateloop_depth= 4, \n","    condition_on_text = True\n",")\n","total_params = sum(p.numel() for p in transformer.parameters())\n","print(f\"Total parameters: {total_params}\") "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["dataset.embed_texts(transformer)\n","dataset.generate_codes(autoencoder,1)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[" \n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,   dataset = dataset,\n","                                 learning_rate = 1e-1, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)   \n","\n"," \n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  dataset = dataset,\n","                                 learning_rate = 1e-2, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)    \n","\n","trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100,  dataset = dataset,\n","                                 learning_rate = 1e-4, batch_size=2)\n","trainer.train(80,stop_at_loss = 0.00009)   \n","\n","trainer.save(f'./mesh-transformer_2_{loss:.3f}.pt')    "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["unique_values = set(item[\"texts\"] for item in dataset.data)\n","print(len(unique_values))  \n","coords = []\n","for text in unique_values: \n","    print(f\"doing {text}\")\n","    faces_coordinates = transformer.generate(texts = [text]) \n","    coords.append(faces_coordinates)\n","    tensor_data = faces_coordinates[0].cpu()\n","    \n","    numpy_data = tensor_data.numpy().reshape(-1, 3)\n","    \n","    obj_file_content = \"\"\n","    \n","    for vertex in numpy_data:\n","        obj_file_content += f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\"\n","\n","    for i in range(1, len(numpy_data), 3):\n","        obj_file_content += f\"f {i} {i + 1} {i + 2}\\n\"\n","\n","    # Save to a file\n","    obj_file_path = f'./tests/3d_output_{text}.obj'\n","    with open(obj_file_path, \"w\") as file:\n","        file.write(obj_file_content)\n","\n","    print(obj_file_path) \n","    \n","    \n","all_vertices = []\n","all_faces = []\n","vertex_offset = 0\n"," \n","translation_distance = 0.3  \n","\n","for r, faces_coordinates in enumerate(coords): \n","    tensor_data = faces_coordinates[0].cpu()\n","\n","    numpy_data = tensor_data.numpy().reshape(-1, 3)\n","\n","    # Translate the model to avoid overlapping\n","    numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  # Adjust X coordinate\n","\n","    # Accumulate vertices\n","    for vertex in numpy_data:\n","        all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","    # Accumulate faces with adjusted indices\n","    for i in range(1, len(numpy_data), 3):\n","        all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n","\n","    # Update the vertex offset for the next model\n","    vertex_offset += len(numpy_data)\n","\n","# Combine vertices and faces\n","obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","# Save to a single file\n","obj_file_path = f\"./tests/3d_models_all.obj\"\n","with open(obj_file_path, \"w\") as file:\n","    file.write(obj_file_content)\n","\n","print(obj_file_path)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","coords_all = []\n","for text in set(item[\"texts\"] for item in dataset.data): \n","    print(f\"Doing {text}\")\n","    coords = []\n","    for r in np.arange(0, 1.0, 0.1):\n","        faces_coordinates = transformer.generate(temperature=r, texts = [text]) \n","        coords.append(faces_coordinates)\n","    coords_all.append(coords)\n","    \n","    all_vertices = []\n","    all_faces = []\n","    vertex_offset = 0\n","\n","    # Translation distance for each model\n","    translation_distance = 0.3  # Adjust as needed\n","\n","    for r, faces_coordinates in enumerate(coords): \n","        tensor_data = faces_coordinates[0].cpu()\n","\n","        numpy_data = tensor_data.numpy().reshape(-1, 3)\n","\n","        # Translate the model to avoid overlapping\n","        numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  # Adjust X coordinate\n","\n","        # Accumulate vertices\n","        for vertex in numpy_data:\n","            all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n","\n","        # Accumulate faces with adjusted indices\n","        for i in range(1, len(numpy_data), 3):\n","            all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n","\n","        # Update the vertex offset for the next model\n","        vertex_offset += len(numpy_data)\n","\n","    # Combine vertices and faces\n","    obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n","\n","    # Save to a single file\n","    obj_file_path = f\"./results/3d_models_{text}_temps.obj\"\n","    with open(obj_file_path, \"w\") as file:\n","        file.write(obj_file_content)\n","\n","    print(obj_file_path)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","def loadModels():\n","    autoencoder = MeshAutoencoder(\n","        dim = 576,\n","        encoder_depth = 6,\n","        decoder_depth = 6,\n","        num_discrete_coors = 128  ,\n","        local_attn_depth =0, \n","        \n","    )\n","    autoencoder_trainer = MeshAutoencoderTrainer(model = autoencoder,\n","                                    learning_rate = 1e-1, \n","                                                checkpoint_every_epoch= 5,\n","                                                warmup_steps = 10,\n","                                                dataset = dataset,  \n","                                                num_train_steps=100,\n","                                                batch_size=2,\n","                                                grad_accum_every=1)\n","\n","    autoencoder_trainer.load(r\"mesh-encoder_last.pt\")\n","    encoder = autoencoder_trainer.model\n","    max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n","    max_seq =  max_length * 6  \n","    \n","    transformer = MeshTransformer(\n","        autoencoder,\n","        dim = 768,\n","        max_seq_len = max_seq,\n","        condition_on_text = True)\n","     \n","    trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100, checkpoint_folder = r\"F:\\MachineLearning\\Mesh\\MeshGPT\\checkpoints\" , dataset = dataset,\n","                                    learning_rate = 1e-3, batch_size=2) \n","    trainer.load(r\"mesh-transformer.pt\")\n","    transformer = trainer.model\n","    return transformer, encoder\n","\n","#transformer, autoencoder =  loadModels() "]}],"metadata":{"kaggle":{"accelerator":"gpu","dataSources":[{"datasetId":4189032,"sourceId":7234116,"sourceType":"datasetVersion"}],"dockerImageVersionId":30627,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"}},"nbformat":4,"nbformat_minor":4}
diff --git a/MeshGPT_demo.ipynb b/MeshGPT_demo.ipynb
new file mode 100644
index 00000000..ca2a016d
--- /dev/null
+++ b/MeshGPT_demo.ipynb
@@ -0,0 +1,866 @@
+{
+   "cells":[
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "!pip install  git+https://github.com/MarcusLoppe/classifier-free-guidance-pytorch.git\n",
+            "!pip install  -q git+https://github.com/MarcusLoppe/meshgpt-pytorch.git"
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "import torch\n",
+            "import trimesh\n",
+            "import numpy as np\n",
+            "import os\n",
+            "from collections import OrderedDict\n",
+            "\n",
+            "from meshgpt_pytorch import (\n",
+            "    MeshTransformerTrainer,\n",
+            "    MeshAutoencoderTrainer,\n",
+            "    MeshAutoencoder,\n",
+            "    MeshTransformer\n",
+            ")\n",
+            "from meshgpt_pytorch.data import ( \n",
+            "    derive_face_edges_from_faces\n",
+            ") \n",
+            "\n",
+            "def get_mesh(file_path): \n",
+            "    mesh = trimesh.load(file_path, force='mesh')\n",
+            "     \n",
+            "    vertices = mesh.vertices.tolist()\n",
+            "    faces = mesh.faces.tolist()\n",
+            "    centered_vertices = vertices - np.mean(vertices, axis=0)\n",
+            " \n",
+            "    max_abs = np.max(np.abs(centered_vertices))\n",
+            "    vertices = centered_vertices / (max_abs / 0.95)  \n",
+            "       \n",
+            "    def sort_vertices(vertex):\n",
+            "        return vertex[2], vertex[0], vertex[1]   \n",
+            " \n",
+            "    seen = OrderedDict()\n",
+            "    for point in vertices: \n",
+            "      key = tuple(point)\n",
+            "      if key not in seen:\n",
+            "        seen[key] = point\n",
+            "        \n",
+            "    unique_vertices =  list(seen.values()) \n",
+            "    sorted_vertices = sorted(unique_vertices, key=sort_vertices)\n",
+            "     \n",
+            "    vertices_as_tuples = [tuple(v) for v in vertices]\n",
+            "    sorted_vertices_as_tuples = [tuple(v) for v in sorted_vertices]\n",
+            "\n",
+            "    vertex_map = {old_index: new_index for old_index, vertex_tuple in enumerate(vertices_as_tuples) for new_index, sorted_vertex_tuple in enumerate(sorted_vertices_as_tuples) if vertex_tuple == sorted_vertex_tuple}\n",
+            " \n",
+            "\n",
+            "    reindexed_faces = [[vertex_map[face[0]], vertex_map[face[1]], vertex_map[face[2]]] for face in faces] \n",
+            "    return np.array(sorted_vertices), np.array(reindexed_faces)\n",
+            " \n",
+            " \n",
+            "\n",
+            "def augment_mesh(vertices, scale_factor):     \n",
+            "    jitter_factor=0.01 \n",
+            "    possible_values = np.arange(-jitter_factor, jitter_factor , 0.0005) \n",
+            "    offsets = np.random.choice(possible_values, size=vertices.shape) \n",
+            "    vertices = vertices + offsets   \n",
+            "    \n",
+            "    vertices = vertices * scale_factor \n",
+            "    return vertices\n",
+            "\n",
+            "\n",
+            "#load_shapenet(\"./shapenet\", \"./shapenet_csv_files\", 10, 10)   \n",
+            "#Find the csv files with the labels in the ShapeNetCore.v1.zip, download at  https://huggingface.co/datasets/ShapeNet/ShapeNetCore-archive  \n",
+            "def load_shapenet(directory, csv_files, per_category, variations ):\n",
+            "    obj_datas = []   \n",
+            "    chosen_models_count = {}   \n",
+            "    id_info = {} \n",
+            "    print(f\"per_category: {per_category} min {min}\")\n",
+            "     \n",
+            "    for csv_filename in os.listdir(csv_files):\n",
+            "        if csv_filename.endswith(\".csv\"): \n",
+            "            csv_path = os.path.join(csv_files, csv_filename) \n",
+            "            with open(csv_path, newline='', encoding=\"utf-8\") as csvfile:\n",
+            "                csv_reader = csv.DictReader(csvfile)\n",
+            "                for row in csv_reader: \n",
+            "                    full_id = row['fullId'].replace(\"3dw.\", \"\")\n",
+            "                    wn_lemmas = row['wnlemmas'].replace(\",\", \" \")\n",
+            "                    id_info[full_id] = wn_lemmas \n",
+            "    \n",
+            "    possible_values = np.arange(0.75, 1.0 , 0.005) \n",
+            "    scale_factors = np.random.choice(possible_values, size=variations) \n",
+            "    \n",
+            "    for category in os.listdir(directory): \n",
+            "        category_path = os.path.join(directory, category)   \n",
+            "         \n",
+            "        num_files_in_category = len(os.listdir(category_path))\n",
+            "        print(f\"{category_path} got {num_files_in_category} files\") \n",
+            "        chosen_models_count[category] = 0  \n",
+            "        \n",
+            "        for filename in os.listdir(category_path)[:per_category]:\n",
+            "            if filename.endswith((\".obj\", \".glb\", \".off\")):\n",
+            "                file_path = os.path.join(category_path, filename)\n",
+            "                \n",
+            "                if os.path.getsize(file_path) >  20 * 1024: # 20 kb limit = less then 400-600 faces\n",
+            "                    continue\n",
+            "                    \n",
+            "                if filename[:-4] not in id_info:\n",
+            "                    print(\"Unable to find id info for \", filename)\n",
+            "                    continue \n",
+            "                chosen_models_count[category] += 1\n",
+            "                vertices, faces = get_mesh(file_path)   \n",
+            "                textName =  id_info[filename[:-4]]  \n",
+            "                \n",
+            "                if len(faces) > 800: \n",
+            "                    continue\n",
+            "                \n",
+            "                for scale_factor in scale_factors: \n",
+            "                    aug_vertices = augment_mesh(vertices.copy(), scale_factor)  \n",
+            "                    obj_data = {\"vertices\": torch.tensor(aug_vertices.tolist(), dtype=torch.float).to(\"cuda\"), \"faces\":  torch.tensor(faces.tolist(), dtype=torch.long).to(\"cuda\"),\"texts\": textName } \n",
+            "                    obj_datas.append(obj_data)\n",
+            "                    \n",
+            "    print(\"=\"*25)\n",
+            "    print(\"Chosen models count for each category:\")\n",
+            "    for category, count in chosen_models_count.items():\n",
+            "        print(f\"{category}: {count}\")\n",
+            "    \n",
+            "    total_chosen_models = sum(chosen_models_count.values())\n",
+            "    print(f\"Total number of chosen models: {total_chosen_models}\")\n",
+            "    return obj_datas\n",
+            "\n",
+            "  \n",
+            "   \n",
+            "def load_filename(directory, variations):\n",
+            "    obj_datas = []   \n",
+            "    chosen_models_count = {}      \n",
+            "    possible_values = np.arange(0.75, 1.0 , 0.005) \n",
+            "    scale_factors = np.random.choice(possible_values, size=variations) \n",
+            "    \n",
+            "    for filename in os.listdir(directory):\n",
+            "        if filename.endswith((\".obj\", \".glb\", \".off\")): \n",
+            "            file_path = os.path.join(directory, filename) \n",
+            "            vertices, faces = get_mesh(file_path)  \n",
+            "            \n",
+            "            faces = torch.tensor(faces.tolist(), dtype=torch.long).to(\"cuda\")\n",
+            "            face_edges =  derive_face_edges_from_faces(faces)  \n",
+            "            texts, ext = os.path.splitext(filename)     \n",
+            "            \n",
+            "            for scale_factor in scale_factors: \n",
+            "                aug_vertices = augment_mesh(vertices.copy(), scale_factor)  \n",
+            "                obj_data = {\"vertices\": torch.tensor(aug_vertices.tolist(), dtype=torch.float).to(\"cuda\"), \"faces\":  faces, \"face_edges\" : face_edges, \"texts\": texts } \n",
+            "                obj_datas.append(obj_data)\n",
+            "                     \n",
+            "    print(f\"[create_mesh_dataset] Returning {len(obj_data)} meshes\")\n",
+            "    return obj_datas"
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "from pathlib import Path \n",
+            "import gc    \n",
+            "import torch\n",
+            "import os\n",
+            "from meshgpt_pytorch import MeshDataset\n",
+            "\n",
+            "torch.cuda.empty_cache()\n",
+            "gc.collect()  \n",
+            "\n",
+            " \n",
+            "project_name = \"polygen_demo\" \n",
+            "\n",
+            "working_dir = f'.\\{project_name}'\n",
+            "\n",
+            "working_dir = Path(working_dir)\n",
+            "working_dir.mkdir(exist_ok = True, parents = True)\n",
+            "dataset_path = working_dir / (project_name + \".npz\")\n",
+            "\n",
+            "\n",
+            "if not os.path.isfile(dataset_path):\n",
+            "    data = load_filename(\"./demo_mesh\",20)\n",
+            "      \n",
+            "    dataset = MeshDataset(data) \n",
+            "    dataset.generate_face_edges() \n",
+            "    print(set(item[\"texts\"] for item in dataset.data)  ) \n",
+            "    dataset.save(dataset_path)\n",
+            " \n",
+            "dataset = MeshDataset.load(dataset_path) \n",
+            "print(dataset.data[0].keys())\n",
+            " "
+         ]
+      },
+      {
+         "cell_type":"markdown",
+         "metadata":{
+            
+         },
+         "source":[
+            "### Inspect"
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "from pathlib import Path\n",
+            " \n",
+            "folder = working_dir / f'renders' \n",
+            "obj_file_path = Path(folder)\n",
+            "obj_file_path.mkdir(exist_ok = True, parents = True)\n",
+            "   \n",
+            "all_vertices = []\n",
+            "all_faces = []\n",
+            "vertex_offset = 0\n",
+            "translation_distance = 0.5  \n",
+            "\n",
+            "for r, item in enumerate(data): \n",
+            "    vertices_copy =  np.copy(item['vertices'])\n",
+            "    vertices_copy += translation_distance * (r / 0.2 - 1) \n",
+            "    \n",
+            "    for vert in vertices_copy:\n",
+            "        vertex = vert.to('cpu')\n",
+            "        all_vertices.append(f\"v {float(vertex[0])}  {float(vertex[1])}  {float(vertex[2])}\\n\") \n",
+            "    for face in item['faces']:\n",
+            "        all_faces.append(f\"f {face[0]+1+ vertex_offset} {face[1]+ 1+vertex_offset} {face[2]+ 1+vertex_offset}\\n\")  \n",
+            "    vertex_offset = len(all_vertices)\n",
+            " \n",
+            "obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n",
+            " \n",
+            "obj_file_path = f'{folder}/3d_models_inspect.obj'\n",
+            "\n",
+            "with open(obj_file_path, \"w\") as file:\n",
+            "    file.write(obj_file_content)    \n",
+            "    "
+         ]
+      },
+      {
+         "cell_type":"markdown",
+         "metadata":{
+            
+         },
+         "source":[
+            "### Train!"
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "autoencoder = MeshAutoencoder().to(\"cuda\")      "
+         ]
+      },
+      {
+         "cell_type":"markdown",
+         "metadata":{
+            
+         },
+         "source":[
+            "**Have at least 400-2000 items in the dataset, use this to multiply the dataset**  "
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "dataset.data = [dict(d) for d in dataset.data] * 10\n",
+            "print(len(dataset.data))"
+         ]
+      },
+      {
+         "cell_type":"markdown",
+         "metadata":{
+            
+         },
+         "source":[
+            "*Load previous saved model if you had to restart session*"
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder ,warmup_steps = 10, dataset = dataset, num_train_steps=100, batch_size=8,  grad_accum_every=1, learning_rate = 1e-4) \n",
+            "autoencoder_trainer.load(f'{working_dir}\\mesh-encoder_{project_name}.pt')   \n",
+            "autencoder = autoencoder_trainer.model\n",
+            "for param in autoencoder.parameters():\n",
+            "    param.requires_grad = True"
+         ]
+      },
+      {
+         "cell_type":"markdown",
+         "metadata":{
+            
+         },
+         "source":[
+            "**Train to about 0.3 loss if you are using a small dataset**"
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "\n",
+            "autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder ,warmup_steps = 10, dataset = dataset, num_train_steps=100,\n",
+            "                                             batch_size=8,\n",
+            "                                             grad_accum_every=2,\n",
+            "                                             learning_rate = 1e-2) \n",
+            "loss = autoencoder_trainer.train(280,stop_at_loss = 0.7, diplay_graph= True)   \n",
+            "  \n",
+            "autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder ,warmup_steps = 10, dataset = dataset, num_train_steps=100,\n",
+            "                                             batch_size=8,\n",
+            "                                             grad_accum_every=2,\n",
+            "                                             learning_rate = 4e-3) \n",
+            "loss = autoencoder_trainer.train(280,stop_at_loss = 0.28, diplay_graph= True)     "
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "autoencoder_trainer.save(f'{working_dir}\\mesh-encoder_{project_name}.pt')   "
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "import gc  \n",
+            "torch.cuda.empty_cache()\n",
+            "gc.collect()  \n",
+            "\n",
+            "max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n",
+            "max_seq = max_length * 6  \n",
+            "print(\"Highest face count:\" , max_length)\n",
+            "print(\"Max token sequence:\" , max_seq) \n",
+            "\n",
+            "transformer = MeshTransformer(\n",
+            "    autoencoder,\n",
+            "    dim = 512,\n",
+            "    coarse_pre_gateloop_depth = 6,\n",
+            "    fine_pre_gateloop_depth= 4, \n",
+            "   # attn_depth = 24,\n",
+            "    max_seq_len = max_seq, \n",
+            "    condition_on_text = True, \n",
+            "    gateloop_use_heinsen = False,\n",
+            "    text_condition_model_types = \"t5\", ## Change or remove this line if you are using:  https://github.com/MarcusLoppe/classifier-free-guidance-pytorch\n",
+            "    text_condition_cond_drop_prob = 0.0\n",
+            ") \n",
+            "\n",
+            "total_params = sum(p.numel() for p in transformer.decoder.parameters())\n",
+            "total_params = f\"{total_params / 1000000:.1f}M\"\n",
+            "print(f\"Decoder total parameters: {total_params}\") \n",
+            "total_params = sum(p.numel() for p in transformer.parameters())\n",
+            "total_params = f\"{total_params / 1000000:.1f}M\"\n",
+            "print(f\"Total parameters: {total_params}\") "
+         ]
+      },
+      {
+         "cell_type":"markdown",
+         "metadata":{
+            
+         },
+         "source":[
+            "## **Required!**, embed the text and run generate_codes to save 4-96 GB VRAM (dependant on dataset) ##\n",
+            "\n",
+            "**If you don't;** <br>\n",
+            "During each during each training step the autoencoder will generate the codes and the text encoder will embed the text.\n",
+            "<br>\n",
+            "After these fields are generate: **they will be deleted and next time it generates the code again:**<br>\n",
+            "\n",
+            "This is due to the dataloaders nature, it writes this information to a temporary COPY of the dataset\n"
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "labels = set(item[\"texts\"] for item in dataset.data)\n",
+            "print(labels)\n",
+            "dataset.embed_texts(transformer)\n",
+            "dataset.generate_codes(autoencoder)\n",
+            "print(dataset.data[0].keys())"
+         ]
+      },
+      {
+         "cell_type":"markdown",
+         "metadata":{
+            
+         },
+         "source":[
+            "*Load previous saved model if you had to restart session*"
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100, dataset = dataset, learning_rate = 1e-1, batch_size=2)\n",
+            "trainer.load(f'{working_dir}\\mesh-transformer_{project_name}.pt')  \n",
+            "transformer = trainer.model"
+         ]
+      },
+      {
+         "cell_type":"markdown",
+         "metadata":{
+            
+         },
+         "source":[
+            "**Train to about 0.0001 loss (or less) if you are using a small dataset**"
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=4,num_train_steps=100, dataset = dataset,\n",
+            "                                 learning_rate = 1e-3, batch_size=2)  \n",
+            "loss = trainer.train(100, stop_at_loss = 0.009) \n",
+            "\n",
+            "trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=4,num_train_steps=100, dataset = dataset,\n",
+            "                                 learning_rate = 5e-4, batch_size=2)\n",
+            "loss = trainer.train(200, stop_at_loss = 0.00001)  "
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            " \n",
+            "trainer.save(f'{working_dir}\\mesh-transformer_{project_name}.pt')   "
+         ]
+      },
+      {
+         "cell_type":"markdown",
+         "metadata":{
+            
+         },
+         "source":[
+            "## Generate and view mesh"
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "def combind_mesh(path, mesh):\n",
+            "    all_vertices = []\n",
+            "    all_faces = []\n",
+            "    vertex_offset = 0\n",
+            "    translation_distance = 0.5  \n",
+            "\n",
+            "    for r, faces_coordinates in enumerate(mesh): \n",
+            "        numpy_data = faces_coordinates[0].cpu().numpy().reshape(-1, 3)   \n",
+            "    \n",
+            "        for vertex in numpy_data:\n",
+            "            all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n",
+            "    \n",
+            "        for i in range(1, len(numpy_data), 3):\n",
+            "            all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n",
+            "    \n",
+            "        vertex_offset += len(numpy_data)\n",
+            "    \n",
+            "    obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n",
+            "     \n",
+            "    with open(path , \"w\") as file:\n",
+            "        file.write(obj_file_content)   \n",
+            " \n",
+            "def combind_mesh_with_rows(path, meshes):\n",
+            "    all_vertices = []\n",
+            "    all_faces = []\n",
+            "    vertex_offset = 0\n",
+            "    translation_distance = 0.5  \n",
+            "    \n",
+            "    for row, mesh in enumerate(meshes): \n",
+            "        for r, faces_coordinates in enumerate(mesh): \n",
+            "            numpy_data = faces_coordinates[0].cpu().numpy().reshape(-1, 3)  \n",
+            "            numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  \n",
+            "            numpy_data[:, 2] += translation_distance * (row / 0.2 - 1)  \n",
+            "        \n",
+            "            for vertex in numpy_data:\n",
+            "                all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n",
+            "        \n",
+            "            for i in range(1, len(numpy_data), 3):\n",
+            "                all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n",
+            "        \n",
+            "            vertex_offset += len(numpy_data)\n",
+            "        \n",
+            "        obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n",
+            "     \n",
+            "    with open(path , \"w\") as file:\n",
+            "        file.write(obj_file_content)   \n",
+            "        \n",
+            "        \n",
+            "def write_mesh_output(path, coords):\n",
+            "    numpy_data = faces_coordinates[0].cpu().numpy().reshape(-1, 3)  \n",
+            "    obj_file_content = \"\"\n",
+            "    \n",
+            "    for vertex in numpy_data:\n",
+            "        obj_file_content += f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\"\n",
+            "\n",
+            "    for i in range(1, len(numpy_data), 3):\n",
+            "        obj_file_content += f\"f {i} {i + 1} {i + 2}\\n\"\n",
+            " \n",
+            "    with open(path, \"w\") as file:\n",
+            "        file.write(obj_file_content) \n",
+            "         "
+         ]
+      },
+      {
+         "cell_type":"markdown",
+         "metadata":{
+            
+         },
+         "source":[
+            "**Using only text**"
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            " \n",
+            "from pathlib import Path\n",
+            " \n",
+            "folder = working_dir / 'renders'\n",
+            "obj_file_path = Path(folder)\n",
+            "obj_file_path.mkdir(exist_ok = True, parents = True)  \n",
+            " \n",
+            "text_coords = [] \n",
+            "for text in labels:\n",
+            "    print(f\"Generating {text}\")\n",
+            "    faces_coordinates = transformer.generate(texts = [text],  temperature = 0.0) \n",
+            "    text_coords.append(faces_coordinates)\n",
+            "    \n",
+            "    write_mesh_output(f'{folder}/3d_output_{text}.obj', faces_coordinates)  \n",
+            "     \n",
+            " \n",
+            "combind_mesh(f'{folder}/3d_models_all.obj', text_coords)"
+         ]
+      },
+      {
+         "cell_type":"markdown",
+         "metadata":{
+            
+         },
+         "source":[
+            "**Text + prompt of tokens**"
+         ]
+      },
+      {
+         "cell_type":"markdown",
+         "metadata":{
+            
+         },
+         "source":[
+            "Grab fresh copy of dataset"
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "dataset = MeshDataset.load(dataset_path)\n",
+            "dataset.generate_codes(autoencoder)"
+         ]
+      },
+      {
+         "cell_type":"markdown",
+         "metadata":{
+            
+         },
+         "source":[
+            "**Prompt with 10% of codes/tokens**"
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "from pathlib import Path\n",
+            "token_length_procent = 0.10 \n",
+            "codes = []\n",
+            "texts = []\n",
+            "for label in labels:\n",
+            "    for item in dataset.data: \n",
+            "        if item['texts'] == label:\n",
+            "            num_tokens = int(item[\"codes\"].shape[0] * token_length_procent) \n",
+            "            \n",
+            "            texts.append(item['texts']) \n",
+            "            codes.append(item[\"codes\"].flatten()[:num_tokens].unsqueeze(0))  \n",
+            "            break\n",
+            "        \n",
+            "folder = working_dir / f'renders/text+codes'\n",
+            "obj_file_path = Path(folder)\n",
+            "obj_file_path.mkdir(exist_ok = True, parents = True)  \n",
+            "\n",
+            "coords = [] \n",
+            "\n",
+            "\n",
+            "\n",
+            "for text, prompt in zip(texts, codes): \n",
+            "    print(f\"Generating {text} with {prompt.shape[1]} tokens\")\n",
+            "    faces_coordinates = transformer.generate(texts = [text],  prompt = prompt, temperature = 0) \n",
+            "    coords.append(faces_coordinates) \n",
+            "    \n",
+            "    obj_file_path = f'{folder}/{text}_{prompt.shape[1]}_tokens.obj'\n",
+            "    write_mesh_output(obj_file_path, faces_coordinates)\n",
+            "        \n",
+            "    print(obj_file_path)\n",
+            "     \n",
+            " \n",
+            "combind_mesh(f'{folder}/text+prompt_all.obj', coords)\n",
+            "\n",
+            "if text_coords is not None: \n",
+            "    combind_mesh_with_rows(f'{folder}/both_verisons.obj', [text_coords , coords])"
+         ]
+      },
+      {
+         "cell_type":"markdown",
+         "metadata":{
+            
+         },
+         "source":[
+            "**Prompt with 0% to 80% of tokens**"
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "from pathlib import Path\n",
+            " \n",
+            "folder = working_dir / f'renders/text+codes_rows'\n",
+            "obj_file_path = Path(folder)\n",
+            "obj_file_path.mkdir(exist_ok = True, parents = True)   \n",
+            "\n",
+            "mesh_rows = []\n",
+            "for token_length_procent in np.arange(0, 0.8, 0.1):\n",
+            "    codes = []\n",
+            "    texts = []\n",
+            "    for label in labels:\n",
+            "        for item in dataset.data: \n",
+            "            if item['texts'] == label:\n",
+            "                num_tokens = int(item[\"codes\"].shape[0] * token_length_procent) \n",
+            "                \n",
+            "                texts.append(item['texts']) \n",
+            "                codes.append(item[\"codes\"].flatten()[:num_tokens].unsqueeze(0))  \n",
+            "                break\n",
+            "            \n",
+            "    coords = []   \n",
+            "    for text, prompt in zip(texts, codes): \n",
+            "        \n",
+            "        print(f\"Generating {text} with {prompt.shape[1]} tokens\")\n",
+            "        faces_coordinates = transformer.generate(texts = [text],  prompt = prompt, temperature = 0) \n",
+            "        coords.append(faces_coordinates)\n",
+            "        \n",
+            "        obj_file_path = f'{folder}/{text}_{prompt.shape[1]}_tokens.obj'\n",
+            "        write_mesh_output(obj_file_path, coords)  \n",
+            "        print(obj_file_path)\n",
+            "        \n",
+            "        \n",
+            "    mesh_rows.append(coords) \n",
+            "    combind_mesh(f'{folder}/text+prompt_all_{token_length_procent}.obj', coords)\n",
+            "    \n",
+            "combind_mesh_with_rows(f'{folder}/all.obj', mesh_rows)\n",
+            " "
+         ]
+      },
+      {
+         "cell_type":"markdown",
+         "metadata":{
+            
+         },
+         "source":[
+            "**Just some testing for text embedding similarity**"
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "import numpy as np \n",
+            "texts = list(labels)\n",
+            "vectors = [transformer.conditioner.text_models[0].embed_text([text], return_text_encodings = False).cpu().flatten() for text in texts]\n",
+            " \n",
+            "max_label_length = max(len(text) for text in texts)\n",
+            " \n",
+            "# Print the table header\n",
+            "print(f\"{'Text':<{max_label_length}} |\", end=\" \")\n",
+            "for text in texts:\n",
+            "    print(f\"{text:<{max_label_length}} |\", end=\" \")\n",
+            "print()\n",
+            "\n",
+            "# Print the similarity matrix as a table with fixed-length columns\n",
+            "for i in range(len(texts)):\n",
+            "    print(f\"{texts[i]:<{max_label_length}} |\", end=\" \")\n",
+            "    for j in range(len(texts)):\n",
+            "        # Encode the texts and calculate cosine similarity manually\n",
+            "        vector_i = vectors[i]\n",
+            "        vector_j = vectors[j]\n",
+            "        \n",
+            "        dot_product = torch.sum(vector_i * vector_j)\n",
+            "        norm_vector1 = torch.norm(vector_i)\n",
+            "        norm_vector2 = torch.norm(vector_j)\n",
+            "        similarity_score = dot_product / (norm_vector1 * norm_vector2)\n",
+            "        \n",
+            "        # Print with fixed-length columns\n",
+            "        print(f\"{similarity_score.item():<{max_label_length}.4f} |\", end=\" \")\n",
+            "    print()"
+         ]
+      }
+   ],
+   "metadata":{
+      "kaggle":{
+         "accelerator":"gpu",
+         "dataSources":[],
+		 
+         "dockerImageVersionId":30627,
+         "isGpuEnabled":true,
+         "isInternetEnabled":true,
+         "language":"python",
+         "sourceType":"notebook"
+      },
+      "kernelspec":{
+         "display_name":"Python 3",
+         "language":"python",
+         "name":"python3"
+      },
+      "language_info":{
+         "codemirror_mode":{
+            "name":"ipython",
+            "version":3
+         },
+         "file_extension":".py",
+         "mimetype":"text/x-python",
+         "name":"python",
+         "nbconvert_exporter":"python",
+         "pygments_lexer":"ipython3",
+         "version":"3.11.5"
+      }
+   },
+   "nbformat":4,
+   "nbformat_minor":4
+}
\ No newline at end of file
diff --git a/demo_mesh/cone.obj b/demo_mesh/cone.obj
new file mode 100644
index 00000000..22efc7cb
--- /dev/null
+++ b/demo_mesh/cone.obj
@@ -0,0 +1,66 @@
+v 0.000000 -1.000000 -1.000000
+v 0.195090 -1.000000 -0.980785
+v 0.382683 -1.000000 -0.923880
+v 0.555570 -1.000000 -0.831470
+v 0.707107 -1.000000 -0.707107
+v 0.831470 -1.000000 -0.555570
+v 0.923880 -1.000000 -0.382683
+v 0.980785 -1.000000 -0.195090
+v 1.000000 -1.000000 -0.000000
+v 0.980785 -1.000000 0.195090
+v 0.923880 -1.000000 0.382683
+v 0.831470 -1.000000 0.555570
+v 0.707107 -1.000000 0.707107
+v 0.555570 -1.000000 0.831470
+v 0.382683 -1.000000 0.923880
+v 0.195090 -1.000000 0.980785
+v -0.000000 -1.000000 1.000000
+v -0.195091 -1.000000 0.980785
+v -0.382684 -1.000000 0.923879
+v -0.555571 -1.000000 0.831469
+v -0.707107 -1.000000 0.707106
+v -0.831470 -1.000000 0.555570
+v -0.923880 -1.000000 0.382683
+v 0.000000 1.000000 0.000000
+v -0.980785 -1.000000 0.195089
+v -1.000000 -1.000000 -0.000001
+v -0.980785 -1.000000 -0.195091
+v -0.923879 -1.000000 -0.382684
+v -0.831469 -1.000000 -0.555571
+v -0.707106 -1.000000 -0.707108
+v -0.555569 -1.000000 -0.831470
+v -0.382682 -1.000000 -0.923880
+v -0.195089 -1.000000 -0.980786
+f 1 24 2
+f 2 24 3
+f 3 24 4
+f 4 24 5
+f 5 24 6
+f 6 24 7
+f 7 24 8
+f 8 24 9
+f 9 24 10
+f 10 24 11
+f 11 24 12
+f 12 24 13
+f 13 24 14
+f 14 24 15
+f 15 24 16
+f 16 24 17
+f 17 24 18
+f 18 24 19
+f 19 24 20
+f 20 24 21
+f 21 24 22
+f 22 24 23
+f 23 24 25
+f 25 24 26
+f 26 24 27
+f 27 24 28
+f 28 24 29
+f 29 24 30
+f 30 24 31
+f 31 24 32
+f 32 24 33
+f 33 24 1
+f 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 25 26 27 28 29 30 31 32 33
diff --git a/demo_mesh/cube.obj b/demo_mesh/cube.obj
new file mode 100644
index 00000000..9855ecdd
--- /dev/null
+++ b/demo_mesh/cube.obj
@@ -0,0 +1,14 @@
+v -1.000000 -1.000000 1.000000
+v -1.000000 1.000000 1.000000
+v -1.000000 -1.000000 -1.000000
+v -1.000000 1.000000 -1.000000
+v 1.000000 -1.000000 1.000000
+v 1.000000 1.000000 1.000000
+v 1.000000 -1.000000 -1.000000
+v 1.000000 1.000000 -1.000000
+f 1 2 4 3
+f 3 4 8 7
+f 7 8 6 5
+f 5 6 2 1
+f 3 7 5 1
+f 8 4 2 6
diff --git a/demo_mesh/cylinder.obj b/demo_mesh/cylinder.obj
new file mode 100644
index 00000000..4fad316c
--- /dev/null
+++ b/demo_mesh/cylinder.obj
@@ -0,0 +1,98 @@
+v 0.000000 -1.000000 -1.000000
+v 0.000000 1.000000 -1.000000
+v 0.195090 -1.000000 -0.980785
+v 0.195090 1.000000 -0.980785
+v 0.382683 -1.000000 -0.923880
+v 0.382683 1.000000 -0.923880
+v 0.555570 -1.000000 -0.831470
+v 0.555570 1.000000 -0.831470
+v 0.707107 -1.000000 -0.707107
+v 0.707107 1.000000 -0.707107
+v 0.831470 -1.000000 -0.555570
+v 0.831470 1.000000 -0.555570
+v 0.923880 -1.000000 -0.382683
+v 0.923880 1.000000 -0.382683
+v 0.980785 -1.000000 -0.195090
+v 0.980785 1.000000 -0.195090
+v 1.000000 -1.000000 -0.000000
+v 1.000000 1.000000 -0.000000
+v 0.980785 -1.000000 0.195090
+v 0.980785 1.000000 0.195090
+v 0.923880 -1.000000 0.382683
+v 0.923880 1.000000 0.382683
+v 0.831470 -1.000000 0.555570
+v 0.831470 1.000000 0.555570
+v 0.707107 -1.000000 0.707107
+v 0.707107 1.000000 0.707107
+v 0.555570 -1.000000 0.831470
+v 0.555570 1.000000 0.831470
+v 0.382683 -1.000000 0.923880
+v 0.382683 1.000000 0.923880
+v 0.195090 -1.000000 0.980785
+v 0.195090 1.000000 0.980785
+v -0.000000 -1.000000 1.000000
+v -0.000000 1.000000 1.000000
+v -0.195091 -1.000000 0.980785
+v -0.195091 1.000000 0.980785
+v -0.382684 -1.000000 0.923879
+v -0.382684 1.000000 0.923879
+v -0.555571 -1.000000 0.831469
+v -0.555571 1.000000 0.831469
+v -0.707107 -1.000000 0.707106
+v -0.707107 1.000000 0.707106
+v -0.831470 -1.000000 0.555570
+v -0.831470 1.000000 0.555570
+v -0.923880 -1.000000 0.382683
+v -0.923880 1.000000 0.382683
+v -0.980785 -1.000000 0.195089
+v -0.980785 1.000000 0.195089
+v -1.000000 -1.000000 -0.000001
+v -1.000000 1.000000 -0.000001
+v -0.980785 -1.000000 -0.195091
+v -0.980785 1.000000 -0.195091
+v -0.923879 -1.000000 -0.382684
+v -0.923879 1.000000 -0.382684
+v -0.831469 -1.000000 -0.555571
+v -0.831469 1.000000 -0.555571
+v -0.707106 -1.000000 -0.707108
+v -0.707106 1.000000 -0.707108
+v -0.555569 -1.000000 -0.831470
+v -0.555569 1.000000 -0.831470
+v -0.382682 -1.000000 -0.923880
+v -0.382682 1.000000 -0.923880
+v -0.195089 -1.000000 -0.980786
+v -0.195089 1.000000 -0.980786
+f 1 2 4 3
+f 3 4 6 5
+f 5 6 8 7
+f 7 8 10 9
+f 9 10 12 11
+f 11 12 14 13
+f 13 14 16 15
+f 15 16 18 17
+f 17 18 20 19
+f 19 20 22 21
+f 21 22 24 23
+f 23 24 26 25
+f 25 26 28 27
+f 27 28 30 29
+f 29 30 32 31
+f 31 32 34 33
+f 33 34 36 35
+f 35 36 38 37
+f 37 38 40 39
+f 39 40 42 41
+f 41 42 44 43
+f 43 44 46 45
+f 45 46 48 47
+f 47 48 50 49
+f 49 50 52 51
+f 51 52 54 53
+f 53 54 56 55
+f 55 56 58 57
+f 57 58 60 59
+f 59 60 62 61
+f 4 2 64 62 60 58 56 54 52 50 48 46 44 42 40 38 36 34 32 30 28 26 24 22 20 18 16 14 12 10 8 6
+f 61 62 64 63
+f 63 64 2 1
+f 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63
diff --git a/demo_mesh/icosphere.obj b/demo_mesh/icosphere.obj
new file mode 100644
index 00000000..1a1506bf
--- /dev/null
+++ b/demo_mesh/icosphere.obj
@@ -0,0 +1,122 @@
+v 0.000000 -1.000000 0.000000
+v 0.723607 -0.447220 0.525725
+v -0.276388 -0.447220 0.850649
+v -0.894426 -0.447216 0.000000
+v -0.276388 -0.447220 -0.850649
+v 0.723607 -0.447220 -0.525725
+v 0.276388 0.447220 0.850649
+v -0.723607 0.447220 0.525725
+v -0.723607 0.447220 -0.525725
+v 0.276388 0.447220 -0.850649
+v 0.894426 0.447216 0.000000
+v 0.000000 1.000000 0.000000
+v -0.162456 -0.850654 0.499995
+v 0.425323 -0.850654 0.309011
+v 0.262869 -0.525738 0.809012
+v 0.850648 -0.525736 0.000000
+v 0.425323 -0.850654 -0.309011
+v -0.525730 -0.850652 0.000000
+v -0.688189 -0.525736 0.499997
+v -0.162456 -0.850654 -0.499995
+v -0.688189 -0.525736 -0.499997
+v 0.262869 -0.525738 -0.809012
+v 0.951058 0.000000 0.309013
+v 0.951058 0.000000 -0.309013
+v 0.000000 0.000000 1.000000
+v 0.587786 0.000000 0.809017
+v -0.951058 0.000000 0.309013
+v -0.587786 0.000000 0.809017
+v -0.587786 0.000000 -0.809017
+v -0.951058 0.000000 -0.309013
+v 0.587786 0.000000 -0.809017
+v 0.000000 0.000000 -1.000000
+v 0.688189 0.525736 0.499997
+v -0.262869 0.525738 0.809012
+v -0.850648 0.525736 0.000000
+v -0.262869 0.525738 -0.809012
+v 0.688189 0.525736 -0.499997
+v 0.162456 0.850654 0.499995
+v 0.525730 0.850652 0.000000
+v -0.425323 0.850654 0.309011
+v -0.425323 0.850654 -0.309011
+v 0.162456 0.850654 -0.499995
+f 1 14 13
+f 2 14 16
+f 1 13 18
+f 1 18 20
+f 1 20 17
+f 2 16 23
+f 3 15 25
+f 4 19 27
+f 5 21 29
+f 6 22 31
+f 2 23 26
+f 3 25 28
+f 4 27 30
+f 5 29 32
+f 6 31 24
+f 7 33 38
+f 8 34 40
+f 9 35 41
+f 10 36 42
+f 11 37 39
+f 39 42 12
+f 39 37 42
+f 37 10 42
+f 42 41 12
+f 42 36 41
+f 36 9 41
+f 41 40 12
+f 41 35 40
+f 35 8 40
+f 40 38 12
+f 40 34 38
+f 34 7 38
+f 38 39 12
+f 38 33 39
+f 33 11 39
+f 24 37 11
+f 24 31 37
+f 31 10 37
+f 32 36 10
+f 32 29 36
+f 29 9 36
+f 30 35 9
+f 30 27 35
+f 27 8 35
+f 28 34 8
+f 28 25 34
+f 25 7 34
+f 26 33 7
+f 26 23 33
+f 23 11 33
+f 31 32 10
+f 31 22 32
+f 22 5 32
+f 29 30 9
+f 29 21 30
+f 21 4 30
+f 27 28 8
+f 27 19 28
+f 19 3 28
+f 25 26 7
+f 25 15 26
+f 15 2 26
+f 23 24 11
+f 23 16 24
+f 16 6 24
+f 17 22 6
+f 17 20 22
+f 20 5 22
+f 20 21 5
+f 20 18 21
+f 18 4 21
+f 18 19 4
+f 18 13 19
+f 13 3 19
+f 16 17 6
+f 16 14 17
+f 14 1 17
+f 13 15 3
+f 13 14 15
+f 14 2 15
diff --git a/meshgpt_pytorch/__init__.py b/meshgpt_pytorch/__init__.py
index 37db1d82..f2253628 100644
--- a/meshgpt_pytorch/__init__.py
+++ b/meshgpt_pytorch/__init__.py
@@ -12,4 +12,7 @@
 from meshgpt_pytorch.data import (
     DatasetFromTransforms,
     cache_text_embeds_for_dataset
-)
\ No newline at end of file
+)
+from meshgpt_pytorch.mesh_dataset import (
+    MeshDataset
+)
diff --git a/meshgpt_pytorch/mesh_dataset.py b/meshgpt_pytorch/mesh_dataset.py
index 9ce58ee0..201f311b 100644
--- a/meshgpt_pytorch/mesh_dataset.py
+++ b/meshgpt_pytorch/mesh_dataset.py
@@ -66,25 +66,25 @@ def load(cls, path):
         for item in loaded_data["arr_0"]:
             data.append(item)  
         print(f"[MeshDataset] Loaded {len(data)} entrys")
-        return cls(data)
-     
-
-            
-    def generate_face_edges(self): 
-        for i in range(0, len(self.data)):  
-            item = self.data[i]
-            item['face_edges'] =  derive_face_edges_from_faces(item['faces']) 
-            
-        desired_order = ['vertices', 'faces', 'face_edges', 'texts'] 
+        return cls(data) 
+    def sort_dataset_keys(self):
+        desired_order = ['vertices', 'faces', 'face_edges', 'texts','text_embeds','codes'] 
         self.data = [
-            {key: d[key] for key in desired_order} for d in self.data
+            {key: d[key] for key in desired_order if key in d} for d in self.data
         ]
-        print(f"[MeshDataset] Generated face_edges for {len(self.data)} entrys")
+       
+    def generate_face_edges(self): 
+        i = 0
+        for item in self.data:
+            if 'face_edges' not in item:
+                item['face_edges'] =  derive_face_edges_from_faces(item['faces']) 
+                i += 1
+            
+        self.sort_dataset_keys()
+        print(f"[MeshDataset] Generated face_edges for {i}/{len(self.data)} entrys")
 
     def generate_codes(self, autoencoder : MeshAutoencoder): 
-        for i in range(0, len(self.data)):  
-            item = self.data[i]  
-             
+        for item in self.data: 
             codes = autoencoder.tokenize(
                 vertices = item['vertices'],
                 faces = item['faces'],
@@ -92,6 +92,7 @@ def generate_codes(self, autoencoder : MeshAutoencoder):
             ) 
             item['codes'] = codes  
  
+        self.sort_dataset_keys()
         print(f"[MeshDataset] Generated codes for {len(self.data)} entrys")
     
     def embed_texts(self, transformer : MeshTransformer): 
@@ -102,6 +103,7 @@ def embed_texts(self, transformer : MeshTransformer):
         text_embedding_dict = dict(zip(unique_texts, text_embeddings))
  
         for item in self.data:
-            text_value = item['texts']
-            item['text_embeds'] = text_embedding_dict.get(text_value, None)
-            del item['texts']
\ No newline at end of file
+            if 'texts' in item:  
+                item['text_embeds'] = text_embedding_dict.get(item['texts'], None)
+                del item['texts'] 
+        self.sort_dataset_keys()
\ No newline at end of file

From 8364938b4ccfbd60d5950556f36fee15ebae1abd Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Fri, 5 Jan 2024 20:30:31 +0100
Subject: [PATCH 42/84] demo, missing csv import

---
 MeshGPT_demo.ipynb | 474 ++++++++++++++++++---------------------------
 1 file changed, 184 insertions(+), 290 deletions(-)

diff --git a/MeshGPT_demo.ipynb b/MeshGPT_demo.ipynb
index ca2a016d..63625165 100644
--- a/MeshGPT_demo.ipynb
+++ b/MeshGPT_demo.ipynb
@@ -1,33 +1,26 @@
 {
-   "cells":[
+   "cells": [
       {
-         "cell_type":"code",
-         "execution_count":null,
-         "metadata":{
-            
-         },
-         "outputs":[
-            
-         ],
-         "source":[
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
             "!pip install  git+https://github.com/MarcusLoppe/classifier-free-guidance-pytorch.git\n",
             "!pip install  -q git+https://github.com/MarcusLoppe/meshgpt-pytorch.git"
          ]
       },
       {
-         "cell_type":"code",
-         "execution_count":null,
-         "metadata":{
-            
-         },
-         "outputs":[
-            
-         ],
-         "source":[
+         "cell_type": "code",
+         "execution_count": 11,
+         "metadata": {},
+         "outputs": [],
+         "source": [
             "import torch\n",
             "import trimesh\n",
             "import numpy as np\n",
             "import os\n",
+            "import csv\n",
             "from collections import OrderedDict\n",
             "\n",
             "from meshgpt_pytorch import (\n",
@@ -106,21 +99,26 @@
             "    \n",
             "    for category in os.listdir(directory): \n",
             "        category_path = os.path.join(directory, category)   \n",
-            "         \n",
+            "        if os.path.isdir(category_path) == False:\n",
+            "            continue \n",
             "        num_files_in_category = len(os.listdir(category_path))\n",
             "        print(f\"{category_path} got {num_files_in_category} files\") \n",
             "        chosen_models_count[category] = 0  \n",
             "        \n",
-            "        for filename in os.listdir(category_path)[:per_category]:\n",
+            "        for filename in os.listdir(category_path):\n",
             "            if filename.endswith((\".obj\", \".glb\", \".off\")):\n",
             "                file_path = os.path.join(category_path, filename)\n",
             "                \n",
+            "                if chosen_models_count[category] >= per_category:\n",
+            "                    break\n",
+            "                \n",
             "                if os.path.getsize(file_path) >  20 * 1024: # 20 kb limit = less then 400-600 faces\n",
             "                    continue\n",
             "                    \n",
             "                if filename[:-4] not in id_info:\n",
             "                    print(\"Unable to find id info for \", filename)\n",
             "                    continue \n",
+            "                \n",
             "                chosen_models_count[category] += 1\n",
             "                vertices, faces = get_mesh(file_path)   \n",
             "                textName =  id_info[filename[:-4]]  \n",
@@ -169,15 +167,11 @@
          ]
       },
       {
-         "cell_type":"code",
-         "execution_count":null,
-         "metadata":{
-            
-         },
-         "outputs":[
-            
-         ],
-         "source":[
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
             "from pathlib import Path \n",
             "import gc    \n",
             "import torch\n",
@@ -198,8 +192,7 @@
             "\n",
             "\n",
             "if not os.path.isfile(dataset_path):\n",
-            "    data = load_filename(\"./demo_mesh\",20)\n",
-            "      \n",
+            "    data = load_filename(\"./demo_mesh\",20)  \n",
             "    dataset = MeshDataset(data) \n",
             "    dataset.generate_face_edges() \n",
             "    print(set(item[\"texts\"] for item in dataset.data)  ) \n",
@@ -211,24 +204,18 @@
          ]
       },
       {
-         "cell_type":"markdown",
-         "metadata":{
-            
-         },
-         "source":[
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
             "### Inspect"
          ]
       },
       {
-         "cell_type":"code",
-         "execution_count":null,
-         "metadata":{
-            
-         },
-         "outputs":[
-            
-         ],
-         "source":[
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
             "from pathlib import Path\n",
             " \n",
             "folder = working_dir / f'renders' \n",
@@ -261,69 +248,51 @@
          ]
       },
       {
-         "cell_type":"markdown",
-         "metadata":{
-            
-         },
-         "source":[
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
             "### Train!"
          ]
       },
       {
-         "cell_type":"code",
-         "execution_count":null,
-         "metadata":{
-            
-         },
-         "outputs":[
-            
-         ],
-         "source":[
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
             "autoencoder = MeshAutoencoder().to(\"cuda\")      "
          ]
       },
       {
-         "cell_type":"markdown",
-         "metadata":{
-            
-         },
-         "source":[
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
             "**Have at least 400-2000 items in the dataset, use this to multiply the dataset**  "
          ]
       },
       {
-         "cell_type":"code",
-         "execution_count":null,
-         "metadata":{
-            
-         },
-         "outputs":[
-            
-         ],
-         "source":[
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
             "dataset.data = [dict(d) for d in dataset.data] * 10\n",
             "print(len(dataset.data))"
          ]
       },
       {
-         "cell_type":"markdown",
-         "metadata":{
-            
-         },
-         "source":[
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
             "*Load previous saved model if you had to restart session*"
          ]
       },
       {
-         "cell_type":"code",
-         "execution_count":null,
-         "metadata":{
-            
-         },
-         "outputs":[
-            
-         ],
-         "source":[
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
             "autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder ,warmup_steps = 10, dataset = dataset, num_train_steps=100, batch_size=8,  grad_accum_every=1, learning_rate = 1e-4) \n",
             "autoencoder_trainer.load(f'{working_dir}\\mesh-encoder_{project_name}.pt')   \n",
             "autencoder = autoencoder_trainer.model\n",
@@ -332,24 +301,18 @@
          ]
       },
       {
-         "cell_type":"markdown",
-         "metadata":{
-            
-         },
-         "source":[
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
             "**Train to about 0.3 loss if you are using a small dataset**"
          ]
       },
       {
-         "cell_type":"code",
-         "execution_count":null,
-         "metadata":{
-            
-         },
-         "outputs":[
-            
-         ],
-         "source":[
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
             "\n",
             "autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder ,warmup_steps = 10, dataset = dataset, num_train_steps=100,\n",
             "                                             batch_size=8,\n",
@@ -365,28 +328,20 @@
          ]
       },
       {
-         "cell_type":"code",
-         "execution_count":null,
-         "metadata":{
-            
-         },
-         "outputs":[
-            
-         ],
-         "source":[
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
             "autoencoder_trainer.save(f'{working_dir}\\mesh-encoder_{project_name}.pt')   "
          ]
       },
       {
-         "cell_type":"code",
-         "execution_count":null,
-         "metadata":{
-            
-         },
-         "outputs":[
-            
-         ],
-         "source":[
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
             "import gc  \n",
             "torch.cuda.empty_cache()\n",
             "gc.collect()  \n",
@@ -418,11 +373,9 @@
          ]
       },
       {
-         "cell_type":"markdown",
-         "metadata":{
-            
-         },
-         "source":[
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
             "## **Required!**, embed the text and run generate_codes to save 4-96 GB VRAM (dependant on dataset) ##\n",
             "\n",
             "**If you don't;** <br>\n",
@@ -434,15 +387,11 @@
          ]
       },
       {
-         "cell_type":"code",
-         "execution_count":null,
-         "metadata":{
-            
-         },
-         "outputs":[
-            
-         ],
-         "source":[
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
             "labels = set(item[\"texts\"] for item in dataset.data)\n",
             "print(labels)\n",
             "dataset.embed_texts(transformer)\n",
@@ -451,48 +400,36 @@
          ]
       },
       {
-         "cell_type":"markdown",
-         "metadata":{
-            
-         },
-         "source":[
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
             "*Load previous saved model if you had to restart session*"
          ]
       },
       {
-         "cell_type":"code",
-         "execution_count":null,
-         "metadata":{
-            
-         },
-         "outputs":[
-            
-         ],
-         "source":[
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
             "trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100, dataset = dataset, learning_rate = 1e-1, batch_size=2)\n",
             "trainer.load(f'{working_dir}\\mesh-transformer_{project_name}.pt')  \n",
             "transformer = trainer.model"
          ]
       },
       {
-         "cell_type":"markdown",
-         "metadata":{
-            
-         },
-         "source":[
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
             "**Train to about 0.0001 loss (or less) if you are using a small dataset**"
          ]
       },
       {
-         "cell_type":"code",
-         "execution_count":null,
-         "metadata":{
-            
-         },
-         "outputs":[
-            
-         ],
-         "source":[
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
             "trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=4,num_train_steps=100, dataset = dataset,\n",
             "                                 learning_rate = 1e-3, batch_size=2)  \n",
             "loss = trainer.train(100, stop_at_loss = 0.009) \n",
@@ -503,38 +440,28 @@
          ]
       },
       {
-         "cell_type":"code",
-         "execution_count":null,
-         "metadata":{
-            
-         },
-         "outputs":[
-            
-         ],
-         "source":[
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
             " \n",
             "trainer.save(f'{working_dir}\\mesh-transformer_{project_name}.pt')   "
          ]
       },
       {
-         "cell_type":"markdown",
-         "metadata":{
-            
-         },
-         "source":[
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
             "## Generate and view mesh"
          ]
       },
       {
-         "cell_type":"code",
-         "execution_count":null,
-         "metadata":{
-            
-         },
-         "outputs":[
-            
-         ],
-         "source":[
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
             "def combind_mesh(path, mesh):\n",
             "    all_vertices = []\n",
             "    all_faces = []\n",
@@ -599,24 +526,18 @@
          ]
       },
       {
-         "cell_type":"markdown",
-         "metadata":{
-            
-         },
-         "source":[
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
             "**Using only text**"
          ]
       },
       {
-         "cell_type":"code",
-         "execution_count":null,
-         "metadata":{
-            
-         },
-         "outputs":[
-            
-         ],
-         "source":[
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
             " \n",
             "from pathlib import Path\n",
             " \n",
@@ -637,56 +558,42 @@
          ]
       },
       {
-         "cell_type":"markdown",
-         "metadata":{
-            
-         },
-         "source":[
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
             "**Text + prompt of tokens**"
          ]
       },
       {
-         "cell_type":"markdown",
-         "metadata":{
-            
-         },
-         "source":[
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
             "Grab fresh copy of dataset"
          ]
       },
       {
-         "cell_type":"code",
-         "execution_count":null,
-         "metadata":{
-            
-         },
-         "outputs":[
-            
-         ],
-         "source":[
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
             "dataset = MeshDataset.load(dataset_path)\n",
             "dataset.generate_codes(autoencoder)"
          ]
       },
       {
-         "cell_type":"markdown",
-         "metadata":{
-            
-         },
-         "source":[
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
             "**Prompt with 10% of codes/tokens**"
          ]
       },
       {
-         "cell_type":"code",
-         "execution_count":null,
-         "metadata":{
-            
-         },
-         "outputs":[
-            
-         ],
-         "source":[
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
             "from pathlib import Path\n",
             "token_length_procent = 0.10 \n",
             "codes = []\n",
@@ -726,24 +633,18 @@
          ]
       },
       {
-         "cell_type":"markdown",
-         "metadata":{
-            
-         },
-         "source":[
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
             "**Prompt with 0% to 80% of tokens**"
          ]
       },
       {
-         "cell_type":"code",
-         "execution_count":null,
-         "metadata":{
-            
-         },
-         "outputs":[
-            
-         ],
-         "source":[
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
             "from pathlib import Path\n",
             " \n",
             "folder = working_dir / f'renders/text+codes_rows'\n",
@@ -783,24 +684,18 @@
          ]
       },
       {
-         "cell_type":"markdown",
-         "metadata":{
-            
-         },
-         "source":[
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
             "**Just some testing for text embedding similarity**"
          ]
       },
       {
-         "cell_type":"code",
-         "execution_count":null,
-         "metadata":{
-            
-         },
-         "outputs":[
-            
-         ],
-         "source":[
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
             "import numpy as np \n",
             "texts = list(labels)\n",
             "vectors = [transformer.conditioner.text_models[0].embed_text([text], return_text_encodings = False).cpu().flatten() for text in texts]\n",
@@ -832,35 +727,34 @@
          ]
       }
    ],
-   "metadata":{
-      "kaggle":{
-         "accelerator":"gpu",
-         "dataSources":[],
-		 
-         "dockerImageVersionId":30627,
-         "isGpuEnabled":true,
-         "isInternetEnabled":true,
-         "language":"python",
-         "sourceType":"notebook"
-      },
-      "kernelspec":{
-         "display_name":"Python 3",
-         "language":"python",
-         "name":"python3"
-      },
-      "language_info":{
-         "codemirror_mode":{
-            "name":"ipython",
-            "version":3
-         },
-         "file_extension":".py",
-         "mimetype":"text/x-python",
-         "name":"python",
-         "nbconvert_exporter":"python",
-         "pygments_lexer":"ipython3",
-         "version":"3.11.5"
+   "metadata": {
+      "kaggle": {
+         "accelerator": "gpu",
+         "dataSources": [],
+         "dockerImageVersionId": 30627,
+         "isGpuEnabled": true,
+         "isInternetEnabled": true,
+         "language": "python",
+         "sourceType": "notebook"
+      },
+      "kernelspec": {
+         "display_name": "Python 3",
+         "language": "python",
+         "name": "python3"
+      },
+      "language_info": {
+         "codemirror_mode": {
+            "name": "ipython",
+            "version": 3
+         },
+         "file_extension": ".py",
+         "mimetype": "text/x-python",
+         "name": "python",
+         "nbconvert_exporter": "python",
+         "pygments_lexer": "ipython3",
+         "version": "3.11.5"
       }
    },
-   "nbformat":4,
-   "nbformat_minor":4
-}
\ No newline at end of file
+   "nbformat": 4,
+   "nbformat_minor": 4
+}

From bf9d0a7cefdd29d05557f34233f2a8ed94f19b37 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Sat, 6 Jan 2024 01:51:55 +0100
Subject: [PATCH 43/84] demo comments

---
 MeshGPT_demo.ipynb | 32 +++++++++++++++++---------------
 1 file changed, 17 insertions(+), 15 deletions(-)

diff --git a/MeshGPT_demo.ipynb b/MeshGPT_demo.ipynb
index 63625165..971ef28b 100644
--- a/MeshGPT_demo.ipynb
+++ b/MeshGPT_demo.ipynb
@@ -12,7 +12,7 @@
       },
       {
          "cell_type": "code",
-         "execution_count": 11,
+         "execution_count": 33,
          "metadata": {},
          "outputs": [],
          "source": [
@@ -38,13 +38,15 @@
             "     \n",
             "    vertices = mesh.vertices.tolist()\n",
             "    faces = mesh.faces.tolist()\n",
+            "    # Center\n",
             "    centered_vertices = vertices - np.mean(vertices, axis=0)\n",
-            " \n",
+            "    # Limit vertices to [-0.95, 0.95]\n",
             "    max_abs = np.max(np.abs(centered_vertices))\n",
             "    vertices = centered_vertices / (max_abs / 0.95)  \n",
-            "       \n",
+            "    \n",
+            "    # Sort by Z, Y,X where Z is vertical    \n",
             "    def sort_vertices(vertex):\n",
-            "        return vertex[2], vertex[0], vertex[1]   \n",
+            "        return vertex[2], vertex[1], vertex[0]   \n",
             " \n",
             "    seen = OrderedDict()\n",
             "    for point in vertices: \n",
@@ -54,15 +56,15 @@
             "        \n",
             "    unique_vertices =  list(seen.values()) \n",
             "    sorted_vertices = sorted(unique_vertices, key=sort_vertices)\n",
-            "     \n",
+            "      \n",
             "    vertices_as_tuples = [tuple(v) for v in vertices]\n",
             "    sorted_vertices_as_tuples = [tuple(v) for v in sorted_vertices]\n",
             "\n",
-            "    vertex_map = {old_index: new_index for old_index, vertex_tuple in enumerate(vertices_as_tuples) for new_index, sorted_vertex_tuple in enumerate(sorted_vertices_as_tuples) if vertex_tuple == sorted_vertex_tuple}\n",
-            " \n",
-            "\n",
-            "    reindexed_faces = [[vertex_map[face[0]], vertex_map[face[1]], vertex_map[face[2]]] for face in faces] \n",
-            "    return np.array(sorted_vertices), np.array(reindexed_faces)\n",
+            "    vertex_map = {old_index: new_index for old_index, vertex_tuple in enumerate(vertices_as_tuples) for new_index, sorted_vertex_tuple in enumerate(sorted_vertices_as_tuples) if vertex_tuple == sorted_vertex_tuple} \n",
+            "    reindexed_faces = [[vertex_map[face[0]], vertex_map[face[1]], vertex_map[face[2]]] for face in faces]   \n",
+            "    sorted_faces = [sorted(sub_arr) for sub_arr in reindexed_faces]  \n",
+            "    \n",
+            "    return np.array(sorted_vertices), np.array(sorted_faces)\n",
             " \n",
             " \n",
             "\n",
@@ -182,7 +184,7 @@
             "gc.collect()  \n",
             "\n",
             " \n",
-            "project_name = \"polygen_demo\" \n",
+            "project_name = \"demo_mesh\" \n",
             "\n",
             "working_dir = f'.\\{project_name}'\n",
             "\n",
@@ -192,7 +194,7 @@
             "\n",
             "\n",
             "if not os.path.isfile(dataset_path):\n",
-            "    data = load_filename(\"./demo_mesh\",20)  \n",
+            "    data = load_filename(\"./demo_mesh\",10)  \n",
             "    dataset = MeshDataset(data) \n",
             "    dataset.generate_face_edges() \n",
             "    print(set(item[\"texts\"] for item in dataset.data)  ) \n",
@@ -354,13 +356,13 @@
             "transformer = MeshTransformer(\n",
             "    autoencoder,\n",
             "    dim = 512,\n",
-            "    coarse_pre_gateloop_depth = 6,\n",
+            "    coarse_pre_gateloop_depth = 6, # Better performance using more gateloop layers\n",
             "    fine_pre_gateloop_depth= 4, \n",
-            "   # attn_depth = 24,\n",
+            "    #attn_depth = 24, # GPT-2 medium have 24 layer depth, change if needed\n",
             "    max_seq_len = max_seq, \n",
             "    condition_on_text = True, \n",
             "    gateloop_use_heinsen = False,\n",
-            "    text_condition_model_types = \"t5\", ## Change or remove this line if you are using:  https://github.com/MarcusLoppe/classifier-free-guidance-pytorch\n",
+            "    text_condition_model_types = \"bge\", ## Change or remove this line if you are using:  https://github.com/MarcusLoppe/classifier-free-guidance-pytorch\n",
             "    text_condition_cond_drop_prob = 0.0\n",
             ") \n",
             "\n",

From c5e2c1405b264168d04e41e8f2ac63e2b6e6ecef Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Thu, 15 Feb 2024 01:31:55 +0100
Subject: [PATCH 44/84] shuffle as arg

---
 demo_mesh/bar chair.glb             | Bin 0 -> 11076 bytes
 demo_mesh/circle chair.glb          | Bin 0 -> 10204 bytes
 demo_mesh/cone.obj                  |  66 ---------------
 demo_mesh/corner table.glb          | Bin 0 -> 9780 bytes
 demo_mesh/cube.obj                  |  14 ----
 demo_mesh/cylinder.obj              |  98 ----------------------
 demo_mesh/designer chair.glb        | Bin 0 -> 10804 bytes
 demo_mesh/designer sloped chair.glb | Bin 0 -> 11832 bytes
 demo_mesh/glass table.glb           | Bin 0 -> 9916 bytes
 demo_mesh/high chair.glb            | Bin 0 -> 10200 bytes
 demo_mesh/icosphere.obj             | 122 ----------------------------
 demo_mesh/office table.glb          | Bin 0 -> 10368 bytes
 demo_mesh/tv table.glb              | Bin 0 -> 11432 bytes
 meshgpt_pytorch/trainer.py          |   6 +-
 14 files changed, 4 insertions(+), 302 deletions(-)
 create mode 100644 demo_mesh/bar chair.glb
 create mode 100644 demo_mesh/circle chair.glb
 delete mode 100644 demo_mesh/cone.obj
 create mode 100644 demo_mesh/corner table.glb
 delete mode 100644 demo_mesh/cube.obj
 delete mode 100644 demo_mesh/cylinder.obj
 create mode 100644 demo_mesh/designer chair.glb
 create mode 100644 demo_mesh/designer sloped chair.glb
 create mode 100644 demo_mesh/glass table.glb
 create mode 100644 demo_mesh/high chair.glb
 delete mode 100644 demo_mesh/icosphere.obj
 create mode 100644 demo_mesh/office table.glb
 create mode 100644 demo_mesh/tv table.glb

diff --git a/demo_mesh/bar chair.glb b/demo_mesh/bar chair.glb
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diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index cdf0874c..099e942f 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -75,6 +75,7 @@ def __init__(
         grad_accum_every: int,
         val_dataset: Optional[Dataset] = None,
         val_every: int = 100,
+        shuffle: bool = True,
         val_num_batches: int = 5,
         learning_rate: float = 1e-4,
         weight_decay: float = 0.,
@@ -140,7 +141,7 @@ def __init__(
             self.val_dataloader = DataLoader(
                 val_dataset,
                 batch_size = batch_size,
-                shuffle = True,
+                shuffle = shuffle,
                 drop_last = True,
                 collate_fn = partial(custom_collate, pad_id = model.pad_id)
             )
@@ -431,6 +432,7 @@ def __init__(
         val_dataset: Optional[Dataset] = None,
         val_every = 1,
         val_num_batches = 5,
+        shuffle: bool = True,
         scheduler: Optional[Type[_LRScheduler]] = None,
         scheduler_kwargs: dict = dict(),
         ema_kwargs: dict = dict(),
@@ -480,7 +482,7 @@ def __init__(
         self.dataloader = DataLoader(
             dataset,
             batch_size = batch_size,
-            shuffle = True,
+            shuffle = shuffle,
             drop_last = True,
             collate_fn = partial(custom_collate, pad_id = model.pad_id)
         )

From 5b42b8de9811dcf9182acd42a678babaf4f48323 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Thu, 15 Feb 2024 01:32:52 +0100
Subject: [PATCH 45/84] shuffle as arg

---
 MeshGPT_demo.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/MeshGPT_demo.ipynb b/MeshGPT_demo.ipynb
index 971ef28b..144ec786 100644
--- a/MeshGPT_demo.ipynb
+++ b/MeshGPT_demo.ipynb
@@ -194,7 +194,7 @@
             "\n",
             "\n",
             "if not os.path.isfile(dataset_path):\n",
-            "    data = load_filename(\"./demo_mesh\",10)  \n",
+            "    data = load_filename(\"./demo_mesh\",50)  \n",
             "    dataset = MeshDataset(data) \n",
             "    dataset.generate_face_edges() \n",
             "    print(set(item[\"texts\"] for item in dataset.data)  ) \n",

From 6e65de48a4caf3ce126ec4212f65ba2b8382f3f5 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Thu, 15 Feb 2024 01:44:54 +0100
Subject: [PATCH 46/84] shuffle as arg

---
 meshgpt_pytorch/trainer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 099e942f..bf575bc9 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -125,7 +125,7 @@ def __init__(
         self.dataloader = DataLoader(
             dataset,
             batch_size = batch_size,
-            shuffle = True,
+            shuffle = shuffle,
             drop_last = True,
             collate_fn = partial(custom_collate, pad_id = model.pad_id)
         )
@@ -498,7 +498,7 @@ def __init__(
             self.val_dataloader = DataLoader(
                 val_dataset,
                 batch_size = batch_size,
-                shuffle = True,
+                shuffle = shuffle,
                 drop_last = True,
                 collate_fn = partial(custom_collate, pad_id = model.pad_id)
             )

From f8c087a5b54263629d097440722cc5b7705857cf Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Sat, 17 Feb 2024 04:29:02 +0100
Subject: [PATCH 47/84] sageconv fix

---
 meshgpt_pytorch/meshgpt_pytorch.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/meshgpt_pytorch/meshgpt_pytorch.py b/meshgpt_pytorch/meshgpt_pytorch.py
index deffbbea..7f96e25d 100644
--- a/meshgpt_pytorch/meshgpt_pytorch.py
+++ b/meshgpt_pytorch/meshgpt_pytorch.py
@@ -476,7 +476,7 @@ def __init__(
 
         # initial sage conv
 
-        sageconv_kwargs = {**sageconv_kwargs, 'sageconv_dropout' : sageconv_dropout}
+        sageconv_kwargs = {**sageconv_kwargs }
 
         init_encoder_dim, *encoder_dims_through_depth = encoder_dims_through_depth
         curr_dim = init_encoder_dim

From 60fb3122343e4519080d878e51f248bc26e29b10 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Mon, 19 Feb 2024 03:52:59 +0100
Subject: [PATCH 48/84] Optional Squeeze

---
 meshgpt_pytorch/meshgpt_pytorch.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/meshgpt_pytorch/meshgpt_pytorch.py b/meshgpt_pytorch/meshgpt_pytorch.py
index 7f96e25d..b8936f3d 100644
--- a/meshgpt_pytorch/meshgpt_pytorch.py
+++ b/meshgpt_pytorch/meshgpt_pytorch.py
@@ -293,6 +293,7 @@ def forward(self, x, mask = None):
 class ResnetBlock(Module):
     def __init__(
         self,
+        use_squeezeExcite,
         dim,
         dim_out = None,
         *,
@@ -300,10 +301,12 @@ def __init__(
         dropout = 0.
     ):
         super().__init__()
+        self.use_squeezeExcite = use_squeezeExcite
         dim_out = default(dim_out, dim)
         self.block1 = Block(dim, dim_out, groups = groups, dropout = dropout)
         self.block2 = Block(dim_out, dim_out, groups = groups, dropout = dropout)
-        self.excite = SqueezeExcite(dim_out)
+        if use_squeezeExcite:
+            self.excite = SqueezeExcite(dim_out)
         self.residual_conv = nn.Conv1d(dim, dim_out, 1) if dim != dim_out else nn.Identity()
 
     def forward(
@@ -314,7 +317,8 @@ def forward(
         res = self.residual_conv(x)
         h = self.block1(x, mask = mask)
         h = self.block2(h, mask = mask)
-        h = self.excite(h, mask = mask)
+        if self.use_squeezeExcite:
+            h = self.excite(h, mask = mask)
         return h + res
 
 # gateloop layers
@@ -424,7 +428,7 @@ def __init__(
         ),
         use_linear_attn = True,
         pad_id = -1,
-        flash_attn = True,
+        use_squeezeExcite = True,
         sageconv_dropout = 0.,
         attn_dropout = 0.,
         ff_dropout = 0.,
@@ -570,7 +574,7 @@ def __init__(
         self.decoders = ModuleList([])
 
         for dim_layer in decoder_dims_through_depth:
-            resnet_block = ResnetBlock(curr_dim, dim_layer, dropout = resnet_dropout)
+            resnet_block = ResnetBlock(use_squeezeExcite, curr_dim, dim_layer, dropout = resnet_dropout)
 
             self.decoders.append(resnet_block)
             curr_dim = dim_layer

From d35228edc46b550dc8faeefb832db7bd43a23c2a Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Thu, 29 Feb 2024 21:47:28 +0100
Subject: [PATCH 49/84] layernorm

---
 meshgpt_pytorch/meshgpt_pytorch.py | 30 +++++++++++++-----------------
 1 file changed, 13 insertions(+), 17 deletions(-)

diff --git a/meshgpt_pytorch/meshgpt_pytorch.py b/meshgpt_pytorch/meshgpt_pytorch.py
index b8936f3d..be5580b7 100644
--- a/meshgpt_pytorch/meshgpt_pytorch.py
+++ b/meshgpt_pytorch/meshgpt_pytorch.py
@@ -264,49 +264,47 @@ def __init__(
         self,
         dim,
         dim_out = None,
-        groups = 8,
         dropout = 0.
     ):
         super().__init__()
         dim_out = default(dim_out, dim)
 
         self.proj = nn.Conv1d(dim, dim_out, 3, padding = 1)
-        self.norm = nn.GroupNorm(groups, dim_out)
+        self.norm = nn.LayerNorm(dim_out)
         self.dropout = nn.Dropout(dropout)
         self.act = nn.SiLU()
 
     def forward(self, x, mask = None):
         if exists(mask):
-            x = x.masked_fill(~mask, 0.)
+            x = x.masked_fill(~mask, 0.0)
 
         x = self.proj(x)
 
         if exists(mask):
-            x = x.masked_fill(~mask, 0.)
+            x = x.masked_fill(~mask, 0.0)
 
+        x = rearrange(x, "b c n -> b n c")
         x = self.norm(x)
+        x = rearrange(x, "b n c -> b c n")
+
         x = self.act(x)
-        x = self.dropout(x)
 
+        x = self.dropout(x)
         return x
 
 class ResnetBlock(Module):
     def __init__(
         self,
-        use_squeezeExcite,
         dim,
         dim_out = None,
         *,
-        groups = 8,
         dropout = 0.
     ):
         super().__init__()
-        self.use_squeezeExcite = use_squeezeExcite
         dim_out = default(dim_out, dim)
-        self.block1 = Block(dim, dim_out, groups = groups, dropout = dropout)
-        self.block2 = Block(dim_out, dim_out, groups = groups, dropout = dropout)
-        if use_squeezeExcite:
-            self.excite = SqueezeExcite(dim_out)
+        self.block1 = Block(dim, dim_out, dropout = dropout)
+        self.block2 = Block(dim_out, dim_out,  dropout = dropout)
+        self.excite = SqueezeExcite(dim_out)
         self.residual_conv = nn.Conv1d(dim, dim_out, 1) if dim != dim_out else nn.Identity()
 
     def forward(
@@ -316,9 +314,8 @@ def forward(
     ):
         res = self.residual_conv(x)
         h = self.block1(x, mask = mask)
-        h = self.block2(h, mask = mask)
-        if self.use_squeezeExcite:
-            h = self.excite(h, mask = mask)
+        h = self.block2(h, mask = mask) 
+        h = self.excite(h, mask = mask)
         return h + res
 
 # gateloop layers
@@ -428,7 +425,6 @@ def __init__(
         ),
         use_linear_attn = True,
         pad_id = -1,
-        use_squeezeExcite = True,
         sageconv_dropout = 0.,
         attn_dropout = 0.,
         ff_dropout = 0.,
@@ -574,7 +570,7 @@ def __init__(
         self.decoders = ModuleList([])
 
         for dim_layer in decoder_dims_through_depth:
-            resnet_block = ResnetBlock(use_squeezeExcite, curr_dim, dim_layer, dropout = resnet_dropout)
+            resnet_block = ResnetBlock(curr_dim, dim_layer, dropout = resnet_dropout)
 
             self.decoders.append(resnet_block)
             curr_dim = dim_layer

From 1a8b0b1c0e667c8e19fb35ef36b5a427cee76197 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Fri, 8 Mar 2024 03:48:19 +0100
Subject: [PATCH 50/84] block-forward fix

---
 meshgpt_pytorch/meshgpt_pytorch.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/meshgpt_pytorch/meshgpt_pytorch.py b/meshgpt_pytorch/meshgpt_pytorch.py
index 555e1f15..b86a95c4 100644
--- a/meshgpt_pytorch/meshgpt_pytorch.py
+++ b/meshgpt_pytorch/meshgpt_pytorch.py
@@ -284,24 +284,23 @@ def __init__(
         self.dropout = nn.Dropout(dropout)
         self.act = nn.SiLU()
 
+
     def forward(self, x, mask = None):
         if exists(mask):
-            x = x.masked_fill(~mask, 0.0)
+            x = x.masked_fill(~mask, 0.)
 
         x = self.proj(x)
 
         if exists(mask):
-            x = x.masked_fill(~mask, 0.0)
+            x = x.masked_fill(~mask, 0.)
 
-        x = rearrange(x, "b c n -> b n c")
         x = self.norm(x)
-        x = rearrange(x, "b n c -> b c n")
-
         x = self.act(x)
-
         x = self.dropout(x)
+
         return x
 
+
 class ResnetBlock(Module):
     def __init__(
         self,

From 738a71c90b11489e8975824304a45777105132f2 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Fri, 8 Mar 2024 23:55:29 +0100
Subject: [PATCH 51/84] demo update

---
 MeshGPT_demo.ipynb | 200 ++++++++++++++++++++++++++++++++++++++++-----
 1 file changed, 178 insertions(+), 22 deletions(-)

diff --git a/MeshGPT_demo.ipynb b/MeshGPT_demo.ipynb
index 144ec786..ed948d8e 100644
--- a/MeshGPT_demo.ipynb
+++ b/MeshGPT_demo.ipynb
@@ -37,16 +37,30 @@
             "    mesh = trimesh.load(file_path, force='mesh')\n",
             "     \n",
             "    vertices = mesh.vertices.tolist()\n",
+            "    if \".off\" in file_path:  # ModelNet dataset\n",
+            "       mesh.vertices[:, [1, 2]] = mesh.vertices[:, [2, 1]]\n",
+            " \n",
+            "       rotation_matrix = trimesh.transformations.rotation_matrix(np.radians(-90), [0, 1, 0])\n",
+            "       mesh.apply_transform(rotation_matrix)\n",
+            "\n",
+            "        # Extract vertices and faces from the rotated mesh\n",
+            "       vertices = mesh.vertices.tolist()\n",
+            "            \n",
             "    faces = mesh.faces.tolist()\n",
             "    # Center\n",
             "    centered_vertices = vertices - np.mean(vertices, axis=0)\n",
             "    # Limit vertices to [-0.95, 0.95]\n",
             "    max_abs = np.max(np.abs(centered_vertices))\n",
-            "    vertices = centered_vertices / (max_abs / 0.95)  \n",
+            "    vertices = centered_vertices / (max_abs / 0.95)   \n",
+            "     \n",
+            "    # To ensure that the mesh models are on the \"ground\", I think this might help the models learn better if they always knows the ground plane\n",
+            "    # If you dont do this, the models will 'hover' in the air \n",
+            "    min_y = np.min(vertices[:, 1]) \n",
+            "    difference = -0.95 - min_y \n",
+            "    vertices[:, 1] += difference\n",
             "    \n",
-            "    # Sort by Z, Y,X where Z is vertical    \n",
             "    def sort_vertices(vertex):\n",
-            "        return vertex[2], vertex[1], vertex[0]   \n",
+            "        return vertex[1], vertex[2], vertex[0]   \n",
             " \n",
             "    seen = OrderedDict()\n",
             "    for point in vertices: \n",
@@ -61,7 +75,9 @@
             "    sorted_vertices_as_tuples = [tuple(v) for v in sorted_vertices]\n",
             "\n",
             "    vertex_map = {old_index: new_index for old_index, vertex_tuple in enumerate(vertices_as_tuples) for new_index, sorted_vertex_tuple in enumerate(sorted_vertices_as_tuples) if vertex_tuple == sorted_vertex_tuple} \n",
-            "    reindexed_faces = [[vertex_map[face[0]], vertex_map[face[1]], vertex_map[face[2]]] for face in faces]   \n",
+            "    reindexed_faces = [[vertex_map[face[0]], vertex_map[face[1]], vertex_map[face[2]]] for face in faces]\n",
+            "    # Code for cyclically permuted faces, used in paper (not recommended if you are using datasets from many sources)    \n",
+            "    #sorted_faces = [sub_arr[sub_arr.index(min(sub_arr)):] + sub_arr[:sub_arr.index(min(sub_arr))] for sub_arr in reindexed_faces]\n",
             "    sorted_faces = [sorted(sub_arr) for sub_arr in reindexed_faces]  \n",
             "    \n",
             "    return np.array(sorted_vertices), np.array(sorted_faces)\n",
@@ -74,7 +90,13 @@
             "    offsets = np.random.choice(possible_values, size=vertices.shape) \n",
             "    vertices = vertices + offsets   \n",
             "    \n",
-            "    vertices = vertices * scale_factor \n",
+            "    vertices = vertices * scale_factor\n",
+            "    \n",
+            "    # To ensure that the mesh models are on the \"ground\", I think this might help the models learn better if they always knows the ground plane\n",
+            "    # If you dont do this, the models will 'hover' in the air \n",
+            "    min_y = np.min(vertices[:, 1])  \n",
+            "    difference = -0.95 - min_y \n",
+            "    vertices[:, 1] += difference\n",
             "    return vertices\n",
             "\n",
             "\n",
@@ -120,17 +142,18 @@
             "                if filename[:-4] not in id_info:\n",
             "                    print(\"Unable to find id info for \", filename)\n",
             "                    continue \n",
-            "                \n",
-            "                chosen_models_count[category] += 1\n",
-            "                vertices, faces = get_mesh(file_path)   \n",
-            "                textName =  id_info[filename[:-4]]  \n",
-            "                \n",
+            "                vertices, faces = get_mesh(file_path) \n",
             "                if len(faces) > 800: \n",
             "                    continue\n",
             "                \n",
+            "                chosen_models_count[category] += 1  \n",
+            "                textName = id_info[filename[:-4]]  \n",
+            "                \n",
+            "                \n",
+            "                face_edges =  derive_face_edges_from_faces(faces)  \n",
             "                for scale_factor in scale_factors: \n",
-            "                    aug_vertices = augment_mesh(vertices.copy(), scale_factor)  \n",
-            "                    obj_data = {\"vertices\": torch.tensor(aug_vertices.tolist(), dtype=torch.float).to(\"cuda\"), \"faces\":  torch.tensor(faces.tolist(), dtype=torch.long).to(\"cuda\"),\"texts\": textName } \n",
+            "                    aug_vertices = augment_mesh(vertices.copy(), scale_factor)   \n",
+            "                    obj_data = {\"vertices\": torch.tensor(aug_vertices.tolist(), dtype=torch.float).to(\"cuda\"), \"faces\":  torch.tensor(faces.tolist(), dtype=torch.long).to(\"cuda\"), \"face_edges\" : face_edges, \"texts\": textName }  \n",
             "                    obj_datas.append(obj_data)\n",
             "                    \n",
             "    print(\"=\"*25)\n",
@@ -145,8 +168,7 @@
             "  \n",
             "   \n",
             "def load_filename(directory, variations):\n",
-            "    obj_datas = []   \n",
-            "    chosen_models_count = {}      \n",
+            "    obj_datas = []    \n",
             "    possible_values = np.arange(0.75, 1.0 , 0.005) \n",
             "    scale_factors = np.random.choice(possible_values, size=variations) \n",
             "    \n",
@@ -168,6 +190,56 @@
             "    return obj_datas"
          ]
       },
+      {
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
+            "import gzip,json\n",
+            "from tqdm import tqdm\n",
+            "\n",
+            "def load_objverse(directory, variations ):\n",
+            "    obj_datas = []     \n",
+            "    id_info = {}   \n",
+            "    with open('./objaverse/metadata.json' , 'r') as f:\n",
+            "        id_info = json.load(f) \n",
+            "        \n",
+            "    possible_values = np.arange(0.75, 1.0) \n",
+            "    scale_factors = np.random.choice(possible_values, size=variations) \n",
+            "    \n",
+            "    for folder in os.listdir(directory):  \n",
+            "        full_folder_path = os.path.join(directory, folder)   \n",
+            "        if os.path.isdir(full_folder_path) == False:\n",
+            "            continue    \n",
+            "         \n",
+            "        for filename in tqdm(os.listdir(full_folder_path)):  \n",
+            "            if filename.endswith((\".obj\", \".glb\", \".off\")):\n",
+            "                file_path = os.path.join(full_folder_path, filename)\n",
+            "                kb = os.path.getsize(file_path)  / 1024 \n",
+            "                if kb < 1 or kb > 30:\n",
+            "                    continue\n",
+            "                  \n",
+            "                if filename[:-4] not in id_info: \n",
+            "                    continue   \n",
+            "                textName =  id_info[filename[:-4]]['name']\n",
+            "                try:    \n",
+            "                    vertices, faces = get_mesh(file_path)   \n",
+            "                except Exception as e:\n",
+            "                    continue\n",
+            "                \n",
+            "                if len(faces) > 250 or len(faces) < 50: \n",
+            "                    continue\n",
+            "                \n",
+            "                faces = torch.tensor(faces.tolist(), dtype=torch.long).to(\"cuda\")\n",
+            "                face_edges = derive_face_edges_from_faces(faces)   \n",
+            "                for scale_factor in scale_factors: \n",
+            "                    aug_vertices = augment_mesh(vertices.copy(), scale_factor)   \n",
+            "                    obj_data = {\"filename\": filename, \"vertices\": torch.tensor(aug_vertices.tolist(), dtype=torch.float).to(\"cuda\"), \"faces\":  faces, \"face_edges\" : face_edges, \"texts\": textName }   \n",
+            "                    obj_datas.append(obj_data)  \n",
+            "    return obj_datas"
+         ]
+      },
       {
          "cell_type": "code",
          "execution_count": null,
@@ -175,14 +247,9 @@
          "outputs": [],
          "source": [
             "from pathlib import Path \n",
-            "import gc    \n",
-            "import torch\n",
+            "import gc     \n",
             "import os\n",
-            "from meshgpt_pytorch import MeshDataset\n",
-            "\n",
-            "torch.cuda.empty_cache()\n",
-            "gc.collect()  \n",
-            "\n",
+            "from meshgpt_pytorch import MeshDataset \n",
             " \n",
             "project_name = \"demo_mesh\" \n",
             "\n",
@@ -205,6 +272,75 @@
             " "
          ]
       },
+      {
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "#### Objaverse, instructions to download meshes: https://github.com/MarcusLoppe/Objaverse-downloader/tree/main"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
+            "from pathlib import Path \n",
+            "import gc    \n",
+            "import torch\n",
+            "import os\n",
+            "from meshgpt_pytorch import MeshDataset \n",
+            "\n",
+            "project_name = \"objaverse_dataset\"  \n",
+            "\n",
+            "working_dir = f'./datasets/{project_name}' \n",
+            "working_dir = Path(working_dir)\n",
+            "working_dir.mkdir(exist_ok = True, parents = True)\n",
+            "dataset_path = working_dir / (project_name + \".npz\")\n",
+            "\n",
+            "\n",
+            "if not os.path.isfile(dataset_path):\n",
+            "    data = load_objverse(\"./objaverse\",10)  \n",
+            "    objverse_dataset = MeshDataset(data) \n",
+            "    objverse_dataset.generate_face_edges() \n",
+            "    print(set(item[\"texts\"] for item in objverse_dataset.data)  ) \n",
+            "    objverse_dataset.save(dataset_path)\n",
+            " \n",
+            "objverse_dataset = MeshDataset.load(dataset_path) \n",
+            "print(objverse_dataset.data[0].keys()) "
+         ]
+      },
+      {
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "### Combind datasets"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
+            "from pathlib import Path \n",
+            "import gc    \n",
+            "import torch \n",
+            "from meshgpt_pytorch import MeshDataset\n",
+            "torch.cuda.empty_cache()\n",
+            "gc.collect()  \n",
+            " \n",
+            "project_name = \"objverse_shapenet_combined\" \n",
+            "\n",
+            "working_dir = f'./datasets/{project_name}' \n",
+            "working_dir = Path(working_dir)\n",
+            "working_dir.mkdir(exist_ok = True, parents = True)\n",
+            "dataset_path = working_dir / (project_name + \".npz\")\n",
+            " \n",
+            "objverse_dataset.data.extend(dataset.data) \n",
+            "objverse_dataset.save(dataset_path)    "
+         ]
+      },
       {
          "cell_type": "markdown",
          "metadata": {},
@@ -262,7 +398,27 @@
          "metadata": {},
          "outputs": [],
          "source": [
-            "autoencoder = MeshAutoencoder().to(\"cuda\")      "
+            "num_layers = 23 \n",
+            "autoencoder = MeshAutoencoder(     \n",
+            "        decoder_dims_through_depth =  (128,) * 3 + (192,) * 4 + (256,) * num_layers + (384,) * 3,  \n",
+            "        encoder_dims_through_depth =  (64,) * 2 + (128,) *3 + (256,) *4 + (576,) * 1,  \n",
+            "        dim_codebook = 192 , \n",
+            "        codebook_size = 16384 , \n",
+            "        dim_area_embed = 16,\n",
+            "        dim_coor_embed = 16, \n",
+            "        dim_normal_embed = 16,\n",
+            "        dim_angle_embed = 8\n",
+            "    ).to(\"cuda\")    \n",
+            "\n",
+            "total_params = sum(p.numel() for p in autoencoder.decoders.parameters()) \n",
+            "total_params = f\"{total_params / 1000000:.1f}M\"\n",
+            "print(f\"decoder Total parameters: {total_params}\") \n",
+            "total_params = sum(p.numel() for p in autoencoder.encoders.parameters()) \n",
+            "total_params = f\"{total_params / 1000000:.1f}M\"\n",
+            "print(f\"encoders Total parameters: {total_params}\")   \n",
+            "total_params = sum(p.numel() for p in autoencoder.parameters()) \n",
+            "total_params = f\"{total_params / 1000000:.1f}M\"\n",
+            "print(f\"Total parameters: {total_params}\")"
          ]
       },
       {

From 0201134f2680381987dee411efc45a54da4bd38e Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Sat, 9 Mar 2024 00:03:16 +0100
Subject: [PATCH 52/84] labels

---
 MeshGPT_demo.ipynb   | 23 ++++++++---------------
 shapenet_labels.json |  1 +
 2 files changed, 9 insertions(+), 15 deletions(-)
 create mode 100644 shapenet_labels.json

diff --git a/MeshGPT_demo.ipynb b/MeshGPT_demo.ipynb
index ed948d8e..6a0d3a6d 100644
--- a/MeshGPT_demo.ipynb
+++ b/MeshGPT_demo.ipynb
@@ -6,7 +6,6 @@
          "metadata": {},
          "outputs": [],
          "source": [
-            "!pip install  git+https://github.com/MarcusLoppe/classifier-free-guidance-pytorch.git\n",
             "!pip install  -q git+https://github.com/MarcusLoppe/meshgpt-pytorch.git"
          ]
       },
@@ -21,6 +20,7 @@
             "import numpy as np\n",
             "import os\n",
             "import csv\n",
+            "import json\n",
             "from collections import OrderedDict\n",
             "\n",
             "from meshgpt_pytorch import (\n",
@@ -102,21 +102,13 @@
             "\n",
             "#load_shapenet(\"./shapenet\", \"./shapenet_csv_files\", 10, 10)   \n",
             "#Find the csv files with the labels in the ShapeNetCore.v1.zip, download at  https://huggingface.co/datasets/ShapeNet/ShapeNetCore-archive  \n",
-            "def load_shapenet(directory, csv_files, per_category, variations ):\n",
+            "def load_shapenet(directory, per_category, variations ):\n",
             "    obj_datas = []   \n",
-            "    chosen_models_count = {}   \n",
-            "    id_info = {} \n",
-            "    print(f\"per_category: {per_category} min {min}\")\n",
-            "     \n",
-            "    for csv_filename in os.listdir(csv_files):\n",
-            "        if csv_filename.endswith(\".csv\"): \n",
-            "            csv_path = os.path.join(csv_files, csv_filename) \n",
-            "            with open(csv_path, newline='', encoding=\"utf-8\") as csvfile:\n",
-            "                csv_reader = csv.DictReader(csvfile)\n",
-            "                for row in csv_reader: \n",
-            "                    full_id = row['fullId'].replace(\"3dw.\", \"\")\n",
-            "                    wn_lemmas = row['wnlemmas'].replace(\",\", \" \")\n",
-            "                    id_info[full_id] = wn_lemmas \n",
+            "    chosen_models_count = {}    \n",
+            "    print(f\"per_category: {per_category} variations {variations}\")\n",
+            "    \n",
+            "    with open('shapenet_labels.json' , 'r') as f:\n",
+            "        id_info = json.load(f) \n",
             "    \n",
             "    possible_values = np.arange(0.75, 1.0 , 0.005) \n",
             "    scale_factors = np.random.choice(possible_values, size=variations) \n",
@@ -125,6 +117,7 @@
             "        category_path = os.path.join(directory, category)   \n",
             "        if os.path.isdir(category_path) == False:\n",
             "            continue \n",
+            "        \n",
             "        num_files_in_category = len(os.listdir(category_path))\n",
             "        print(f\"{category_path} got {num_files_in_category} files\") \n",
             "        chosen_models_count[category] = 0  \n",
diff --git a/shapenet_labels.json b/shapenet_labels.json
new file mode 100644
index 00000000..0512434d
--- /dev/null
+++ b/shapenet_labels.json
@@ -0,0 +1 @@
+{"a4678e6798e768c3b6a66ea321171690": "biplane propeller plane airplane aeroplane plane", "d8a43017132c210cc1006ed55bc1a3fc": "biplane propeller plane airplane aeroplane plane", "af55f398af2373aa18b14db3b83de9ff": "biplane airplane aeroplane plane", "8de793e2e964f40a26c713777861983a": "biplane propeller plane airplane aeroplane plane fighter fighter aircraft attack aircraft", "17c86b46990b54b65578b8865797aa0": "biplane propeller plane airplane aeroplane plane", "67eac921334c130c336fa8434b94be14": "biplane propeller plane airplane aeroplane plane", "c353ad7ae6ad2b9df1cb19f636b1c2bd": "biplane propeller plane airplane aeroplane plane", "2d01483c696c0a1688be2a30dd556a09": "biplane propeller plane airplane aeroplane plane", "5f9b4ffc555c9915a3451bc89763f63c": "biplane propeller plane airplane aeroplane plane", "b2bb5a56b3d805b298b8c800ae001b66": "biplane propeller plane airplane aeroplane plane", "445404a75a0ba2ab1fc43d5b32fa230f": "delta wing airplane aeroplane plane", "5a37bc42a52130a18f52dc705c3109b9": "delta wing fighter fighter aircraft attack aircraft bomber", "69a46dafabb616191f9b3085a256a338": "delta wing fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "647e76622faa22b626b85a24a73a298a": "delta wing fighter fighter aircraft attack aircraft", "1a6ad7a24bb89733f412783097373bdc": "delta wing fighter fighter aircraft attack aircraft", "24d4c063f7a361bacbc6ff5546f4ec42": "delta wing fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "4d2d87e61a18760ff64801ad2940cdd5": "delta wing fighter fighter aircraft attack aircraft", "4bd5f77521e76e6a2e690fa6dfd5d610": "delta wing fighter fighter aircraft attack aircraft", "150fd58e55111034761c6d3861a25da2": "delta wing airplane aeroplane plane", "3adbafd59a34d393eccd82bb51193a7f": "delta wing fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "54711bb9127ddf7ef412783097373bdc": "delta wing fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane bomber", "562a94308f7bf09b8aa0062ef5480a6d": "delta wing fighter fighter aircraft attack aircraft bomber", "4bf0b3df529d5093551df482db927edc": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "3948ac8a29ae42c761f027f2a55df6ea": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "740e79be16a92efbf1f2e10bfa81e2b": "delta wing fighter fighter aircraft attack aircraft", "22acc443fd007fce6e80138ae17d7d07": "delta wing jet jet plane jet-propelled plane transport airplane airplane aeroplane plane", "2d9a7863dcfb5663d208f79049825a82": "delta wing fighter fighter aircraft attack aircraft", "75e9e2d6970f5ee71927c29d6faf370a": "delta wing fighter fighter aircraft attack aircraft", "4d84619c0da53326e90916c8815b5c43": "delta wing fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "1f9b49f320eeb2f5d0226d12d397045": "delta wing fighter fighter aircraft attack aircraft", "40192d0e50b4d2c1f27a705edb2f9ba6": "delta wing fighter fighter aircraft attack aircraft", "35f43f566f2f14651713426437a9f7cd": "delta wing jet jet plane jet-propelled plane bomber", "486f1238321ffd2825eb6beb311c44e1": "delta wing bomber", "6a3028e1c7205b22ad6a38fcc21e6e9e": "delta wing airplane aeroplane plane", "556363167281c6e486ecff2582325794": "delta wing airplane aeroplane plane", "5c10e37453733ddb46d83d16be057f3e": "delta wing airliner", "6058d6701a0ca4e748e8405d6c51a908": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft", "4a21d3920b11cdbf1592d7a04a86fa53": "delta wing airplane aeroplane plane", "3b31e28ac1eb36fe1eb4be4ad34a6dbd": "delta wing airplane aeroplane plane", "3f3cd5461f7ec6edfa8a0c9a1860ff01": "delta wing fighter fighter aircraft attack aircraft", "3a82056ea319a442f64801ad2940cdd5": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft", "73bcbc2d147291451e7b1f533ce75": "delta wing fighter fighter aircraft attack aircraft", "4b4782c572fa8b11a20c7111a5d0d7fc": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "6615bb23e68159c193d4024985440d4c": "delta wing fighter fighter aircraft attack aircraft", "77f5111d348bf051368d7e7849f8df62": "delta wing bomber airplane aeroplane plane", "273c9c0bd43443c3b4f192eea1889928": "delta wing fighter fighter aircraft attack aircraft", "4f3a64164fbe16f54c2c88971423d0be": "delta wing airplane aeroplane plane", "32b6448c0864812729348d14ca881f7d": "delta wing fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "1ea8a685cdc71effb8494b55ada518dc": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "2c1f66380af03e4c5d1df55cbe0874aa": "delta wing fighter fighter aircraft attack aircraft", "5fed73635306ad9f14ac58bc87dcf2c2": "delta wing fighter fighter aircraft attack aircraft", "65cde29553fe7d763a63e8ba97b45ddc": "delta wing airplane aeroplane plane", "457c12e05d0f5d15762c3bd1a2b3f377": "delta wing fighter fighter aircraft attack aircraft", "35131f7ea7256373879c08e5cc6e64bc": "delta wing airplane aeroplane plane", "421f45774389984bea6586b61968eac": "delta wing fighter fighter aircraft attack aircraft", "33d955301966e4215ebedace13b486c4": "delta wing fighter fighter aircraft attack aircraft bomber", "3fa5f65a92a5e521d87c63d8b3018b58": "delta wing fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "2c5bdd9a08122f9e2023ec956848b741": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft", "5cd14216765393f18b96ae1a0a8b84ec": "delta wing jet jet plane jet-propelled plane", "33b8b6af08696fdea616caf97d73fa02": "delta wing airplane aeroplane plane", "1d4ff34cdf90d6f9aa2d78d1b8d0b45c": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "5678fc24603d25cb74745e8fbb11e3df": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "3b86245a5cd388ccf12b4513d8540d7c": "delta wing airplane aeroplane plane", "16689e54c884aa01639c7058eaf33ae": "delta wing fighter fighter aircraft attack aircraft", "77fea5bd4008e5f09a6e43b878d5b335": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft", "5f6b6f649b419cea71f4784575c35350": "delta wing jet jet plane jet-propelled plane", "61fe19a62a786c96950d2b3eef73a70e": "delta wing fighter fighter aircraft attack aircraft", "220a911e2e303865f64801ad2940cdd5": "delta wing bomber", "3ecea45bfa541b8e4a4dd08ffc16eb81": "delta wing jet jet plane jet-propelled plane", "24e79ed92be932e19bfb78d2af523ae": "delta wing fighter fighter aircraft attack aircraft", "22d0561f06900e165140beb2199de2af": "delta wing fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "5d2f0bdbd08be6fa3105bdad0abb9e24": "delta wing jet jet plane jet-propelled plane transport airplane airliner", "67979ab8dc522028788e57551eae0744": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft bomber", "66e0fbcab927f21a875d37e2f5bce5e8": "delta wing fighter fighter aircraft attack aircraft", "6a868213168421c6f0985368fed75674": "delta wing fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "21feae1212b07575f23c3116d040903f": "delta wing fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "3764de22af04fd32a993db466b6d73d3": "delta wing fanjet fan-jet turbofan turbojet fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "57575cb42132554af64801ad2940cdd5": "delta wing fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "56ace4b209c9ee823bb911cbca3906a3": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft", "34ddff243ac3783521b85e5214b0d6a7": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft", "538f0b5ee7c786e6d3b936925082270f": "delta wing fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "776bd07271792d1b131c2ccc2e4397": "delta wing fighter fighter aircraft attack aircraft bomber", "4d13a1b180422efc2d0334ee3a22c9fc": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "4f8952ff04d33784f64801ad2940cdd5": "delta wing fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "4ed01f44e3e422dff64801ad2940cdd5": "delta wing fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "3daca58825ce2ff6dc82b8fee1057b30": "delta wing jet jet plane jet-propelled plane", "2b0a2bb1556c96b94f762cd8ae1b1c4b": "delta wing airplane aeroplane plane jet jet plane jet-propelled plane bomber", "2628b6cfcf1a53465569af4484881d20": "delta wing fighter fighter aircraft attack aircraft", "4653be69e215a1a4b378822897b79a81": "delta wing fighter fighter aircraft attack aircraft bomber", "162ed8d0d989f3acc1ccec171a275967": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "7317dec9c1cd67a815ce9e432a3a77b8": "delta wing airplane aeroplane plane", "5cbe5be753b5c7faf389d19fad321c37": "delta wing bomber", "743670f7f3a8042e6ad6a28101cd3ecd": "delta wing jet jet plane jet-propelled plane transport airplane bomber", "372c95e7685363ca18e0889038e4fb6": "delta wing fighter fighter aircraft attack aircraft", "16868a30ebe634053dc22b596edc161e": "delta wing fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "3bad4bd2c944d78391d77854c55fb8fc": "delta wing airplane aeroplane plane", "68f26c36ba5340ede58ca160a93fe29b": "delta wing bomber", "34a89777594d3a61b2440702f5566974": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "1c26ecb4cd01759dc1006ed55bc1a3fc": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "62ca091091053afd9a6e43b878d5b335": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "3c52f7f687ce7efe3df325aac2f73830": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft", "5e6c986605a2817ed4837a4534bf020b": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft", "48e477d5904bb7bb1ad94eee1d03defc": "delta wing fighter fighter aircraft attack aircraft", "37f2f187a1582704a29fef5d2b2f3d7": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft", "1cfada8b8ad2428fcabcecce1c335df1": "delta wing fighter fighter aircraft attack aircraft bomber", "66b9ccf5842ed7f79330265a9fff38de": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "d3b39374c97d39da7dc01d89d8a05b74": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "96409ee8309b4c679c72d95bbb12b7e8": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "873f4d2e92681d12709eb7790ef48e0c": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "b6d2c4beaec0520e9a6e43b878d5b335": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane", "5d7b52a58c667ca835fc197bbabcd5bd": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "45574293b59c62ff301fa0a0663ee996": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "5f9707e5f6b820275823df672991ed66": "jet jet plane jet-propelled plane swept wing transport airplane", "842e5dcd452f34aa8caa71b1fbf7fb98": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "ff725af6df1b76207b164268a44f7712": "jet jet plane jet-propelled plane swept wing transport airplane propeller plane airplane aeroplane plane", "6d432caaa8eba4fb44b2fa2cac0778f5": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "cb744ec78dd320efd2c2bfa672ed621f": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane", "1a29042e20ab6f005e9e2656aff7dd5b": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "f8bc6483dd3c87085df50a69f2f8e096": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "1f47381312c9bebc9bff604afc480b65": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "41afa42c77cd167c2b5147716975ed8a": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "3baa3ca477d17e1a61f1ef59130c405d": "jet jet plane jet-propelled plane swept wing transport airplane", "30c0995dcb7c10039a6e43b878d5b335": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane", "f1ef7546cc85a1815823df672991ed66": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "b9006dadc7ae7f7d21afc48d963f897": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "427240c0fde25a90e6901f9a264cdbc0": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "4d97f6fcb6886f49cc14f1e6f4f4f49b": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "18d0da47a238945abc0909d98a1ff2b4": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "24fbe7a49fd786c5fb5c1b0f759e2bc1": "jet jet plane jet-propelled plane straight wing transport airplane bomber", "3a756cbf87c9a6c64d210d9468aedaf2": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "b4d64b689e870d1b828204947d78b9af": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "47a4ed133dd37264521546825315c695": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane airliner", "de45798ef57fe2d131b4f9e586a6d334": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "a82400edb160c18fc8727b27ee96a4b7": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "43c5f85e9a10071cb1bb46d2556ba67d": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "80770c90ba84524e825b20c2472ad90a": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane", "ffef991d85e3136a9a6e43b878d5b335": "jet jet plane jet-propelled plane straight wing transport airplane", "29b3b168322ac49448d2ba4615e03b21": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "52a6ae9074397d5f65f50257ecdfa5c7": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "da12fdb456d5fb588b96ae1a0a8b84ec": "jet jet plane jet-propelled plane swept wing transport airplane", "eee96e21bf51ee6ce719b5362fe06bbb": "jet jet plane jet-propelled plane swept wing transport airplane", "5fe313e070d6fc4e2620819ddac40644": "jet jet plane jet-propelled plane swept wing transport airplane widebody aircraft wide-body aircraft wide-body twin-aisle airplane airplane aeroplane plane", "1b300fd9ad4050e6301fa0a0663ee996": "jet jet plane jet-propelled plane swept wing transport airplane widebody aircraft wide-body aircraft wide-body twin-aisle airplane jumbojet jumbo jet airplane aeroplane plane airliner", "d4d61a35e8b568fb7f1f82f6fc8747b8": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "bd066f14adf6c0a9f6639976815d96b": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "909f59399d056983a0a3307f8f7721fc": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "35055f849da2cf0ec3fe7930fce05ded": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "55b7a2661bffdf9d55a352cb563d3195": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "137acaae47b50659348e240586a3f6f8": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "5c306ddfcc08ea1b230ac8907b9b7f90": "jet jet plane jet-propelled plane straight wing transport airplane bomber", "420eb46efaa81427b60ce16063f0788e": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "fc0dbd045c9391ce4a29fef5d2b2f3d7": "jet jet plane jet-propelled plane airplane aeroplane plane", "4396510f1346f13050586f69625e08a2": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "2c77ff96a9b46996b963df94d2f21069": "jet jet plane jet-propelled plane swept wing transport airplane", "995d06b3afb755eef446f92b52bbd82a": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "4de653411d132abdf1e2558bb9cfc329": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "48bcce07b0baf689d9e6f00e848ea18": "jet jet plane jet-propelled plane swept wing transport airplane fanjet fan-jet turbofan turbojet airplane aeroplane plane airliner", "5aa22f8c52d2f777a80799ffaf21ea7d": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "839a950d0264cbb89a162c818d22a620": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "11def84f9fe8ab0acd726e804e5d7a": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "b5d0ae4f723bce81f119374ee5d5f944": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "b6da5542f0a81fb5aae46a5cc955c766": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "b0599ecbf78aa615a8e091cb544689d5": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "98ca06c300ad5a6bdc9181d6fa468db3": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "2d4a57a467307d675e9e2656aff7dd5b": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "af6cf1d2b6c00b1233de69c9cd670995": "jet jet plane jet-propelled plane swept wing transport airplane widebody aircraft wide-body aircraft wide-body twin-aisle airplane jumbojet jumbo jet airplane aeroplane plane airliner", "1de008320c90274d366b1ebd023111a8": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "1825c91c5f2da4bab12962b43f188cbb": "jet jet plane jet-propelled plane swept wing transport airplane airliner jumbojet jumbo jet airplane aeroplane plane", "34ef846a22c6a6ac5e9e2656aff7dd5b": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "ee30cf94a209a45b91d43bb96e1bb0b1": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "ba784e6de35a5c6b4f8a08a5e6b028e7": "jet jet plane jet-propelled plane straight wing transport airplane airliner", "e85cd06b4a14d3ffbc0909d98a1ff2b4": "jet jet plane jet-propelled plane swept wing transport airplane", "e039c9760ca026cc6aa3bf023a2b42a5": "jet jet plane jet-propelled plane swept wing transport airplane widebody aircraft wide-body aircraft wide-body twin-aisle airplane airplane aeroplane plane airliner", "6311138687290f8a505ae8d422ba349d": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "76788a2758e938f435fc197bbabcd5bd": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane airliner", "d583d6f23c590f3ec672ad25c77a396": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "e2f91794c302f5c03221f63172285740": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "85462aa8b58d5a1ae753c4c9af8a19d7": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "a5cdc19967a8f69fedf24d76037ce02d": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "596c6e1e27b102aedf9d17a71b5ddec2": "jet jet plane jet-propelled plane swept wing transport airplane narrowbody aircraft narrow-body aircraft narrow-body airliner", "f771c5a1b9bbac21f4fb6842b3610149": "jet jet plane jet-propelled plane swept wing transport airplane delta wing airplane aeroplane plane", "2f7253f8fd1a6a999a6e43b878d5b335": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane fighter fighter aircraft attack aircraft", "d629589ee52d48ee4d210d9468aedaf2": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "ce9246d409f1c688bc0909d98a1ff2b4": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "2494cb5facfe7b1f85e0a2007a11e92f": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "2bc9a8aa5826f7335fc197bbabcd5bd": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "eae54f8d47cf07c0aeec93871310e650": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "688c645ad207773f5c3e79a10705422c": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "845364350601ca0cbdf0b44e2f8f4e6d": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "3cd1b98f12d2a22bf3ad4b0977199f23": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "b3135ae5bad8cc23cbaac693398e53fa": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "f16381a160f20bc4a3b534252984039": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "26bc67e3ecda6aa1adbb4c4e06ad649": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "985e020b26adf663acd7e882b88d2b": "jet jet plane jet-propelled plane swept wing transport airplane", "6c3593d0cc398e715d058822e4c8a2a8": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner jumbojet jumbo jet", "130934b3dd2fddfaaf4f36f817f09501": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "97e58a98eeee184565d73e2944dbef1d": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "3547cdf24eeabdc925194e2abdb4c403": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "34d467fc26232f53f36024ed2c0874a7": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "c76f2214e133a81adbafc7250d0654e": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "ffdaed95c3063cdf3ce4891c7dcdfb1c": "jet jet plane jet-propelled plane swept wing", "b51c1bb30371852fa325f626d0051e24": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "3cb63efff711cfc035fc197bbabcd5bd": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "a90230831ba263e5f7fe0727acb55c39": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "fd7c74a05072d3befef192e05c55dcd3": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "50040de261741893fb5c1b0f759e2bc1": "jet jet plane jet-propelled plane straight wing transport airplane bomber", "74ebf601d9c872a7828204947d78b9af": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "5b815e2726656685823df672991ed66": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "6ba3a181a40ac7aa35fc197bbabcd5bd": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "94b0675bb9f2a7fdf8a8bda5d07839b5": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "14161a05a21cbd6f62b600da24e0965": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "ad85ce4dde9fde14a13757cba42c461a": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "103c9e43cdf6501c62b600da24e0965": "jet jet plane jet-propelled plane swept wing transport airplane airliner airplane aeroplane plane", "6dfde67c8b905939bc0909d98a1ff2b4": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "f9fb41a3b1f59bb027745e9b6b461e93": "jet jet plane jet-propelled plane swept wing airplane aeroplane plane", "1850f4acdfb738bd7456183f198fd1e9": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane", "f97fa7329969bcd0ebf1d9fd44798b9b": "jet jet plane jet-propelled plane fighter fighter aircraft attack aircraft", "f89b085c37779a5997517c313146c4ab": "jet jet plane jet-propelled plane airplane aeroplane plane", "c61f67c3f03b10537f3efc94c2d31dc5": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "3b9c905771244df7b6ed9420d56b12a9": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane bomber", "f222d51d7c6fedc2db73445864b72015": "jet jet plane jet-propelled plane swept wing transport airplane", "3692c62cd29c6f1378a64d47db225ea2": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "1b171503b1d0a074bc0909d98a1ff2b4": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "1943ee06ecb139819330265a9fff38de": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "16b2f62791bd9f003554ccf8c30febe7": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "fb48754430b17846e3375a6b96a1d765": "jet jet plane jet-propelled plane", "37d32db3143814fd9a55c6af6f4b2bb3": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "5b76fbabde77889c99399ee63318a21b": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "16079ddc92f4c9efd677715e63c14038": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "96a69c2add6d55379a6e43b878d5b335": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "23616b4d03c5bac61232236eecec447b": "jet jet plane jet-propelled plane swept wing transport airplane", "2c4228b79cf44778b96ae1a0a8b84ec": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "b45e5b92bb4623ff151f2df200a24ac": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "3e8fc3155bbc4225c1ccec171a275967": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "ffce3c994052ffea339b6c8cc24f619d": "jet jet plane jet-propelled plane airplane aeroplane plane fighter fighter aircraft attack aircraft", "5f2f19cff434d86ceee9782f3a645bc4": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "98b5a3a8e2ab94ccce19d55bc5e6a406": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "fbee89bee5338798d4837a4534bf020b": "jet jet plane jet-propelled plane airplane aeroplane plane fighter fighter aircraft attack aircraft", "6b8bd13bb6c2589db61f9d3f64979662": "jet jet plane jet-propelled plane straight wing transport airplane bomber", "95f7a9754cba79469a6e43b878d5b335": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "519f1ddcbf942a76a71b0c9b506dc672": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "61c804d4751079962df73a6189a3b87": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "3ca058682dfe98f7f678b53750b6d181": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "a80a6ec94928f7dfe87d0cb113e517d2": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "b9e1ba3782922bab6ad6a28101cd3ecd": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane", "5eb91263da1f0bf3e816c75152573ee0": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "460cf3a75d8467d1bb579d1d8d989550": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "77a1744ee3c37f9b6aa3bf023a2b42a5": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "29ff751f989bdcfc9bff604afc480b65": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "e409d714620161c132fa725d7a62a02a": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "1807d521bc2164cd8c4f5e741c2f9cdb": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "1304ef60fe9793f685e0a2007a11e92f": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "12a1ac26d29ed1083554ccf8c30febe7": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "52ef4ccb24accbb295673ce3194af95e": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "53c27d3031f6f97e402be9c8dff728f8": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "df8bb967a346ccc25e9e2656aff7dd5b": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "4063fb031d8fb78e505ae8d422ba349d": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "124062ccaeae95085e9e2656aff7dd5b": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "9618b78610150b53825b20c2472ad90a": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "de863b025e53c9a63554ccf8c30febe7": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "32e8412ce01e63c1a47575cd04a1d851": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "bce1b10c92fb9fc81b7a4091d85f2e8d": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "611c65a9a9099c6121e570d3da54bf9d": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "b9c8536f91ec3bcd3aff44a57f77b39": "jet jet plane jet-propelled plane swept wing transport airplane widebody aircraft wide-body aircraft wide-body twin-aisle airplane airliner", "85d3691b7bde76548b96ae1a0a8b84ec": "jet jet plane jet-propelled plane swept wing transport airplane", "7dc614fd957186cf216b327569dd806e": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "ab8eadee08cba8d8303d346f4be7d4f": "jet jet plane jet-propelled plane swept wing transport airplane widebody aircraft wide-body aircraft wide-body twin-aisle airplane airplane aeroplane plane airliner", "72d96bac651bc74c7af159eaf7625abf": "jet jet plane jet-propelled plane swept wing transport airplane", "5ff1cd7d4fc48c864b6d6a3a20b9f7f": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "24c499191b85dd55bf4fc6675b9d12fc": "jet jet plane jet-propelled plane swept wing transport airplane", "e8ceb64406509714e5dcd47593c60221": "jet jet plane jet-propelled plane transport airplane airplane aeroplane plane fighter fighter aircraft attack aircraft", "5a12bc3a78c510cb366a5992755f2322": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "7a80f3950b5e31f6b0d51feef5761dce": "jet jet plane jet-propelled plane swept wing transport airplane", "3cbc83ba49edeccebc0909d98a1ff2b4": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "229cd78f9ac3e77c8b96ae1a0a8b84ec": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "7cabdedabd1d65bdfb86dddee1d6ac3": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "647a8f5c79b54910f6c0d5cf01fdaf45": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "29374d5ffdb23451e1f2a1daf140ac9f": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "3998242c3442e04265e04abc9923b374": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "177fadcfa6b4a8346aa3bf023a2b42a5": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "dbc521b1d4c2d308303d346f4be7d4f": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "a5beb33ba75edd59485e40e44ec62786": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "47e48c70040beb9c8db97d9bc169245": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "7054f98cc8af397f9a6e43b878d5b335": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane", "146533404a778665c93b40751084c22": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "552871abb864d06d35fc197bbabcd5bd": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "52a1b6e8177805cf53a728ba6e36dfae": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "45801aa10db0636d4694b8455e89054": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane airliner", "589e5b24b3b0e30e2620819ddac40644": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "aa3a317001bb8617f7fe0727acb55c39": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "eb2fbd46563e23635fc197bbabcd5bd": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "226e3f0a844a3b4a77fd7318510b8627": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "1e8c56bac4e66672e85e222f6559b47": "jet jet plane jet-propelled plane swept wing transport airplane airliner airplane aeroplane plane", "92b7d0035cefb816d13ef00338ba8c52": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", 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"1597cbeb8266af206aa3bf023a2b42a5": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "86590fb6310cf7361f1ef59130c405d": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "20d97069df784e19a80799ffaf21ea7d": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "efc7d4fb87937413dc13452e3008005b": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "1026dd1b26120799107f68a9cb8e3c": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "425619faec43ba86a000a5a61b867bca": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "18e86ba0172154f3bc0909d98a1ff2b4": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "3b95867a47a8afafe593bc205a118b49": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "987e67ef16969b93663e90eaf6b4ca52": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane", "b7c11baa48456c23149f1a143758cddb": "jet jet plane jet-propelled plane swept wing transport airplane delta wing airliner", "f998bb1b62da4d06d3a1a47fb9b678": "jet jet plane jet-propelled plane straight wing airplane aeroplane plane", "1818597d398db8f7f1f82f6fc8747b8": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "8bb8876d55433eed216b327569dd806e": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "62e29787d3623618575bf8a4b14be4f4": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane", "1496504f42de2eef8b96ae1a0a8b84ec": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "48fc59725119ac1133de69c9cd670995": "jet jet plane jet-propelled plane swept wing transport airplane narrowbody aircraft narrow-body aircraft narrow-body airliner", "cf4c2d7d836c781e5a59e179e114b9a7": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "472e6c2a77198432e1211756a8e887aa": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "a3927542339a2c491b1cc145b0144991": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "bee504b4909890df1dfabee9ba27dc70": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "1b3c6b2fbcf834cf62b600da24e0965": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "3a18489f9615a350e768735f27170bc4": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "3a3d4a90a2db90b4203936772104a82d": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane", "142acab4424009a123fc69eefd95e6d3": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "c68efb547224f1dbed4212b3ec7c4371": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "2e235eafe787ad029a6e43b878d5b335": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "2de92a57e410020cc231d70e1e0cc567": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "1954754c791e4571873ec74c119307b9": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "a96c1589731b06a1efbd0f357ff7c40a": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane", "40278c4bbf6c1f3d9642905e5096dbcb": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "14ec1da5f5499d83bc0909d98a1ff2b4": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane fanjet fan-jet turbofan turbojet", "6d119be1286c7b129a6e43b878d5b335": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane", "abb3c5d793e4c26d7aeb2194e56698b4": "jet jet plane jet-propelled plane swept wing transport airplane widebody aircraft wide-body aircraft wide-body twin-aisle airplane airliner", "23c8d6c73cf78ab23cf128905a10d59d": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "561bab672a9e12352c1752d7966dcc0": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "133b74393a3349aa70c4138179d9ed97": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "ef26ad836cd29010f2acf00738ce9d0d": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "420efeb902ba9c9f47f9199352b2dddb": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "d31a0f4f945775de5823df672991ed66": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "a0d3a490ee1b74762a4947824596dce3": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane fighter fighter aircraft attack aircraft bomber", "21e9ae6cdd3ddc27e1c97634acf0214": "jet jet plane jet-propelled plane swept wing transport airplane", "a5be00166e57ce5dd92de1e594e3dc28": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "84615b34a2c8a2868699933784576e73": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "eb2ac022b65f827e5e0925f0d15a8f74": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "4ce24828099fdb5e744ca709c13b7abe": "jet jet plane jet-propelled plane swept wing transport airplane", "df411aa240fe48d5855eb7906a7a7a04": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "eb4396ab17012b3c9a6e43b878d5b335": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "4ed5983b7721f1da7f79d93424b708f0": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane airliner", "209e9845a482333c77c06b43df2749d": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "32fb1aca48b3a0d197eae247c9b1df47": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "a87adc4fb1229b7f6d0f2528dd6f85b9": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "12d15ac778df6e4562b600da24e0965": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "347d86d7001cef01232236eecec447b": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "b8df5a1fb0fa198a9a162c818d22a620": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "121b5c1c81aa77906b153e6e0582b3ac": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "380e9e8c3ef07372acd7e882b88d2b": "jet jet plane jet-propelled plane swept wing transport airplane", "d60ec60e091e9d7ade125dc914c4bb9c": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "5958285db82a82cbc0909d98a1ff2b4": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "47b409c5c80dd7c533de69c9cd670995": "jet jet plane jet-propelled plane swept wing transport airplane narrowbody aircraft narrow-body aircraft narrow-body airplane aeroplane plane airliner", "3656c9bbd79f6e29f6439977935c2784": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "19fc1b9ff4b6456f1232236eecec447b": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "e17a696c47d4292393db03f6b4e68f17": "jet jet plane jet-propelled plane swept wing transport airplane bomber", "8def338aaf898d6c26479a182158bae5": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "b4b21950f0627213fba066e52ee0e502": "jet jet plane jet-propelled plane transport airplane airplane aeroplane plane", "6047f2c5854dd38526cb540828830330": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "da6faddc7079ce8ab6befd5df74fffa2": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane", "1482d7a9e529dde7340461a254c1a95": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "85743f17e13271e0709eb7790ef48e0c": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "13ea0a2ac279dbaa5e9e2656aff7dd5b": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "9ff7d7d71bcf50ff4fb6842b3610149": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "f7f69ecc4ff199ec5e9e2656aff7dd5b": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "2767994ce3078824f38bce64a8733419": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "75fd28838e0fc8cf5b1edf5d4f643136": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "1272dd98c66f13579a6e43b878d5b335": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane", "13daaef458d6bb11e1873a3963e0d14": "jet jet plane jet-propelled plane swept wing transport airplane airliner airplane aeroplane plane", "84b396dde837c81994445a3e8b9de59d": "jet jet plane jet-propelled plane swept wing transport airplane", "aa5cdbe94abd2ee15042e5fc5b753f1": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "2aac22a33893fad8b96ae1a0a8b84ec": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane", "54d49185bcda732a91e8833d3c500d73": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "3cdf7ea70c5a8471f446f92b52bbd82a": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "132111c575000a3c146e44cc99f07c52": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "1490a4ae04d453784f856435f28995af": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "7401bcbb1a184bf548627a76b6268107": "jet jet plane jet-propelled plane straight wing transport airplane airplane aeroplane plane airliner", "3788ee24009c65cb4d210d9468aedaf2": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "46a1b199bd63c0b4505ae8d422ba349d": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "67b2e9282e92abe110ac482828ea39f": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "2ce02b8bb91ecd05c503a3776fc280fe": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "816935cac027310d5e9e2656aff7dd5b": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "1345cd9d0da6d149c6f6da58b133bae0": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "48d03ffabd0399f4303510f9a56d94fe": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "4b20c1736440ff9d90dd6eb126f6cbbb": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "2b62a8a0bcac6f22e1873a3963e0d14": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "fae6ae6ef62cfa107b164268a44f7712": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "e60b86c7a4015490e5a760ec65004efd": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "a839663396fe378a896facc4c7b99761": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "ede14aa589c68280216b327569dd806e": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "ec4cd947eb2ee3103554ccf8c30febe7": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "2cf6583a74dc4d1e373ed874fe97420b": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "4e4ae13865bf47f41adbb4c4e06ad649": "jet jet plane jet-propelled plane swept wing transport airplane airliner", "895939bb0da197f8303d346f4be7d4f": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane", "f350f4836dcd13541b1cc145b0144991": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "5a3344eee5b27e670da4e1131665650": "jet jet plane jet-propelled plane swept wing transport airplane airplane aeroplane plane airliner", "28da27a6bebc81df62b600da24e0965": "propeller plane straight wing airplane aeroplane plane", "89a6407ce86efaeee1211756a8e887aa": "propeller plane straight wing transport airplane flying boat airplane aeroplane plane", "6adda4af6b2b7bf9f315217de00e37bb": "propeller plane straight wing airplane aeroplane plane", "d581b2e10f8bc6aecc1d733a19631a1": "propeller plane straight wing transport airplane airplane aeroplane plane", "60af3c930040c8b915d4d2fdca08573e": "propeller plane straight wing airplane aeroplane plane", "dab7d624889233a863dc0bc8d259b20e": "propeller plane straight wing airplane aeroplane plane fighter fighter aircraft attack aircraft", "7a95a024f5616009ab21e26e992b2c94": "propeller plane straight wing jet jet plane jet-propelled plane", "caab31ecfe961086bc0909d98a1ff2b4": "propeller plane straight wing transport airplane airplane aeroplane plane", "688930b9d0e06ead9a6e43b878d5b335": "propeller plane straight wing transport airplane airplane aeroplane plane", "edb916c2cdc2d3b9ce2cec85ea6ae00d": "propeller plane straight wing bomber", "157bb84c08754307dff9b4d1071b12d7": "propeller plane straight wing airplane aeroplane plane fighter fighter aircraft attack aircraft", "f0137228f926dd464f8327dbe71ce85c": "propeller plane swept wing airplane aeroplane plane jet jet plane jet-propelled plane", "2b8bcb6e208a69b16a3383b58c4a9330": "propeller plane straight wing bomber", "bb244dba6323f80a126bd036879e7101": "propeller plane straight wing bomber airplane aeroplane plane", "a68e67e5fa7ae694d9a7daf2d70e57bd": "propeller plane straight wing airplane aeroplane plane", "7713f76f4ef99f19954990879891d2b": "propeller plane straight wing airplane aeroplane plane", "e2a6bed8b8920586c7a2c209f9742f15": "propeller plane straight wing airplane aeroplane plane", "1a74b169a76e651ebc0909d98a1ff2b4": "propeller plane straight wing transport airplane airplane aeroplane plane", "fd0e59013a16eb5184306f3bf0a1f2d7": "propeller plane straight wing airplane aeroplane plane", "dc28756381ac019ac2912ef1a1302821": "propeller plane straight wing transport airplane fighter fighter aircraft attack aircraft bomber", "46c52edc6107d1a3505ae8d422ba349d": "propeller plane straight wing transport airplane airplane aeroplane plane", "27c409ead0c4e34c9a6e43b878d5b335": "propeller plane straight wing transport airplane flying boat airplane aeroplane plane", "284e6431669d46fd44797ce00623b3fd": "propeller plane straight wing transport airplane airplane aeroplane plane airliner", "194098fb0aefc4a0666bd6da67d3abc2": "propeller plane straight wing jet jet plane jet-propelled plane", "6dd16a06f0e9a286bfc945be58e73eac": "propeller plane straight wing airplane aeroplane plane", "487c60f57489506d5d515fd83cfe3891": "propeller plane straight wing transport airplane airplane aeroplane plane airliner", "cc14512d5fe6e8489a6e43b878d5b335": "propeller plane straight wing airplane aeroplane plane", "dfdcc024d1043c73d5dc0e7cb9b4e7b6": "propeller plane straight wing airplane aeroplane plane", "2c97e6b2c92913cac1ccec171a275967": "propeller plane straight wing airplane aeroplane plane", "49e9aa7ffa2b1a25e9d8a35d7a1fb08d": "propeller plane straight wing transport airplane airplane aeroplane plane", "515b1b36d9f7721a903a13c314c66013": "propeller plane straight wing airplane aeroplane plane bomber", "787d2fbb247c04266818a2bd5aa39c80": "propeller plane straight wing airplane aeroplane plane", "adad9536c34c673862b600da24e0965": "propeller plane straight wing transport airplane airliner", "e1be99a614c85c3e2016648502c798bf": "propeller plane straight wing airplane aeroplane plane jet jet plane jet-propelled plane", "48836d07312f930223e2b52fb0c019e0": "propeller plane straight wing airplane aeroplane plane", "645b2bf682e865ab9a6e43b878d5b335": "propeller plane straight wing airplane aeroplane plane", "3fc5fb97d23b6bf111f78b196c636566": "propeller plane straight wing transport airplane bomber airplane aeroplane plane", "5afdfdbb4161ce0a7456183f198fd1e9": "propeller plane straight wing transport airplane airplane aeroplane plane", "3fc25f2cd192f7d262b600da24e0965": "propeller plane straight wing transport airplane airplane aeroplane plane", "3c98bc293d33375a9a6e43b878d5b335": "propeller plane straight wing airplane aeroplane plane", "558d3d7e6e78f2d941756cc47b70ca20": "propeller plane straight wing transport airplane airplane aeroplane plane airliner", "b42620c214ca7cfc44deee06752bbfaa": "propeller plane straight wing airplane aeroplane plane", "3fa6a05b62169e0d2350d07acbcc057a": "propeller plane straight wing transport airplane airplane aeroplane plane bomber", "de7cc3442b1775d46559a92f80e3672e": "propeller plane straight wing transport airplane airplane aeroplane plane", "dc03e743739c4f87c27f2d9f006d69eb": "propeller plane straight wing airplane aeroplane plane fighter fighter aircraft attack aircraft", "dc3bfc83bc2d1db643afdb9c81ff2967": "propeller plane straight wing transport airplane airliner", "228fcd24cea34febc0909d98a1ff2b4": "propeller plane straight wing transport airplane airliner", "c85e3f6c572581de7d3b11085e75c7ad": "propeller plane straight wing airplane aeroplane plane", "d1e3bba19cb9447dcf6c095014f481a4": "propeller plane straight wing transport airplane jet jet plane jet-propelled plane airplane aeroplane plane", "7a3392340af9ec7b62b600da24e0965": "propeller plane straight wing transport airplane airliner jet jet plane jet-propelled plane", "8f4416cb67c3807bcf155ddc002a8f77": "propeller plane straight wing airplane aeroplane plane", "266ba93989037e6d48e8405d6c51a908": "propeller plane straight wing airplane aeroplane plane", "fbd800d43c5f0d74250cb4f7fcd9ec03": "propeller plane straight wing jet jet plane jet-propelled plane", "da7354a90e6596e6139336f03eb955d2": "propeller plane straight wing airplane aeroplane plane", "a1017765200c668c6ecd5ddc73f570a8": "propeller plane straight wing bomber airplane aeroplane plane", "3390c5050cda83c09a6e43b878d5b335": "propeller plane straight wing transport airplane bomber jet jet plane jet-propelled plane", "739c8cadb1db6db39a6e43b878d5b335": "propeller plane straight wing transport airplane airplane aeroplane plane airliner", "d130dd0c96152140bc0909d98a1ff2b4": "propeller plane straight wing transport airplane airplane aeroplane plane", "7b6f528acc10d3fbcf5b9c6e0c7ce895": "propeller plane straight wing transport airplane fighter fighter aircraft attack aircraft bomber", "db567b8afbaaa95060a762246a709d46": "propeller plane straight wing fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane airplane aeroplane plane", "1c93b0eb9c313f5d9a6e43b878d5b335": "propeller plane straight wing transport airplane airplane aeroplane plane", "297316f1c8a6f59bd007e7980e2b01ba": "propeller plane straight wing transport airplane airplane aeroplane plane", "d438a4539d8068fd44601c64497858af": "propeller plane straight wing jet jet plane jet-propelled plane", "7c4e1ab21e8d6549a46ec065c01b381": "propeller plane straight wing airplane aeroplane plane airliner", "9912b47f0efc512b8d548f89c1029ea3": "propeller plane straight wing airplane aeroplane plane", "1659db64bbf46bcdc1955ee4b5680e91": "propeller plane straight wing airplane aeroplane plane", "f5a667f5796b9a0b9a6e43b878d5b335": "propeller plane straight wing transport airplane flying boat", "14453b29e139d81f6d07c55cf995503e": "propeller plane straight wing airplane aeroplane plane", "3b529633bdd49dc1e5dbe91fa3801c4c": "propeller plane straight wing transport airplane airplane aeroplane plane jet jet plane jet-propelled plane", "228478022f30a2bef1f87579d4dedbb4": "propeller plane straight wing airplane aeroplane plane", "6ecf2dd001e3b029dc53c0dc42fb387b": "propeller plane straight wing transport airplane airplane aeroplane plane", "d172705764e25e20884a857d19f7439f": "propeller plane straight wing airplane aeroplane plane", "a28b92f0ed237d6d13256b8319a93100": "propeller plane swept wing transport airplane airplane aeroplane plane jet jet plane jet-propelled plane", "6e7bc6995080fe6542ca265836030911": "propeller plane straight wing transport airplane airplane aeroplane plane", "23d5975dd63000eb4e901b3386f76d65": "propeller plane straight wing airplane aeroplane plane", "9facc516914dcfee6c32d954b1c7f41e": "propeller plane straight wing airplane aeroplane plane fighter fighter aircraft attack aircraft", "29925f23c2050efa7def3dd22d9ef72": "propeller plane straight wing flying boat airplane aeroplane plane", "60232dad38a6ec709a6e43b878d5b335": "propeller plane straight wing airplane aeroplane plane", "3ac64a2c67cb44f19777d69c8d47140": "propeller plane straight wing transport airplane airplane aeroplane plane", "d1d308692cb4b6059a6e43b878d5b335": "propeller plane straight wing transport airplane airplane aeroplane plane", "723921d0060203a344d33ea02fa0f619": "propeller plane straight wing transport airplane airplane aeroplane plane fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane bomber", "fb68077d405c233ef879f4163a3ec7b": "propeller plane straight wing airplane aeroplane plane fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "1974f6955c36c2aece2cec85ea6ae00d": "propeller plane straight wing airplane aeroplane plane", "89a6af782a025acc62b600da24e0965": "propeller plane straight wing transport airplane jet jet plane jet-propelled plane", "ddc14c7e0a228663796843908eee8f7d": "propeller plane straight wing transport airplane bomber", "130d3f27fb083eebc0909d98a1ff2b4": "propeller plane straight wing transport airplane airplane aeroplane plane", "20f2a121d9dbbf9e98494085d68ad6a0": "propeller plane straight wing airplane aeroplane plane", "45251b4f76c9a840139336f03eb955d2": "propeller plane straight wing airplane aeroplane plane", "ed35478403ae873943cf31d2bcc8f4": "propeller plane straight wing transport airplane bomber", "71ad36bd32ad44a0c503a3776fc280fe": "propeller plane straight wing transport airplane airplane aeroplane plane", "7628e525228e5a2d9a46ec065c01b381": "propeller plane straight wing airplane aeroplane plane", "395cb914880adc105b50b4451364cc2c": "propeller plane straight wing fighter fighter aircraft attack aircraft", "bb8c3e64da2788b98057ec2068d8fa1": "propeller plane straight wing airplane aeroplane plane", "2228845a400dbad30fb60bd5470eb25": "propeller plane straight wing airplane aeroplane plane", "79a62c385180cc51da67790f4274f014": "propeller plane straight wing airplane aeroplane plane", "c1fd4eb9f911851f4352ea917328a5b": "propeller plane straight wing transport airplane airplane aeroplane plane", "14282db0ca4238429a6e43b878d5b335": "propeller plane straight wing transport airplane airplane aeroplane plane", "6ca40d19374c9d735c5f85af1b625f04": "propeller plane straight wing airplane aeroplane plane", "95240a3e33d607bd88803e631d9fa455": "propeller plane straight wing airplane aeroplane plane", "5d5aefde5935fc9eaa5d0ddd6a2781ea": "propeller plane straight wing airplane aeroplane plane", "d48064100e793155f56a7ca118af1bd1": "propeller plane straight wing bomber", "7182efccab0d3553c27f2d9f006d69eb": "straight wing fighter fighter aircraft attack aircraft airplane aeroplane plane jet jet plane jet-propelled plane", "c88275e49bc23ee41af5817af570225e": "straight wing fighter fighter aircraft attack aircraft airplane aeroplane plane jet jet plane jet-propelled plane", "4afcc2cf695baea99a6e43b878d5b335": "straight wing propeller plane airplane aeroplane plane", "40b916f7c41ad451494ac047ec5b052e": "straight wing airplane aeroplane plane", "1021a0914a7207aff927ed529ad90a11": "straight wing airplane aeroplane plane jet jet plane jet-propelled plane", "65960aceecc9d6f88388594197e843d8": "straight wing jet jet plane jet-propelled plane airplane aeroplane plane", "1890f6391df25286394b1e418d5c594": "straight wing airplane aeroplane plane fighter fighter aircraft attack aircraft bomber", "9e8ab1862683399f570726399d67ccc3": "straight wing fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "56bb8130f49b481e230ac8907b9b7f90": "straight wing airplane aeroplane plane", "1322a9cabb1517219a6e43b878d5b335": "straight wing airplane aeroplane plane jet jet plane jet-propelled plane", "653665e9c401e7ce6b310cbbfd03b8ce": "straight wing airplane aeroplane plane", "523f1c2392338cf2b7f9c6f6b7bee458": "straight wing airplane aeroplane plane jet jet plane jet-propelled plane", "49e21b81565a51e6493c5f51f14398c7": "straight wing bomber", "40dc09d726aa6325ce2cec85ea6ae00d": "straight wing fanjet fan-jet turbofan turbojet airplane aeroplane plane", "9c7268dd8ec3a3bb590874dcd9dc8481": "straight wing jet jet plane jet-propelled plane bomber", "ba0f83a82b18613d2350d07acbcc057a": "straight wing airplane aeroplane plane fighter fighter aircraft attack aircraft", "1914552110aa5a61c1006ed55bc1a3fc": "straight wing bomber", "6a9b8fd1f4ddd87f152be1f5910e33df": "straight wing bomber airplane aeroplane plane fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "1bea1445065705eb37abdc1aa610476c": "straight wing airplane aeroplane plane", "5c6590461085c93ea91e80f26309099e": "straight wing jet jet plane jet-propelled plane", "117830993cc5887726587cb13c78fb9b": "straight wing airplane aeroplane plane", "4853e07c17127af7df92be10876fa000": "straight wing airplane aeroplane plane", "1eb3af39d93c8bf2ddea2f4a0e0f0d2e": "straight wing fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "223b0ac8399bf9c5ea1bd6f2443b43e7": "straight wing jet jet plane jet-propelled plane", "a692309cd39291106055329cd9432d36": "straight wing airplane aeroplane plane", "cdeb868930b32468f879f4163a3ec7b": "straight wing airplane aeroplane plane airliner fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "a85dbb5bb889881419bf80b167cee3a3": "straight wing bomber", "4fccf49d6356c756b833a96759a020e2": "straight wing airplane aeroplane plane jet jet plane jet-propelled plane", 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aircraft jet jet plane jet-propelled plane", "36393e03ed11f78461f1ef59130c405d": "swept wing airplane aeroplane plane", "5c7729a3c482d77542c20743f866e1a6": "swept wing fanjet fan-jet turbofan turbojet", "7826147390af0f9d1fbc781ac25c5c7a": "swept wing airplane aeroplane plane", "e7c6e2f7c5fd372e8e9091d822257500": "swept wing airplane aeroplane plane fighter fighter aircraft attack aircraft", "e3b06cf8bea9a6a5c1006ed55bc1a3fc": "swept wing airplane aeroplane plane fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "1f86a689ea6c35f0d9da70c3ab6dcf3b": "swept wing transport airplane airplane aeroplane plane", "2a801b1918ef23f1121ca0b13e917b22": "swept wing fighter fighter aircraft attack aircraft", "7981eae34c05e8409a6e43b878d5b335": "swept wing airplane aeroplane plane fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "1a32f10b20170883663e90eaf6b4ca52": "swept wing transport airplane airplane aeroplane plane", 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airliner jet jet plane jet-propelled plane", "d3b9114df1d8a3388e415c6cf89025f0": "airplane aeroplane plane jet jet plane jet-propelled plane", "951358ab93e7380c1ccec171a275967": "airplane aeroplane plane jet jet plane jet-propelled plane", "b71bd3c61315abf9ad8a06dbee1d115": "airplane aeroplane plane airliner", "a00ed5d078c6244c37678474be485ca": "airplane aeroplane plane airliner jet jet plane jet-propelled plane jumbojet jumbo jet", "30d8f7fe0da9d5d0c164bdcf3c9661ee": "airplane aeroplane plane jet jet plane jet-propelled plane jumbojet jumbo jet", "3a72adcf14ccec9965f50257ecdfa5c7": "airplane aeroplane plane airliner", "ac75db84aa4e72e28f21c6f032637775": "airplane aeroplane plane", "ce0c461237a21cefdb22b838c125a50b": "airplane aeroplane plane airliner", "b9f486be9d0160349a6e43b878d5b335": "airplane aeroplane plane jet jet plane jet-propelled plane", "9e75560008080a8529348d14ca881f7d": "airplane aeroplane plane jet jet plane jet-propelled plane", "bdbc01a1bea50bdba6cb562d18000d3d": "airplane aeroplane plane", "1e7dbf0057e067586e88b250ea6544d0": "airplane aeroplane plane jet jet plane jet-propelled plane jumbojet jumbo jet", "34ffc5e29472358cae81a3776126c079": "airplane aeroplane plane fanjet fan-jet turbofan turbojet airliner jet jet plane jet-propelled plane", "cf17d0ce34f09f572722fc1bdd7e0e51": "airplane aeroplane plane", "cbacce3a17e061251ab9df4be75138d0": "airplane aeroplane plane", "7175100f99a61c9646322bce65ca3756": "airplane aeroplane plane", "b71c16266f1674cd884ccdf529614144": "airplane aeroplane plane", "a5ca77bfabfad028b56059027b9b30d2": "airplane aeroplane plane airliner jet jet plane jet-propelled plane", "aabbf242aabd1af7766c6046535346e7": "airplane aeroplane plane airliner jet jet plane jet-propelled plane", "cc1e143cf7da051a6a6dc7e2bbcd098": "airplane aeroplane plane jet jet plane jet-propelled plane", "4561def0c651631122309ea5a3ab0f04": "airplane aeroplane plane", "2f576be042897eae38f859e2bc79169c": "airplane aeroplane plane", 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"18666bda4244d22ca7aff2c3136e8e59": "airliner widebody aircraft wide-body aircraft wide-body twin-aisle airplane airplane aeroplane plane jet jet plane jet-propelled plane", "3b3eb57af5f995c268aa0dbe2db661e2": "airliner jet jet plane jet-propelled plane delta wing airplane aeroplane plane", "66493aa4ae7dbe40b6b87e72ead132ed": "airliner", "d9d39f688eb9ba58b3f2b01de37c0b29": "airliner narrowbody aircraft narrow-body aircraft narrow-body airplane aeroplane plane jet jet plane jet-propelled plane", "388c9b9f1cf24ff84e61a0c2eaaabe87": "airliner airplane aeroplane plane", "bad8f0b4602df33fbe4274edc10c1c8e": "airliner jet jet plane jet-propelled plane jumbojet jumbo jet", "2bfd3dc8b0630de9e1873a3963e0d14": "airliner airplane aeroplane plane jet jet plane jet-propelled plane", "1c27d282735f81211063b9885ddcbb1": "airliner airplane aeroplane plane", "afd02e6d4cf0a342c1ccec171a275967": "airliner airplane aeroplane plane jet jet plane jet-propelled plane", "2d4c147d4d1cc40c582bf3113c3974ef": "airliner airplane aeroplane plane", "1560968d05cd8887cc14f1e6f4f4f49b": "airliner airplane aeroplane plane jet jet plane jet-propelled plane", "5ad5fbe84d7d8ef790b1d6deb98feec6": "airliner airplane aeroplane plane", "aad1733e7bc7152bcc14f1e6f4f4f49b": "airliner airplane aeroplane plane jet jet plane jet-propelled plane", "750000e25d4078f64e61a0c2eaaabe87": "airliner airplane aeroplane plane jet jet plane jet-propelled plane", "4fbdfec0f9ee078dc1ccec171a275967": "airliner airplane aeroplane plane jet jet plane jet-propelled plane", "6ad44d7abe7492b7d89ea85a4a66c0f9": "airliner airplane aeroplane plane", "1caa02b831cccff090baeef8ba5b93e5": "airliner airplane aeroplane plane jet jet plane jet-propelled plane", "97bd6259fd4dd69a90baeef8ba5b93e5": "airliner airplane aeroplane plane jet jet plane jet-propelled plane", "41aafedd84a6fa7490baeef8ba5b93e5": "airliner airplane aeroplane plane jet jet plane jet-propelled plane", "ca8e8a54b54b41eceb1a0518c224975f": "airliner", "3fe8243b660d5e8fbc0909d98a1ff2b4": "airliner fanjet fan-jet turbofan turbojet", "17e66cd463ff0de126360e1e29a956c7": "airliner jet jet plane jet-propelled plane", "4e66465abe540ee326ccedfb0470de8f": "airliner airplane aeroplane plane jet jet plane jet-propelled plane", "6e66c96067f3c631ce58b9b5a8f9e3b2": "airliner airplane aeroplane plane jet jet plane jet-propelled plane", "d1b28579fde95f19e1873a3963e0d14": "airliner jumbojet jumbo jet airplane aeroplane plane jet jet plane jet-propelled plane", "7f837b389e885af471b4c018296f73c7": "airliner fanjet fan-jet turbofan turbojet airplane aeroplane plane jet jet plane jet-propelled plane jumbojet jumbo jet", "2103dcb213151791acf77e718d93f3e1": "airliner", "7336bffce72ea58b90baeef8ba5b93e5": "airliner airplane aeroplane plane jet jet plane jet-propelled plane", "7f2d03635180db2137678474be485ca": "airliner airplane aeroplane plane", "735466165c04f71f62b600da24e0965": "airliner", "76b492b77a03882d431e5b4ad135fb8": "airliner airplane aeroplane plane", "b151573056013eaf71d03b466c72ce41": "airliner airplane aeroplane plane", "76a86e664e302f32fca1f1143bb6bc17": "airliner airplane aeroplane plane", "f186d2998485c6ed5e9e2656aff7dd5b": "airliner airplane aeroplane plane", "520382dade7695c4906bca4ffd4af837": "airliner fanjet fan-jet turbofan turbojet jet jet plane jet-propelled plane", "351e24a503c49840cc0472b5df09bf08": "airliner", "52712e1c07ea494419ba010ddb4974fe": "airliner airplane aeroplane plane", "b98a46b40d030e8a26360e1e29a956c7": "airliner jet jet plane jet-propelled plane", "c64e43e18b01fb5eca8607f540cc62ba": "airliner", "96291ea39a5c6eda203936772104a82d": "airliner", "df7313e87ef2905d37678474be485ca": "airliner airplane aeroplane plane jet jet plane jet-propelled plane", "b522108c4c08430a71d03b466c72ce41": "airliner airplane aeroplane plane", "24968851e483feb237678474be485ca": "airliner", "b1696ffd98c753ccea88a0a7eb1222bb": "airliner airplane aeroplane plane", "cf93f33b52900c64bbf3143b1cb6076a": "airliner", 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"fighter fighter aircraft attack aircraft", "ae4fff8877a5a64b3ae1361afc45ac52": "fighter fighter aircraft attack aircraft airplane aeroplane plane bomber", "7ea57db538494a2fc1ccec171a275967": "fighter fighter aircraft attack aircraft airplane aeroplane plane jet jet plane jet-propelled plane", "61159c0458d41ac8e341b5704aa568bd": "fighter fighter aircraft attack aircraft", "dd9a7dd5e2ea3389938204d34a891739": "fighter fighter aircraft attack aircraft", "a287dc5d0e28d3d3325212819caa597d": "fighter fighter aircraft attack aircraft", "d1b1c13fdec4d69ccfd264a25791a5e1": "fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "40a828b10b1b092bd208f79049825a82": "fighter fighter aircraft attack aircraft", "8bb827904cd9acd36c1cd53dbc9f7b8e": "fighter fighter aircraft attack aircraft airplane aeroplane plane bomber", "9f90f3298d7b6c6f938204d34a891739": "fighter fighter aircraft attack aircraft", "fcd7a8834a7f26f15069db69b8c1c70": "fighter fighter aircraft attack aircraft", "f47c5c54c4e49f62e0a768dae1badf69": "fighter fighter aircraft attack aircraft airplane aeroplane plane jet jet plane jet-propelled plane", "4dfe2199fdd333d09837a3d08a6a1966": "fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "8df8e2580639b093c1006ed55bc1a3fc": "fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "5fc63354b0156d113136bac5fdb5050a": "fighter fighter aircraft attack aircraft fanjet fan-jet turbofan turbojet", "7b39d993f6934a96b08e958a32ef3184": "fighter fighter aircraft attack aircraft", "166c9abfb0f5256bbf46baa68c6e37d4": "fighter fighter aircraft attack aircraft bomber airplane aeroplane plane", "a26639a06e2ae1ede7d756565593b850": "fighter fighter aircraft attack aircraft", "f485dcbd466f217286774cad40dd456a": "fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane airplane aeroplane plane", "a19a5a459d234919c1ccec171a275967": "fighter fighter aircraft attack aircraft airplane aeroplane plane jet jet plane jet-propelled plane", "9378f4e9b3bc187e621c837076d6923a": "fighter fighter aircraft attack aircraft bomber", "51f51906f12681a1dc82b8fee1057b30": "fighter fighter aircraft attack aircraft fanjet fan-jet turbofan turbojet airplane aeroplane plane jet jet plane jet-propelled plane", "8a050edf3d971a617b17a3f88bbfbb61": "fighter fighter aircraft attack aircraft airplane aeroplane plane jet jet plane jet-propelled plane", "c5143d3be7f1d0eb27745e9b6b461e93": "fighter fighter aircraft attack aircraft airplane aeroplane plane", "a839884fba87173ce25031ee39d82b94": "fighter fighter aircraft attack aircraft", "9f25e4e0aa598c7fabda204c62559ac3": "fighter fighter aircraft attack aircraft airplane aeroplane plane jet jet plane jet-propelled plane", "794fe891e20800d37bbc9fbc6d1fd31d": "fighter fighter aircraft attack aircraft", "1f7a516fdf80dcdc1006ed55bc1a3fc": "fighter fighter aircraft attack aircraft bomber", "3a3403fb14bdb46823e79e99b949a341": "fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "47a40ce42bb9077579dc7d843e2be5ed": "fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "e430f24c3125997339cb1b92881e8e76": "fighter fighter aircraft attack aircraft", "2a3d485b0214d6a182389daa2190d234": "fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "f8fb835bdc55ffffdc82b8fee1057b30": "fighter fighter aircraft attack aircraft", "6da4668de7ccdd0d4d10a13d437fced6": "fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "57eaa54ff3e4d6f211f78b196c636566": "fighter fighter aircraft attack aircraft bomber", "764894168678b36efc1619e612ccfb0b": "fighter fighter aircraft attack aircraft", "8615fe6e2a959ac1efe5f291bc2f5fd0": "fighter fighter aircraft attack aircraft", "f894972de285088da0896c4491cb3db6": "fighter fighter aircraft attack aircraft", "c9584d90a1da19f723a665a253ac8cae": "fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "ddd6c2a51c25036c8a43cd65b179a1ff": "fighter fighter aircraft attack aircraft", "e88ce288726375ff87bf26680c510802": "fighter fighter aircraft attack aircraft", "960d086565fd36def0a9e4c23bc75c0": "jet jet plane jet-propelled plane propeller plane airplane aeroplane plane", "f34a66147493f0633d5158710d6fb8": "jet jet plane jet-propelled plane", "63f3edb47c67caa29a6e43b878d5b335": "jet jet plane jet-propelled plane airplane aeroplane plane airliner", "1f672d2fd5e3f4e78026abe712c1ab05": "jet jet plane jet-propelled plane airplane aeroplane plane fighter fighter aircraft attack aircraft", "a4cbb0489a3a680eca8607f540cc62ba": "jet jet plane jet-propelled plane airplane aeroplane plane airliner", "74b0d11834cd8ec258a4fbb6b625be1": "jet jet plane jet-propelled plane airplane aeroplane plane", "46ae88cad17edca7ae7c0d0e12bd33da": "jet jet plane jet-propelled plane airplane aeroplane plane airliner", "c235f2c6d1e8c80e7f436bc8bb8973a3": "jet jet plane jet-propelled plane fanjet fan-jet turbofan turbojet airplane aeroplane plane bomber", "b26d238e335c59b567b13a456b2224a6": "jet jet plane jet-propelled plane propeller plane fighter fighter aircraft attack aircraft", "2d0945065aa2a427eec7473defebb526": "jet jet plane jet-propelled plane airplane aeroplane plane fighter fighter aircraft attack aircraft", "2fb78c8d2f27598826360e1e29a956c7": "jet jet plane jet-propelled plane airplane aeroplane plane airliner", "20b6bc7d69902db4d3ccdbb72feef61f": "jet jet plane jet-propelled plane airplane aeroplane plane", "86099a00dd63ac05aac413eef609e99b": "jet jet plane jet-propelled plane", "a61a59a4c48154db37678474be485ca": "jet jet plane jet-propelled plane airplane aeroplane plane airliner", "8e2e03ed888e0eace4f2488af2c37f8d": "jet jet plane jet-propelled plane airplane aeroplane plane fighter fighter aircraft attack aircraft", "e533a1a758a1752b187ea8c086d9f2c": "jet jet plane jet-propelled plane", "61330ac001ced49f64801ad2940cdd5": "jet jet plane jet-propelled plane delta wing airplane aeroplane plane bomber", "db758090dd739ee9ca68a659ecae961c": "jet jet plane jet-propelled plane", "7d928af41b7dd26e1d0f8853f6d023e3": "jet jet plane jet-propelled plane fighter fighter aircraft attack aircraft", "7db10020f24a3232abf03860b8d62488": "jet jet plane jet-propelled plane", "dbdca81a0f9079096d511e9563e4bbe7": "jet jet plane jet-propelled plane fighter fighter aircraft attack aircraft", "776c423005dbb55d354aed6327ff24db": "jet jet plane jet-propelled plane", "657439efe649f57f47f8deec944763f7": "jet jet plane jet-propelled plane airplane aeroplane plane fighter fighter aircraft attack aircraft", "23fb92d8cc0f8ac4def547874c3364e3": "jet jet plane jet-propelled plane airplane aeroplane plane fighter fighter aircraft attack aircraft", "25668f59015af0de56a7f2952cdf4b81": "jet jet plane jet-propelled plane airplane aeroplane plane", "e74b8862468d887c6d9b5a419d9131": "jet jet plane jet-propelled plane", "8855c5531c093275146f724acb952fba": "narrowbody aircraft narrow-body aircraft narrow-body airplane aeroplane plane airliner jet jet plane jet-propelled plane", "92cf58c641a4e5af56a793e48de27b07": "narrowbody aircraft narrow-body aircraft narrow-body airplane aeroplane plane", "4e67529b0ca7bd4fb3f2b01de37c0b29": "narrowbody aircraft narrow-body aircraft narrow-body airplane aeroplane plane airliner", "5739574537212a72b3f2b01de37c0b29": "narrowbody aircraft narrow-body aircraft narrow-body airplane aeroplane plane jet jet plane jet-propelled plane", "31d40e6e24511386d6d949577c389a84": "narrowbody aircraft narrow-body aircraft narrow-body widebody aircraft wide-body aircraft wide-body twin-aisle airplane delta wing fanjet fan-jet turbofan turbojet airplane aeroplane plane airliner jet jet plane jet-propelled plane", "bc33901245943313d6d949577c389a84": "narrowbody aircraft narrow-body aircraft narrow-body widebody aircraft wide-body aircraft wide-body twin-aisle airplane delta wing fanjet fan-jet turbofan turbojet airplane aeroplane plane airliner jet jet plane jet-propelled plane", "9912e7eaab960bf0e3639a60ffa58b1e": "narrowbody aircraft narrow-body aircraft narrow-body fanjet fan-jet turbofan turbojet airplane aeroplane plane", "ab2f47cda3622573c231d70e1e0cc567": "narrowbody aircraft narrow-body aircraft narrow-body airplane aeroplane plane airliner jet jet plane jet-propelled plane", "a611fb88b28c3f2ec231d70e1e0cc567": "narrowbody aircraft narrow-body aircraft narrow-body airplane aeroplane plane airliner jet jet plane jet-propelled plane", "d919249737749c36417568af9ae9577f": "narrowbody aircraft narrow-body aircraft narrow-body airplane aeroplane plane airliner", "e6236c5ec784a48111dcece1819895c0": "widebody aircraft wide-body aircraft wide-body twin-aisle airplane airplane aeroplane plane jet jet plane jet-propelled plane", "a097428376f298abd872dc56d048665c": "widebody aircraft wide-body aircraft wide-body twin-aisle airplane airplane aeroplane plane airliner jet jet plane jet-propelled plane", "d22521d217d89f8d5b1bb801ea1e2db7": "widebody aircraft wide-body aircraft wide-body twin-aisle airplane airliner", "d1cdd239dcbfd018bbf3143b1cb6076a": "widebody aircraft wide-body aircraft wide-body twin-aisle airplane", "10eeb119fd5508e0d6d949577c389a84": "widebody aircraft wide-body aircraft wide-body twin-aisle airplane fanjet fan-jet turbofan turbojet airplane aeroplane plane airliner jet jet plane jet-propelled plane", "b4de0e888e562f40873ec74c119307b9": "widebody aircraft wide-body aircraft wide-body twin-aisle airplane airplane aeroplane plane jet jet plane jet-propelled plane", "b53ea9af10f2a151bc0909d98a1ff2b4": "widebody aircraft wide-body aircraft wide-body twin-aisle airplane airliner", "4e2769ec84c0bd0454eefcdc602d4520": "widebody aircraft wide-body aircraft wide-body twin-aisle airplane jumbojet jumbo jet airplane aeroplane plane jet jet plane jet-propelled plane", "4457652c2853e0bea8cba409c4409ba9": "widebody aircraft wide-body aircraft wide-body twin-aisle airplane jet jet plane jet-propelled plane", "aad69c419b27d6744d8e7d3c15c98c3b": "widebody aircraft wide-body aircraft wide-body twin-aisle airplane jumbojet jumbo jet airplane aeroplane plane airliner jet jet plane jet-propelled plane", "a6aa2281ebfd822e9b2acbfd8323f804": "widebody aircraft wide-body aircraft wide-body twin-aisle airplane airplane aeroplane plane airliner", "f3cc7d5c4089b842df6e254b0245ddcb": "widebody aircraft wide-body aircraft wide-body twin-aisle airplane airliner", "9f75309b9744f1b54eefcdc602d4520": "widebody aircraft wide-body aircraft wide-body twin-aisle airplane jumbojet jumbo jet airplane aeroplane plane airliner jet jet plane jet-propelled plane", "a487d7527a0d0c2076b0a025d05432d": "widebody aircraft wide-body aircraft wide-body twin-aisle airplane airplane aeroplane plane", "3ee352a759364cdfbd46d022fd7d80aa": "widebody aircraft wide-body aircraft wide-body twin-aisle airplane delta wing airplane aeroplane plane airliner jet jet plane jet-propelled plane", "ad9059778f3e34ed4c3a35cee92bb95b": "widebody aircraft wide-body aircraft wide-body twin-aisle airplane airplane aeroplane plane", "c3bd5ab4d3ac6a5a5c351e299b24e355": "bomber airplane aeroplane plane", "be96646f774c7d0e2a23d93958262ccc": "bomber fighter fighter aircraft attack aircraft", "9eef6307dc504b88392b84e3285cce39": "bomber airplane aeroplane plane", "fd95b99d4699430a7395d58407f193ba": "bomber", "6859dc976c0528a831fec775e1dce6b3": "bomber airplane aeroplane plane", "c78cbd0e53900a1ef34db3de24263f32": "bomber", "cbdf611a2146031823a665a253ac8cae": "bomber", "9b60b20ea5fe6f002a2fdfabfb4182ed": "bomber fighter fighter aircraft attack aircraft", "e6f0811f15286120cedbd07f4cf21a81": "bomber jet jet plane jet-propelled plane", "d2c7b95823938833339bd6b570381585": "bomber", "61bd590e917928f6b6ecbbb2e4d05900": "bomber fighter fighter aircraft attack aircraft", "aba51f26f4da1b44b785bc4e6e3fb739": "bomber", "420f3bb771e8e75ed878249aca2571f": "bomber airplane aeroplane plane", "64dbeac5250de2bfc5d50480246d2c40": "bomber", "ce12e8c1a5f5ce1df58f507a9f1e3e03": "bomber", "a9b95631bcbefe9ad225a1c252daae25": "bomber", "e218bb755f35da1bb4cdad9a5bf52dd5": "bomber jet jet plane jet-propelled plane", "47d958a8a781144b132a08135eefe76d": "bomber", "43edf9f02a027ed162f11c7bf54cc1ed": "bomber", "3636483aff4b7977b7d7aa9b9ebcccb0": "delta wing", "59bbe6a07a5a68d5d96b332cac4e78f7": "delta wing jet jet plane jet-propelled plane", "a273a18b004c209c90b1d6deb98feec6": "delta wing airliner", "844d36a369cdeed3ac4f72bf08dc79a6": "delta wing bomber", "4470a28f2dd0cca9d9fba06863881b8d": "delta wing fighter fighter aircraft attack aircraft", "5963e4385e6e5c0925c10fa44a32fd7": "delta wing", "a4391c7977d990142c20743f866e1a6": "delta wing fighter fighter aircraft attack aircraft bomber", "a2c5e769f19c7e97b7d7aa9b9ebcccb0": "delta wing", "8b594934c14ac5db66cf1b4a8fc3914e": "delta wing fighter fighter aircraft attack aircraft", "879a8f69cd931fe9f64801ad2940cdd5": "delta wing fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "d18f2aeae4146464bd46d022fd7d80aa": "delta wing airliner", "54885d5beee4e76ab63969f9f8d6f075": "delta wing", "c00d38c09ac5e6b8405970680284869": "delta wing airplane aeroplane plane fighter fighter aircraft attack aircraft bomber", "4d223d7a0e8162cabd46d022fd7d80aa": "delta wing airliner jet jet plane jet-propelled plane", "2f4133664133746cf64801ad2940cdd5": "delta wing fighter fighter aircraft attack aircraft", "7f3446564689ac918f69a600441c970": "delta wing fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "69f1d9c3987d429e5e9e2656aff7dd5b": "delta wing airliner", "aa780af4d9cc83c3669c7dec71cfc5b0": "delta wing fighter fighter aircraft attack aircraft", "1628b65a9f3cd7c05e9e2656aff7dd5b": "delta wing airplane aeroplane plane airliner", "47331e4c26dd1dbc66cf1b4a8fc3914e": "fanjet fan-jet turbofan turbojet jet jet plane jet-propelled plane", "ae837be3456bd54f71d03b466c72ce41": "fanjet fan-jet turbofan turbojet airplane aeroplane plane", "c8dcaeda94da8da962b600da24e0965": "fanjet fan-jet turbofan turbojet", "e5fe5ed2cdc8b11d62b600da24e0965": "fanjet fan-jet turbofan turbojet", "483207a72a39129771d03b466c72ce41": "fanjet fan-jet turbofan turbojet airplane aeroplane plane", "2c3ba3f35c5d2b0ce77e43d0a92bdc06": "fanjet fan-jet turbofan turbojet airplane aeroplane plane", "8adc6a0f45a1ef2e71d03b466c72ce41": "fanjet fan-jet turbofan turbojet airliner", "dadf41579d385b0aacf77e718d93f3e1": "fanjet fan-jet turbofan turbojet airliner", "b97b9cde30c23b4d71d03b466c72ce41": "fanjet fan-jet turbofan turbojet airplane aeroplane plane airliner", "a1ce38065b93520335fc197bbabcd5bd": "fanjet fan-jet turbofan turbojet airliner", "1d96d1c7cfb1085e61f1ef59130c405d": "fanjet fan-jet turbofan turbojet airplane aeroplane plane airliner", "439c0ad637f11af762b600da24e0965": "fanjet fan-jet turbofan turbojet airliner jet jet plane jet-propelled plane", "fbe788465e564e46bc0909d98a1ff2b4": "fanjet fan-jet turbofan turbojet airplane aeroplane plane airliner", "399449fd9e6149de62b600da24e0965": "fanjet fan-jet turbofan turbojet airplane aeroplane plane airliner", "e0df97dfc068e3664bf88973bd147a26": "fanjet fan-jet turbofan turbojet airplane aeroplane plane fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane bomber", "da3ed25d281fc4c065fc5f76098aceb0": "fanjet fan-jet turbofan turbojet", "393cfa7e090b972dce2cec85ea6ae00d": "fanjet fan-jet turbofan turbojet airplane aeroplane plane fighter fighter aircraft attack aircraft bomber", "2e468cc6afe2da70bc0909d98a1ff2b4": "fanjet fan-jet turbofan turbojet jumbojet jumbo jet airliner jet jet plane jet-propelled plane", "8938ab05c9dd830cb422638f4c34a0ac": "fanjet fan-jet turbofan turbojet", "46c311894026d9a762b600da24e0965": "fanjet fan-jet turbofan turbojet", "9ac7fb5963ae673f7d64cad20089f027": "fanjet fan-jet turbofan turbojet airplane aeroplane plane airliner jet jet plane jet-propelled plane", "856a4d86ea105d97be13ce34aa7c0c1c": "fanjet fan-jet turbofan turbojet airliner", "845d7718879d26f9a45fa73059b12336": "fanjet fan-jet turbofan turbojet jumbojet jumbo jet airplane aeroplane plane airliner jet jet plane jet-propelled plane", "4cb164f1fce7849762b600da24e0965": "fanjet fan-jet turbofan turbojet", "7f6e55daa567aade3a1cddb101d3e1ea": "fanjet fan-jet turbofan turbojet airplane aeroplane plane", "b80131dacfa19f77337e02e3502c7536": "fanjet fan-jet turbofan turbojet airplane aeroplane plane fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane", "c3a03137955f65c5cf67c0b63878e138": "fanjet fan-jet turbofan turbojet jet jet plane jet-propelled plane", "b356ef92a2052648b8494b55ada518dc": "fanjet fan-jet turbofan turbojet airplane aeroplane plane bomber", "cb145792217196e27d64cad20089f027": "fanjet fan-jet turbofan turbojet airplane aeroplane plane fighter fighter aircraft attack aircraft", "cef4d41c07613b658168857903a9552b": "fanjet fan-jet turbofan turbojet fighter fighter aircraft attack aircraft", "d405b9e5f942fed5efe5d5ae25ee424e": "fanjet fan-jet turbofan turbojet fighter fighter aircraft attack aircraft", "52e27aecdd55c1bf5b03388497f76a9e": "fanjet fan-jet turbofan turbojet airplane aeroplane plane", "967b1afce4221816cf67c0b63878e138": "jumbojet jumbo jet airplane aeroplane plane jet jet plane jet-propelled plane", "a660b01d17f929f54e61a0c2eaaabe87": "jumbojet jumbo jet airplane aeroplane plane airliner jet jet plane jet-propelled plane", "8406b2372f6e1478d62bfd1579f9a1c2": "jumbojet jumbo jet airplane aeroplane plane airliner jet jet plane jet-propelled plane", "78feacec03272b28854fbae23376cbfe": "jumbojet jumbo jet airplane aeroplane plane jet jet plane jet-propelled plane", "4515ac65d312fdbdb63361ca9ddfc9ae": "jumbojet jumbo jet airplane aeroplane plane jet jet plane jet-propelled plane", "e594754cc2a264d63da2ae40e7681e7e": "jumbojet jumbo jet airliner jet jet plane jet-propelled plane", "149702d4e275568c18b14db3b83de9ff": "jumbojet jumbo jet airplane aeroplane plane jet jet plane jet-propelled plane", "6b84749eaca0e657f37f38dedb2f1219": "jumbojet jumbo jet airplane aeroplane plane jet jet plane jet-propelled plane propeller plane", "79e06d1fe2e217b2cf67c0b63878e138": "jumbojet jumbo jet airplane aeroplane plane airliner jet jet plane jet-propelled plane", "77dfd8d4ee3c54c3e99ea3c41458bc01": "jumbojet jumbo jet jet jet plane jet-propelled plane", "69ed3801f4f6f4d1e1873a3963e0d14": "jumbojet jumbo jet airplane aeroplane plane airliner jet jet plane jet-propelled plane", "365747560fafc7e847b5c8181a9fe9a3": "jumbojet jumbo jet jet jet plane jet-propelled plane", "4e1dfdfdd417479f49e1f7e01fe2ed1": "jumbojet jumbo jet airliner jet jet plane jet-propelled plane", "f405858991b190173af8c56bd4352c9": "jumbojet jumbo jet jet jet plane jet-propelled plane", "aaefbfb4765df684cf9f662004cc77d8": "jumbojet jumbo jet airplane aeroplane plane jet jet plane jet-propelled plane", "29c0b705d6368b6ef0483511b875f0d2": "jumbojet jumbo jet airliner jet jet plane jet-propelled plane", "3109b3740ac1a056df55310efbf4c0e4": "propeller plane airplane aeroplane plane fanjet fan-jet turbofan turbojet", "e559a626d0ef8b4f982014dd9aabdeeb": "propeller plane airplane aeroplane plane", "c1260a89eee28413f2acf00738ce9d0d": "propeller plane jet jet plane jet-propelled plane", "a0095db392a8b520884a857d19f7439f": "propeller plane airplane aeroplane plane", "634de14a7721145fb3f2b01de37c0b29": "propeller plane airplane aeroplane plane fighter fighter aircraft attack aircraft", "422700fbef58a0ee1fd12d3807585791": "propeller plane fighter fighter aircraft attack aircraft", "ae3556f7151098b0f64801ad2940cdd5": "propeller plane airplane aeroplane plane bomber", "121e9fceb90440efed79d3bd546890bd": "propeller plane airplane aeroplane plane", "ddfbeb997ef83cab884a857d19f7439f": "propeller plane airplane aeroplane plane", "c7b2f5b125bc41056d07c55cf995503e": "propeller plane airplane aeroplane plane", "fbebcde2d8fbf81ee7cf320ab5761d45": "propeller plane airplane aeroplane plane fighter fighter aircraft attack aircraft", "9b09169e0a2b8650dcadc22bf3c23ace": "propeller plane airplane aeroplane plane", "7edf83742bece6be7cc4b32c704a58d2": "propeller plane airplane aeroplane plane airliner", "4ec2aff45e8c495a667f1f26ed74631c": "propeller plane", "697b4a3b6a380443c503a3776fc280fe": "propeller plane airplane aeroplane plane", "7b4b249f1d3400e488be2a30dd556a09": "propeller plane airplane aeroplane plane fighter fighter aircraft attack aircraft jet jet plane jet-propelled plane bomber", "49611834b200ece04c3a35cee92bb95b": "propeller plane airplane aeroplane plane airliner", "a60cd34f71c3c0549288437ebbe47284": "propeller plane airplane aeroplane plane", "133937bd45f953748be6919d4632fec1": "propeller plane airplane aeroplane plane", "6615d159d2e250376969ef231a00a44b": "propeller plane airplane aeroplane plane", "32d9fe9366da899b90b73cf2bf8fe3d1": "propeller plane airplane aeroplane plane", "f27927b066b0696d1f95d2b537942ba5": "propeller plane airplane aeroplane plane jet jet plane jet-propelled plane", "a3c1b9b0c75af9a65cbd1a70b5b90a8": "propeller plane airplane aeroplane plane", "7191b4709d5142a1f86c2bd4e090c60d": "propeller plane", "9c7395d87c59aa54a79f2ed56427c6e6": "propeller plane", "a1e5e9561b501f2a19e06ee1b2e7b1b4": "propeller plane", "7089929d7a778403e91b0867f5d9629f": "propeller plane airplane aeroplane plane", "f50eba69a3be1a1e536cfc00d8c31ac5": "propeller plane airplane aeroplane plane jet jet plane jet-propelled plane", "645cccbc057c79cdcc57882dfaef8a57": "propeller plane airplane aeroplane plane fighter fighter aircraft attack aircraft", "5608c1b40a0c2fc9c2912ef1a1302821": "flying boat airplane aeroplane plane", "7feab568d879270dd8d90babf3c5509a": "flying boat", "480d027eea9830b511c04c8a3e94088": "flying boat", "b5b6f5ed2031f34cec7a415ac918303f": "flying boat airplane aeroplane plane bomber", "d37781e47470fa3d664a56105f5816ce": "flying boat airplane aeroplane plane jet jet plane jet-propelled plane", "4ba7b11cd496e3be69176f0174b7620c": "flying 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"26334c57fc4b83b543e68d98c50c8932": "birdhouse", "b6348429292d990be67195cc1ecf4787": "birdhouse", "be167ea4f0718058b4501871f1444d0b": "birdhouse", "4566d6e03bcd174b5fd335a0d0b003d5": "birdhouse", "7f53db3b31fe08283c2748dd7bf1793a": "birdhouse", "88d4727a4c13ceb5613af97d40709241": "birdhouse", "bf9a6c379d2dd1ce5bdd1b5cdc787a49": "birdhouse", "92f6d4d1dc94f4db973037aa80f5ba45": "birdhouse", "c815baf0ba9306cca9af96dabf6167b0": "birdhouse", "45c547298f85241b959ad2f80f4dbcca": "birdhouse", "ed8eecb25a61ce8ec30aa7d959330a5f": "birdhouse", "1025dd84537d737febed407fccfaf6d8": "birdhouse", "76ea6ce7dd6c1298805346b85ff6b7a": "birdhouse", "75bf6f6a98aea049ba5342d638d0c267": "birdhouse", "c5a9713e045f91363c04c1aafcac3ee2": "birdhouse", "cca3e35970a665e56dcb36a8108f189": "birdhouse", "8a159ab6dc7b409cc211ff482a022b4d": "birdhouse", "c9a3538b8408c66a33f19716eab42752": "birdhouse", "d5171f20e202b8aef74a754b7c345792": "birdhouse", "ece71b326dcb50bdfcfdb70b6c560685": "birdhouse", "8fdcf46df1b30e1179f82e70cc0e3b57": "birdhouse", "9c038a6c2564f3b0560e99aa033eed2e": "birdhouse", "514e3ac98b1d18565ae27b5e83f8a47a": "birdhouse", "a36e1456c6d68281132a08135eefe76d": "birdhouse", "b68f37561d77468da6ffec31b6fb775": "birdhouse", "304394899d284fe1ec1e281e84d8961f": "birdhouse", "fb7402f91e300ead8ac033353d37592a": "birdhouse", "1b3a8fa303445f3e4ff4a2772e8deea": "ship tender ship's boat pinnace cutter boat", "3176313945e3739e10508e1f7e97aa01": "ship tender ship's boat pinnace cutter boat sea boat cruise ship cruise liner", "47133569866031669268271e4d570275": "ship cabin cruiser cruiser pleasure boat pleasure craft tender ship's boat pinnace cutter boat sailboat sailing boat sailing vessel sailing ship yacht racing yacht", "e72c59779e1a85f05308b39b1b1978d6": "ship tender ship's boat pinnace cutter boat destroyer guided missile destroyer submarine pigboat sub U-boat sea boat container ship containership container vessel cargo ship cargo vessel liner ocean liner man-of-war ship of the line warship war vessel combat ship", "1b4268c4d812e54943bd01645155d3ee": "ship boat vessel watercraft ferry ferryboat sea boat container ship containership container vessel cruise ship cruise liner liner ocean liner passenger ship weather ship", "9902e521bbd9ff3348d84ab7c5cd4c19": "ship speedboat tender ship's boat pinnace cutter boat", "c2d6e8710215b74c82689dfa8a7d7e7f": "ship boat destroyer guided missile destroyer submarine pigboat sub U-boat vessel watercraft small boat sailing vessel sailing ship oil tanker oiler tanker tank ship cargo ship cargo vessel tender supply ship destroyer escort warship war vessel combat ship", "4af786ed4226705279863338881ed398": "ship boat", "36fba9c2f4c256dc4387c5ea62cbbe8b": "ship boat", "246335e0dfc3a0ea834ac3b5e36b95c": "ship boat vessel watercraft sea boat tender ship's boat pinnace cutter", "6834c4f307753dd1427c8de062c1497a": "ship pilot boat small boat tender ship's boat pinnace cutter boat destroyer guided missile destroyer", "b31884906c966b4fa54923b7d85aee7d": "ship tender ship's boat pinnace cutter boat fishing boat fishing smack fishing vessel", "355a85d0156984c75e559927dcb9417c": "ship boat", "160271b40b1d6822c40386bdd72f9a0": "cabin cruiser cruiser pleasure boat pleasure craft tender ship's boat pinnace cutter boat sea boat", "58c05eceed8e0a48f3e39f2e17005efc": "cabin cruiser cruiser pleasure boat pleasure craft", "5bc0bff4ead8277b421dcde4f714e772": "cabin cruiser cruiser pleasure boat pleasure craft battle cruiser tender ship's boat pinnace cutter boat vessel watercraft", "63d91bce2f3d26abdeb3e56da26a6974": "cabin cruiser cruiser pleasure boat pleasure craft boat liner ocean liner yacht racing yacht", "d57e124826aabbbfc6cb0dfa2f14f9d4": "cabin cruiser cruiser pleasure boat pleasure craft", "79a13d8bffa87b8ba8ae9698506bed5": "cabin cruiser cruiser pleasure boat pleasure craft sailing vessel sailing ship ship tender ship's boat pinnace cutter boat yacht racing yacht", "19df5aa5324f265d4de203ef6842ee61": "cabin cruiser cruiser pleasure boat pleasure craft speedboat boat", "54196fe702cd0f5ba27ad5a09bb4c2bf": "cabin cruiser cruiser pleasure boat pleasure craft", "f2192f3c34a877f1d01eaac447e2e9b": "cabin cruiser cruiser pleasure boat pleasure craft liner ocean liner ship", "b1991078d16797eb221fc8bb1161b418": "cabin cruiser cruiser pleasure boat pleasure craft warship war vessel combat ship ship", "567e385bc5c40247e414f982f9c422f7": "cabin cruiser cruiser pleasure boat pleasure craft cruiser vessel watercraft ship", "68e60ab321e4198ab437d385637c4e1c": "tender ship's boat pinnace cutter boat rowing boat dinghy dory rowboat small boat fishing boat fishing smack fishing vessel", "66fcc3b9b53e0564d619719db5285416": "tender ship's boat pinnace cutter catamaran ship boat sailboat sailing boat sailing vessel sailing ship yacht racing yacht", "9c50b10fb651e57fdd93d77eaf89012": "tender ship's boat pinnace cutter sailboat sailing boat pirate pirate ship ship boat", "6cc258399daf767ae1942965937d3cef": "tender ship's boat pinnace cutter sea boat ship", "60ecf4adf9e115f91eff78a25c58572c": "tender ship's boat pinnace cutter", "2bf9436a79954087bc585c195913193c": "tender ship's boat pinnace cutter river boat liner ocean liner boat", "667c662b55519e4650cff0d5bea1684": "tender ship's boat pinnace cutter small boat ship", "1e3400a7a099823371feee345701e9eb": "tender ship's boat pinnace cutter pirate pirate ship submarine pigboat sub U-boat", "512251a635bd548f3463d0a80e47bc52": "tender ship's boat pinnace cutter boat sea boat", "a06a7f4b82beb87df6fc999231665677": "tender ship's boat pinnace cutter ferry ferryboat ship boat", "203c2cac2c46e06c320fa6e2cd857828": "tender ship's boat pinnace cutter outboard motorboat outboard dinghy dory rowboat small boat boat", "c2d6eb0899e5d10dff531859cd52e4b5": "tender ship's boat pinnace cutter pirate pirate ship boat", "e19c3bb778bc8e9ed372f295778d1591": "tender ship's boat pinnace cutter cruise ship cruise liner liner ocean liner ship ferry ferryboat", "61df71a58a9d0161202de8d12c6f7633": "tender ship's boat pinnace cutter boat vessel watercraft", "efdfd88f3fcbc162b4c316c21a905ad4": "tender ship's boat pinnace cutter outboard motorboat outboard", "62b67cadf45841ab82b5804533e545b1": "tender ship's boat pinnace cutter boat", "18f650bbd1068f0589c6f1079da7a8d4": "tender ship's boat pinnace cutter yacht racing yacht", "372ff5cab89350d23217bd20648fc12d": "tender ship's boat pinnace cutter ship", "490ba339e10c0b7f4de203ef6842ee61": "tender ship's boat pinnace cutter small boat boat", "97dd02cb0afa088d485fd5204f716152": "tender ship's boat pinnace cutter yacht racing yacht sea boat", "cc03dbbf18adc483bcf964ea747b39ec": "tender ship's boat pinnace cutter small boat tender supply ship boat cruise ship cruise liner yacht racing yacht", "a668bb9ccffe8cb627ca60ff81c74bf5": "tender ship's boat pinnace cutter sea boat ship boat", "a5fa8ae8f743d5498052128bafa4f7d8": "tender ship's boat pinnace cutter sea boat", "d7b1c89328c8f0099c42421cfc900873": "tender ship's boat pinnace cutter boat sailboat sailing boat tender supply ship", "a5a16ec8ebc811f1aff407511d7dfc96": "tender ship's boat pinnace cutter outboard motorboat outboard boat", "50ec883d9c1f715614edc3c9b2489f4": "tender ship's boat pinnace cutter boat small boat yacht racing yacht", "5a3924c1fb78bb908dea00b146cfa3de": "tender ship's boat pinnace cutter boat sea boat rowing boat dinghy dory rowboat fishing boat fishing smack fishing vessel tender supply ship", "d95c49195e51912056f316a86bec8b19": "tender ship's boat pinnace cutter sailboat sailing boat boat", "cfb7ca78b0b65c4f2d615e80824301ca": "tender ship's boat pinnace cutter boat river boat sea boat", "c74dbde6656efb4eb4248d66f92b2feb": "tender ship's boat pinnace cutter sea boat sailing vessel sailing ship pirate pirate ship ship", "eab41e8a5c5fbdff151300a2afaaffe0": "tender ship's boat pinnace cutter fishing boat fishing smack fishing vessel ship", "ab01205090242c7277945fcb3f0ff3f2": "tender ship's boat pinnace cutter vessel watercraft liner ocean liner ship", "2d189012a5b5a64aeb5178df7a9bd7f4": "tender ship's boat pinnace cutter oil tanker oiler tanker tank ship cruise ship cruise liner tender supply ship ship submarine pigboat sub U-boat liner ocean liner", "f61132a535bd608c85f7a3de54751f1b": "tender ship's boat pinnace cutter", "c46b2f2069ce543c4bbcee86e282d431": "tender ship's boat pinnace cutter sea boat sailing vessel sailing ship pirate pirate ship surface ship ship", "edb4574369f95b50689cbb453f479f9f": "boat river boat tender ship's boat pinnace cutter", "cb497aa6439c5a058c8c82196ef75032": "boat guard boat outboard motorboat outboard sea boat", "87a750e55b04395e668e250d23d1f86a": "boat fishing boat fishing smack fishing vessel", "94a0345790869eace074faa875b76f82": "boat vessel watercraft small boat sailboat sailing boat sailing vessel sailing ship ship tender ship's boat pinnace cutter", "3ff4ba746d73fdd08f845c46c317c0e2": "boat", "5b6d80dac7d626baa218acf6a387078": "boat dinghy dory rowboat small boat", "6e739e28803bbf257446ad526c7486ad": "boat kayak", "6c1458eec06ad0c147a67ad1fb1cf329": "boat sea boat small boat sailboat sailing boat", "8b3cf3842a88136c67c16c85d5f8c7d7": "boat submarine pigboat sub U-boat small boat fishing boat fishing smack fishing vessel", "cb7bc049274173df73a75ef44d66eede": "boat vessel watercraft pontoon ship tender ship's boat pinnace cutter", "51537c56f71ec82acfd826dd468a5497": "boat yacht racing yacht", "93304640e3a2a60d611cd70ef25f5235": "boat", "f2bf8fdce1a33c66a60ce6f0a0028c13": "boat tender ship's boat pinnace cutter", "5b6044746e12fb6733b68f5ec4e8a690": "boat", "684d665be29eaa49a1e649e7e3e5f054": "boat", "677ffbc1ee40a0a0db5a9ea3574bb285": "boat tender ship's boat pinnace cutter", "d350936cad33863bf2ad837508eb2db7": "boat submarine pigboat sub U-boat", "e3206eef4da407e7c08fee43ebed0bfa": "boat", "e8d1da5077f0fd3754f4583ac182c2": "boat", "fd850819ad05f139bd4f838682e34d2a": "boat", "65577c66f22fd84d6eb9a4c3b55eb0c4": "boat speedboat ship tender ship's boat pinnace cutter small boat", "ca2e4bd483c006a5c9f3294669fff77d": "boat", "fddb69f72a853e2f8ff5243f1df2529": "boat", "60a1ffde62956a96e4723800c586d63": "boat speedboat", "705df680888e17ed8841beb2e0280e42": "boat ship tender ship's boat pinnace cutter", "80c6a14accb189a9c2c2c81e2232aa95": "boat", "38b55fc61393e40653d45bb9e1743653": "boat small boat sailboat sailing boat", "8db75d7703406e81e4f8f268ed99350e": "boat submarine pigboat sub U-boat sea boat warship war vessel combat ship", "9b02ecc129d89000f4841afd366e16cb": "boat rowing boat", "6b87ed6710a0fc8d8aae7c4db7244339": "boat outboard motorboat outboard fishing boat fishing smack fishing vessel", "9fd71a532f7c732fb036cb639ea80765": "boat", "50c0bd9933f7259fe59358c5e6239c84": "boat", "64ceb6b8d122c2b3c9b5311300e82c12": "boat", "68f118940e88df383ac122c0078b9b60": "aircraft carrier carrier flattop attack aircraft carrier ship boat", "4e8a28d0659e430845aff212946058fc": "aircraft carrier carrier flattop attack aircraft carrier ship tender ship's boat pinnace cutter sea boat", "82242062558a24bc97a9c690ec97cc81": "aircraft carrier carrier flattop attack aircraft carrier submarine pigboat sub U-boat canal boat narrow boat narrowboat ship tender ship's boat pinnace cutter destroyer guided missile destroyer sea boat", "876a4bea4fc35db067fd6c1ab7f8324": "aircraft carrier carrier flattop attack aircraft carrier cargo ship cargo vessel ship tender ship's boat pinnace cutter boat submarine pigboat sub U-boat", "48cfd8b4bc4919e6cbc6ff5546f4ec42": "cruiser cabin cruiser cruiser pleasure boat pleasure craft cruise ship cruise liner", "e74a9275bf24d3e47dd35385411db37c": "cruiser ship cabin cruiser cruiser pleasure boat pleasure craft", "5ecf8b2c217b840fef5e991e1c32e94a": "cruiser cabin cruiser cruiser pleasure boat pleasure craft", "7fb484e04409f7323f3cc56bc9bfcde3": "cruiser battle cruiser cabin cruiser cruiser pleasure boat pleasure craft", "6cae5a0551aca4fdb39afb07adb2a544": "destroyer guided missile destroyer ship tender ship's boat pinnace cutter boat", "35e1639eac2c9b96cfea3d4fc15719ea": "destroyer guided missile destroyer patrol boat patrol ship ship cabin cruiser cruiser pleasure boat pleasure craft aircraft carrier carrier flattop attack aircraft carrier", "176cea94ab59ce9949f0e3dd7767881": "destroyer guided missile destroyer destroyer escort warship war vessel combat ship ship tender ship's boat pinnace cutter submarine pigboat sub U-boat vessel watercraft", "99ecd71c5103416e698137c717c4ad11": "destroyer guided missile destroyer cabin cruiser cruiser pleasure boat pleasure craft", "bf8995f756b59102698137c717c4ad11": "destroyer guided missile destroyer battle cruiser ship cabin cruiser cruiser pleasure boat pleasure craft", "70b4666e2da7ca32cfea3d4fc15719ea": "destroyer guided missile destroyer cabin cruiser cruiser pleasure boat pleasure craft", "2570f4db767fa046760fb52676c518d9": "destroyer guided missile destroyer ship tender ship's boat pinnace cutter vessel watercraft warship war vessel combat ship", "c74fe97df26a078facae7037c88ca7f5": "destroyer guided missile destroyer ship tender ship's boat pinnace cutter", "74470719a1b44c9a45aff212946058fc": "destroyer guided missile destroyer ship tender ship's boat pinnace cutter", "294644520ccc2ce27795dd28016933fc": "destroyer guided missile destroyer warship war vessel combat ship ship cabin cruiser cruiser pleasure boat pleasure craft vessel watercraft", "953adc8c58842c1281d9d9b1473043a2": "destroyer guided missile destroyer cabin cruiser cruiser pleasure boat pleasure craft", "a7707ea8482e4c3f925ef26228f6f62": "destroyer guided missile destroyer small boat destroyer escort tender ship's boat pinnace cutter submarine pigboat sub U-boat warship war vessel combat ship", "476bc79bd6fec6b088f5c2d4f2395a95": "destroyer guided missile destroyer cabin cruiser cruiser pleasure boat pleasure craft", "7d2194fb89c42da8ae5c852356538a5e": "destroyer guided missile destroyer pontoon warship war vessel combat ship ship cruise ship cruise liner", "30f5a0395aa8ce14d152039c9a3ad601": "submarine pigboat sub U-boat river boat boat", "73be7ff2caea7fd47c173089abc6401": "submarine pigboat sub U-boat ship tender ship's boat pinnace cutter", "94e216dc57731577c14e2939682bc455": "submarine pigboat sub U-boat outboard motorboat outboard small boat sailboat sailing boat nautilus nuclear submarine nuclear-powered submarine tender ship's boat pinnace cutter", "75d41ee15fc8fa4cca581939cd410ca": "vessel watercraft speedboat boat", "52176ee07927984ab13f6ba928a796f": "vessel watercraft kayak", "845426b7dd1b0e55a9142e7277ee08f1": "vessel watercraft boat", "a0f1e4ef99b57121a9142e7277ee08f1": "vessel watercraft boat sea boat", "bd4614ae4c709729f8527bdfe6691c9": "vessel watercraft ship tender ship's boat pinnace cutter", "f4923b4ce88bdda3a9142e7277ee08f1": "vessel watercraft boat", "209f569f50d42f7e45e2a4e5729b970d": "vessel watercraft boat", "fed3590199534e0f300dee22a3d8ed99": "vessel watercraft sea boat yacht racing yacht", "ec3e0694850f1a923b7cda02ebfc1f62": "vessel watercraft sea boat tender ship's boat pinnace cutter", "645382862cc284869bfdd98fcb85ea6d": "vessel watercraft ferry ferryboat small boat tender ship's boat pinnace cutter boat", "3a68e8dc897fba988332d0d58794c3c4": "vessel watercraft pirate pirate ship ship tender ship's boat pinnace cutter sailing vessel sailing ship", "1f883bf1bf0f6bc7a993db466b6d73d3": "vessel watercraft sea boat cruise ship cruise liner warship war vessel combat ship", "925c05dbefada808cfe472915a175bb": "vessel watercraft container ship containership container vessel cruise ship cruise liner ship tender ship's boat pinnace cutter", "d608bdcd8a87f3af7d2dc2b4ad06dc44": "vessel watercraft sailboat sailing boat sailing vessel sailing ship passenger ship ship tender ship's boat pinnace cutter", "3583b3e2b8dab3f29d8ea13dd61813ef": "vessel watercraft ship boat", "93de96748cab511731205bd848315f04": "vessel watercraft outboard motorboat outboard", "a771ed8115610f0320d75154997de910": "vessel watercraft kayak boat", "67dd862f05f659c71f98035449adc4bb": "houseboat", "4d95b6e392c9d95981e1e9063401fa72": "houseboat yacht racing yacht", "daa1781b81a74852bddd6eea3463dff0": "pontoon boat vessel watercraft", "82180d1a6a7edf815e585de41f56d4fd": "pontoon", "88ea6d51dcf2cdee3a6f102e22936e2c": "pontoon", "a8f5ce05ab3aac91cbd32f07d6724eb4": "pontoon", "ecdd85e25c18ea404d73ca91c97c0cb3": "pontoon", "f3a1538193bfc54fad3766494450e53a": "pontoon", "c78d6f5c908e2ff1b05dfc874f00d5f3": "pontoon", "ff7b2ba047233a5b149489fae2289e6b": "pontoon", "bc691ae1f2d6cf0ba628de2fd7989b40": "pontoon boat", "33692290561c798e780bc2708a85ba9a": "pontoon boat", "5810aaf1a0818553b05dfc874f00d5f3": "pontoon", "e5a3728a3b5a7ecd1d2d2f3b717d35d5": "pontoon", "b91a01dfd51d615b4f84436cd4bc7d29": "pontoon", "92be906c57e20430fd87075a257732c6": "pontoon", "55cf3c1207e421a29e3e34eaf1f39535": "pontoon ship tender ship's boat pinnace cutter boat", "a443271055ad0ebccc9f5b25407f3c72": "pontoon river boat cabin cruiser cruiser pleasure boat pleasure craft", "13a9352a47bab8c98cfe472915a175bb": "barge flatboat hoy lighter river boat cargo ship cargo vessel ship tender ship's boat pinnace cutter boat vessel watercraft", "7e7ec66ebdd4df7ed1745f739fc0fa03": "barge flatboat hoy lighter", "9a20ab78eb6a5e7af7ff315fbee3c6eb": "barge flatboat hoy lighter", "863fd298e6ea46a5614edc3c9b2489f4": "barge flatboat hoy lighter", "1c4ea31563b54bc8b77192b0405b4a6d": "canal boat narrow boat narrowboat", "99a27d7e2553c629b0828c4cf6b95ee9": "canal boat narrow boat narrowboat boat", "859daa33e58191bee2d4c157e5bf1dde": "canal boat narrow boat narrowboat river boat boat", "1c7f49bab676187480901e43af4435c7": "canal boat narrow boat narrowboat boat", "b5d1a551525c6b7876ed13a8bcf33762": "canal boat narrow boat narrowboat", "bbf1507f7126733665224ccd01ad35d4": "canal boat narrow boat narrowboat boat", "429dea3aadb0c3bdc753f4f2b4288d6": "canal boat narrow boat narrowboat", "3f3912cf68cd28eed7259ae7450dc47b": "canal boat narrow boat narrowboat", "dc422a7fdde68d8bbfc744c1348ee017": "ferry ferryboat", "c386c703e0f06a9b3a980283f1a7085f": "ferry ferryboat steamer steamship warship war vessel combat ship ship vessel watercraft", "7e0d128b6c8ce8b59e5ca238b1551011": "ferry ferryboat", "969c3262a594bda487a9e46bb7812fbd": "ferry ferryboat", "dcb390b2e8e5093def84f4e0accfb14e": "ferry ferryboat", "e687e0074b0dc5bc470938161c06eb7d": "ferry ferryboat ship cruise ship cruise liner", "d0ed110df0d56672b26ad4ebd4ebcb88": "ferry ferryboat cruise ship cruise liner liner ocean liner passenger ship ship", "db3a31bb1fb188c727ca48a52046110e": "ferry ferryboat", "f6762e2b8bef4ffa476a01ad767951be": "ferry ferryboat", "f9cb8429f822b8469ae563bb6367e47c": "ferry ferryboat ship", "408cdd476e8bb202852ae095a967f0ca": "ferry ferryboat passenger ship ship tender ship's boat pinnace cutter vessel watercraft", "27f0d2bf42b0e3374ed242fafe836df1": "ferry ferryboat ship", "c7fb35a9ca583715ef0445a3d98d3d35": "ferry ferryboat", "4cb135ddc5d3211c713a67cd30c1e1dc": "ferry ferryboat", "9a9bd517b3d9c60f9e23f6fc87a31098": "ferry ferryboat cruise ship cruise liner liner ocean liner ship", "4bf2dba91007f0285ce1e6c1c914b7a9": "ferry ferryboat cruise ship cruise liner man-of-war ship of the line ship", "1b86008df6322c5d438d829603922fbc": "ferry ferryboat cargo ship cargo vessel cruise ship cruise liner ship tender ship's boat pinnace cutter submarine pigboat sub U-boat", "63f170670881b2deaf6320700e3cf173": "ferry ferryboat man-of-war ship of the line ship", "5a7635031cd518daefa33903c8f77ab8": "ferry ferryboat sailing vessel sailing ship cruise ship cruise liner passenger ship ship tender ship's boat pinnace cutter boat vessel watercraft", "840d9078c31c188feae47c2aade5a70b": "ferry ferryboat ship", "3d396cf4279378456d165da625f3894c": "ferry ferryboat steamer steamship warship war vessel combat ship ship vessel watercraft", "7fbdbc374263bc0e44fe68fe7fbc12cf": "ferry ferryboat ship vessel watercraft", "5456c7546e3f3c3d9c5408f4f799fe72": "ferry ferryboat yacht racing yacht", "5e8276dfb4c1db936f31e247620349e3": "ferry ferryboat", "e6a9f9135e36b6c17c0ab7347b9e831a": "ferry ferryboat", "b93e07e84bdfd620f4000b62f164055f": "ferry ferryboat", "147fd27a40793d7e9bbe4f1047e9e5fd": "ferry ferryboat ship tender ship's boat pinnace cutter vessel watercraft", "7d7fe630419cb3e3ff217e1345ac0f8": "ferry ferryboat liner ocean liner cruise ship cruise liner", "dd012d1ee91543ff500cc506a763c18": "ferry ferryboat", "772835e02fec3a93629f9e3e495fed76": "ferry ferryboat", "7f05275eddecedd06f3d18c7cf5f8b6": "ferry ferryboat ship tender ship's boat pinnace cutter boat vessel watercraft", "d24616fb070d105d3a10f0b86a89bfc3": "ferry ferryboat", "74093f3d2583f3be7b0299baad14624c": "ferry ferryboat", "58b6ab987872ed42f36dc23129445ac0": "ferry ferryboat ship", "a346005de2c522e9c9078664355a9ff2": "ferry ferryboat", "3a71bdf6dd3e42bd6d08077e9257af3": "ferry ferryboat ship tender ship's boat pinnace cutter", "b4ea73d8daa2789699c1d6f5c0b53b84": "ferry ferryboat", "161d9d8a1550390ff4000b62f164055f": "ferry ferryboat", "8c611205ba2d189068b6f3ae18faf6b7": "ferry ferryboat passenger ship man-of-war ship of the line", "db01f2f8cff7b7e1c517f0fe59ec7ee": "ferry ferryboat ship", "4fbc7c634bd4488798b8c800ae001b66": "ferry ferryboat passenger ship ship vessel watercraft", "1b00f29471a41f59e92b1dc10fc46551": "ferry ferryboat", "6f3384e934f3588ccc37416b6163cbbd": "ferry ferryboat", "cf254a334ba1af0aaf96425967f2d458": "guard boat guard ship ship tender ship's boat pinnace cutter boat", "3625522559a753b78dd2cbb9124e6d20": "guard boat police boat sea boat small boat fishing boat fishing smack fishing vessel patrol boat patrol ship sailboat sailing boat oil tanker oiler tanker tank ship ship tender ship's boat pinnace cutter boat vessel watercraft yacht racing yacht", "b4d3624a4e2651ac6bfc202176cd1f2c": "guard boat outboard motorboat outboard speedboat police boat boat", "1b00e4c41b4195807e1c97634acf0214": "guard boat patrol boat patrol ship boat", "11766a7b1f86adf36bfc202176cd1f2c": "guard boat outboard motorboat outboard speedboat police boat boat", "8508ec8efeedf4a41ff8f5b5b24b7b46": "guard boat small boat patrol boat patrol ship guard ship ship boat sea boat", "6556015faea5ba45e0f0f0669675011": "guard boat boat", "a58fb21bfa4e2f451ddea1d5aef06a06": "guard boat river boat patrol boat patrol ship guard ship ship tender ship's boat pinnace cutter boat sea boat", "611824dac2873f5b59f280bab375e8b4": "guard boat guard ship ship tender ship's boat pinnace cutter boat yacht racing yacht", "7a9ad733a68b93bbb1bb46d2556ba67d": "outboard motorboat outboard", "ac2e609dec5745b9e00ca2cd74ddfc7a": "outboard motorboat outboard boat", "54daf1472c51bb47a97a590141e0046": "outboard motorboat outboard boat", "c19f98f149ee5da8b1bb46d2556ba67d": "outboard motorboat outboard small boat boat fishing boat fishing smack fishing vessel yacht racing yacht", "c0c1dce07f56384136da2d0ca22ed9df": "outboard motorboat outboard boat fishing boat fishing smack fishing vessel", "6367d10f3cb043e1cdcba7385a96c2c8": "outboard motorboat outboard fishing boat fishing smack fishing vessel boat", "91eda08cecf2e0f1c6cb0dfa2f14f9d4": "outboard motorboat outboard small boat boat", "aa05992294efaa26a47eca0fafc43370": "outboard motorboat outboard speedboat boat", "5b0e4acffe94b6ebd283df6ee191ad24": "outboard motorboat outboard boat small boat", "4c6e4bc79308e23e480060a68009533d": "outboard motorboat outboard boat", "3ffae5def89153e911141f4b3ef0acd0": "outboard motorboat outboard", "aa6a256cce6f8248b1bb46d2556ba67d": "outboard motorboat outboard fishing boat fishing smack fishing vessel boat", "c8dcc1e4f2276ef1ca6895dabdbc0ada": "outboard motorboat outboard boat dinghy dory rowboat", "fcb10d77160ccc67dfaef96860d73193": "outboard motorboat outboard speedboat", "51f002bbc33e4b34aff407511d7dfc96": "outboard motorboat outboard", "7a4fb637dc511c19b1bb46d2556ba67d": "outboard motorboat outboard boat", "573c69987f0508d210572d5e96d039f0": "outboard motorboat outboard", "ddd02c6fb780d4f6c683d3e7114aaa37": "outboard motorboat outboard boat", "bff4e5d71e9b695dc6a303e6239bc3b0": "outboard motorboat outboard boat", "3d33ac667a885a1a7856807e3e4b01ad": "outboard motorboat outboard boat", "4e45c5a16f45c3bad9356bb4e82527e5": "outboard motorboat outboard boat", "7114f947ac296d8034d05799e615961a": "speedboat", "5c46f6ce5efa21175060c1b57f88c056": "speedboat boat sea boat", "f10162679968fb0d8f21fab201b7ef8d": "speedboat boat", "5edcd7984de7fd4b42cdbb9140b25a0d": "speedboat boat", "bcf9572e3f21a9505c0be177939e290": "speedboat boat small boat", "8d8e8de880b4d7dd12ce033f7610a23e": "speedboat cabin cruiser cruiser pleasure boat pleasure craft boat", "a8dd1e23448a492aad67d63ac9e4b707": "speedboat", "3bd4a80ef718b9c9a069f11e22e91bb4": "speedboat boat", "65f8bb163e351bfa9399f7cdb64577ad": "speedboat boat", "9a6b9049576b97649bbc565383eee97c": "speedboat", "a230c555488ec2664b07b7d8c0d68c50": "speedboat", "30cf2b36478a21ee701c1f58e22b85e8": "speedboat", "5cee5c3afee327cf360d3819686769c9": "speedboat tender ship's boat pinnace cutter", "39c41184f49edccbc381729395a38d7b": "speedboat boat", "212a323fdfc0d8e59399f7cdb64577ad": "speedboat boat", "efa85ab5aa4704d299429033f40f0f8b": "speedboat sailboat sailing boat cabin cruiser cruiser pleasure boat pleasure craft boat", "f8ec76f3861f7c68b76982957f05710f": "speedboat", "7bdcc3c9462eed64482170356260f668": "speedboat boat", "a48181b53d207f702ec88d3440bc2d77": "speedboat", "3b60a8a4d71e3f192063cebb14246130": "speedboat boat", "973b398bbcc97c3fea9bd4954e1c8c49": "speedboat police boat small boat patrol boat patrol ship boat", "24ce8f07f05d14bfb274bf54d5e89093": "speedboat boat", "7abde2cf79febf6efe4db18443aaee4d": "speedboat boat", "722b68d90bc3955d2e5eb68672f87e15": "speedboat", "f2db3a2fa09b459a91001ec6a6afb362": "speedboat boat", "c55868566d2cd547d8d2db57c9e192a": "speedboat boat", "75078bd36089fd98243f62fe7d2ba690": "speedboat boat", "a53eb39c51a7e0a2dcb7d7c06abe0da3": "speedboat boat", "adb1b651af34dc6f42534f02c8e8b5ac": "speedboat boat", "88baa5e1db4d1c624c24aa820d0994e": "speedboat", "e57c1a72d265147548c2e07e979bc75": "speedboat", "13c289d915523f2348132c07d51cd81a": "speedboat", "3fd6e59f9c95a24d88b3e1662276b5b": "speedboat tender ship's boat pinnace cutter boat", "4f513b39cfce2d269eddf484109e2999": "speedboat", "52f3ad585a3900792de1329cfb242f46": "speedboat", "6bbd0b0e09dd43017ddd35a83509257f": "speedboat boat", "b3090ee38204289cb90142192ec795d": "speedboat ship tender ship's boat pinnace cutter boat small boat tender supply ship", "9c4ae8501ef74e82cffae3eeba6126e6": "speedboat boat", "991dab2b2339600a27da61c271c9c6a1": "speedboat river boat boat", "956c3b989bdd0603158a3417d0510bc": "speedboat", "b791549dfe175150a7d88fdd8b14a0fa": "speedboat vessel watercraft", "c3648d0cd141035428cf87eb5ebe5e6": "speedboat ship tender ship's boat pinnace cutter boat", "b7c24dca7db1d2985c00e52236fed2eb": "speedboat boat", "f4aa1a1c7216fdfcd3d8a17e1b074875": "speedboat yacht racing yacht", "b6ec93c690df4cac711722c32e47b894": "pilot boat boat", "4cbffb314a589346b9b184da9c0040b5": "pilot boat", "cb9ff07ef4769dba1d055b8893d6182": "police boat boat", "c6e255195b9a8691ea9bd4954e1c8c49": "police boat patrol boat patrol ship boat", "206c86e4cf614dd5d03c0ce6ad42cc93": "police boat boat", "397f37249263000990960087ed86644": "punt", "5aaec5938224841f36cf5f476d32636f": "river boat boat", "94ddf20a9a6e035e85f7a3de54751f1b": "river boat small boat ship tender ship's boat pinnace cutter boat", "e49935adf322de2f77e672c4996ec4a3": "river boat steamer steamship boat", "f7220f2e7b880c2492ab202de6ff04fa": "river boat boat yacht racing yacht", "bf5112a856a8f6a3819d76d0172b8abf": "river boat boat", "6729875f6c9b48cbe4707b1374ec004d": "river boat boat", "ccf527bf6ea742f0afe1d4530f4c6e24": "river boat", "1baadd62642d9f03a134736201a79843": "river boat boat", "249d543a30a88020be7995d5b4bc81b7": "river boat rowing boat", "4e341338a4425ea391fb4dce28166bd1": "river boat boat", "aeb094e328468631d3cf225db57b7a46": "river boat boat vessel watercraft", "355f5aa01c79fe1458a8952c93ff9829": "river boat ship tender ship's boat pinnace cutter boat", "4a01d52c0d8346fda8215f78849a813c": "river boat rowing boat boat", "7a188a5ed26af4b360a251068e7dcf6d": "river boat fishing boat fishing smack fishing vessel ship tender ship's boat pinnace cutter boat submarine pigboat sub U-boat", "7a5de2857213a2547ea0f302610f14a5": "sea boat", "988e1d7106b705d692336765b86c93d9": "sea boat cruise ship cruise liner liner ocean liner ship", "eaaeb5f4195eb55c1cec93449f0f44dc": "sea boat dinghy dory rowboat boat sailboat sailing boat", "8cbb360233696342309e1bb5976741f2": "sea boat boat vessel watercraft", "ed397dd3d2ca4c8a69852296a4bb593c": "sea boat boat", "cfffdac8ecda7575de208b4547dba0e": "sea boat ship tender ship's boat pinnace cutter", "d10cd8bfe5eb85572aec0b439528eb79": "sea boat tender ship's boat pinnace cutter yacht racing yacht", "da57d296d06af50d6fbde5631e3e226": "sea boat boat", "215e4b59683247378a3e8bd3a4e48433": "sea boat sailboat sailing boat pirate pirate ship tender ship's boat pinnace cutter sailing vessel sailing ship", "40752c807c2be827c924fc2f78f9cea0": "sea boat ship tender ship's boat pinnace cutter boat fishing boat fishing smack fishing vessel", "c3119babc97b4cb374f75d9e92dd466": "sea boat boat fishing boat fishing smack fishing vessel", "77e304ed79aded1e454ff46d89fe2a0a": "sea boat ship tender ship's boat pinnace cutter boat", "66fc4d3e245a643c4f7b88e5a2748214": "dugout canoe dugout pirogue canoe", "5d301491ba435b71257fc1c453f165b6": "dugout canoe dugout pirogue canoe", "9c2c87ceba2a465f86b234e3f0128df2": "kayak", "f69f4b2abc707f667610cb1c03c9c3ea": "kayak boat", "768bb9fbc3a5e3724f42eadbd9a0b820": "kayak canoe boat", "d214cdddcfaeb0cbcf8dc8d39d90900d": "kayak canoe boat", "fda1e57da58ec9bee88464b900e0eced": "kayak", "2d07f2ed9c3d78b33f222a37719e9945": "kayak", "ce6e40c533159b2db9e8bdc9c4a49aa2": "kayak canoe boat", "dffd4f1a6a4d3647568f91064f82e7ec": "kayak canoe boat", "ec4a394fd782e66b749227c7279794e": "kayak", "3f3bf820fa5d171bd45d4d5673b732bd": "kayak", "e9c603efb5aea8087610cb1c03c9c3ea": "kayak", "b472526267976d94e145e5e8ca0d926": "kayak canoe boat", "55d87dea079d2b991bc665188b93587b": "kayak canoe", "8d53d8f4cacbff77853339423b00f029": "kayak", "f531f4b2ca36d626e18dd06c1afbc426": "kayak boat", "a980a2cc1ece321968165c64a4129512": "kayak", "7bfd756573de367176098e230fa177d": "canoe boat", "d80bd439608923e07610cb1c03c9c3ea": "canoe boat", "7b0ed74742d27ff3ccc6f1de97150965": "canoe boat", "72f4c3c433492d585001cb19c4a0eee4": "canoe boat vessel watercraft", "e0845c8579c5d4acc3e7754d5a998758": "canoe", "31a41e6a73c5d019efffdb45d12d0585": "canoe", "2a492069b6009dec37363131ac7e8139": "canoe boat", "133c9fa2562498d28ae10bd53dffee76": "canoe", "aba35304010a7ca093f1a6337ed3a54c": "canoe", "223d2316bb8b74a979170a5f8beda902": "canoe boat", "d830527d616f47c811b9a587ace886f7": "canoe", "d0652ef190720c84f4e9dade6d46323d": "canoe", "59fefd79eba35dbb723877c00e9cdc03": "canoe boat", "c8bce97e59665f2811b9a587ace886f7": "canoe", "efa50c4e3e691d3bda1b3e013aeb1e59": "canoe", "40d4cf2e4c0042b81f6b8c144863b7d": "canoe boat", "8a779f2127dee13b28db2e450917f8b5": "canoe", "a10a3ed95539d9e81f0c83f3777b8bde": "canoe", "d3c26b292a4a6a82621153a3cbe4a77f": "canoe", "a034dc83750e7b8144c1f09f7df84b24": "canoe boat", "82be57a43d5d1e14fa286e2f06a75d07": "canoe", "bf4386d3e823a09e57a4e26112104eae": "canoe tender ship's boat pinnace cutter boat", "2fcb8c472f1cbdef185cce17ce166549": "canoe boat", "f72222b43e40e7e84cd259bd328e92a": "canoe", "1522b8c3c28a9d57ace571be2585c620": "canoe", "ff404dc428ac3cb7102f71c17954fcf0": "canoe boat fishing boat fishing smack fishing vessel", "597975f72770523eedb8bbed3955f432": "canoe boat", "fad2e708e354b0315e585de41f56d4fd": "canoe", "64ccd647293ba2a75e73d168e741c7c8": "canoe boat", "ac4286c2e92e3367d5ba5130450bf929": "canoe", "6edd37977522996a9b8e79aa9da22fd7": "rowing boat small boat fishing boat fishing smack fishing vessel tender ship's boat pinnace cutter", "c9bb36ffd5ccf7584cbfeb2946087e59": "rowing boat boat", "c2eb5277d66ff1af79953c8c5ce3505b": "rowing boat boat", "3f35daa80c8b364fb7b1411b0ad3bd0d": "rowing boat boat", "e9da320a14afc7098031a892cba81629": "rowing boat boat", "1b89cb2463ccaf15e1e2fc42e32162ff": "rowing boat dinghy dory rowboat boat", "70dcc0d460cacce0e63ec060b551ac57": "rowing boat dinghy dory rowboat small boat boat sea boat", "cd67f7d1ba943b162f84cb7932f866fd": "rowing boat boat", "2c7a0d5909d8dd46a3599da6d4b3696d": "rowing boat boat", "90a71287364b3d1bccb9d5278825aef9": "rowing boat tender ship's boat pinnace cutter boat", "5fa144c3a8cb5234379339ae6512a12": "rowing boat", "adbbaa33c52c3b0d5c0be177939e290": "rowing boat", "47a07536cc3ff3219c42421cfc900873": "dinghy dory rowboat small boat man-of-war ship of the line tender ship's boat pinnace cutter boat sailboat sailing boat sailing vessel sailing ship yacht racing yacht", "31f7c298ea24bb99847c4e55bb965ab0": "dinghy dory rowboat sailing vessel sailing ship tender ship's boat pinnace cutter sailboat sailing boat yacht racing yacht", "3e5e782373516ad760ddc468fe733ed1": "dinghy dory rowboat small boat boat", "d66b7128600af54e7afee304cce81d6f": "dinghy dory rowboat boat", "9798a8da8fb83d63847c4e55bb965ab0": "dinghy dory rowboat ship tender ship's boat pinnace cutter boat", "d617c5194daf4f7298a97ddfb532e08c": "dinghy dory rowboat boat", "485f63613911c93fd316c98a55307c24": "dinghy dory rowboat", "66a90b7b92ff2549f2635cfccf45023": "dinghy dory rowboat boat", "5c9d5cedcb75cd2bad7daee13cc76c38": "dinghy dory rowboat small boat", "203d75b8112369d66a4afad911312b2b": "dinghy dory rowboat sailboat sailing boat", "b78f1dd3083c35a1a716de2121565fe": "dinghy dory rowboat catamaran sailboat sailing boat yacht racing yacht boat submarine pigboat sub U-boat", "563d66d82ad3f27e324be6ae66f7fdf": "dinghy dory rowboat small boat sailboat sailing boat", "c521d7e4482fc7f7a716de2121565fe": "dinghy dory rowboat yacht racing yacht sailboat sailing boat", "6abd7ebbc9020871bfbc6e788697e036": "dinghy dory rowboat boat", "6f4a328af926532b642f9e27aaf0c47a": "small boat boat", "81a7b820899ccbf15ddefb7321dceac0": "small boat boat", "ab40191e935c415cef0a5cdc2cab422d": "small boat boat", "5bc8a432a5911a4c14621506c22882a0": "small boat boat", "849f7c682fe9160d8c1a083ca347f005": "small boat ship tender ship's boat pinnace cutter", "ad440e99d3a9bed3cd202a4790115f0c": "small boat boat", "1dabc979fd569a0e850e00840470903a": "small boat boat", "e364eac9ec762c54de203ef6842ee61": "small boat ship cabin cruiser cruiser pleasure boat pleasure craft boat", "d0afe316f1648d22b5d16f91270f7fa2": "small boat boat", "e964c2fe232f6c3e3ece64552d44d319": "small boat container ship containership container vessel cargo ship cargo vessel ship tender ship's boat pinnace cutter vessel watercraft", "e0fc9177fe0aab2fb50d0c6a0c254040": "small boat sailboat sailing boat boat fishing boat fishing smack fishing vessel", "3ff7e02233affcc350eb615eb69452b": "small boat sailboat sailing boat boat", "dc3336883fae5239d76134f29a265550": "small boat boat sailboat sailing boat", "3da3c73abafe7a9eb9e86d477b879ac0": "small boat boat", "2f4d7a1873384f5a7301f2b895d332ac": "small boat boat", "4818277b913b682a3c3bd8d7abbd3fc8": "small boat ship tender ship's boat pinnace cutter vessel watercraft", "9119eaa9b5996cd3b1bb46d2556ba67d": "small boat yacht racing yacht boat", "13b7b299d346020a4de203ef6842ee61": "small boat tender ship's boat pinnace cutter", "341905f73563a75717e4d86bbbb01f9f": "small boat ship tender ship's boat pinnace cutter boat", "83d2a7a4cd616008e19ccaf2c9aa161d": "small boat patrol boat patrol ship sea boat", "9798a8b84987bd661851363a5850bdc": "small boat", "110acfe04edbd526969f70ba15f2ef4f": "small boat warship war vessel combat ship vessel watercraft container ship containership container vessel", "16f53391dba14c9829ebc23acefb248a": "small boat ship boat yacht racing yacht", "35e014dccdd4fed12c791a7edd1fdff6": "small boat pirate pirate ship ship tender ship's boat pinnace cutter sailboat sailing boat sailing vessel sailing ship", "c6ef6b859205ab53609e2d916fa0da27": "small boat patrol boat patrol ship ship destroyer guided missile destroyer submarine pigboat sub U-boat cruise ship cruise liner", "f791188138166b4d9db44e8b39ef337": "small boat sailboat sailing boat", "53fb6683aac2191fa8a3d44052f8cb25": "small boat", "a40a870c5abd8c83d84b8ba651dfb8ac": "small boat boat", "9bdb0cf464f5c65956b5365bdce185d": "small boat sailboat sailing boat yacht racing yacht", "6e3db071ed95552b3faeafe37c2ce4f6": "fishing boat fishing smack fishing vessel boat", "9b91a683da8c4781f990230c33049243": "fishing boat fishing smack fishing vessel ship tender ship's boat pinnace cutter boat submarine pigboat sub U-boat vessel watercraft sea boat", "da4003977c998e81f74f6fb7142d37cb": "fishing boat fishing smack fishing vessel boat", "1442afdc0aade741c46814fb26e284d4": "fishing boat fishing smack fishing vessel ship tender ship's boat pinnace cutter boat vessel watercraft", "804e29433fda9657746ffcbc44510229": "fishing boat fishing smack fishing vessel ship cabin cruiser cruiser pleasure boat pleasure craft tender ship's boat pinnace cutter boat", "143df6dce1d90696874f0e42f84eb4f5": "fishing boat fishing smack fishing vessel ship tender ship's boat pinnace cutter boat vessel watercraft", "7cf500d15ff839e92f35bf9c34b31609": "fishing boat fishing smack fishing vessel boat", "7f8d800b3fce12a7874f0e42f84eb4f5": "fishing boat fishing smack fishing vessel ship tender ship's boat pinnace cutter vessel watercraft", "85ca7456e200e8cb874f0e42f84eb4f5": "fishing boat fishing smack fishing vessel ship tender ship's boat pinnace cutter boat vessel watercraft", "8dd5ad35a45f72a64bad9b80eddb4cd1": "fishing boat fishing smack fishing vessel boat", "812c1a3dacdce93ab4b5d7d0cd89b504": "fishing boat fishing smack fishing vessel small boat", "769a3afbadaa92648bfbe54b5d01550": "fishing boat fishing smack fishing vessel boat", "5da7daeff2b24f23206066da291e8981": "fishing boat fishing smack fishing vessel tender ship's boat pinnace cutter boat", "8410626d841dca3218b14db3b83de9ff": "fishing boat fishing smack fishing vessel ship boat", "1ce3bf4b1ee8c4b41542b16dedc43949": "fishing boat fishing smack fishing vessel ship boat sea boat", "c6bc9330b0a75d83cc73edf5d735208": "fishing boat fishing smack fishing vessel boat", "3e233d02a6943be81c99ec30e7a67a36": "fishing boat fishing smack fishing vessel ship boat", "12b5302a8aa697a138b0c448fc291f4a": "fishing boat fishing smack fishing vessel boat", "bdd8d8f6e6536c0d4d16452ef1302b59": "fishing boat fishing smack fishing vessel tender ship's boat pinnace cutter", "c0f619a710e4cccd15c8f969616647e5": "fishing boat fishing smack fishing vessel boat", "6f5df8d6d0d77039be286ae2fb79c470": "fishing boat fishing smack fishing vessel boat", "a848decbfe26833c4a27b0b4657ac8b5": "patrol boat patrol ship ship tender ship's boat pinnace cutter boat vessel watercraft", "e5c3835b9f3ae3634a27b0b4657ac8b5": "patrol boat patrol ship boat vessel watercraft sea boat", "a89cda9712b67a97d1e97c10e2464c2d": "patrol boat patrol ship guard ship tender ship's boat pinnace cutter vessel watercraft", "24ab70fe2e704df45b76db9a8a523dba": "patrol boat patrol ship boat", "ad251b2cd68290a41c9545c3c7193d55": "patrol boat patrol ship ship tender ship's boat pinnace cutter boat sailboat sailing boat", "c00cee4431f3c2db89d39b85991ba25a": "clipper clipper ship tender ship's boat pinnace cutter sailing vessel sailing ship", "388c5175593b057a66bc841ba9cad258": "clipper clipper ship boat vessel watercraft sailing vessel sailing ship", "806834c2b3c0bce7989a4a13be837150": "clipper clipper ship sailboat sailing boat tender ship's boat pinnace cutter boat sailing vessel sailing ship", "80d381a6760185d8c45977b13fbe7645": "clipper clipper ship boat sailboat sailing boat sailing vessel sailing ship", "860e632b27b9d2469f1deb04fb8ae481": "catamaran boat sailboat sailing boat", "a6571d7e2c8f6113b05dfc874f00d5f3": "catamaran boat", "968848dda6faa772a9c79146773bd1b6": "catamaran boat", "2f682d31b26398cc2218383c174cbd7c": "catamaran ferry ferryboat", "736e45984ce6645cda5505f71d8f8d5b": "catamaran boat sailboat sailing boat yacht racing yacht", "7ba19c8354c66edbb74e6fa236ab84b4": "catamaran weather ship tender ship's boat pinnace cutter boat", "3807ccfaad3716ec59d8a2cd36b3278": "catamaran boat", "217966c23fe43ab83bde759c61eecdb6": "catamaran boat", "6d96d3f9f621292b7eabc15108a128c0": "catamaran sailboat sailing boat yacht racing yacht boat", "e6fa4d02d2df5a9b1bb46d2556ba67d": "catamaran boat yacht racing yacht", "81170170d0cf36eaa5c5aafe837d3f8f": "sailboat sailing boat boat", "9b5077b81457cfc2986bcbcf53ae184f": "sailboat sailing boat boat", "cc7369fee843cbfc8d1322963ff40d1e": "sailboat sailing boat boat", "77331918e10b8eacd07153997ca82b26": "sailboat sailing boat boat", "d5b2fe7ceeed1ff59268271e4d570275": "sailboat sailing boat sailing vessel sailing ship ship cabin cruiser cruiser pleasure boat pleasure craft tender ship's boat pinnace cutter boat yacht racing yacht", "26bdd8852a95cd2d4e4ba49b2943b913": "sailboat sailing boat boat", "52492bb52701aa4eac05cacef62577ec": "sailboat sailing boat ship boat sailing vessel sailing ship", "176d386905bf227da56b564158d089de": "sailboat sailing boat boat", "70d4ca23f31a5c4373c03aee5e8475d4": "sailboat sailing boat boat", "bec39afc5bad2dc45ef003dbb9fb9184": "sailboat sailing boat ship tender ship's boat pinnace cutter boat sailing vessel sailing ship", "8b92073eb06f0365e18960ef54b079a3": "sailboat sailing boat pirate pirate ship tender ship's boat pinnace cutter boat sailing vessel sailing ship", "2b1d09f8412f647899c0080e6a5589fa": "sailboat sailing boat boat", "afcafaf311af722a5446fdbcaa866773": "sailboat sailing boat boat", "de55eb89c357cd5baff407511d7dfc96": "sailboat sailing boat tender ship's boat pinnace cutter boat sailing vessel sailing ship", "dded8a613927539cd72535fe84d882f0": "sailboat sailing boat tender ship's boat pinnace cutter boat", "e42655032562701e746ffcbc44510229": "sailboat sailing boat yacht racing yacht boat", "ed94229d6bb18b783f351db9d8c0e76": "sailboat sailing boat warship war vessel combat ship ship tender ship's boat pinnace cutter boat", "94e1c24d7f205a39779adafea0303e70": "sailboat sailing boat boat", "6e0f329ced87d7a3c9cfe13d43d30dc": "sailing vessel sailing ship tender ship's boat pinnace cutter boat cruise ship cruise liner", "cd1d407bf8637a019a6249baee317a57": "sailing vessel sailing ship tender ship's boat pinnace cutter sailboat sailing boat", "29a131678dfba5339a6e43b878d5b335": "sailing vessel sailing ship school ship training ship ship tender ship's boat pinnace cutter boat sailboat sailing boat", "8bc922537410e1ce847c4e55bb965ab0": "sailing vessel sailing ship boat yacht racing yacht", "5573567872450417d61794222ef2800f": "sailing vessel sailing ship tender ship's boat pinnace cutter vessel watercraft sea boat sailboat sailing boat pirate pirate ship", "de100dcbc3be32f2baeac688ed8ea6cd": "sailing vessel sailing ship cruise ship cruise liner ship cabin cruiser cruiser pleasure boat pleasure craft tender ship's boat pinnace cutter boat sea boat liner ocean liner", "dc27c81f14e3a89c84306f3bf0a1f2d7": "container ship containership container vessel cargo ship cargo vessel man-of-war ship of the line ship tender ship's boat pinnace cutter vessel watercraft sea boat", "591d1b833b9ebfe798b8c800ae001b66": "container ship containership container vessel cargo ship cargo vessel ship tender ship's boat pinnace cutter boat vessel watercraft sea boat", "1a2b1863733c2ca65e26ee427f1e5a4c": "container ship containership container vessel ship tender ship's boat pinnace cutter boat sea boat", "42a8428a3d909fd64de203ef6842ee61": "container ship containership container vessel ship tender ship's boat pinnace cutter small boat", "a64d2249cbe63b5f9cbca1f73829701e": "container ship containership container vessel tender ship's boat pinnace cutter", "5cf29e4dff6535c394eb4a27c704b88": "container ship containership container vessel oil tanker oiler tanker tank ship cargo ship cargo vessel ship tender ship's boat pinnace cutter", "2e4aeae716401155bf9913347b9df76": "oil tanker oiler tanker tank ship ship tender ship's boat pinnace cutter", "ff2b8253ca3190d5d65fb76f5f0a1db7": "oil tanker oiler tanker tank ship tender ship's boat pinnace cutter", "12e012fc28fc8d784de203ef6842ee61": "oil tanker oiler tanker tank ship ship tender ship's boat pinnace cutter boat", "5d48d75153eb221b476c772fd813166d": "oil tanker oiler tanker tank ship ship tender ship's boat pinnace cutter boat", "fdddf0bea9cc33ae81d9d9b1473043a2": "oil tanker oiler tanker tank ship ship cabin cruiser cruiser pleasure boat pleasure craft", "65f78142a6c33a89ea7dce1646d86149": "oil tanker oiler tanker tank ship ship tender ship's boat pinnace cutter boat sea boat cargo ship cargo vessel", "78e347c044ac743de2d4c157e5bf1dde": "oil tanker oiler tanker tank ship ship tender ship's boat pinnace cutter boat", "a6d232b7443609fd20ded1536778b0f": "oil tanker oiler tanker tank ship tender ship's boat pinnace cutter", "c48e3ab1cc01521cdfaef96860d73193": "oil tanker oiler tanker tank ship ship tender ship's boat pinnace cutter aircraft carrier carrier flattop attack aircraft carrier", "1e0c84d82fd52f8c394eb4a27c704b88": "cargo ship cargo vessel tender ship's boat pinnace cutter", "7dc72dc46c46410168ac0d25269a2c67": "cargo ship cargo vessel ship tender ship's boat pinnace cutter boat", "19a72473173bf26ff7bc9a344bb7bae5": "cargo ship cargo vessel ship tender ship's boat pinnace cutter boat aircraft carrier carrier flattop attack aircraft carrier submarine pigboat sub U-boat", "1d8adfa96dca83849143982c80da1e75": "cargo ship cargo vessel ship tender ship's boat pinnace cutter boat", "bb79e21e85306ba19e51f6d13f6d609a": "cargo ship cargo vessel ship tender ship's boat pinnace cutter boat ferry ferryboat", "e3a49772e431fa6a4911559f9f073abb": "cruise ship cruise liner yacht racing yacht ship tender ship's boat pinnace cutter boat", "3948782a95f084b92336765b86c93d9": "cruise ship cruise liner liner ocean liner ship tender ship's boat pinnace cutter boat", "e86aed19f3272118e8340a4561cb4030": "cruise ship cruise liner boat", "20e8718d1c6d5a665a525c461820c6b3": "cruise ship cruise liner liner ocean liner tender ship's boat pinnace cutter boat vessel watercraft", "8c4fb998d99465b48cfe472915a175bb": "cruise ship cruise liner ship tender ship's boat pinnace cutter boat", "14fff3ebab1e144d6b77971fd22cc80d": "cruise ship cruise liner yacht racing yacht ship tender ship's boat pinnace cutter", "6ec6d6a7124542c1973b07394b864922": "cruise ship cruise liner ship tender ship's boat pinnace cutter", "e2ee62e09af461498d84ac376021f1": "cruise ship cruise liner liner ocean liner ship submarine pigboat sub U-boat sea boat", "a88f4ec8440e412c92336765b86c93d9": "cruise ship cruise liner liner ocean liner passenger ship yacht racing yacht ship boat", "bbad61a484ffb3ecf0e4a921fd6a03e7": "cruise ship cruise liner tender ship's boat pinnace cutter vessel watercraft", "b3aa94c4aac0534ec34b1b7198ff8321": "cruise ship cruise liner yacht racing yacht ship tender ship's boat pinnace cutter submarine pigboat sub U-boat sea boat", "4944328c0ea895c53f464857aba8c0eb": "cruise ship cruise liner ship tender ship's boat pinnace cutter submarine pigboat sub U-boat sea boat", "19b29e973ad05c71e0a1fe48a8d148cb": "liner ocean liner tender ship's boat pinnace cutter", "d7fc7ed59558b7b3f3e39f2e17005efc": "liner ocean liner cabin cruiser cruiser pleasure boat pleasure craft", "141b2e976df2d4ed4ccd729f8bcb378": "liner ocean liner tender ship's boat pinnace cutter", "b9a6e086fa26907897eb0d3a9685d3ed": "liner ocean liner passenger ship man-of-war ship of the line ship tender ship's boat pinnace cutter cruise ship cruise liner", "59585673328efdf2efa33903c8f77ab8": "liner ocean liner sister ship ship tender ship's boat pinnace cutter boat vessel watercraft sea boat cruise ship cruise liner", "44b4972b96604e08a2fe1981a00251e0": "liner ocean liner man-of-war ship of the line ship tender ship's boat pinnace cutter", "733105c0a74ab0a2b83ec978f31c7f76": "liner ocean liner passenger ship ship tender ship's boat pinnace cutter boat", "2e19e953c01ddd69d5bb7fc09b61862a": "liner ocean liner ship tender ship's boat pinnace cutter boat vessel watercraft sea boat cruise ship cruise liner yacht racing yacht", "61142394175b9e2b6a42e03bb7759e0c": "passenger ship ship cabin cruiser cruiser pleasure boat pleasure craft tender ship's boat pinnace cutter vessel watercraft", "2458e4e41633c95642f9e27aaf0c47a": "passenger ship ship tender ship's boat pinnace cutter", "8e4fbd72746ed71c20d966555ebf57d2": "passenger ship tender ship's boat pinnace cutter sea boat", "62cebab704dbc0d02b76c9fef45435c7": "passenger ship ship tender ship's boat pinnace cutter boat", "269ada03c39d200498a4434c6f79eefc": "passenger ship tender ship's boat pinnace cutter boat", "58f13d9c96d702c72d5c18abc132bda5": "passenger ship ship sea boat cruise ship cruise liner", "8742b1778f2a6e7deb0cc8797c62d3c8": "pirate pirate ship tender ship's boat pinnace cutter small boat", "88cc060aa20b340611b3e656ab4bd115": "pirate pirate ship tender ship's boat pinnace cutter", "8497e02fa1662113776d8bc79b9caa2c": "pirate pirate ship tender ship's boat pinnace cutter boat sailboat sailing boat sailing vessel sailing ship", "731b8fb370639bec2c8b4a1ac32beb2d": "pirate pirate ship tender ship's boat pinnace cutter sea boat", "f1d39ce0d8c25403b4caa017b521f7": "steamer steamship tender ship's boat pinnace cutter", "de7c3942e8a460ffa9bd9d33180e7ac0": "steamer steamship boat", "2a3e2ecc6c6d3be83b2c1416b6e149a5": "steamer steamship boat", "c8f5e3891a7b0d1188d5590328ce0ddf": "tender supply ship destroyer escort ship cabin cruiser cruiser pleasure boat pleasure craft destroyer guided missile destroyer vessel watercraft", "491a1618a4891fc73d1328bfe2caad0a": "capital ship ship tender ship's boat pinnace cutter tender supply ship warship war vessel combat ship", "3899f8b55ec9f1db8a1ec28cb7d97871": "battle cruiser ship cabin cruiser cruiser pleasure boat pleasure craft tender ship's boat pinnace cutter boat vessel watercraft", "92fec5e70e9c972130327a4f3a474384": "battle cruiser tender ship's boat pinnace cutter", "77327d5b8b56083415a0ff0aad35ca87": "battle cruiser warship war vessel combat ship ship cabin cruiser cruiser pleasure boat pleasure craft vessel watercraft", "25677ae07e0257955dd2c581ea62184": "battle cruiser cabin cruiser cruiser pleasure boat pleasure craft warship war vessel combat ship", "c4830136a2451f8f45aff212946058fc": "battle cruiser ship cabin cruiser cruiser pleasure boat pleasure craft destroyer guided missile destroyer", "1f1fb65b78e0f6ac816e21307f82dd39": "battle cruiser cabin cruiser cruiser pleasure boat pleasure craft", "c746175e0993abc19a6e43b878d5b335": "battle cruiser cabin cruiser cruiser pleasure boat pleasure craft tender ship's boat pinnace cutter boat warship war vessel combat ship", "556a76a5d29e124635952e349f57d": "battle cruiser cabin cruiser cruiser pleasure boat pleasure craft warship war vessel combat ship", "3c3161464bb86b2d3ece64552d44d319": "guard ship ship tender ship's boat pinnace cutter boat warship war vessel combat ship", "2467cd9f4ae70875e26aef76db45b56": "man-of-war ship of the line ship cabin cruiser cruiser pleasure boat pleasure craft cruise ship cruise liner", "eddb01638a0c8be89c9cb44b983e47a": "man-of-war ship of the line ship cabin cruiser cruiser pleasure boat pleasure craft cruise ship cruise liner", "e70fa867da6276efa65562f270f71c81": "man-of-war ship of the line ship tender ship's boat pinnace cutter boat", "cfd92436ff0d0779b011f864ecc25526": "man-of-war ship of the line tender ship's boat pinnace cutter boat cruise ship cruise liner liner ocean liner", "8480865aa92d13dd46687b3834d255fa": "nautilus nuclear submarine nuclear-powered submarine boat submarine pigboat sub U-boat vessel watercraft warship war vessel combat ship", "ad8b5aaa3eb9f2f69330265a9fff38de": "warship war vessel combat ship ship tender ship's boat pinnace cutter", "3cbb8096ac9bef3533ffceb8a4acbfe1": "warship war vessel combat ship ship tender ship's boat pinnace cutter boat", "955507bbb3aa0b97b1bb46d2556ba67d": "yacht racing yacht boat", "f66bca54b97bf719b1bb46d2556ba67d": "yacht racing yacht tender ship's boat pinnace cutter boat submarine pigboat sub U-boat", "f45d51939d8ee9122698fe11741f2ba4": "yacht racing yacht tender ship's boat pinnace cutter boat sailing vessel sailing ship", "ecbfa1faba336185bc33bb3e21836dd7": "yacht racing yacht ship tender ship's boat pinnace cutter boat cruise ship cruise liner", "5a84b0529edd7101d3ca4877ecd2c01b": "yacht racing yacht tender ship's boat pinnace cutter sea boat", "544ed8713bb6b697846f1b9d68a7eecd": "yacht racing yacht ship tender ship's boat pinnace cutter", "85481518c0938a6eb1bb46d2556ba67d": "yacht racing yacht boat vessel watercraft", "d00fe7ce177483ed5cb65f793213d431": "yacht racing yacht boat sailboat sailing boat", "f65e78e69d1d5eac465721b273720f4c": "yacht racing yacht tender ship's boat pinnace cutter", "80d9d9fc9c6138c4800a0120f3e757a2": "yacht racing yacht boat sailboat sailing boat", "1d6bcf04874000a9ea96bfd37cd8bdfb": "yacht racing yacht boat", "a8db7dcc16750af73c83f6e156ad107a": "yacht racing yacht boat", "a88fa5daf540c14a4f6190a680acb783": "yacht racing yacht ship tender ship's boat pinnace cutter boat", "ae8ccc2a90c7d57ff107f18bfa25e296": "yacht racing yacht boat", "9f34d1dbf06b4d989330265a9fff38de": "ship tender ship's boat pinnace cutter boat sea boat", "ead18d83024fab8deb2c303a0e0d287b": "ship tender ship's boat pinnace cutter cruise ship cruise liner", "d262a9cfdeca256e228ca1071b304db": "ship boat", "bb248dedd82b2f28deed0e4a55ad5dd6": "ship tender ship's boat pinnace cutter vessel watercraft container ship containership container vessel", "8e06600c02764ee98aa3ca9bfd776ccf": "ship tender ship's boat pinnace cutter vessel watercraft", "43f7e9f8ad44e8236557d610dab4f1b2": "ship tender ship's boat pinnace cutter boat small boat", "eb1b91b2e6458a813f6d8df212adba77": "ship tender ship's boat pinnace cutter", "2dbe5ea82a45443b71f3cc81eb6c076e": "ship tender ship's boat pinnace cutter destroyer guided missile destroyer submarine pigboat sub U-boat", "fcf21e1176459664806b90e3f08c9a28": "ship tender ship's boat pinnace cutter cruise ship cruise liner yacht racing yacht", "d6cf4f71059b98a13d9a3c3392922df1": "ship cabin cruiser cruiser pleasure boat pleasure craft tender ship's boat pinnace cutter sea boat cruise ship cruise liner", "d25531bd6b7b8274ceb0dd441125df3e": "ship tender ship's boat pinnace cutter boat warship war vessel combat ship", "c189e1768a4f291d4de203ef6842ee61": "ship tender ship's boat pinnace cutter container ship containership container vessel", "bc8cfba04c86ab537117d5d39e7f9235": "ship tender ship's boat pinnace cutter cruise ship cruise liner", "9951a6732eb8438a79662f01dd94fba1": "ship tender ship's boat pinnace cutter", "8e7fd92d91a9974a2d220e1baafcedd8": "ship tender ship's boat pinnace cutter warship war vessel combat ship", "850d6a53ae03a9aab2640ff8dd6f4f82": "ship tender ship's boat pinnace cutter boat", "91a124454518abb7f2ad837508eb2db7": "ship tender ship's boat pinnace cutter submarine pigboat sub U-boat cruise ship cruise liner", "eb214ca65059e8d8fb8de648691848ea": "ship tender ship's boat pinnace cutter boat yacht racing yacht", "6dd448ee6a2288ff5a680bf43a0499b7": "ship tender ship's boat pinnace cutter", "3bcfc7f00e9fb213846571d66928670": "ship tender ship's boat pinnace cutter sea boat sailboat sailing boat sailing vessel sailing ship", "9e653d3b0b8f8c6b28f998b2aa4c5983": "ship tender ship's boat pinnace cutter boat yacht racing yacht", "47130fa6563a4712af6320700e3cf173": "ship ferry ferryboat", "5295994daf6e427dbec3353c29751fdf": "ship tender ship's boat pinnace cutter small boat sailing vessel sailing ship", "390de3a1bd0191c881d9d9b1473043a2": "ship cabin cruiser cruiser pleasure boat pleasure craft", "366c8a02305d2a0f4c9b81664e789bca": "ship tender ship's boat pinnace cutter boat", "f8cf09473d99d5ebfb7214357e073eb7": "ship tender ship's boat pinnace cutter boat yacht racing yacht", "fa18c9283443b67388fb22c0c1b2b173": "ship tender ship's boat pinnace cutter boat warship war vessel combat ship", "e969147de0934040b2533fe4d795f40c": "ship ferry ferryboat", "eb01f5a73f0239a348c096ae17d885c9": "ship tender ship's boat pinnace cutter boat vessel watercraft yacht racing yacht", "eadf347da5aa8877da97052ff1f36504": "ship tender ship's boat pinnace cutter boat sea boat", "81d9e9f072f73e42c71a2f2273897a58": "ship tender ship's boat pinnace cutter", 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ferryboat liner ocean liner", "cc4f56a126353cd81985296f8eaaf6ef": "ship tender ship's boat pinnace cutter boat container ship containership container vessel", "c84cc7d62a9112f7b9d24c378f086d93": "ship tender ship's boat pinnace cutter boat container ship containership container vessel cargo ship cargo vessel", "c1825c8ddf2f263ed6f64c5c0b0cdccc": "ship tender ship's boat pinnace cutter boat", "fef52f113f59137dc541ae04c251c7da": "ship boat sea boat", "b8905c60432aadddb4a8f569bbe9f7c5": "ship tender ship's boat pinnace cutter boat fishing boat fishing smack fishing vessel", "8687ac78ffaa38584de203ef6842ee61": "ship tender ship's boat pinnace cutter boat yacht racing yacht", "861048281f4507b45935a8bd0d2fe1f3": "ship tender ship's boat pinnace cutter boat yacht racing yacht", "cd65ea1bb0e091d5a1ea2dd0a4cf317e": "ship tender ship's boat pinnace cutter boat", "aa83008f44e009177795dd28016933fc": "ship tender ship's boat pinnace cutter sea boat", "92e4ae4dfff684832dbef90d406185fa": "ship boat cargo ship cargo vessel", "8ff4c7162259b1d5cf816caaec167fbf": "ship tender ship's boat pinnace cutter boat", "73e6d10ad309d831ddb86907b4b5f05d": "ship tender ship's boat pinnace cutter boat sea boat", "325c2da58c641c12e3bdba9430565083": "ship tender ship's boat pinnace cutter boat", "fa63720ea3f8bd379a6e43b878d5b335": "ship tender ship's boat pinnace cutter sailboat sailing boat sailing vessel sailing ship", "4c1c5200b239b15db700877f4d4ef8cc": "ship tender ship's boat pinnace cutter boat", "969163f17ce6467d9378da473ee38a8d": "ship ferry ferryboat", "8b5ccc3b1eba2c9bdf921d807a1adbf": "ship tender ship's boat pinnace cutter boat", "b6eefb8fe960df93d4dd83fc93961e73": "ship tender ship's boat pinnace cutter sea boat sailboat sailing boat", "bea93ebc1fa965ab34292139f0435d74": "ship tender ship's boat pinnace cutter", "4765a72ac8a0eec3993eb1c02818be1e": "ship ferry ferryboat", "36123091df7600dab1f22ce939b4db68": "ship tender ship's boat pinnace cutter ferry ferryboat", 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tanker oiler tanker tank ship", "7c4ed531d1ecacb156258bc213b2f9c6": "ship boat cargo ship cargo vessel", "46421ef3a412660b428cf87eb5ebe5e6": "ship tender ship's boat pinnace cutter boat", "459decbc3c6bb150ba2abf2ecc57eeda": "ship tender ship's boat pinnace cutter boat", "69c4fbba77ee7891a3c3bb12365964c6": "ship boat sailing vessel sailing ship yacht racing yacht", "1e127a7ca9bfcba86df38a7dd1b448e6": "ship cabin cruiser cruiser pleasure boat pleasure craft patrol boat patrol ship", "2507ed5c9eb161569a704df179502ac7": "ship tender ship's boat pinnace cutter", "5da69ec4bee76b21f3e39f2e17005efc": "ship ferry ferryboat", "cc3ebcb96229bd325f8dd1b77a5d87c4": "ship boat", "65e21949247e36a541db137505613210": "ship tender ship's boat pinnace cutter", "4b501c73f3cb02c63438e17bc657daf1": "ship tender ship's boat pinnace cutter", "268110d849a3aafdec2f14500e179e92": "ship tender ship's boat pinnace cutter boat warship war vessel combat ship", "bc4ab06257678e8db25f0198c2ecfb0e": "ship tender ship's boat pinnace cutter sea boat", "782b29db43c02f7442b917ff8145b491": "ship tender ship's boat pinnace cutter vessel watercraft", "daf5575e801967cdcefe7473eb81422f": "ship tender ship's boat pinnace cutter yacht racing yacht", "f5217865935d63c61f2cd7f4ede43a74": "ship cabin cruiser cruiser pleasure boat pleasure craft warship war vessel combat ship", "e8b93c451e4278277220891f188bc420": "ship tender ship's boat pinnace cutter boat", "7aeadfeb93825b387b3c166137b0d772": "ship tender ship's boat pinnace cutter vessel watercraft", "b4f254a5c49748a2db2b485c26c9f191": "ship tender ship's boat pinnace cutter boat sea boat yacht racing yacht", "71aedddffbb7b1befb4a964cf6619fc2": "ship tender ship's boat pinnace cutter", "85dbbed13875a3a2b90142192ec795d": "ship tender ship's boat pinnace cutter boat", "6a7ce5d7027678c299429033f40f0f8b": "ship cabin cruiser cruiser pleasure boat pleasure craft boat", "cdaff2fe98efb90058a8952c93ff9829": "ship tender ship's boat pinnace cutter boat sailboat sailing boat sailing vessel sailing ship", "4b63a567e41b63974de203ef6842ee61": "ship boat", "8666e4e88dc55c5795ea46728bcdc15d": "ship tender ship's boat pinnace cutter boat sailboat sailing boat sailing vessel sailing ship", "b0e8c331eacdc9bef3e39f2e17005efc": "ship cabin cruiser cruiser pleasure boat pleasure craft tender ship's boat pinnace cutter boat vessel watercraft yacht racing yacht", "5c116a0ca9477e44318f6eb5e296bb88": "ship tender ship's boat pinnace cutter sailing vessel sailing ship", "b10850ed8a61d4dd148bc340784a8c1": "cabin cruiser cruiser pleasure boat pleasure craft warship war vessel combat ship", "d35fc449232d68474163d08524f89006": "cabin cruiser cruiser pleasure boat pleasure craft boat", "cab7f319f0b3cf8e57e59e0ad2d11e74": "cabin cruiser cruiser pleasure boat pleasure craft warship war vessel combat ship", "d271233ccca1e7ee23a3427fc25942e0": "cabin cruiser cruiser pleasure boat pleasure craft", "27d32838007b5a07c63bc8fdb41b8eea": "cabin cruiser cruiser pleasure boat pleasure craft", "c06ec9a0e32cfd11abdc408c54a4b577": "cabin cruiser cruiser pleasure boat pleasure craft", "d0e9f4c4bb582c3cfa0636f27a8b2a9c": "cabin cruiser cruiser pleasure boat pleasure craft", "da675d2149827fe56ba3edb0853a984f": "cabin cruiser cruiser pleasure boat pleasure craft yacht racing yacht", "7e35bae76c10fd7968cdca3005c2cffc": "cabin cruiser cruiser pleasure boat pleasure craft aircraft carrier carrier flattop attack aircraft carrier vessel watercraft warship war vessel combat ship", "2340319ec4d93ae8c1df6b0203ecb359": "cabin cruiser cruiser pleasure boat pleasure craft", "884454f0d5a376c295ea46728bcdc15d": "cabin cruiser cruiser pleasure boat pleasure craft boat sailboat sailing boat yacht racing yacht", "9de5e010b46919293bb25e78abe7f382": "cabin cruiser cruiser pleasure boat pleasure craft boat", "296f0b6a15012e33d87f29c9afcc633e": "cabin cruiser cruiser pleasure boat pleasure craft", "d76ad4ffab09bf27f3e39f2e17005efc": "cabin cruiser cruiser pleasure boat pleasure craft cargo ship cargo vessel", "108d62bb197c948c60c7b57bf6d67c74": "cabin cruiser cruiser pleasure boat pleasure craft", "badc6f1a2c19f724f3e39f2e17005efc": "cabin cruiser cruiser pleasure boat pleasure craft", "6c1c628f4ff60addc5b0cf694baa734f": "cabin cruiser cruiser pleasure boat pleasure craft", "38b6dc2ce1e8cd05e8d6be23f236225a": "cabin cruiser cruiser pleasure boat pleasure craft", "56e2b7332f9011b28fecb9f40039f353": "cabin cruiser cruiser pleasure boat pleasure craft warship war vessel combat ship", "91fd8bfce87a0e7d847c4e55bb965ab0": "cabin cruiser cruiser pleasure boat pleasure craft tender ship's boat pinnace cutter boat sailboat sailing boat sailing vessel sailing ship yacht racing yacht", "a9eda0d90297844cf92c4a072103d4f3": "cabin cruiser cruiser pleasure boat pleasure craft warship war vessel combat ship", "9bb4fa1026322f0f3e39f2e17005efc": "cabin cruiser cruiser pleasure boat pleasure craft", "4f245403e6366d48fb3294f1e40c8a29": "cabin cruiser cruiser pleasure boat pleasure 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"8eeb2bec8499fc6ba60ce6f0a0028c13": "tender ship's boat pinnace cutter boat sea boat", "92e3343250e181e1428cf87eb5ebe5e6": "tender ship's boat pinnace cutter sailboat sailing boat sailing vessel sailing ship", "9472a24df8372cd42e436d38f27146ec": "tender ship's boat pinnace cutter", "a7b07a2aad642f3f18b731fab8bc3743": "tender ship's boat pinnace cutter boat", "abb16b391d1634a84de203ef6842ee61": "tender ship's boat pinnace cutter boat", "ae93355992a29c428064e4301e947b0d": "tender ship's boat pinnace cutter sailing vessel sailing ship", "b04e4650e547e0509803291b0f4bf532": "tender ship's boat pinnace cutter", "b1bc73627283a4076b861850ae66c9d1": "tender ship's boat pinnace cutter", "bc09ba292bd9e684610faa04834ad4e6": "tender ship's boat pinnace cutter cruise ship cruise liner", "bc9448e22c2379d487f08fb94cd2785e": "tender ship's boat pinnace cutter submarine pigboat sub U-boat cruise ship cruise liner", "bffc229892a3d301c8bb4876165f947c": "tender ship's boat pinnace cutter vessel watercraft", "d8acb2681ea3e2466c64e0542c2b0af": "tender ship's boat pinnace cutter", "1b5e114692abf9a5fdbbffe94281448": "tender ship's boat pinnace cutter boat yacht racing yacht", "1e8c9dd30d54392856d301bf1319ad4": "tender ship's boat pinnace cutter sailboat sailing boat sailing vessel sailing ship", "e28a79fa4880c3c5b0df8f7e348f816a": "tender ship's boat pinnace cutter boat sea boat yacht racing yacht", "23c31b1c25689d2a7ba04de7d6d93ce8": "tender ship's boat pinnace cutter cruise ship cruise liner", "291e48a99be418846c03c11657c96c60": "tender ship's boat pinnace cutter boat fishing boat fishing smack fishing vessel", "eb891509436863da874f0e42f84eb4f5": "tender ship's boat pinnace cutter boat vessel watercraft fishing boat fishing smack fishing vessel", "31ddf87b5ef64c9080c9c53be28cd7ba": "tender ship's boat pinnace cutter boat small boat", "32dc0308ca44f256ae9e3df35a893c9": "tender ship's boat pinnace cutter boat vessel watercraft container ship containership container vessel", 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machine motorcar racer race car racing car", "e69a5ad60aad3da2292296f0c84d7087": "convertible hot rod hot-rod roadster runabout two-seater car auto automobile machine motorcar", "2a844013c66dd19d32e3a970ba4fb82b": "convertible car auto automobile machine motorcar", "c6709eb00a557c9785af3668c1cb30b": "convertible car auto automobile machine motorcar roadster runabout two-seater", "2ef03b91692d6dae5828087fef11ba9b": "convertible roadster runabout two-seater car auto automobile machine motorcar", "b9e20b751d869418ce2714c5fa8be19d": "convertible car auto automobile machine motorcar", "a111cef175177b3cd89cde5d5f759ea7": "convertible sedan saloon roadster runabout two-seater touring car phaeton tourer car auto automobile machine motorcar", "385f12fba67e43ada9fe1734a6086750": "convertible roadster runabout two-seater car auto automobile machine motorcar", "bf523e37ff88cb10e23d60a1b706b44f": "convertible car auto automobile machine motorcar", "7e5b55d43322de49295eb8d2a941a3e1": "convertible 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racing car", "3e823f89c611fe57a413908c0e169330": "coupe car auto automobile machine motorcar", "e0bf76446d320aa9aa69dfdc5532bb13": "coupe stock car roadster runabout two-seater car auto automobile machine motorcar", "25f9e0e65b8fae1f9916e41a3ca14d3a": "coupe stock car car auto automobile machine motorcar", "9cea9b79ea255dde56ffccfd33efbc84": "coupe car auto automobile machine motorcar convertible sedan saloon", "8b7b6c2a5c664ca6efe5f291bc2f5fd0": "coupe racer race car racing car stock car car auto automobile machine motorcar", "1213742aa7815fd0e23d60a1b706b44f": "coupe stock car car auto automobile machine motorcar", "12a034b6be9afb50ae983613a2e0a741": "coupe cruiser police cruiser patrol car police car prowl car squad car car auto automobile machine motorcar", "7492ced6cb6289c556de8db8652eec4e": "cruiser police cruiser patrol car police car prowl car squad car panda car car auto automobile machine motorcar", "3a3d23c4d325252aaaafd94b9f216ef6": "cruiser police cruiser patrol car police 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"racer race car racing car car auto automobile machine motorcar", "5ff285283e8ec610434fdb742d7e0cea": "racer race car racing car car auto automobile machine motorcar", "6edcd628ecd0142b4f2f12fbdc4e6f1": "racer race car racing car roadster runabout two-seater", "51b8011a2eaaed85cde8c99e4b182f9": "racer race car racing car car auto automobile machine motorcar", "95d36965fc6d1f7de2d4c157e5bf1dde": "racer race car racing car car auto automobile machine motorcar", "641e43fc49e754edbcfa8eb5a2d224d7": "racer race car racing car car auto automobile machine motorcar", "1284876ac0e2371aa4dbb766064f8bbf": "racer race car racing car car auto automobile machine motorcar", "afc23aacd3e166d9f513bf8e198d377a": "racer race car racing car hot rod hot-rod car auto automobile machine motorcar", "296a15d4e4b903db94b42287616a3ea7": "racer race car racing car car auto automobile machine motorcar", "10f158d2f43926ac80360680c1602c7d": "racer race car racing car stock car car auto automobile machine motorcar 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motorcar", "95a6e03656bdff26f69bc9777f9c3c39": "minivan", "b40c9e44350f95aa1b9234e4ab67a3eb": "minivan", "1e17510abe8f98bea697d949e28d773c": "minivan car auto automobile machine motorcar", "1dc757e77f3cfad0253c03b7df20edd5": "minivan car auto automobile machine motorcar", "498e4295b3aeca9fefddc097534e4553": "minivan car auto automobile machine motorcar", "982448cbcbe56bb78f89cf8d2f8a85b0": "minivan", "20ece5093ff8da0bbda72093f9b5aa73": "minivan car auto automobile machine motorcar", "45186c083231f2207b5338996083748c": "minivan", "c0b9eb823b9fff9a2764cfba57a5de73": "minivan", "d41c66c7b1ff1f57979ef391885ee5d2": "minivan car auto automobile machine motorcar", "a5d32daf96820ca5f63ee8a34069b7c5": "minivan car auto automobile machine motorcar", "ec994111db1da0d6927e12317acf49e7": "pace car car auto automobile machine motorcar racer race car racing car", "9901e5d7520df242c61cbe35764dfac1": "stock car car auto automobile machine motorcar", "6f7cc4940eee83e57bcd1993e004117a": "stock car car auto automobile machine motorcar", "c9e555bb1a17cfbc5d918097f81825e3": "stock car touring car phaeton tourer car auto automobile machine motorcar racer race car racing car jeep landrover", "ecf4e8f49102db90a27a0cbcc84ba703": "stock car car auto automobile machine motorcar", "7ccdf7bc2af0b068e43ab89cb5563740": "stock car car auto automobile machine motorcar sedan saloon", "1724ae84377e0b9ba6c2c95b41a5446d": "stock car car auto automobile machine motorcar coupe", "2307ced410b955a8cf1e58caa15acb49": "stock car racer race car racing car", "84ffded0ab4b499588ed7b6315c3b4a": "stock car car auto automobile machine motorcar racer race car racing car", "12a445770f7d6f2b70a43c2d978e502e": "stock car car auto automobile machine motorcar racer race car racing car", "8fc220461ea512f3abeba927b56dd398": "stock car car auto automobile machine motorcar", "80642f8965edbc51ba01f2d8d5d9ff0": "stock car car auto automobile machine motorcar coupe", "a88d69e53a01aab9ff1370452e3a0154": "stock car sedan 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"roadster runabout two-seater car auto automobile machine motorcar", "e7f40f595169f1b44a383db4764d5432": "roadster runabout two-seater car auto automobile machine motorcar", "d4251f9cf7f1e0a7cac1226cb3e860ca": "touring car phaeton tourer racer race car racing car", "3a735f1a5fe8906cab0fd77f2e9aa584": "touring car phaeton tourer car auto automobile machine motorcar coupe", "1b25c745164e53b822d2fb8fec68f2a1": "touring car phaeton tourer car auto automobile machine motorcar convertible roadster runabout two-seater", "7022a37dd7fc841ce98e65cfc071e8a0": "touring car phaeton tourer car auto automobile machine motorcar", "d691906c87bb2850a9733fa2ac6b95bc": "touring car phaeton tourer car auto automobile machine motorcar", "3bd66fc2782bd2019766e05e7d6c9088": "touring car phaeton tourer car auto automobile machine motorcar racer race car racing car", "53895830484cd75d9a68e1b6bb3d1631": "touring car phaeton tourer car auto automobile machine motorcar", "dbded0816afdfcf776ad064d1c5fdd7c": "touring car phaeton tourer sedan saloon", "49a93fdbcaa0302ae434963ea517a487": "touring car phaeton tourer", "c21cb9575910e30bf0174ad879a5b1cc": "touring car phaeton tourer", "b8c8758f8ee058182a9eb0f146e94477": "touring car phaeton tourer car auto automobile machine motorcar sedan saloon", "38f323fd119d14ed2a9eb0f146e94477": "touring car phaeton tourer car auto automobile machine motorcar sedan saloon", "8df83b28d786ccfc10f2ef3071342a2e": "touring car phaeton tourer car auto automobile machine motorcar sedan saloon", "c17e88a6b0267754c6529edfd0032791": "touring car phaeton tourer car auto automobile machine motorcar coupe", "29793366c4b45bf6f43c51e53897fea": "touring car phaeton tourer car auto automobile machine motorcar", "40b272e537fae1418277d1ad96e14e9a": "touring car phaeton tourer car auto automobile machine motorcar sedan saloon", "c4941d6a2266813c3a2b1a2e5e169910": "touring car phaeton tourer car auto automobile machine motorcar sedan saloon", "f120085da70b2913627279ad10ae805a": "touring car phaeton tourer car auto automobile machine motorcar racer race car racing car", "783577d449be4ded3121f82ae456ac81": "touring car phaeton tourer car auto automobile machine motorcar coupe sedan saloon", "825fb860d71c78a477c771b4b7eed82c": "touring car phaeton tourer car auto automobile machine motorcar", "b0fa729f031a97d2c3c561b608f78503": "touring car phaeton tourer car auto automobile machine motorcar racer race car racing car", "94c0575659c6cdf2091b4cb93147ff9": "touring car phaeton tourer car auto automobile machine motorcar", "791e45f93e9ba58071a126804ae24042": "car auto automobile machine motorcar sedan saloon", "48071634bdeb66e271a126804ae24042": "car auto automobile machine motorcar coupe sedan saloon", "15c785a7505992c3a9be8fd09fb14f4f": "car auto automobile machine motorcar", "145e18e4ec54ed5792abb3e9ac4cd40c": "car auto automobile machine motorcar beach wagon station wagon wagon estate car beach waggon station waggon waggon", "ffe0a5b165461eb66a2345809e2bb169": "car auto automobile machine motorcar", "faeda504709a7af489d347432ecb438c": "car auto automobile machine motorcar", "cb5a320fbd846fac28e9ca8e8fb04e0a": "car auto automobile machine motorcar", "c9bc4690b171e5d7f0ae46ab6a68f9fe": "car auto automobile machine motorcar", "c769d45be62178f516abce8cb03e7794": "car auto automobile machine motorcar sedan saloon beach wagon station wagon wagon estate car beach waggon station waggon waggon", "a944ae569bbff87716abce8cb03e7794": "car auto automobile machine motorcar beach wagon station wagon wagon estate car beach waggon station waggon waggon", "8b2f030e604925c92a9eb0f146e94477": "car auto automobile machine motorcar", "89eb1c5370b80567473f10e6caaeca56": "car auto automobile machine motorcar", "8364ea0a27342c9d16abce8cb03e7794": "car auto automobile machine motorcar coupe", "82d37fceb984256bf200a72c9245aee7": "car auto automobile machine motorcar racer race car racing car", "8070747805908ae62a9eb0f146e94477": "car auto automobile machine motorcar", "7b00e029725c0c96473f10e6caaeca56": "car auto automobile machine motorcar", "721dfe60b48849ec7ac8cd969c3298ce": "car auto automobile machine motorcar", "6e25d6cd4b112d0654ed192f2298faac": "car auto automobile machine motorcar stock car", "53b6f9eb4337276473f10e6caaeca56": "car auto automobile machine motorcar racer race car racing car", "53a604e4037c650beedcafe524c3dc4c": "car auto automobile machine motorcar coupe", "46c504c9760fcf0e7540f431cab4686": "car auto automobile machine motorcar sedan saloon beach wagon station wagon wagon estate car beach waggon station waggon waggon", "45d94dee6634efd516abce8cb03e7794": "car auto automobile machine motorcar sedan saloon", "40a30a8e56fa4a6c7b48e36e31209b9": "car auto automobile machine motorcar convertible coupe sedan saloon", "335b3ad6f7a96bf251a95aaa6caba1d3": "car auto automobile machine motorcar sport utility sport utility vehicle S.U.V. SUV", "287f4c8f87099957473f10e6caaeca56": "car auto automobile machine motorcar racer race car racing car", "1c3c8952b92d567e61c6c61410fc904b": "car auto automobile machine motorcar convertible roadster runabout two-seater", "1a1dcd236a1e6133860800e6696b8284": "car auto automobile machine motorcar coupe", "193ec1089f0a98a36518e6cd1fbc5fab": "car auto automobile machine motorcar", "187b002f0af1ab5a473f10e6caaeca56": "car auto automobile machine motorcar racer race car racing car", "c43b1ebad0ba016a7fd25564c2e888e": "car auto automobile machine motorcar", "382496958e58e9a0658e57fd275f6bed": "car auto automobile machine motorcar", "dfce5b313bebfba3ef8e28849949a11a": "car auto automobile machine motorcar racer race car racing car", "ceb561c72a407ddfdc50a691c68014fd": "car auto automobile machine motorcar", "a8ab20351c2e7542912749d867981a40": "car auto automobile machine motorcar sedan saloon", "a5476137d0f2f930425b2c354eccabaf": "car auto automobile machine motorcar coupe", "6c5c45f7a474daa9a134736201a79843": "car auto automobile machine motorcar coupe", "fd3c767468a825ba860800e6696b8284": "car auto automobile machine motorcar coupe sedan saloon", "f1c277f7fc421492fbe59dad8284dc0": "car auto automobile machine motorcar", "dcdee01d7fe6e1a76ad064d1c5fdd7c": "car auto automobile machine motorcar", "b6345c309ba64f97e97c6bb243ef67d": "car auto automobile machine motorcar", "986ed07c18a2e5592a9eb0f146e94477": "car auto automobile machine motorcar", "9586fcdd105347123ed4cb179e56ad61": "car auto automobile machine motorcar coupe", "7d343bbf3265164fb2d2900ee0ae7e0": "car auto automobile machine motorcar racer race car racing car", "18d9ac72442260e0e97c6bb243ef67d": "car auto automobile machine motorcar", "fb6c92a80dd9af59b528c0abff96f586": "car auto automobile machine motorcar coupe", "f8857237df1717e3aa562f24645e326": "car auto automobile machine motorcar sedan saloon", "eb144af0fef08314f00cd8c386c592fe": "car auto automobile machine motorcar", "e90b0381a7c8841c1c65421fe734e7bc": "car auto automobile machine motorcar", "22654a0cc1ba382c4eace6243432632": "car auto automobile machine motorcar", "bf0e8d944ba4843e94e7e8e27399daf7": "car auto automobile machine motorcar", "8c1664cc8771aa3fce95c44a0ed1e01b": "car auto automobile machine motorcar cruiser police cruiser patrol car police car prowl car squad car sport utility sport utility vehicle S.U.V. SUV beach wagon station wagon wagon estate car beach waggon station waggon waggon jeep landrover", "80e839edb7aeae0bc54427e2c0943fd5": "car auto automobile machine motorcar", "3163c8037d5782365688db6590f826c8": "car auto automobile machine motorcar", "1ee4f19fa617ec5949493d905c02fa86": "car auto automobile machine motorcar", "fb97ee1707f495bbdcff2899f073ea9d": "car auto automobile machine motorcar sport utility sport utility vehicle S.U.V. SUV beach wagon station wagon wagon estate car beach waggon station waggon waggon", "f8dc466677c26ac6f63ee8a34069b7c5": "car auto automobile machine motorcar sport utility sport utility vehicle S.U.V. 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carrier carrier flattop attack aircraft carrier ship", "583ef94812afcd26e025e00c738da634": "aircraft carrier carrier flattop attack aircraft carrier vessel watercraft warship war vessel combat ship", "a2d4f32a82902118aaac2db0a60f403c": "aircraft carrier carrier flattop attack aircraft carrier", "5d0d84924e96eee6b6162efbd23a9d5f": "aircraft carrier carrier flattop attack aircraft carrier ship", "8f9e62cf323515c4ca380c8375c4cdb6": "aircraft carrier carrier flattop attack aircraft carrier", "67da87349baf66e3c1ccec171a275967": "aircraft carrier carrier flattop attack aircraft carrier", "3173f703a1e52d7f38d037116a032a18": "aircraft carrier carrier flattop attack aircraft carrier ship", "486e953ff691d414d8190076748f5b20": "cruiser warship war vessel combat ship", "2f0acfd47622a8fa419fe456f622e6b2": "cruiser ship", "e51dc5bb6b97700747d2f4d1390fa93e": "destroyer guided missile destroyer ship", "da738558b1230b8288d5590328ce0ddf": "destroyer guided missile destroyer warship war vessel combat ship ship", "8595fde74bf250a5d9fd1bb6b75d9865": "destroyer guided missile destroyer surface ship warship war vessel combat ship", "3b8c16861667747fcfea3d4fc15719ea": "destroyer guided missile destroyer surface ship ship", "395471e88200756d88d5590328ce0ddf": "destroyer guided missile destroyer ship warship war vessel combat ship", "4a523f749fd08ffa698137c717c4ad11": "destroyer guided missile destroyer ship", "99450835aba6e4c4da2951c4977eb283": "destroyer guided missile destroyer warship war vessel combat ship ship", "1f7f1d7c3882f638fb64f9487ce62dd2": "destroyer guided missile destroyer ship", "c6e36ffba5630fcd7301f2b895d332ac": "destroyer guided missile destroyer warship war vessel combat ship", "2005e1613f1a46b5c083d5272d526845": "destroyer guided missile destroyer destroyer escort ship", "5bd0346e72866c8256f77b6bc6731cda": "destroyer guided missile destroyer", "a2e82524f7920e12d3ded2945a65f23f": "destroyer guided missile destroyer ship vessel watercraft tender supply ship", "1b2a8980c17fdd97c897e89b561760b1": "destroyer guided missile destroyer ship", "34b77143d6313e3f717ad2ef4c4af2e5": "destroyer guided missile destroyer", "5702b12bfba4b9eb9223657c918f0a45": "submarine pigboat sub U-boat", "5c0e6663c131610a2c3770cecdd58208": "submarine pigboat sub U-boat", "313aaf9d79105fea82fd5ed7e39258c7": "submarine pigboat sub U-boat", "b9dde09099e081e65f76b0776a50c136": "submarine pigboat sub U-boat", "a77c64b41bcc66bbca4b459ebb5d9bcb": "submarine pigboat sub U-boat", "340bed24d1dbd33ba9142e7277ee08f1": "vessel watercraft", "6fbf3621aa1d3a8e49f0e3dd7767881": "vessel watercraft sailing vessel sailing ship", "ebb7451625233d772afc5c10dbf83b25": "vessel watercraft container ship containership container vessel liner ocean liner ship", "a7fdfc15268ff4d68413778731d6cd28": "vessel watercraft", "6fbb26f9372ee87648192cebc0015590": "vessel watercraft", "8ecf57984f77fe84583ae8949bf11b56": "vessel watercraft passenger ship ship cruise ship cruise liner", 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"8b335b0be37debefd85e5191b992b560": "fishing boat fishing smack fishing vessel", "e177d4186bff96ea6dae8586082d789": "patrol boat patrol ship", "b7f288e9187a72ea5d3426ee101e9f8e": "patrol boat patrol ship vessel watercraft", "a53312711cd84c9fcfea3d4fc15719ea": "patrol boat patrol ship", "d4882179bb3a1868abc6b16fdb345d24": "patrol boat patrol ship", "194ea4b2297cc2ce90c91984b829ab51": "patrol boat patrol ship", "6436ddeaa11efffdcdd34f38f87d26cf": "patrol boat patrol ship ship", "5c54100c798dd681bfeb646a8eadb57": "patrol boat patrol ship", "2e447f96f152a33a7428866500a95dd8": "patrol boat patrol ship", "99ccd811a0e3052634e1b4a71050e6de": "patrol boat patrol ship ship", "18761559208a970188d5590328ce0ddf": "patrol boat patrol ship submarine pigboat sub U-boat", "1fe84ad7b78667e47428866500a95dd8": "patrol boat patrol ship", "f2b504b2e368bee6d3c2fcae9650358c": "bark barque", "7703ba29545cc381acf9e4b0d73d2434": "bark barque", "6974280e14023bd6bf984c00fef39915": "clipper clipper ship", 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"377497ba926616fbfd7a80a80a3ebd45": "catamaran", "7eefcf457d32b9b03146aa85e47ab8e1": "catamaran", "f5fc09f67a5fdb0142d0e83abdfab1fd": "catamaran", "aac813074775281a4163d08524f89006": "catamaran", "3d4b80858a5ae3d694daa4565dd30535": "catamaran", "94aeb294adb9be58b05dfc874f00d5f3": "catamaran sailboat sailing boat yacht racing yacht", "1c89943a24952d1737f4612124cea14f": "catamaran", "29af666e04825f66576378847ca0b69": "catamaran", "a31a0ca1247e23ceb1bb46d2556ba67d": "sailboat sailing boat yacht racing yacht", "c554a1d4895b67b457f5c96598c7eb45": "sailboat sailing boat sailing vessel sailing ship", "fe09fbe9e43aa889a08db804545b684": "sailboat sailing boat", "fe24ea1bc3383631370d3273b84cb8bf": "sailboat sailing boat", "8b57b62c984575334f89761f054e2019": "sailboat sailing boat", "55dce6844c2bb0145368bed34e5564d5": "sailboat sailing boat ship sailing vessel sailing ship", "cc3957e0605cd684bb48c7922d71f3d0": "sailboat sailing boat sailing vessel sailing ship pirate pirate ship", "7c4b6e4ef996f109a2b4e6eee41ad14a": "sailboat sailing boat", "d6ad84cfb652f5dd27ac692756be9e24": "sailboat sailing boat", "dbe05209a14fca8fdf72e713dd4f492a": "sailboat sailing boat", "a3459aeef2676d7653b838b6058a8d95": "sailboat sailing boat", "3b662a6ae2eab330d7ea446be069b8cf": "sailing vessel sailing ship pirate pirate ship ship", "76dc1a35fee28991a5103c3a2f33a5e9": "sailing vessel sailing ship", "4ce56405f7f67d3c38cd6a6c9620e96d": "sailing vessel sailing ship", "379fa7ba7c753376619ef21bc094a137": "sailing vessel sailing ship yacht racing yacht", "4adb08a459076889574b6f4c9eeb8eea": "container ship containership container vessel cargo ship cargo vessel", "4c433697f371db56459e601c4b110252": "container ship containership container vessel", "799d446d33e604f990f7927ebadab5fc": "container ship containership container vessel tender supply ship cargo ship cargo vessel", "78c2790d999b0f9c1403af6fa4ab17f0": "oil tanker oiler tanker tank ship", "2ca06dc9165d4cdf5fbe3e7e4d41e98c": "oil tanker oiler tanker tank ship", "79284d5d39e4738394749f185cc702f2": "oil tanker oiler tanker tank ship", "6b2fb8cbc5365f5c8cfe472915a175bb": "oil tanker oiler tanker tank ship man-of-war ship of the line ship", "b1b5e16d0c96f34c46b7a006accd201": "oil tanker oiler tanker tank ship", "8500272b43352ae6f844b7e844d01ddc": "oil tanker oiler tanker tank ship cargo ship cargo vessel", "9c3810b8bc98c59426fa791c6af15500": "oil tanker oiler tanker tank ship warship war vessel combat ship", "d841f12d8e22039f56f77b6bc6731cda": "oil tanker oiler tanker tank ship", "2fe634902b6ba945b4e4ece897f04daa": "oil tanker oiler tanker tank ship", "5e0e9215f91c163d7c513e87d3f6c6c": "oil tanker oiler tanker tank ship", "e853f137948055b188d5590328ce0ddf": "oil tanker oiler tanker tank ship cargo ship cargo vessel tender supply ship ship vessel watercraft warship war vessel combat ship", "e64bb04b54022e708d7bd537eb907025": "oil tanker oiler tanker tank ship", "f1800fb3a9873af1e64f09105f9c8a56": "oil tanker oiler tanker tank ship", "a6af4bc3629fc5101998595492ff7acf": "oil tanker oiler tanker tank ship", "c2b394de8407988bb538a15c6737824e": "oil tanker oiler tanker tank ship ship cargo ship cargo vessel", "dcaf4238da8ef379106f24d0bacaba69": "cargo ship cargo vessel ship", "44d1e432239591887b2cd6c1f3acedb0": "cargo ship cargo vessel vessel watercraft", "112743160fae5bd7ad83f267e8f0b536": "cargo ship cargo vessel", "577acbe57ebdcbbdeeb8eb76c515e836": "cargo ship cargo vessel ship vessel watercraft container ship containership container vessel", "aec5ec913aa2084b543941eaaf04581f": "cargo ship cargo vessel vessel watercraft", "f7f62b9657aecbe77f00b68989ad3ebc": "cargo ship cargo vessel", "9b300840d96e46f08c173f7caf7ef0ff": "cargo ship cargo vessel", "943567584893fd5218591932caa46ff6": "hospital ship ship", "598f1f474f1cedcf33827bf0303796ff": "hospital ship", "320683da0d8bd1f394a6427195fa0bd9": "cruise ship cruise liner ship", "5eb2d085267142f26192896700aa3bd4": "cruise ship cruise liner ship", "38189a2101b5e6c3da32b7c218678a0d": "cruise ship cruise liner ship", "e3431dc06c6f4c22441d5d15cc0399df": "cruise ship cruise liner ship", "99b9eb5e1a7dd3b9e4614310319ea50": "liner ocean liner", "33a9a04e7d8ab8ce861ba49603151327": "liner ocean liner cruise ship cruise liner", "d23e71adcc315d56c748af2814b558c4": "liner ocean liner", "4869abf7743f09b71f29de30ec1bf6a3": "liner ocean liner man-of-war ship of the line ship cruise ship cruise liner", "7488e965278a8b8524e54dbd7430a489": "liner ocean liner ship cruise ship cruise liner", "677fb8c312d9c5642712ffd807ced208": "liner ocean liner man-of-war ship of the line ship", "3efda6b0c4e70f7decdcd70d755bfc70": "liner ocean liner cruise ship cruise liner", "d3043fff20dad5c41dc762869682f4f": "liner ocean liner passenger ship ship vessel watercraft cruise ship cruise liner", "fa8af8f2bedb0d461f29de30ec1bf6a3": "liner ocean liner ship cruise ship cruise liner", "be357b15497cc2133a18a4a0def966f": "liner ocean liner", 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pirate ship", "e5bb455414be46bbe62f291403035429": "pirate pirate ship ship", "6de5985910b222f8afa96d6d0eac8962": "pirate pirate ship", "37ea55e2d023f9ae9588e71e21de9f30": "pirate pirate ship", "337381e4b5c11c4f8c9312de4e9cb8a8": "pirate pirate ship", "15d805635f77420ef724cdbbd599557b": "pirate pirate ship ship", "5bcfb42ffc3490c4af802a9d0ab09410": "pirate pirate ship ship", "3f6340ffdc0de35caba19ce79eba1cbe": "pirate pirate ship", "d3de6c76e6d96f7fc5fc3eafa9cdfb3f": "sister ship steamer steamship man-of-war ship of the line", "35857ac61388f9fec2912ef1a1302821": "sister ship ship", "e3fe6f0e74fa641c2247d987054751d2": "sister ship ship", "80e8c7703883a40c9e4614310319ea50": "sister ship", "5e9890e2f4cab96dbc6cd96a5e6546c": "sister ship man-of-war ship of the line ship cruise ship cruise liner liner ocean liner", "a02fb3d1e8e9d34ffdd29529d1a15514": "steamer steamship", "1e047e25506b4885eb5715ea2e14242a": "steamer steamship ship cargo ship cargo vessel", "4a5ad5b71ce08abc687e743787d2aa6b": 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cruise liner liner ocean liner", "a74f3cc7adbdaad2a072c5ad67cebc91": "ship cruise ship cruise liner", "9848322f786fc6d9eb2c303a0e0d287b": "ship yacht racing yacht", "950ebca8ad7d94051fba2cab1ada6bf6": "ship cruise ship cruise liner", "8c62487a3593dc337810d5f988f90f49": "ship cruise ship cruise liner", "75198f9e1c5a075c1e6afa6efa6add34": "ship cruise ship cruise liner", "6cf0d9cb4e2d607856f77b6bc6731cda": "ship", "e0ddb444604f573d48260190ec06324e": "ship", "9d737c1e2872b322d2ff3af4b7716b6": "ship cruise ship cruise liner", "d152611e5b14420460e26fdb6baad14d": "ship", "67f3fa7b1ae6df7bd36a8aec7c2382cc": "ship", "fbc2e96b16209642fc86577bf738024a": "ship", "99b36b2d2174f061ff8f632f1c753803": "ship yacht racing yacht", "d939109a33e6e2d175517226b23c384c": "ship", "90bb60a421727973e3852f9948e4ef35": "ship", "5adf37321156a7bcbfe1cbc47170312": "ship pirate pirate ship", "410c336c78ce00ebe2f628ca3c07d462": "ship", "234541314abebbd02783b84b5d2d6e46": "ship", "1f2e662c92843dc9c083d5272d526845": 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warship war vessel combat ship", "7ba457477629beb8888d5ca4abf8aff": "ship aircraft carrier carrier flattop attack aircraft carrier", "ee3d521376d89f408c954439655c356e": "ship", "c9b7da24378152d082e68e57491bae0e": "ship", "c172ec6226c1374c43a8324aee7340e": "ship", "ad8c4e406743c29a5b84e0ded5ffcc87": "ship", "461517433312704299986cc2d8a2c28b": "ship", "d2f2ccde17f68aa6f0a2921fde94663d": "ship", "4abbc9f36affe95dbcbc3fd6f72b94c": "ship cargo ship cargo vessel", "da73f51f17f0a8bfb0d00cfc385a039b": "ship man-of-war ship of the line", "df15e931e12c32c38a5d92735846f6c": "ship", "d3eaeb69557892657828ce3c6ecfce8b": "ship", "3970dff155f0a8731aa129fffe673b3e": "ship warship war vessel combat ship", "2212a794bfca650384d5ba37e7a649b7": "ship", "b9d541ece25778e12783b84b5d2d6e46": "ship", "5b542fec3cd96c872e28b2b96a4985ba": "ship", "cdb70178e2562e7a80ac63732535bbcc": "ship oil tanker oiler tanker tank ship", "35960e065788a31b123c06ea72522927": "aircraft carrier carrier flattop attack aircraft carrier", "73c6ef5ee071b7f9791ed0cd6a81047": "aircraft carrier carrier flattop attack aircraft carrier", "c89c8eca488d5072cdd34f38f87d26cf": "aircraft carrier carrier flattop attack aircraft carrier", "bacbb452a0f6917a663e90eaf6b4ca52": "aircraft carrier carrier flattop attack aircraft carrier", "370ab590d5f9f6e12998de2923cbf8ed": "aircraft carrier carrier flattop attack aircraft carrier", "9262aa413df7b369d735fa1ab17311ec": "aircraft carrier carrier flattop attack aircraft carrier", "887be2603a56d8acc13b4b8de5611825": "aircraft carrier carrier flattop attack aircraft carrier", "c15004cb38217b33af018ffb2ed4ff85": "aircraft carrier carrier flattop attack aircraft carrier", "b093af9dabb14b70cfd826dd468a5497": "aircraft carrier carrier flattop attack aircraft carrier", "beacf34e9b0c0597894b951d14f8e56": "aircraft carrier carrier flattop attack aircraft carrier", "dace99ad1144747eaec7ac3c7340a2f9": "aircraft carrier carrier flattop attack aircraft carrier", 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"2988a53fc376653aae529ab4a75c4af": "submarine pigboat sub U-boat", "93c013c922b90649ec08eb6485163157": "submarine pigboat sub U-boat", "847e8fedddf6bee5518c49741aed1bc1": "submarine pigboat sub U-boat", "9a7b0effb53375625f647c1b0a6369bc": "submarine pigboat sub U-boat", "df73ce22935c8081b04c44c0f84a941e": "submarine pigboat sub U-boat", "ff77dc4561e1a8a531d119333f70ecc8": "submarine pigboat sub U-boat", "95de565a937cc4ccdb024f6e72f57630": "submarine pigboat sub U-boat", "4214845c44563c5fb1bb46d2556ba67d": "submarine pigboat sub U-boat", "409ff4fbba1ba94adab1bb7802748e9e": "submarine pigboat sub U-boat", "d45f39e14c365801892294bafc22e040": "submarine pigboat sub U-boat", "377ea2491f8db13c4808655181d482fb": "submarine pigboat sub U-boat", "9e3c0b7fb69ec3997cd1f8dd6fbce8fb": "submarine pigboat sub U-boat", "703c1f85dc01baad9fb3e3631a88cdab": "submarine pigboat sub U-boat", "2067c22906b79faff877f04737ced5b5": "submarine pigboat sub U-boat", "f91e712b124915514b6c44ccda750d2e": "submarine pigboat sub U-boat", "2e620c48f90f6fb2f569f587daa38158": "submarine pigboat sub U-boat", "e0d1f833e8dc5c604947b319a9e09fd": "submarine pigboat sub U-boat", "8d5e3b659cf0df154327dc86b5ea9517": "submarine pigboat sub U-boat", "2212bb44240a9f0b57cf4c4836e6df44": "vessel watercraft", "7890b89eba662af0ce90854bc9efdc95": "vessel watercraft", "c8f5f746daa5bd96b34ff70ac6c9e4d5": "vessel watercraft", "1abf3b20f05ed8ea902d6f4ac8edb5f4": "vessel watercraft", "6fd433c69f9c498a12d1578498a3b488": "vessel watercraft", "7aba61c9096ce60b7002bddf0942f8e": "vessel watercraft", "411da0cd0b0089362b249fd16295bbfb": "vessel watercraft", "e93a47089f5c03fb7220891f188bc420": "vessel watercraft", "59a363d3852757572b249fd16295bbfb": "vessel watercraft yacht racing yacht", "f99537c4b6d50126d87c63d8b3018b58": "vessel watercraft", "d09202ccf8fa18d7de6289e76b045123": "vessel watercraft liner ocean liner", "3e7923c788b0b9a67297c18bca670e89": "vessel watercraft sailboat sailing boat sailing vessel sailing ship", "c993c40ab88b161e3cedf90fdd80720e": "vessel watercraft", "9696dd7f30247c5c573080510ce7a6bb": "vessel watercraft cargo ship cargo vessel", "f8b80289dbba94bdb7002bddf0942f8e": "vessel watercraft", "ec685f1f4bd71a191bf585548743bf8": "vessel watercraft sailing vessel sailing ship", "e88c7403ff401716b7002bddf0942f8e": "vessel watercraft warship war vessel combat ship", "c680824e615c772d18d5c097fe7ed300": "vessel watercraft", "aeb021c1e1838706b44ca19ee8c5531e": "vessel watercraft liner ocean liner", "921a5d88994aa99c71327f667b2179b0": "vessel watercraft", "1f846bab69b20a23561fc06c3ebd59d2": "vessel watercraft", "1d5ff455b6ebd04e413559acd5524c40": "vessel watercraft", "3c835bfc9201041a34f2450730697f14": "fishing boat fishing smack fishing vessel", "8676b1989d68f3e970c565b5a7d76650": "fishing boat fishing smack fishing vessel", "a755dbab93afbb3a1a05bdebf75f301d": "fishing boat fishing smack fishing vessel", "5c313324778eb79ad976d6ec0305833a": "fishing boat fishing smack fishing vessel", 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ship cargo vessel", "22a66588db3822831d20b5ad5ef1cb1b": "cruise ship cruise liner", "307a956fb5eaf7a629d81e9641dd535d": "cruise ship cruise liner liner ocean liner", "52ad452f1c63aee5a66bcb069f7626a7": "cruise ship cruise liner", "54da496566d05afb568b456e2715b08f": "cruise ship cruise liner", "6a5f7a634b13ea7a94daa4565dd30535": "cruise ship cruise liner", "7ee49cd8ad72bfd2c620cf302459a7d3": "cruise ship cruise liner", "123c021333485d698a75485f44bcc744": "cruise ship cruise liner", "1eac28fe0b2d620bf0f75e254acd7ce4": "cruise ship cruise liner", "8faf5fbe39f67f3dfa2eb3102eea4e72": "cruise ship cruise liner", "9bedc0d32c657b5a95003e7aecc71b93": "cruise ship cruise liner", "b7d831d7b1dcad77a8596b6b008107ab": "cruise ship cruise liner", "c8f85606ab95f0bd75086ae01906f5ab": "cruise ship cruise liner", "ca129502c7b2b8186d54dce3f3e9f18b": "cruise ship cruise liner", "ddf03991440ea0d15eb13ba95b07c9cb": "cruise ship cruise liner", "ffacadade68cec7b926a1ee5a429907": "cruise ship cruise liner", 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washer washing machine", "725bbfc557d3e1b9b5af85a5be833ee": "washer automatic washer washing machine", "6b056e83f0ae99f3792911e05e8927e7": "washer automatic washer washing machine", "6e606fb36a1fa72370011ebcb36fd95d": "washer automatic washer washing machine", "a6f649b55bb9fdfa7a1aca5b147accc5": "washer automatic washer washing machine", "7de9dc34c7230ca54d3df84a5eca1a73": "washer automatic washer washing machine", "fa4162988208d07a1cc00550ccb8f129": "washer automatic washer washing machine", "226f73828ad8562f4c86b1fdc94892eb": "washer automatic washer washing machine", "b04403b592cf3516df3a1b0d597ce76e": "washer automatic washer washing machine", "8797bf2bcd0c914dd6b976d6c56a2c0c": "washer automatic washer washing machine", "f0a1e9e6ad4b47b60a634b9aafd6dbd": "washer automatic washer washing machine", "dbd0ba5a349a8849fb0f1b7266d79466": "washer automatic washer washing machine", "265d042dcfed6f15c357c21161963e89": "washer automatic washer washing machine", "aa9d1a55c1a546a1df3a1b0d597ce76e": "washer automatic washer washing machine", "9b1b90b033d6c6a42bf3161ae4fda942": "washer automatic washer washing machine", "58bc4edaefdf3b0e1db42cf774e49bf": "washer automatic washer washing machine", "9cd0227494eeaafabd2a8bf2cac51544": "washer automatic washer washing machine", "41f9be4d80af2c709c12d6260da9ac2b": "washer automatic washer washing machine", "1ab22863c5f183eab00a4eba25ea081": "washer automatic washer washing machine", "9b50ed5a7b980dea4572fbc715b752ca": "washer automatic washer washing machine", "1ba5e13255aa9d4840b559ef47048b86": "washer automatic washer washing machine", "63b49123983e4cf67a31dd5af93fdb5c": "washer automatic washer washing machine", "50c639910aa3d75f29d8eecf3827c486": "washer automatic washer washing machine", "c1e9cbae0d223f9135e770f723375070": "washer automatic washer washing machine", "36594769b62b24d63b1e413ac12356ae": "washer automatic washer washing machine", "c0a1a342e0ca945edb04615d958147f5": "washer automatic washer washing machine", "d4305fddf4c03812625f0e5788fbff74": "washer automatic washer washing machine", "46749d40640967a94b77d9f59f49ab0": "washer automatic washer washing machine", "2d65189ff15fe1538a8c921f75bfa58c": "washer automatic washer washing machine", "8ba3bb4e5f54bc15eb2160f94b208571": "washer automatic washer washing machine", "52c23e4a42c5f058ffc6e457221b9271": "washer automatic washer washing machine", "29561b3ac0b7596cb23717c90dbac0d": "washer automatic washer washing machine", "53e2b5c6f9a2103e3fb0920a55a7e805": "washer automatic washer washing machine", "6bb2e6f20f189167d48855885373053b": "washer automatic washer washing machine", "abaca4a16b203ccadf3a1b0d597ce76e": "washer automatic washer washing machine", "3ccba2b436b03aa635325a5f07346e1b": "washer automatic washer washing machine", "cbf8ea778b70d1c5ae0f84ab89df135e": "washer automatic washer washing machine", "bb609567ff4d46a0a44768a940557d1c": "washer automatic washer washing machine", "ef6d0cac2707dd6ee262a7452d41c334": "washer automatic washer washing machine", "434fc8832f3ade492d5c22cc9b713d25": "washer automatic washer washing machine", "1b77c0248add1d5540b559ef47048b86": "washer automatic washer washing machine", "39e2a2eac45d9ee9961469926610c5fc": "washer automatic washer washing machine", "957ddf9eb718876dc2180858eb6a5f28": "washer automatic washer washing machine", "9e991b898c6362977123ef821e961857": "washer automatic washer washing machine", "177501db32f76aa4a2ef01d38c90dbd6": "washer automatic washer washing machine", "2ba54a5656c25096174c6fb6a5678c41": "washer automatic washer washing machine", "3e4b1aaca677501b91219aad82abc053": "washer automatic washer washing machine", "5e411987081d3f3af44ef865a534c948": "washer automatic washer washing machine", "8340c44f09408180d122457e1745c167": "washer automatic washer washing machine", "a7c8177faa40c43a87be36ce60737655": "washer automatic washer washing machine", "aef595f6f7c14cf4fe43ff1e45af424d": "washer automatic washer washing machine", "c667f9cf4f72ef6fbdabe30acfd2c07": "washer automatic washer washing machine", "83546b11f6c0cbd0c6e1273d5c613c62": "washer automatic washer washing machine", "49a0abbe22fb557bee526fc96b277c69": "washer automatic washer washing machine", "e66739cb1df7e6e92da2064a99bad66a": "washer automatic washer washing machine", "81ecf0670ab97ff5fb23f72ec839c310": "washer automatic washer washing machine", "8fb4749a25eca524cba07fb2573fff75": "washer automatic washer washing machine", "969c60ef5f2c2fe696d5fa1ee6ada8d1": "washer automatic washer washing machine", "4c90de8c1b850265bf258aa99523f66b": "washer automatic washer washing machine", "1a53e51455e0812e44a7fc53aa1d411c": "washer automatic washer washing machine", "452f7e16dcf5b2cf46cde67a6a1ea328": "washer automatic washer washing machine", "d2711c36636e9b1eeacaf08e3ee3b2b3": "washer automatic washer washing machine", "5947c6f05f5f74437b3ec0bc4f26f615": "washer automatic washer washing machine", "5e04ab0e24e0882272a5adfd0bef5d10": "washer automatic washer washing machine", "81c849960bdf3f1e91219aad82abc053": "washer automatic washer washing machine", "9b744b01aaed2d603ebd14c45634ff71": "washer automatic washer washing machine", "a9b39195ae8c707474e92060c1aed1a2": "washer automatic washer washing machine", "bf1fc3ec1617fb0064f9059364710e5": "washer automatic washer washing machine", "c18dffc90a4633d191219aad82abc053": "washer automatic washer washing machine", "ed34c7ded72ba0a346cde67a6a1ea328": "washer automatic washer washing machine", "ea961e30236b2b76a0ea52c7b90036b2": "washer automatic washer washing machine", "a4773b8eac98c358cc9b47ce449d317": "washer automatic washer washing machine", "a339c3e29128a402c8e4135ffa0bbfdd": "washer automatic washer washing machine", "64e1c85034dfe93ab7dc65d7950bd55b": "washer automatic washer washing machine", "3f60bb32239ffa303bf7bae2651d2cf": "washer automatic washer washing machine", "ac2fd0b05d956f7a589577ccf07b31e7": "washer automatic washer washing machine", "2010a39c4b0c58b714b35a7d7cea7130": "washer automatic washer washing machine", "30925e14af2a92814b35a7d7cea7130": "washer automatic washer washing machine", "60b8c29d75c28ce214b35a7d7cea7130": "washer automatic washer washing machine", "30468019d1d90687b361c88e264d57a6": "washer automatic washer washing machine", "33d3f4a7b2df861248bfbf0a3bc453a5": "washer automatic washer washing machine", "bdfbadd16a5ddceb8e55c35c514cb2f7": "washer automatic washer washing machine", "5fc3cef0cf926f5b9303a353ee6dfc42": "washer automatic washer washing machine", "ba5ea28f0065c71cdf3a1b0d597ce76e": "washer automatic washer washing machine", "9ac5425b6c547d3d99373190f2d703c0": "washer automatic washer washing machine", "5aa97c0e9c11bcd1677ce0c660f811e0": "washer automatic washer washing machine", "b91045b62241ebd158c88d30f502a452": "washer automatic washer washing machine", "aaf971855cff72731a1a2dbbd3d54d53": "washer automatic washer washing machine", "3cafaabfd4924ee47e5b110192149385": "washer automatic washer washing machine", "592e665fa7e0a2bcc9614070d3269898": "washer automatic washer washing machine", "62e22f4d1846d8c1fdc6c1669e5c540": "washer automatic washer washing machine", "7da46c2fa47deac5f509767312277f4e": "washer automatic washer washing machine", "8405ab2d49b428b191219aad82abc053": "washer automatic washer washing machine", "e994852d9f4502674a16879fb3ac81f": "washer automatic washer washing machine", "25b6e21ae9aa70824657977e55055ba0": "washer automatic washer washing machine", "5f8991a5039ca4385c8adc541f88498": "washer automatic washer washing machine", "ccd7d621279425b8d1b2557b80a0d3f0": "washer automatic washer washing machine", "da98f4f303a500856e27419626f050f1": "washer automatic washer washing machine", "e3c637980c8c22eaf4a3d771dd0a0ffa": "washer automatic washer washing machine", "17dd78a433c3840b51229e7d6fc0cda8": "washer automatic washer washing machine", "24d50c4d744c20494a084dbeb79b6bb2": "washer automatic washer washing machine", "ded40c1866a00ea02b5631520b7d94fe": "washer automatic washer washing machine", "60f7d79d10e19134a3e7878f2bb5f0ca": "washer automatic washer washing machine", "677a00ae2e3f7a2145ca70e41d71c65d": "washer automatic washer washing machine", "933dc825712acc8ab5a3b8119a3da34a": "washer automatic washer washing machine", "c709c8a6cb97c8e55c67382e5c978dae": "washer automatic washer washing machine", "eca8612dc1cc153c4df5aaa9df719285": "washer automatic washer washing machine", "5517868119f44388a027ae047c88a1b9": "washer automatic washer washing machine", "4713da50446a7595df3a1b0d597ce76e": "washer automatic washer washing machine", "360e2cb74c9c4d38df3a1b0d597ce76e": "washer automatic washer washing machine", "8f9232a493ccf77e46cde67a6a1ea328": "washer automatic washer washing machine", "d5ad82f383a9536d46cde67a6a1ea328": "washer automatic washer washing machine", "8c122f99b5a0aa047ca3f903b5926fb1": "washer automatic washer washing machine", "aba19f953a07b5d3f589dfa369e10caa": "washer automatic washer washing machine", "d1aa6a6c230b5150f0ddc6174ecef5c": "washer automatic washer washing machine", "d49400b5298fb56adf3a1b0d597ce76e": "washer automatic washer washing machine", "6b5bd7b97b0fafb83bd5bed2701d5593": "washer automatic washer washing machine", "71f26627a3eece3e151c656115fe24ee": "washer automatic washer washing machine", "bcf112d7f50c222685842deebe63f964": "washer automatic washer washing machine", "2e9e2ff2b757fcbc23a5a908e4c7c27c": "washer automatic washer washing machine", "8dee44b4cd1f34ac856e3ad6b24f68b2": "washer automatic washer washing machine", "880a6bcedaeaed1fd095418bcfa348eb": "washer automatic washer washing machine", "91e432daef378b24553e9c84d5d40de4": "washer automatic washer washing machine", "cccfe2cec3069c449c72dfe3cb318a90": "washer automatic washer washing machine", "d78b4c3dc263a8f9c5243a0ab191990d": "washer automatic washer washing machine", "e51ad19bf1dc680b525bdb2a29d19760": "washer automatic washer washing machine", "74dc1a59c3585ed746cde67a6a1ea328": "washer automatic washer washing machine", "c1d4c8c438c4bfb346cde67a6a1ea328": "washer automatic washer washing machine", "2ecde1b0c35fe63b4e4ed13543f85ef0": "washer automatic washer washing machine", "4b5552c17c1fee56b6c98c3c1d4f4fcb": "washer automatic washer washing machine", "5a94498353af7763e169a380d7bfbfc": "washer automatic washer washing machine", "5e071ea7184305122c5cdbc97c64ef41": "washer automatic washer washing machine", "3a289ed95f5902fb46cde67a6a1ea328": "washer automatic washer washing machine", "828f6f95c6e8651691219aad82abc053": "washer automatic washer washing machine", "c6f9e7650c898b017b197b8311051a3c": "washer automatic washer washing machine", "8103431c1feeedcd42163310d48ef535": "washer automatic washer washing machine", "da021d3f1eceeea49ffad002249ee384": "washer automatic washer washing machine", "22c6787bb43651f0f6eb1856550bd229": "washer automatic washer washing machine", "8a0a7c14b9cd6489d202e705a59e5585": "washer automatic washer washing machine", "c2eb080246a97865c4c2cb2195d7bfe5": "washer automatic washer washing machine", "2d856dae6ae9373add5cf959690bb73f": "washer automatic washer washing machine", "8d3cd0d2cf88b2c2a83d16be6f14bd8": "washer automatic washer washing machine", "b0b77a2d6816230c99bb3947ecdf7d2b": "washer automatic washer washing machine", "cb6febe6334cbf7791219aad82abc053": "washer automatic washer washing machine", "da0179a5b68f13586a6a687121d74e50": "washer automatic washer washing machine", "14bb2e591332db56b0be6ed024602be5": "washer automatic washer washing machine", "e0bb2eefa09251a6149c1768202b4e64": "washer automatic washer washing machine"}
\ No newline at end of file

From a3a2588abb09599d41f404970ac42ea1025d8516 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Tue, 12 Mar 2024 17:28:49 +0100
Subject: [PATCH 53/84] updated demo render functions

---
 MeshGPT_demo.ipynb              | 170 +++++++++++++++-----------------
 meshgpt_pytorch/__init__.py     |   5 +
 meshgpt_pytorch/mesh_dataset.py |  47 ++++++---
 meshgpt_pytorch/mesh_render.py  |  63 ++++++++++++
 4 files changed, 178 insertions(+), 107 deletions(-)
 create mode 100644 meshgpt_pytorch/mesh_render.py

diff --git a/MeshGPT_demo.ipynb b/MeshGPT_demo.ipynb
index 6a0d3a6d..507368bd 100644
--- a/MeshGPT_demo.ipynb
+++ b/MeshGPT_demo.ipynb
@@ -487,6 +487,71 @@
             "autoencoder_trainer.save(f'{working_dir}\\mesh-encoder_{project_name}.pt')   "
          ]
       },
+      {
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "### Inspect how the autoencoder can encode and then provide the decoder with the codes to reconstruct the mesh"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
+            "import torch\n",
+            "import random\n",
+            "from tqdm import tqdm \n",
+            "from meshgpt_pytorch import mesh_render \n",
+            "\n",
+            "min_mse, max_mse = float('inf'), float('-inf')\n",
+            "min_coords, min_orgs, max_coords, max_orgs = None, None, None, None\n",
+            "random_samples = []\n",
+            "random_samples_pred = []\n",
+            "all_random_samples = []\n",
+            "total_mse = 0.0 \n",
+            "sample_size = 200\n",
+            "\n",
+            "random.shuffle(dataset.data)\n",
+            "\n",
+            "for item in tqdm(dataset.data[:sample_size]):\n",
+            "    codes = autoencoder.tokenize(\n",
+            "                vertices = item['vertices'],\n",
+            "                faces = item['faces'],\n",
+            "                face_edges = item['face_edges']\n",
+            "    ) \n",
+            "    codes =codes.flatten().unsqueeze(0)\n",
+            "    codes = codes[:, :codes.shape[-1] // 2 * 2]\n",
+            "\n",
+            "    coords, mask = autoencoder.decode_from_codes_to_faces(codes)\n",
+            "    orgs = item['vertices'][item['faces']].unsqueeze(0)\n",
+            "\n",
+            "    mse = torch.mean((orgs.view(-1, 3).cpu() - coords.view(-1, 3).cpu())**2)\n",
+            "    total_mse += mse\n",
+            "   \n",
+            "\n",
+            "    if mse < min_mse:\n",
+            "        min_mse, min_coords, min_orgs = mse, coords, orgs\n",
+            "\n",
+            "    if mse > max_mse:\n",
+            "        max_mse, max_coords, max_orgs = mse, coords, orgs\n",
+            " \n",
+            "    if len(random_samples) <= 30:\n",
+            "        random_samples.append(coords)\n",
+            "        random_samples_pred.append(orgs)\n",
+            "    else:\n",
+            "        all_random_samples.append(random_samples_pred)\n",
+            "        all_random_samples.append(random_samples)\n",
+            "        random_samples = []\n",
+            "        random_samples_pred = []\n",
+            "\n",
+            "avg_mse = total_mse / sample_size \n",
+            "print(f'MSE AVG: {avg_mse:.10f}, Min: {min_mse:.10f}, Max: {max_mse:.10f}')   \n",
+            "\n",
+            "mesh_render.combind_mesh_with_rows(f'{working_dir}\\mse_rows.obj', all_random_samples)  "
+         ]
+      },
       {
          "cell_type": "code",
          "execution_count": null,
@@ -607,75 +672,6 @@
             "## Generate and view mesh"
          ]
       },
-      {
-         "cell_type": "code",
-         "execution_count": null,
-         "metadata": {},
-         "outputs": [],
-         "source": [
-            "def combind_mesh(path, mesh):\n",
-            "    all_vertices = []\n",
-            "    all_faces = []\n",
-            "    vertex_offset = 0\n",
-            "    translation_distance = 0.5  \n",
-            "\n",
-            "    for r, faces_coordinates in enumerate(mesh): \n",
-            "        numpy_data = faces_coordinates[0].cpu().numpy().reshape(-1, 3)   \n",
-            "    \n",
-            "        for vertex in numpy_data:\n",
-            "            all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n",
-            "    \n",
-            "        for i in range(1, len(numpy_data), 3):\n",
-            "            all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n",
-            "    \n",
-            "        vertex_offset += len(numpy_data)\n",
-            "    \n",
-            "    obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n",
-            "     \n",
-            "    with open(path , \"w\") as file:\n",
-            "        file.write(obj_file_content)   \n",
-            " \n",
-            "def combind_mesh_with_rows(path, meshes):\n",
-            "    all_vertices = []\n",
-            "    all_faces = []\n",
-            "    vertex_offset = 0\n",
-            "    translation_distance = 0.5  \n",
-            "    \n",
-            "    for row, mesh in enumerate(meshes): \n",
-            "        for r, faces_coordinates in enumerate(mesh): \n",
-            "            numpy_data = faces_coordinates[0].cpu().numpy().reshape(-1, 3)  \n",
-            "            numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  \n",
-            "            numpy_data[:, 2] += translation_distance * (row / 0.2 - 1)  \n",
-            "        \n",
-            "            for vertex in numpy_data:\n",
-            "                all_vertices.append(f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\")\n",
-            "        \n",
-            "            for i in range(1, len(numpy_data), 3):\n",
-            "                all_faces.append(f\"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\\n\")\n",
-            "        \n",
-            "            vertex_offset += len(numpy_data)\n",
-            "        \n",
-            "        obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n",
-            "     \n",
-            "    with open(path , \"w\") as file:\n",
-            "        file.write(obj_file_content)   \n",
-            "        \n",
-            "        \n",
-            "def write_mesh_output(path, coords):\n",
-            "    numpy_data = faces_coordinates[0].cpu().numpy().reshape(-1, 3)  \n",
-            "    obj_file_content = \"\"\n",
-            "    \n",
-            "    for vertex in numpy_data:\n",
-            "        obj_file_content += f\"v {vertex[0]} {vertex[1]} {vertex[2]}\\n\"\n",
-            "\n",
-            "    for i in range(1, len(numpy_data), 3):\n",
-            "        obj_file_content += f\"f {i} {i + 1} {i + 2}\\n\"\n",
-            " \n",
-            "    with open(path, \"w\") as file:\n",
-            "        file.write(obj_file_content) \n",
-            "         "
-         ]
-      },
       {
          "cell_type": "markdown",
          "metadata": {},
@@ -690,6 +686,7 @@
          "outputs": [],
          "source": [
             " \n",
+            "from meshgpt_pytorch import mesh_render \n",
             "from pathlib import Path\n",
             " \n",
             "folder = working_dir / 'renders'\n",
@@ -702,10 +699,10 @@
             "    faces_coordinates = transformer.generate(texts = [text],  temperature = 0.0) \n",
             "    text_coords.append(faces_coordinates)\n",
             "    \n",
-            "    write_mesh_output(f'{folder}/3d_output_{text}.obj', faces_coordinates)  \n",
+            "    mesh_render.write_mesh_output(f'{folder}/3d_output_{text}.obj', faces_coordinates)  \n",
             "     \n",
             " \n",
-            "combind_mesh(f'{folder}/3d_models_all.obj', text_coords)"
+            "mesh_render.combind_mesh(f'{folder}/3d_models_all.obj', text_coords)"
          ]
       },
       {
@@ -745,7 +742,8 @@
          "metadata": {},
          "outputs": [],
          "source": [
-            "from pathlib import Path\n",
+            "from pathlib import Path \n",
+            "from meshgpt_pytorch import mesh_render \n",
             "token_length_procent = 0.10 \n",
             "codes = []\n",
             "texts = []\n",
@@ -762,25 +760,19 @@
             "obj_file_path = Path(folder)\n",
             "obj_file_path.mkdir(exist_ok = True, parents = True)  \n",
             "\n",
-            "coords = [] \n",
-            "\n",
-            "\n",
-            "\n",
+            "coords = []  \n",
             "for text, prompt in zip(texts, codes): \n",
             "    print(f\"Generating {text} with {prompt.shape[1]} tokens\")\n",
             "    faces_coordinates = transformer.generate(texts = [text],  prompt = prompt, temperature = 0) \n",
-            "    coords.append(faces_coordinates) \n",
-            "    \n",
-            "    obj_file_path = f'{folder}/{text}_{prompt.shape[1]}_tokens.obj'\n",
-            "    write_mesh_output(obj_file_path, faces_coordinates)\n",
+            "    coords.append(faces_coordinates)  \n",
             "        \n",
             "    print(obj_file_path)\n",
             "     \n",
             " \n",
-            "combind_mesh(f'{folder}/text+prompt_all.obj', coords)\n",
+            "mesh_render.combind_mesh(f'{folder}/text+prompt_all.obj', coords)\n",
             "\n",
             "if text_coords is not None: \n",
-            "    combind_mesh_with_rows(f'{folder}/both_verisons.obj', [text_coords , coords])"
+            "    mesh_render.combind_mesh_with_rows(f'{folder}/both_verisons.obj', [text_coords , coords])"
          ]
       },
       {
@@ -797,6 +789,7 @@
          "outputs": [],
          "source": [
             "from pathlib import Path\n",
+            "from meshgpt_pytorch import mesh_render \n",
             " \n",
             "folder = working_dir / f'renders/text+codes_rows'\n",
             "obj_file_path = Path(folder)\n",
@@ -816,21 +809,16 @@
             "                break\n",
             "            \n",
             "    coords = []   \n",
-            "    for text, prompt in zip(texts, codes): \n",
-            "        \n",
+            "    for text, prompt in zip(texts, codes):  \n",
             "        print(f\"Generating {text} with {prompt.shape[1]} tokens\")\n",
             "        faces_coordinates = transformer.generate(texts = [text],  prompt = prompt, temperature = 0) \n",
-            "        coords.append(faces_coordinates)\n",
-            "        \n",
-            "        obj_file_path = f'{folder}/{text}_{prompt.shape[1]}_tokens.obj'\n",
-            "        write_mesh_output(obj_file_path, coords)  \n",
-            "        print(obj_file_path)\n",
+            "        coords.append(faces_coordinates) \n",
             "        \n",
             "        \n",
             "    mesh_rows.append(coords) \n",
-            "    combind_mesh(f'{folder}/text+prompt_all_{token_length_procent}.obj', coords)\n",
+            "    mesh_render.combind_mesh(f'{folder}/text+prompt_all_{token_length_procent}.obj', coords)\n",
             "    \n",
-            "combind_mesh_with_rows(f'{folder}/all.obj', mesh_rows)\n",
+            "mesh_render-combind_mesh_with_rows(f'{folder}/all.obj', mesh_rows)\n",
             " "
          ]
       },
diff --git a/meshgpt_pytorch/__init__.py b/meshgpt_pytorch/__init__.py
index 206b732d..3ecc9bca 100644
--- a/meshgpt_pytorch/__init__.py
+++ b/meshgpt_pytorch/__init__.py
@@ -17,4 +17,9 @@
 from meshgpt_pytorch.mesh_dataset import (
     MeshDataset
 )
+from meshgpt_pytorch.mesh_render import (
+    combind_mesh_with_rows,
+    write_mesh_output,
+    combind_mesh
+)
  
diff --git a/meshgpt_pytorch/mesh_dataset.py b/meshgpt_pytorch/mesh_dataset.py
index 201f311b..084754a5 100644
--- a/meshgpt_pytorch/mesh_dataset.py
+++ b/meshgpt_pytorch/mesh_dataset.py
@@ -1,5 +1,7 @@
 from torch.utils.data import Dataset
 import numpy as np  
+from torch.nn.utils.rnn import pad_sequence
+from tqdm import tqdm
 from meshgpt_pytorch import ( 
     MeshAutoencoder,
     MeshTransformer
@@ -83,27 +85,40 @@ def generate_face_edges(self):
         self.sort_dataset_keys()
         print(f"[MeshDataset] Generated face_edges for {i}/{len(self.data)} entrys")
 
-    def generate_codes(self, autoencoder : MeshAutoencoder): 
-        for item in self.data: 
-            codes = autoencoder.tokenize(
-                vertices = item['vertices'],
-                faces = item['faces'],
-                face_edges = item['face_edges']
-            ) 
-            item['codes'] = codes  
- 
+    def generate_codes(self, autoencoder : MeshAutoencoder, batch_size = 25): 
+        total_batches = (len(self.data) + batch_size - 1) // batch_size
+
+        for i in tqdm(range(0, len(self.data), batch_size), total=total_batches):
+            batch_data = self.data[i:i+batch_size] 
+            
+            padded_batch_vertices = pad_sequence([item['vertices'] for item in batch_data], batch_first=True, padding_value=autoencoder.pad_id)
+            padded_batch_faces = pad_sequence([item['faces'] for item in batch_data], batch_first=True, padding_value=autoencoder.pad_id)
+            padded_batch_face_edges = pad_sequence([item['face_edges'] for item in batch_data], batch_first=True, padding_value=-autoencoder.pad_id)
+            
+            batch_codes = autoencoder.tokenize(
+                vertices=padded_batch_vertices,
+                faces=padded_batch_faces,
+                face_edges=padded_batch_face_edges
+            )
+            
+            for j, item in enumerate(batch_data):
+                item['codes'] = batch_codes[j]
+                
         self.sort_dataset_keys()
         print(f"[MeshDataset] Generated codes for {len(self.data)} entrys")
     
-    def embed_texts(self, transformer : MeshTransformer): 
+    def embed_texts(self, transformer : MeshTransformer, batch_size = 50): 
         unique_texts = set(item['texts'] for item in self.data)
- 
-        text_embeddings = transformer.embed_texts(list(unique_texts))
-        print(f"[MeshDataset] Generated {len(text_embeddings)} text_embeddings") 
-        text_embedding_dict = dict(zip(unique_texts, text_embeddings))
- 
+        embeddings = []
+        for i in range(0,len(unique_texts), batch_size):
+            text_embeddings = transformer.embed_texts(list(unique_texts)[i:i+batch_size])
+            embeddings.extend(text_embeddings)
+            
+        text_embedding_dict = dict(zip(unique_texts, embeddings))
+
         for item in self.data:
             if 'texts' in item:  
                 item['text_embeds'] = text_embedding_dict.get(item['texts'], None)
                 del item['texts'] 
-        self.sort_dataset_keys()
\ No newline at end of file
+        self.sort_dataset_keys()
+        print(f"[MeshDataset] Generated {len(embeddings)} text_embeddings") 
\ No newline at end of file
diff --git a/meshgpt_pytorch/mesh_render.py b/meshgpt_pytorch/mesh_render.py
new file mode 100644
index 00000000..af982534
--- /dev/null
+++ b/meshgpt_pytorch/mesh_render.py
@@ -0,0 +1,63 @@
+def combind_mesh(path, mesh):
+    all_vertices = []
+    all_faces = []
+    vertex_offset = 0
+    translation_distance = 0.5  
+
+    for r, faces_coordinates in enumerate(mesh):  
+        numpy_data = faces_coordinates[0].cpu().numpy().reshape(-1, 3)   
+        numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  
+    
+        for vertex in numpy_data:
+            all_vertices.append(f"v {vertex[0]} {vertex[1]} {vertex[2]}\n")
+    
+        for i in range(1, len(numpy_data), 3):
+            all_faces.append(f"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\n")
+    
+        vertex_offset += len(numpy_data)
+    
+    obj_file_content = "".join(all_vertices) + "".join(all_faces)
+     
+    with open(path , "w") as file:
+        file.write(obj_file_content)   
+ 
+def combind_mesh_with_rows(path, meshes):
+    all_vertices = []
+    all_faces = []
+    vertex_offset = 0
+    translation_distance = 0.5  
+    obj_file_content = ""
+    
+    for row, mesh in enumerate(meshes): 
+        for r, faces_coordinates in enumerate(mesh): 
+            numpy_data = faces_coordinates[0].cpu().numpy().reshape(-1, 3)  
+            numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  
+            numpy_data[:, 2] += translation_distance * (row / 0.2 - 1)  
+        
+            for vertex in numpy_data:
+                all_vertices.append(f"v {vertex[0]} {vertex[1]} {vertex[2]}\n")
+        
+            for i in range(1, len(numpy_data), 3):
+                all_faces.append(f"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\n")
+        
+            vertex_offset += len(numpy_data)
+        
+        obj_file_content = "".join(all_vertices) + "".join(all_faces)
+     
+    with open(path , "w") as file:
+        file.write(obj_file_content)   
+        
+        
+def write_mesh_output(path, coords):
+    numpy_data = coords[0].cpu().numpy().reshape(-1, 3)  
+    obj_file_content = ""
+    
+    for vertex in numpy_data:
+        obj_file_content += f"v {vertex[0]} {vertex[1]} {vertex[2]}\n"
+
+    for i in range(1, len(numpy_data), 3):
+        obj_file_content += f"f {i} {i + 1} {i + 2}\n"
+ 
+    with open(path, "w") as file:
+        file.write(obj_file_content) 
+         
\ No newline at end of file

From 4f9ddd58779db5c0866c701742f20aab6daddda5 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Tue, 12 Mar 2024 22:07:09 +0100
Subject: [PATCH 54/84] training stuff

---
 MeshGPT_demo.ipynb | 66 +++++++++++++++++++++++++++-------------------
 1 file changed, 39 insertions(+), 27 deletions(-)

diff --git a/MeshGPT_demo.ipynb b/MeshGPT_demo.ipynb
index 507368bd..602e360f 100644
--- a/MeshGPT_demo.ipynb
+++ b/MeshGPT_demo.ipynb
@@ -464,18 +464,17 @@
          "metadata": {},
          "outputs": [],
          "source": [
-            "\n",
-            "autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder ,warmup_steps = 10, dataset = dataset, num_train_steps=100,\n",
-            "                                             batch_size=8,\n",
-            "                                             grad_accum_every=2,\n",
-            "                                             learning_rate = 1e-2) \n",
-            "loss = autoencoder_trainer.train(280,stop_at_loss = 0.7, diplay_graph= True)   \n",
-            "  \n",
+            "batch_size=16 # The batch size should be max 64.\n",
+            "grad_accum_every = 4\n",
+            "# So set the maximal batch size (max 64) that your VRAM can handle and then use grad_accum_every to create a effective batch size of 64, e.g  16 * 4 = 64\n",
+            "   \n",
+            "autoencoder.commit_loss_weight = 0.2 # Set dependant on the dataset size, on smaller datasets, 0.1 is fine, otherwise try from 0.25 to 0.4.\n",
             "autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder ,warmup_steps = 10, dataset = dataset, num_train_steps=100,\n",
-            "                                             batch_size=8,\n",
-            "                                             grad_accum_every=2,\n",
-            "                                             learning_rate = 4e-3) \n",
-            "loss = autoencoder_trainer.train(280,stop_at_loss = 0.28, diplay_graph= True)     "
+            "                                             batch_size=batch_size,\n",
+            "                                             grad_accum_every = grad_accum_every,\n",
+            "                                             learning_rate = 1e-4,\n",
+            "                                             checkpoint_every_epoch=1) \n",
+            "loss = autoencoder_trainer.train(480,stop_at_loss = 0.2, diplay_graph= True)        "
          ]
       },
       {
@@ -572,7 +571,7 @@
             "    dim = 512,\n",
             "    coarse_pre_gateloop_depth = 6, # Better performance using more gateloop layers\n",
             "    fine_pre_gateloop_depth= 4, \n",
-            "    #attn_depth = 24, # GPT-2 medium have 24 layer depth, change if needed\n",
+            "    attn_depth = 12, # GPT-2 medium have 24 layer depth, change if needed\n",
             "    max_seq_len = max_seq, \n",
             "    condition_on_text = True, \n",
             "    gateloop_use_heinsen = False,\n",
@@ -610,8 +609,8 @@
          "source": [
             "labels = set(item[\"texts\"] for item in dataset.data)\n",
             "print(labels)\n",
-            "dataset.embed_texts(transformer)\n",
-            "dataset.generate_codes(autoencoder)\n",
+            "dataset.embed_texts(transformer, batch_size = 25)\n",
+            "dataset.generate_codes(autoencoder, batch_size = 50) # Higher batch size = faster but might leave some uncollected garbage in the VRAM\n",
             "print(dataset.data[0].keys())"
          ]
       },
@@ -646,13 +645,17 @@
          "metadata": {},
          "outputs": [],
          "source": [
-            "trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=4,num_train_steps=100, dataset = dataset,\n",
-            "                                 learning_rate = 1e-3, batch_size=2)  \n",
-            "loss = trainer.train(100, stop_at_loss = 0.009) \n",
+            "batch_size = 4 # Max 64\n",
+            "grad_accum_every = 16\n",
             "\n",
-            "trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=4,num_train_steps=100, dataset = dataset,\n",
-            "                                 learning_rate = 5e-4, batch_size=2)\n",
-            "loss = trainer.train(200, stop_at_loss = 0.00001)  "
+            "# Set the maximal batch size (max 64) that your VRAM can handle and then use grad_accum_every to create a effective batch size of 64, e.g  4 * 16 = 64\n",
+            "\n",
+            "trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,num_train_steps=100, dataset = dataset,\n",
+            "                                 grad_accum_every=grad_accum_every,\n",
+            "                                 learning_rate = 1e-4,\n",
+            "                                 batch_size=batch_size,\n",
+            "                                 checkpoint_every_epoch = 1)\n",
+            "loss = trainer.train(300, stop_at_loss = 0.005)  "
          ]
       },
       {
@@ -725,8 +728,7 @@
          "metadata": {},
          "outputs": [],
          "source": [
-            "dataset = MeshDataset.load(dataset_path)\n",
-            "dataset.generate_codes(autoencoder)"
+            "dataset = MeshDataset.load(dataset_path) "
          ]
       },
       {
@@ -750,10 +752,15 @@
             "for label in labels:\n",
             "    for item in dataset.data: \n",
             "        if item['texts'] == label:\n",
-            "            num_tokens = int(item[\"codes\"].shape[0] * token_length_procent) \n",
+            "            tokens = autoencoder.tokenize(\n",
+            "                vertices = item['vertices'],\n",
+            "                faces = item['faces'],\n",
+            "                face_edges = item['face_edges']\n",
+            "            ) \n",
+            "            num_tokens = int(tokens.shape[0] * token_length_procent) \n",
             "            \n",
             "            texts.append(item['texts']) \n",
-            "            codes.append(item[\"codes\"].flatten()[:num_tokens].unsqueeze(0))  \n",
+            "            codes.append(tokens.flatten()[:num_tokens].unsqueeze(0))  \n",
             "            break\n",
             "        \n",
             "folder = working_dir / f'renders/text+codes'\n",
@@ -802,10 +809,15 @@
             "    for label in labels:\n",
             "        for item in dataset.data: \n",
             "            if item['texts'] == label:\n",
-            "                num_tokens = int(item[\"codes\"].shape[0] * token_length_procent) \n",
+            "                tokens = autoencoder.tokenize(\n",
+            "                    vertices = item['vertices'],\n",
+            "                    faces = item['faces'],\n",
+            "                    face_edges = item['face_edges']\n",
+            "                ) \n",
+            "                num_tokens = int(tokens.shape[0] * token_length_procent) \n",
             "                \n",
             "                texts.append(item['texts']) \n",
-            "                codes.append(item[\"codes\"].flatten()[:num_tokens].unsqueeze(0))  \n",
+            "                codes.append(tokens.flatten()[:num_tokens].unsqueeze(0))  \n",
             "                break\n",
             "            \n",
             "    coords = []   \n",
@@ -818,7 +830,7 @@
             "    mesh_rows.append(coords) \n",
             "    mesh_render.combind_mesh(f'{folder}/text+prompt_all_{token_length_procent}.obj', coords)\n",
             "    \n",
-            "mesh_render-combind_mesh_with_rows(f'{folder}/all.obj', mesh_rows)\n",
+            "mesh_render.combind_mesh_with_rows(f'{folder}/all.obj', mesh_rows)\n",
             " "
          ]
       },

From e5db7d9ba8e5344cbfd05f3c745ea12dff41339c Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Tue, 12 Mar 2024 23:51:16 +0100
Subject: [PATCH 55/84] checkpoint name

---
 meshgpt_pytorch/trainer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index bf575bc9..a43dc822 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -394,8 +394,8 @@ def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
             self.print(epochOut) 
 
          
-            if self.checkpoint_every_epoch is not None and epoch != 0 and epoch % self.checkpoint_every_epoch == 0:
-                self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.3f}.pt')
+            if self.checkpoint_every_epoch is not None and (self.checkpoint_every_epoch == 1 or epoch != 0) and epoch % self.checkpoint_every_epoch == 0:
+                self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.5f}_recon_{avg_recon_loss:.4f}_commit_{avg_commit_loss:.4f}.pt')
                 
             if stop_at_loss is not None and avg_epoch_loss < stop_at_loss: 
                 self.print(f'Stopping training at epoch {epoch} with average loss {avg_epoch_loss}')

From e9c6bdfaa167f7d90ffdede126dfc2d539f15bb4 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Tue, 12 Mar 2024 23:53:19 +0100
Subject: [PATCH 56/84] checkpoint name

---
 meshgpt_pytorch/trainer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index a43dc822..a252d0c3 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -394,7 +394,7 @@ def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
             self.print(epochOut) 
 
          
-            if self.checkpoint_every_epoch is not None and (self.checkpoint_every_epoch == 1 or epoch != 0) and epoch % self.checkpoint_every_epoch == 0:
+            if self.checkpoint_every_epoch is not None and (self.checkpoint_every_epoch == 1 or (epoch != 0 and epoch % self.checkpoint_every_epoch == 0)):
                 self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.5f}_recon_{avg_recon_loss:.4f}_commit_{avg_commit_loss:.4f}.pt')
                 
             if stop_at_loss is not None and avg_epoch_loss < stop_at_loss: 
@@ -709,7 +709,7 @@ def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
                 self.wait()
                 self.print(epochOut)
                 
-                if self.checkpoint_every_epoch is not None and epoch != 0 and epoch % self.checkpoint_every_epoch == 0:
+                if self.checkpoint_every_epoch is not None and (self.checkpoint_every_epoch == 1 or (epoch != 0 and epoch % self.checkpoint_every_epoch == 0)):
                     self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.3f}.pt')
                     
                 if stop_at_loss is not None and avg_epoch_loss < stop_at_loss: 

From 6cfc6f966fd90b5d93ce7978801e446826c84576 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Wed, 13 Mar 2024 03:05:43 +0100
Subject: [PATCH 57/84] pad

---
 meshgpt_pytorch/mesh_dataset.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/mesh_dataset.py b/meshgpt_pytorch/mesh_dataset.py
index 084754a5..f9595ba6 100644
--- a/meshgpt_pytorch/mesh_dataset.py
+++ b/meshgpt_pytorch/mesh_dataset.py
@@ -101,8 +101,9 @@ def generate_codes(self, autoencoder : MeshAutoencoder, batch_size = 25):
                 face_edges=padded_batch_face_edges
             )
             
-            for j, item in enumerate(batch_data):
-                item['codes'] = batch_codes[j]
+
+        for codes, item in zip(batch_codes, batch_data): 
+            item['codes'] = [code for code in codes if code != autoencoder.pad_id and code != -1]
                 
         self.sort_dataset_keys()
         print(f"[MeshDataset] Generated codes for {len(self.data)} entrys")

From 89010a97ca2b8b33e2cf8cc85f32fa4b2bbd8cab Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Wed, 13 Mar 2024 03:45:47 +0100
Subject: [PATCH 58/84] pad

---
 meshgpt_pytorch/mesh_dataset.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/mesh_dataset.py b/meshgpt_pytorch/mesh_dataset.py
index f9595ba6..56709977 100644
--- a/meshgpt_pytorch/mesh_dataset.py
+++ b/meshgpt_pytorch/mesh_dataset.py
@@ -102,8 +102,8 @@ def generate_codes(self, autoencoder : MeshAutoencoder, batch_size = 25):
             )
             
 
-        for codes, item in zip(batch_codes, batch_data): 
-            item['codes'] = [code for code in codes if code != autoencoder.pad_id and code != -1]
+            for codes, item in zip(batch_codes, batch_data): 
+                item['codes'] = [code for code in codes if code != autoencoder.pad_id and code != -1]
                 
         self.sort_dataset_keys()
         print(f"[MeshDataset] Generated codes for {len(self.data)} entrys")

From 6c197af207d7a6f00cddef0ada20956b6f6a49d3 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Wed, 13 Mar 2024 04:05:07 +0100
Subject: [PATCH 59/84] pad

---
 meshgpt_pytorch/mesh_dataset.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/mesh_dataset.py b/meshgpt_pytorch/mesh_dataset.py
index 56709977..0f6e0c8c 100644
--- a/meshgpt_pytorch/mesh_dataset.py
+++ b/meshgpt_pytorch/mesh_dataset.py
@@ -102,8 +102,14 @@ def generate_codes(self, autoencoder : MeshAutoencoder, batch_size = 25):
             )
             
 
-            for codes, item in zip(batch_codes, batch_data): 
-                item['codes'] = [code for code in codes if code != autoencoder.pad_id and code != -1]
+            mask = (batch_codes != autoencoder.pad_id).all(dim=-1) 
+            unpadded_batch_codes = batch_codes[mask].view(-1, 2)
+ 
+            code_index = 0
+            for item in batch_data:
+                item_code_length = unpadded_batch_codes.size(0) // len(batch_data)
+                item['codes'] = unpadded_batch_codes[code_index:code_index+item_code_length].tolist()
+                code_index += item_code_length
                 
         self.sort_dataset_keys()
         print(f"[MeshDataset] Generated codes for {len(self.data)} entrys")

From 8231e7b2f48c0399c47f275e765df028b4b58d55 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Wed, 13 Mar 2024 04:38:53 +0100
Subject: [PATCH 60/84] pad

---
 meshgpt_pytorch/mesh_dataset.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/meshgpt_pytorch/mesh_dataset.py b/meshgpt_pytorch/mesh_dataset.py
index 0f6e0c8c..1fb125c6 100644
--- a/meshgpt_pytorch/mesh_dataset.py
+++ b/meshgpt_pytorch/mesh_dataset.py
@@ -108,7 +108,7 @@ def generate_codes(self, autoencoder : MeshAutoencoder, batch_size = 25):
             code_index = 0
             for item in batch_data:
                 item_code_length = unpadded_batch_codes.size(0) // len(batch_data)
-                item['codes'] = unpadded_batch_codes[code_index:code_index+item_code_length].tolist()
+                item['codes'] = unpadded_batch_codes[code_index:code_index+item_code_length]
                 code_index += item_code_length
                 
         self.sort_dataset_keys()

From 2940a469809e1ac81a8163839188bfe8a9d15366 Mon Sep 17 00:00:00 2001
From: Marcus Loren <65302107+MarcusLoppe@users.noreply.github.com>
Date: Thu, 14 Mar 2024 04:25:19 +0100
Subject: [PATCH 61/84] Update README.md

---
 README.md | 186 ++++++++----------------------------------------------
 1 file changed, 27 insertions(+), 159 deletions(-)

diff --git a/README.md b/README.md
index b9615575..7df3efc1 100644
--- a/README.md
+++ b/README.md
@@ -6,173 +6,41 @@ Implementation of <a href="https://arxiv.org/abs/2311.15475">MeshGPT</a>, SOTA M
 
 Will also add text conditioning, for eventual text-to-3d asset
 
-Please join <a href="https://discord.gg/xBPBXfcFHd"><img alt="Join us on Discord" src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white"></a> if you are interested in collaborating with others to replicate this work
 
-## Appreciation
+Please visit the orginal repo for more details: 
+https://github.com/lucidrains/meshgpt-pytorch
+ 
+### Data sources:
+#### ModelNet40: https://www.kaggle.com/datasets/balraj98/modelnet40-princeton-3d-object-dataset/data
 
-- <a href="https://stability.ai/">StabilityAI</a>, <a href="https://a16z.com/supporting-the-open-source-ai-community/">A16Z Open Source AI Grant Program</a>, and <a href="https://huggingface.co/">🤗 Huggingface</a> for the generous sponsorships, as well as my other sponsors, for affording me the independence to open source current artificial intelligence research
+#### ShapeNet - [Extracted model labels](https://github.com/MarcusLoppe/meshgpt-pytorch/blob/main/shapenet_labels.json) Repository: https://huggingface.co/datasets/ShapeNet/shapenetcore-gltf
 
-- <a href="https://github.com/arogozhnikov/einops">Einops</a> for making my life easy
+#### Objaverse - [Downloader](https://github.com/MarcusLoppe/Objaverse-downloader/tree/main) Repository: https://huggingface.co/datasets/allenai/objaverse
+ 
+## Pre-trained autoencoder on the Objaverse dataset (14k meshes, only meshes that have max 250 faces): 
+This is contains only autoencoder model, I'm currently training the transformer model.
 
-- <a href="https://github.com/MarcusLoppe">Marcus</a> for the initial code review (pointing out some missing derived features) as well as running the first successful end-to-end experiments
-
-- <a href="https://github.com/MarcusLoppe">Marcus</a> for the <a href="https://github.com/lucidrains/meshgpt-pytorch/issues/18#issuecomment-1859214710">first successful training</a> of a collection of shapes conditioned on labels
-
-- <a href="https://github.com/qixuema">Quexi Ma</a> for finding numerous bugs with automatic eos handling
-
-- <a href="https://github.com/thuliu-yt16">Yingtian</a> for finding a bug with the gaussian blurring of the positions for spatial label smoothing
-
-- <a href="https://github.com/MarcusLoppe">Marcus</a> yet again for running the experiments to validate that it is possible to extend the system from triangles to <a href="https://github.com/lucidrains/meshgpt-pytorch/issues/54#issuecomment-1906789076">quads</a>
-
-## Install
-
-```bash
-$ pip install meshgpt-pytorch
-```
-
-## Usage
-
-```python
-import torch
-
-from meshgpt_pytorch import (
-    MeshAutoencoder,
-    MeshTransformer
-)
-
-# autoencoder
-
-autoencoder = MeshAutoencoder(
-    num_discrete_coors = 128
-)
-
-# mock inputs
-
-vertices = torch.randn((2, 121, 3))            # (batch, num vertices, coor (3))
-faces = torch.randint(0, 121, (2, 64, 3))      # (batch, num faces, vertices (3))
-
-# make sure faces are padded with `-1` for variable lengthed meshes
-
-# forward in the faces
-
-loss = autoencoder(
-    vertices = vertices,
-    faces = faces
-)
-
-loss.backward()
-
-# after much training...
-# you can pass in the raw face data above to train a transformer to model this sequence of face vertices
-
-transformer = MeshTransformer(
-    autoencoder,
-    dim = 512,
-    max_seq_len = 768
-)
-
-loss = transformer(
-    vertices = vertices,
-    faces = faces
-)
-
-loss.backward()
-
-# after much training of transformer, you can now sample novel 3d assets
-
-faces_coordinates, face_mask = transformer.generate()
-
-# (batch, num faces, vertices (3), coordinates (3)), (batch, num faces)
-# now post process for the generated 3d asset
+https://drive.google.com/drive/folders/1C1l5QrCtg9UulMJE5n_on4A9O9Gn0CC5?usp=sharing
 
 ```
-
-For <a href="https://www.youtube.com/watch?v=NXX0dKw4SjI">text-conditioned 3d shape synthesis</a>, simply set `condition_on_text = True` on your `MeshTransformer`, and then pass in your list of descriptions as the `texts` keyword argument
-
-ex.
-```python
-transformer = MeshTransformer(
-    autoencoder,
-    dim = 512,
-    max_seq_len = 768,
-    condition_on_text = True
-)
-
-
-loss = transformer(
-    vertices = vertices,
-    faces = faces,
-    texts = ['a high chair', 'a small teapot'],
-)
-
-loss.backward()
-
-# after much training of transformer, you can now sample novel 3d assets conditioned on text
-
-faces_coordinates, face_mask = transformer.generate(texts = ['a long table'])
-
-```
-
-If you want to tokenize meshes, for use in your multimodal transformer, simply invoke `.tokenize` on your autoencoder (or same method on autoencoder trainer instance for the exponentially smoothed model)
-
-```python
-
-mesh_token_ids = autoencoder.tokenize(
-    vertices = vertices,
-    faces = faces
-)
-
-# (batch, num face vertices, residual quantized layer)
+num_layers = 23 
+autoencoder = MeshAutoencoder(     
+        decoder_dims_through_depth =  (128,) * 3 + (192,) * 4 + (256,) * num_layers + (384,) * 3,  
+        encoder_dims_through_depth =  (64,) * 2 + (128,) *3 + (256,) *4 + (576,) * 1,  
+        dim_codebook = 192 , 
+        codebook_size = 16384 , 
+        dim_area_embed = 16,
+        dim_coor_embed = 16, 
+        dim_normal_embed = 16,
+        dim_angle_embed = 8,
+
+    attn_decoder_depth  = 8,
+    attn_encoder_depth =4
+    ).to("cuda")    
 ```
 
-## Todo
-
-- [x] autoencoder
-    - [x] encoder sageconv with torch geometric
-    - [x] proper scatter mean accounting for padding for meaning the vertices and RVQ the vertices before gathering back for decoder
-    - [x] complete decoder and reconstruction loss + commitment loss
-    - [x] handle variable lengthed faces
-    - [x] add option to use residual LFQ, latest quantization development that scales code utilization
-    - [x] xcit linear attention in encoder and decoder
-    - [x] figure out how to auto-derive `face_edges` directly from faces and vertices
-    - [x] embed any derived values (area, angles, etc) from the vertices before sage convs
-    - [ ] add an extra graph conv stage in the encoder, where vertices are enriched with their connected vertex neighbors, before aggregating into faces. make optional
-    - [ ] allow for encoder to noise the vertices, so autoencoder is a bit denoising. consider conditioning decoder on noise level, if varying
-
-- [ ] transformer
-    - [x] properly mask out eos logit during generation
-    - [x] make sure it trains
-        - [x] take care of sos token automatically
-        - [x] take care of eos token automatically if sequence length or mask is passed in
-    - [x] handle variable lengthed faces
-        - [x] on forwards
-        - [x] on generation, do all eos logic + substitute everything after eos with pad id
-    - [x] generation + cache kv
-
-- [x] trainer wrapper with hf accelerate
-    - [x] autoencoder - take care of ema
-    - [x] transformer
-
-- [x] text conditioning using own CFG library
-    - [x] complete preliminary text conditioning
-    - [x]  make sure CFG library can support passing in arguments to the two separate calls when cond scaling (as well as aggregating their outputs)
-    - [ ] polish up the magic dataset decorator and see if it can be moved to CFG library
-- [x] hierarchical transformers (using the RQ transformer)
-- [x] fix caching in simple gateloop layer in other repo
-- [x] local attention
-- [x] fix kv caching for two-staged hierarchical transformer - 7x faster now, and faster than original non-hierarchical transformer
-- [x] fix caching for gateloop layers
-- [x] allow for customization of model dimensions of fine vs coarse attention network
-- [x] figure out if autoencoder is really necessary - it is necessary, ablations are in the paper
-    - [x] when mesh discretizer is passed in, one can inject inter-face attention with the relative distance
-    - [x] additional embeddings (angles, area, normal), can also be appended before coarse transformer attention
-
-- [ ] make transformer efficient
-    - [ ] reversible networks
-
-- [ ] speculative decoding option
-
-- [ ] spend a day on documentation
+#### Results, it's about 14k models so with the limited training time and hardware It's a great result.
+![bild](https://github.com/lucidrains/meshgpt-pytorch/assets/65302107/18949b70-a982-4d22-9346-0f40ecf21cae)
 
 ## Citations
 

From 201ce4368c244897043e871210f7112498e7efd6 Mon Sep 17 00:00:00 2001
From: Marcus Loren <65302107+MarcusLoppe@users.noreply.github.com>
Date: Thu, 14 Mar 2024 18:14:56 +0100
Subject: [PATCH 62/84] Update README.md

---
 README.md | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/README.md b/README.md
index 7df3efc1..f548242f 100644
--- a/README.md
+++ b/README.md
@@ -19,9 +19,19 @@ https://github.com/lucidrains/meshgpt-pytorch
  
 ## Pre-trained autoencoder on the Objaverse dataset (14k meshes, only meshes that have max 250 faces): 
 This is contains only autoencoder model, I'm currently training the transformer model.
+Visit the discussions [Pre-trained autoencoder & data sourcing](https://github.com/lucidrains/meshgpt-pytorch/discussions/66) for more information about the training and details about the progression.
 
 https://drive.google.com/drive/folders/1C1l5QrCtg9UulMJE5n_on4A9O9Gn0CC5?usp=sharing
 
+
+The auto-encoder results shows that it's possible to compress many mesh models into tokens which then can be decoded and reconstruct a mesh near perfection!
+The auto-encoder was trained for 9 epochs for 20hrs on a single P100 GPU.
+The more resource heavy part is to train a transformer that can use these tokens learn the auto-encoders 'language'.
+Using the codes as a vocabablity and learn the relationship between the the codes and it's ordering requires more compute during training.
+So by using a single P100 GPU it will probaly take a few weeks till I can get out a pre-trained transformer. 
+
+Let me know if you wish to donate any compute or I can provide you with the dataset + training notebook.
+
 ```
 num_layers = 23 
 autoencoder = MeshAutoencoder(     

From e1f7ffbc61a793bf42a9051b32c1bc6034d1187f Mon Sep 17 00:00:00 2001
From: Marcus Loren <65302107+MarcusLoppe@users.noreply.github.com>
Date: Thu, 14 Mar 2024 18:54:10 +0100
Subject: [PATCH 63/84] Update README.md

---
 README.md | 37 +++++++++++++++++++------------------
 1 file changed, 19 insertions(+), 18 deletions(-)

diff --git a/README.md b/README.md
index f548242f..ec8a6eff 100644
--- a/README.md
+++ b/README.md
@@ -18,35 +18,36 @@ https://github.com/lucidrains/meshgpt-pytorch
 #### Objaverse - [Downloader](https://github.com/MarcusLoppe/Objaverse-downloader/tree/main) Repository: https://huggingface.co/datasets/allenai/objaverse
  
 ## Pre-trained autoencoder on the Objaverse dataset (14k meshes, only meshes that have max 250 faces): 
-This is contains only autoencoder model, I'm currently training the transformer model.
+This is contains only autoencoder model, I'm currently training the transformer model.<br/>
 Visit the discussions [Pre-trained autoencoder & data sourcing](https://github.com/lucidrains/meshgpt-pytorch/discussions/66) for more information about the training and details about the progression.
 
 https://drive.google.com/drive/folders/1C1l5QrCtg9UulMJE5n_on4A9O9Gn0CC5?usp=sharing
 
+<br/>
+The auto-encoder results shows that it's possible to compress many mesh models into tokens which then can be decoded and reconstruct a mesh near perfection!<br/>
+The auto-encoder was trained for 9 epochs for 20hrs on a single P100 GPU.<br/><br/>
 
-The auto-encoder results shows that it's possible to compress many mesh models into tokens which then can be decoded and reconstruct a mesh near perfection!
-The auto-encoder was trained for 9 epochs for 20hrs on a single P100 GPU.
-The more resource heavy part is to train a transformer that can use these tokens learn the auto-encoders 'language'.
-Using the codes as a vocabablity and learn the relationship between the the codes and it's ordering requires more compute during training.
+The more compute heavy part is to train a transformer that can use these tokens learn the auto-encoder 'language'.<br/>
+Using the codes as a vocabablity and learn the relationship between the the codes and it's ordering requires a lot compute to train compared to the auto-encoder.<br/>
 So by using a single P100 GPU it will probaly take a few weeks till I can get out a pre-trained transformer. 
-
+<br/>
 Let me know if you wish to donate any compute or I can provide you with the dataset + training notebook.
+<br/><br/>
 
 ```
 num_layers = 23 
 autoencoder = MeshAutoencoder(     
-        decoder_dims_through_depth =  (128,) * 3 + (192,) * 4 + (256,) * num_layers + (384,) * 3,  
-        encoder_dims_through_depth =  (64,) * 2 + (128,) *3 + (256,) *4 + (576,) * 1,  
-        dim_codebook = 192 , 
-        codebook_size = 16384 , 
-        dim_area_embed = 16,
-        dim_coor_embed = 16, 
-        dim_normal_embed = 16,
-        dim_angle_embed = 8,
-
-    attn_decoder_depth  = 8,
-    attn_encoder_depth =4
-    ).to("cuda")    
+   decoder_dims_through_depth = (128,) * 3 + (192,) * 4 + (256,) * num_layers + (384,) * 3,   
+   dim_codebook = 192 , 
+   codebook_size = 16384 , 
+   dim_area_embed = 16,
+   dim_coor_embed = 16, 
+   dim_normal_embed = 16,
+   dim_angle_embed = 8,
+
+   attn_decoder_depth = 8,
+   attn_encoder_depth = 4
+ ).to("cuda")    
 ```
 
 #### Results, it's about 14k models so with the limited training time and hardware It's a great result.

From cdb0d7fdd7120d3d577a10b1bc3e0ee5ebc396df Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Tue, 2 Apr 2024 17:33:24 +0200
Subject: [PATCH 64/84] tuple error

---
 meshgpt_pytorch/trainer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index a252d0c3..7fd31855 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -88,7 +88,7 @@ def __init__(
         checkpoint_every = 1000,
         checkpoint_every_epoch: Optional[int] = None,
         checkpoint_folder = './checkpoints',
-        data_kwargs: Tuple[str, ...] = ['vertices', 'faces', 'face_edges'],
+        data_kwargs: Tuple[str, ...] = ('vertices', 'faces', 'face_edges'),
         warmup_steps = 1000,
         use_wandb_tracking = False
     ):
@@ -442,7 +442,7 @@ def __init__(
         checkpoint_every = 1000, 
         checkpoint_every_epoch: Optional[int] = None,
         checkpoint_folder = './checkpoints',
-        data_kwargs: Tuple[str, ...] = ['vertices', 'faces', 'face_edges', 'text'],
+        data_kwargs: Tuple[str, ...] = ('vertices', 'faces', 'face_edges', 'text'),
         warmup_steps = 1000,
         use_wandb_tracking = False
     ):

From e5afd9deaa2677f49e21ea0a093914e090651e82 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Mon, 15 Apr 2024 22:30:10 +0200
Subject: [PATCH 65/84] padding issue

---
 meshgpt_pytorch/mesh_dataset.py | 13 +++++--------
 1 file changed, 5 insertions(+), 8 deletions(-)

diff --git a/meshgpt_pytorch/mesh_dataset.py b/meshgpt_pytorch/mesh_dataset.py
index 1fb125c6..34d6c47c 100644
--- a/meshgpt_pytorch/mesh_dataset.py
+++ b/meshgpt_pytorch/mesh_dataset.py
@@ -93,7 +93,7 @@ def generate_codes(self, autoencoder : MeshAutoencoder, batch_size = 25):
             
             padded_batch_vertices = pad_sequence([item['vertices'] for item in batch_data], batch_first=True, padding_value=autoencoder.pad_id)
             padded_batch_faces = pad_sequence([item['faces'] for item in batch_data], batch_first=True, padding_value=autoencoder.pad_id)
-            padded_batch_face_edges = pad_sequence([item['face_edges'] for item in batch_data], batch_first=True, padding_value=-autoencoder.pad_id)
+            padded_batch_face_edges = pad_sequence([item['face_edges'] for item in batch_data], batch_first=True, padding_value=autoencoder.pad_id)
             
             batch_codes = autoencoder.tokenize(
                 vertices=padded_batch_vertices,
@@ -103,13 +103,10 @@ def generate_codes(self, autoencoder : MeshAutoencoder, batch_size = 25):
             
 
             mask = (batch_codes != autoencoder.pad_id).all(dim=-1) 
-            unpadded_batch_codes = batch_codes[mask].view(-1, 2)
- 
-            code_index = 0
-            for item in batch_data:
-                item_code_length = unpadded_batch_codes.size(0) // len(batch_data)
-                item['codes'] = unpadded_batch_codes[code_index:code_index+item_code_length]
-                code_index += item_code_length
+            for item_idx, (item_codes, item_mask) in enumerate(zip(batch_codes, mask)):            
+                item_codes_masked = item_codes[item_mask]
+                item = batch_data[item_idx]
+                item['codes'] = item_codes_masked 
                 
         self.sort_dataset_keys()
         print(f"[MeshDataset] Generated codes for {len(self.data)} entrys")

From fa71927cc050ca2978b0193663f3fbf26b0665a4 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Fri, 19 Apr 2024 22:11:47 +0200
Subject: [PATCH 66/84] multi-gpu checkpoint fix

---
 meshgpt_pytorch/trainer.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 7fd31855..09864c1d 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -334,6 +334,7 @@ def forward(self):
             self.wait()
 
         self.print('training complete')
+        
     def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
         epoch_losses, epoch_recon_losses, epoch_commit_losses = [] , [],[] 
         self.model.train() 
@@ -394,12 +395,12 @@ def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
             self.print(epochOut) 
 
          
-            if self.checkpoint_every_epoch is not None and (self.checkpoint_every_epoch == 1 or (epoch != 0 and epoch % self.checkpoint_every_epoch == 0)):
+            if self.is_main and self.checkpoint_every_epoch is not None and (self.checkpoint_every_epoch == 1 or (epoch != 0 and epoch % self.checkpoint_every_epoch == 0)):
                 self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.5f}_recon_{avg_recon_loss:.4f}_commit_{avg_commit_loss:.4f}.pt')
                 
             if stop_at_loss is not None and avg_epoch_loss < stop_at_loss: 
                 self.print(f'Stopping training at epoch {epoch} with average loss {avg_epoch_loss}')
-                if self.checkpoint_every_epoch is not None:
+                if self.is_main and self.checkpoint_every_epoch is not None:
                     self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.stop_at_loss_avg_loss_{avg_epoch_loss:.3f}.pt') 
                 break   
   
@@ -709,12 +710,12 @@ def train(self, num_epochs, stop_at_loss = None, diplay_graph = False):
                 self.wait()
                 self.print(epochOut)
                 
-                if self.checkpoint_every_epoch is not None and (self.checkpoint_every_epoch == 1 or (epoch != 0 and epoch % self.checkpoint_every_epoch == 0)):
+                if self.is_main and self.checkpoint_every_epoch is not None and (self.checkpoint_every_epoch == 1 or (epoch != 0 and epoch % self.checkpoint_every_epoch == 0)):
                     self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.epoch_{epoch}_avg_loss_{avg_epoch_loss:.3f}.pt')
                     
                 if stop_at_loss is not None and avg_epoch_loss < stop_at_loss: 
                     self.print(f'Stopping training at epoch {epoch} with average loss {avg_epoch_loss}')
-                    if self.checkpoint_every_epoch is not None:
+                    if self.is_main and self.checkpoint_every_epoch is not None:
                         self.save(self.checkpoint_folder / f'mesh-transformer.ckpt.stop_at_loss_avg_loss_{avg_epoch_loss:.3f}.pt') 
                     break   
     

From 780431bdba9a9c32be773967e57fdaf61739e1d5 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Sat, 20 Apr 2024 17:12:22 +0200
Subject: [PATCH 67/84] gateloop_use_heinsen fix

---
 meshgpt_pytorch/meshgpt_pytorch.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/meshgpt_pytorch/meshgpt_pytorch.py b/meshgpt_pytorch/meshgpt_pytorch.py
index b86a95c4..6715d7d6 100644
--- a/meshgpt_pytorch/meshgpt_pytorch.py
+++ b/meshgpt_pytorch/meshgpt_pytorch.py
@@ -1107,7 +1107,7 @@ def __init__(
 
         # address a weakness in attention
 
-        self.fine_gateloop_block = GateLoopBlock(dim, depth = fine_pre_gateloop_depth) if fine_pre_gateloop_depth > 0 else None
+        self.fine_gateloop_block = GateLoopBlock(dim, depth = fine_pre_gateloop_depth, use_heinsen = gateloop_use_heinsen) if fine_pre_gateloop_depth > 0 else None
 
         # decoding the vertices, 2-stage hierarchy
 

From b4fcd8d4bfd3e33ce5c5164a0edc88cacd735ecc Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Tue, 7 May 2024 23:27:00 +0200
Subject: [PATCH 68/84] updated notebook with better instructions

---
 MeshGPT_demo.ipynb | 337 ++++++++++++++++-----------------------------
 1 file changed, 117 insertions(+), 220 deletions(-)

diff --git a/MeshGPT_demo.ipynb b/MeshGPT_demo.ipynb
index 602e360f..7391d1bf 100644
--- a/MeshGPT_demo.ipynb
+++ b/MeshGPT_demo.ipynb
@@ -34,27 +34,21 @@
             ") \n",
             "\n",
             "def get_mesh(file_path): \n",
-            "    mesh = trimesh.load(file_path, force='mesh')\n",
-            "     \n",
+            "    mesh = trimesh.load(file_path, force='mesh') \n",
             "    vertices = mesh.vertices.tolist()\n",
             "    if \".off\" in file_path:  # ModelNet dataset\n",
-            "       mesh.vertices[:, [1, 2]] = mesh.vertices[:, [2, 1]]\n",
-            " \n",
+            "       mesh.vertices[:, [1, 2]] = mesh.vertices[:, [2, 1]] \n",
             "       rotation_matrix = trimesh.transformations.rotation_matrix(np.radians(-90), [0, 1, 0])\n",
-            "       mesh.apply_transform(rotation_matrix)\n",
-            "\n",
+            "       mesh.apply_transform(rotation_matrix) \n",
             "        # Extract vertices and faces from the rotated mesh\n",
             "       vertices = mesh.vertices.tolist()\n",
             "            \n",
             "    faces = mesh.faces.tolist()\n",
-            "    # Center\n",
-            "    centered_vertices = vertices - np.mean(vertices, axis=0)\n",
-            "    # Limit vertices to [-0.95, 0.95]\n",
+            "    \n",
+            "    centered_vertices = vertices - np.mean(vertices, axis=0)  \n",
             "    max_abs = np.max(np.abs(centered_vertices))\n",
-            "    vertices = centered_vertices / (max_abs / 0.95)   \n",
-            "     \n",
-            "    # To ensure that the mesh models are on the \"ground\", I think this might help the models learn better if they always knows the ground plane\n",
-            "    # If you dont do this, the models will 'hover' in the air \n",
+            "    vertices = centered_vertices / (max_abs / 0.95)     # Limit vertices to [-0.95, 0.95]\n",
+            "      \n",
             "    min_y = np.min(vertices[:, 1]) \n",
             "    difference = -0.95 - min_y \n",
             "    vertices[:, 1] += difference\n",
@@ -75,11 +69,8 @@
             "    sorted_vertices_as_tuples = [tuple(v) for v in sorted_vertices]\n",
             "\n",
             "    vertex_map = {old_index: new_index for old_index, vertex_tuple in enumerate(vertices_as_tuples) for new_index, sorted_vertex_tuple in enumerate(sorted_vertices_as_tuples) if vertex_tuple == sorted_vertex_tuple} \n",
-            "    reindexed_faces = [[vertex_map[face[0]], vertex_map[face[1]], vertex_map[face[2]]] for face in faces]\n",
-            "    # Code for cyclically permuted faces, used in paper (not recommended if you are using datasets from many sources)    \n",
-            "    #sorted_faces = [sub_arr[sub_arr.index(min(sub_arr)):] + sub_arr[:sub_arr.index(min(sub_arr))] for sub_arr in reindexed_faces]\n",
-            "    sorted_faces = [sorted(sub_arr) for sub_arr in reindexed_faces]  \n",
-            "    \n",
+            "    reindexed_faces = [[vertex_map[face[0]], vertex_map[face[1]], vertex_map[face[2]]] for face in faces] \n",
+            "    sorted_faces = [sorted(sub_arr) for sub_arr in reindexed_faces]   \n",
             "    return np.array(sorted_vertices), np.array(sorted_faces)\n",
             " \n",
             " \n",
@@ -90,10 +81,8 @@
             "    offsets = np.random.choice(possible_values, size=vertices.shape) \n",
             "    vertices = vertices + offsets   \n",
             "    \n",
-            "    vertices = vertices * scale_factor\n",
-            "    \n",
-            "    # To ensure that the mesh models are on the \"ground\", I think this might help the models learn better if they always knows the ground plane\n",
-            "    # If you dont do this, the models will 'hover' in the air \n",
+            "    vertices = vertices * scale_factor \n",
+            "    # To ensure that the mesh models are on the \"ground\"\n",
             "    min_y = np.min(vertices[:, 1])  \n",
             "    difference = -0.95 - min_y \n",
             "    vertices[:, 1] += difference\n",
@@ -127,11 +116,9 @@
             "                file_path = os.path.join(category_path, filename)\n",
             "                \n",
             "                if chosen_models_count[category] >= per_category:\n",
-            "                    break\n",
-            "                \n",
+            "                    break \n",
             "                if os.path.getsize(file_path) >  20 * 1024: # 20 kb limit = less then 400-600 faces\n",
-            "                    continue\n",
-            "                    \n",
+            "                    continue \n",
             "                if filename[:-4] not in id_info:\n",
             "                    print(\"Unable to find id info for \", filename)\n",
             "                    continue \n",
@@ -140,8 +127,7 @@
             "                    continue\n",
             "                \n",
             "                chosen_models_count[category] += 1  \n",
-            "                textName = id_info[filename[:-4]]  \n",
-            "                \n",
+            "                textName = id_info[filename[:-4]]   \n",
             "                \n",
             "                face_edges =  derive_face_edges_from_faces(faces)  \n",
             "                for scale_factor in scale_factors: \n",
@@ -152,8 +138,7 @@
             "    print(\"=\"*25)\n",
             "    print(\"Chosen models count for each category:\")\n",
             "    for category, count in chosen_models_count.items():\n",
-            "        print(f\"{category}: {count}\")\n",
-            "    \n",
+            "        print(f\"{category}: {count}\") \n",
             "    total_chosen_models = sum(chosen_models_count.values())\n",
             "    print(f\"Total number of chosen models: {total_chosen_models}\")\n",
             "    return obj_datas\n",
@@ -191,12 +176,14 @@
          "source": [
             "import gzip,json\n",
             "from tqdm import tqdm\n",
+            "import pandas as pd\n",
             "\n",
+            "# Instruction to download objverse meshes: https://github.com/MarcusLoppe/Objaverse-downloader/tree/main\n",
             "def load_objverse(directory, variations ):\n",
             "    obj_datas = []     \n",
             "    id_info = {}   \n",
-            "    with open('./objaverse/metadata.json' , 'r') as f:\n",
-            "        id_info = json.load(f) \n",
+            "    pali_captions = pd.read_csv('.\\pali_captions.csv', sep=';') # https://github.com/google-deepmind/objaverse_annotations/blob/main/pali_captions.csv\n",
+            "    pali_captions_dict = pali_captions.set_index(\"object_uid\").to_dict()[\"top_aggregate_caption\"]  \n",
             "        \n",
             "    possible_values = np.arange(0.75, 1.0) \n",
             "    scale_factors = np.random.choice(possible_values, size=variations) \n",
@@ -213,9 +200,9 @@
             "                if kb < 1 or kb > 30:\n",
             "                    continue\n",
             "                  \n",
-            "                if filename[:-4] not in id_info: \n",
+            "                if filename[:-4] not in pali_captions_dict: \n",
             "                    continue   \n",
-            "                textName =  id_info[filename[:-4]]['name']\n",
+            "                textName =  pali_captions_dict[filename[:-4]]\n",
             "                try:    \n",
             "                    vertices, faces = get_mesh(file_path)   \n",
             "                except Exception as e:\n",
@@ -251,94 +238,22 @@
             "working_dir = Path(working_dir)\n",
             "working_dir.mkdir(exist_ok = True, parents = True)\n",
             "dataset_path = working_dir / (project_name + \".npz\")\n",
-            "\n",
-            "\n",
+            " \n",
             "if not os.path.isfile(dataset_path):\n",
             "    data = load_filename(\"./demo_mesh\",50)  \n",
             "    dataset = MeshDataset(data) \n",
-            "    dataset.generate_face_edges() \n",
-            "    print(set(item[\"texts\"] for item in dataset.data)  ) \n",
+            "    dataset.generate_face_edges()  \n",
             "    dataset.save(dataset_path)\n",
             " \n",
             "dataset = MeshDataset.load(dataset_path) \n",
-            "print(dataset.data[0].keys())\n",
-            " "
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "#### Objaverse, instructions to download meshes: https://github.com/MarcusLoppe/Objaverse-downloader/tree/main"
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": null,
-         "metadata": {},
-         "outputs": [],
-         "source": [
-            "from pathlib import Path \n",
-            "import gc    \n",
-            "import torch\n",
-            "import os\n",
-            "from meshgpt_pytorch import MeshDataset \n",
-            "\n",
-            "project_name = \"objaverse_dataset\"  \n",
-            "\n",
-            "working_dir = f'./datasets/{project_name}' \n",
-            "working_dir = Path(working_dir)\n",
-            "working_dir.mkdir(exist_ok = True, parents = True)\n",
-            "dataset_path = working_dir / (project_name + \".npz\")\n",
-            "\n",
-            "\n",
-            "if not os.path.isfile(dataset_path):\n",
-            "    data = load_objverse(\"./objaverse\",10)  \n",
-            "    objverse_dataset = MeshDataset(data) \n",
-            "    objverse_dataset.generate_face_edges() \n",
-            "    print(set(item[\"texts\"] for item in objverse_dataset.data)  ) \n",
-            "    objverse_dataset.save(dataset_path)\n",
-            " \n",
-            "objverse_dataset = MeshDataset.load(dataset_path) \n",
-            "print(objverse_dataset.data[0].keys()) "
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "### Combind datasets"
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": null,
-         "metadata": {},
-         "outputs": [],
-         "source": [
-            "from pathlib import Path \n",
-            "import gc    \n",
-            "import torch \n",
-            "from meshgpt_pytorch import MeshDataset\n",
-            "torch.cuda.empty_cache()\n",
-            "gc.collect()  \n",
-            " \n",
-            "project_name = \"objverse_shapenet_combined\" \n",
-            "\n",
-            "working_dir = f'./datasets/{project_name}' \n",
-            "working_dir = Path(working_dir)\n",
-            "working_dir.mkdir(exist_ok = True, parents = True)\n",
-            "dataset_path = working_dir / (project_name + \".npz\")\n",
-            " \n",
-            "objverse_dataset.data.extend(dataset.data) \n",
-            "objverse_dataset.save(dataset_path)    "
+            "print(dataset.data[0].keys())"
          ]
       },
       {
          "cell_type": "markdown",
          "metadata": {},
          "source": [
-            "### Inspect"
+            "#### Inspect imported meshes (optional)"
          ]
       },
       {
@@ -371,8 +286,7 @@
             " \n",
             "obj_file_content = \"\".join(all_vertices) + \"\".join(all_faces)\n",
             " \n",
-            "obj_file_path = f'{folder}/3d_models_inspect.obj'\n",
-            "\n",
+            "obj_file_path = f'{folder}/3d_models_inspect.obj' \n",
             "with open(obj_file_path, \"w\") as file:\n",
             "    file.write(obj_file_content)    \n",
             "    "
@@ -391,24 +305,18 @@
          "metadata": {},
          "outputs": [],
          "source": [
-            "num_layers = 23 \n",
-            "autoencoder = MeshAutoencoder(     \n",
-            "        decoder_dims_through_depth =  (128,) * 3 + (192,) * 4 + (256,) * num_layers + (384,) * 3,  \n",
-            "        encoder_dims_through_depth =  (64,) * 2 + (128,) *3 + (256,) *4 + (576,) * 1,  \n",
-            "        dim_codebook = 192 , \n",
-            "        codebook_size = 16384 , \n",
+            "autoencoder = MeshAutoencoder(      \n",
+            "        decoder_dims_through_depth =  (128,) * 6 + (192,) * 12 + (256,) * 24 + (384,) * 6,   \n",
+            "        codebook_size = 2048,  # Smaller vocab size will speed up the transformer training, however if you are training on meshes more then 250 triangle, I'd advice to use 16384 codebook size\n",
+            "        dim_codebook = 192,  \n",
             "        dim_area_embed = 16,\n",
             "        dim_coor_embed = 16, \n",
             "        dim_normal_embed = 16,\n",
-            "        dim_angle_embed = 8\n",
-            "    ).to(\"cuda\")    \n",
-            "\n",
-            "total_params = sum(p.numel() for p in autoencoder.decoders.parameters()) \n",
-            "total_params = f\"{total_params / 1000000:.1f}M\"\n",
-            "print(f\"decoder Total parameters: {total_params}\") \n",
-            "total_params = sum(p.numel() for p in autoencoder.encoders.parameters()) \n",
-            "total_params = f\"{total_params / 1000000:.1f}M\"\n",
-            "print(f\"encoders Total parameters: {total_params}\")   \n",
+            "        dim_angle_embed = 8,\n",
+            "    \n",
+            "    attn_decoder_depth  = 4,\n",
+            "    attn_encoder_depth = 2\n",
+            ").to(\"cuda\")     \n",
             "total_params = sum(p.numel() for p in autoencoder.parameters()) \n",
             "total_params = f\"{total_params / 1000000:.1f}M\"\n",
             "print(f\"Total parameters: {total_params}\")"
@@ -444,9 +352,8 @@
          "metadata": {},
          "outputs": [],
          "source": [
-            "autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder ,warmup_steps = 10, dataset = dataset, num_train_steps=100, batch_size=8,  grad_accum_every=1, learning_rate = 1e-4) \n",
-            "autoencoder_trainer.load(f'{working_dir}\\mesh-encoder_{project_name}.pt')   \n",
-            "autencoder = autoencoder_trainer.model\n",
+            "pkg = torch.load(str(f'{working_dir}\\mesh-encoder_{project_name}.pt')) \n",
+            "autoencoder.load_state_dict(pkg['model'])\n",
             "for param in autoencoder.parameters():\n",
             "    param.requires_grad = True"
          ]
@@ -467,12 +374,13 @@
             "batch_size=16 # The batch size should be max 64.\n",
             "grad_accum_every = 4\n",
             "# So set the maximal batch size (max 64) that your VRAM can handle and then use grad_accum_every to create a effective batch size of 64, e.g  16 * 4 = 64\n",
-            "   \n",
+            "learning_rate = 1e-3 # Start with 1e-3 then at staggnation around 0.35, you can lower it to 1e-4.\n",
+            "\n",
             "autoencoder.commit_loss_weight = 0.2 # Set dependant on the dataset size, on smaller datasets, 0.1 is fine, otherwise try from 0.25 to 0.4.\n",
             "autoencoder_trainer = MeshAutoencoderTrainer(model =autoencoder ,warmup_steps = 10, dataset = dataset, num_train_steps=100,\n",
             "                                             batch_size=batch_size,\n",
             "                                             grad_accum_every = grad_accum_every,\n",
-            "                                             learning_rate = 1e-4,\n",
+            "                                             learning_rate = learning_rate,\n",
             "                                             checkpoint_every_epoch=1) \n",
             "loss = autoencoder_trainer.train(480,stop_at_loss = 0.2, diplay_graph= True)        "
          ]
@@ -506,49 +414,47 @@
             "\n",
             "min_mse, max_mse = float('inf'), float('-inf')\n",
             "min_coords, min_orgs, max_coords, max_orgs = None, None, None, None\n",
-            "random_samples = []\n",
-            "random_samples_pred = []\n",
-            "all_random_samples = []\n",
-            "total_mse = 0.0 \n",
-            "sample_size = 200\n",
+            "random_samples, random_samples_pred, all_random_samples = [], [], []\n",
+            "total_mse, sample_size = 0.0, 200\n",
             "\n",
             "random.shuffle(dataset.data)\n",
             "\n",
             "for item in tqdm(dataset.data[:sample_size]):\n",
-            "    codes = autoencoder.tokenize(\n",
-            "                vertices = item['vertices'],\n",
-            "                faces = item['faces'],\n",
-            "                face_edges = item['face_edges']\n",
-            "    ) \n",
-            "    codes =codes.flatten().unsqueeze(0)\n",
-            "    codes = codes[:, :codes.shape[-1] // 2 * 2]\n",
-            "\n",
+            "    codes = autoencoder.tokenize(vertices=item['vertices'], faces=item['faces'], face_edges=item['face_edges']) \n",
+            "    codes = codes.flatten().unsqueeze(0)\n",
+            "    codes = codes[:, :codes.shape[-1] // autoencoder.num_quantizers * autoencoder.num_quantizers] \n",
+            " \n",
             "    coords, mask = autoencoder.decode_from_codes_to_faces(codes)\n",
             "    orgs = item['vertices'][item['faces']].unsqueeze(0)\n",
             "\n",
             "    mse = torch.mean((orgs.view(-1, 3).cpu() - coords.view(-1, 3).cpu())**2)\n",
             "    total_mse += mse\n",
-            "   \n",
-            "\n",
-            "    if mse < min_mse:\n",
-            "        min_mse, min_coords, min_orgs = mse, coords, orgs\n",
             "\n",
-            "    if mse > max_mse:\n",
-            "        max_mse, max_coords, max_orgs = mse, coords, orgs\n",
+            "    if mse < min_mse: min_mse, min_coords, min_orgs = mse, coords, orgs\n",
+            "    if mse > max_mse: max_mse, max_coords, max_orgs = mse, coords, orgs\n",
             " \n",
             "    if len(random_samples) <= 30:\n",
             "        random_samples.append(coords)\n",
             "        random_samples_pred.append(orgs)\n",
             "    else:\n",
-            "        all_random_samples.append(random_samples_pred)\n",
-            "        all_random_samples.append(random_samples)\n",
-            "        random_samples = []\n",
-            "        random_samples_pred = []\n",
+            "        all_random_samples.extend([random_samples_pred, random_samples])\n",
+            "        random_samples, random_samples_pred = [], []\n",
             "\n",
-            "avg_mse = total_mse / sample_size \n",
-            "print(f'MSE AVG: {avg_mse:.10f}, Min: {min_mse:.10f}, Max: {max_mse:.10f}')   \n",
+            "print(f'MSE AVG: {total_mse / sample_size:.10f}, Min: {min_mse:.10f}, Max: {max_mse:.10f}')    \n",
+            "mesh_render.combind_mesh_with_rows(f'{working_dir}\\mse_rows.obj', all_random_samples)"
+         ]
+      },
+      {
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "### Training & fine-tuning\n",
             "\n",
-            "mesh_render.combind_mesh_with_rows(f'{working_dir}\\mse_rows.obj', all_random_samples)  "
+            "**Pre-train:** Train the transformer on the full dataset with all the augmentations, the longer / more epochs will create a more robust model.<br/>\n",
+            "\n",
+            "**Fine-tune:** Since it will take a long time to train on all the possible augmentations of the meshes, I recommend that you remove all the augmentations so you are left with x1 model per mesh.<br/>\n",
+            "Below is the function **filter_dataset** that will return a single copy of each mesh.<br/>\n",
+            "The function can also check for duplicate labels, this may speed up the fine-tuning process (not recommanded) however this most likely will remove it's ability for novel mesh generation."
          ]
       },
       {
@@ -559,32 +465,52 @@
          "source": [
             "import gc  \n",
             "torch.cuda.empty_cache()\n",
-            "gc.collect()  \n",
-            "\n",
-            "max_length =  max(len(d[\"faces\"]) for d in dataset if \"faces\" in d) \n",
-            "max_seq = max_length * 6  \n",
-            "print(\"Highest face count:\" , max_length)\n",
-            "print(\"Max token sequence:\" , max_seq) \n",
+            "gc.collect()   \n",
+            "max_seq = max(len(d[\"faces\"]) for d in dataset if \"faces\" in d)  * (autoencoder.num_vertices_per_face * autoencoder.num_quantizers) \n",
+            "print(\"Max token sequence:\" , max_seq)  \n",
             "\n",
+            "# GPT2-Small model\n",
             "transformer = MeshTransformer(\n",
             "    autoencoder,\n",
-            "    dim = 512,\n",
-            "    coarse_pre_gateloop_depth = 6, # Better performance using more gateloop layers\n",
-            "    fine_pre_gateloop_depth= 4, \n",
-            "    attn_depth = 12, # GPT-2 medium have 24 layer depth, change if needed\n",
+            "    dim = 768,\n",
+            "    coarse_pre_gateloop_depth = 3,  \n",
+            "    fine_pre_gateloop_depth= 3,  \n",
+            "    attn_depth = 12,  \n",
+            "    attn_heads = 12,  \n",
             "    max_seq_len = max_seq, \n",
             "    condition_on_text = True, \n",
             "    gateloop_use_heinsen = False,\n",
-            "    text_condition_model_types = \"bge\", ## Change or remove this line if you are using:  https://github.com/MarcusLoppe/classifier-free-guidance-pytorch\n",
+            "    dropout  = 0.0,\n",
+            "    text_condition_model_types = \"bge\", \n",
             "    text_condition_cond_drop_prob = 0.0\n",
             ") \n",
             "\n",
             "total_params = sum(p.numel() for p in transformer.decoder.parameters())\n",
             "total_params = f\"{total_params / 1000000:.1f}M\"\n",
-            "print(f\"Decoder total parameters: {total_params}\") \n",
-            "total_params = sum(p.numel() for p in transformer.parameters())\n",
-            "total_params = f\"{total_params / 1000000:.1f}M\"\n",
-            "print(f\"Total parameters: {total_params}\") "
+            "print(f\"Decoder total parameters: {total_params}\")"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
+            "def filter_dataset(dataset, unique_labels = False):\n",
+            "    unique_dicts = []\n",
+            "    unique_tensors = set()\n",
+            "    texts = set()\n",
+            "    for d in dataset.data:\n",
+            "        tensor = d[\"faces\"]\n",
+            "        tensor_tuple = tuple(tensor.cpu().numpy().flatten())\n",
+            "        if unique_labels and d['texts'] in texts:\n",
+            "            continue\n",
+            "        if tensor_tuple not in unique_tensors:\n",
+            "            unique_tensors.add(tensor_tuple)\n",
+            "            unique_dicts.append(d)\n",
+            "            texts.add(d['texts'])\n",
+            "    return unique_dicts \n",
+            "#dataset.data = filter_dataset(dataset.data, unique_labels = False)"
          ]
       },
       {
@@ -607,10 +533,9 @@
          "metadata": {},
          "outputs": [],
          "source": [
-            "labels = set(item[\"texts\"] for item in dataset.data)\n",
-            "print(labels)\n",
+            "labels = list(set(item[\"texts\"] for item in dataset.data))\n",
             "dataset.embed_texts(transformer, batch_size = 25)\n",
-            "dataset.generate_codes(autoencoder, batch_size = 50) # Higher batch size = faster but might leave some uncollected garbage in the VRAM\n",
+            "dataset.generate_codes(autoencoder, batch_size = 50)\n",
             "print(dataset.data[0].keys())"
          ]
       },
@@ -627,9 +552,8 @@
          "metadata": {},
          "outputs": [],
          "source": [
-            "trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,grad_accum_every=1,num_train_steps=100, dataset = dataset, learning_rate = 1e-1, batch_size=2)\n",
-            "trainer.load(f'{working_dir}\\mesh-transformer_{project_name}.pt')  \n",
-            "transformer = trainer.model"
+            "pkg = torch.load(str(f'{working_dir}\\mesh-transformer_{project_name}.pt')) \n",
+            "transformer.load_state_dict(pkg['model'])"
          ]
       },
       {
@@ -649,10 +573,11 @@
             "grad_accum_every = 16\n",
             "\n",
             "# Set the maximal batch size (max 64) that your VRAM can handle and then use grad_accum_every to create a effective batch size of 64, e.g  4 * 16 = 64\n",
+            "learning_rate = 1e-2 # Start training with the learning rate at 1e-2 then lower it to 1e-3 at stagnation or at 0.5 loss.\n",
             "\n",
             "trainer = MeshTransformerTrainer(model = transformer,warmup_steps = 10,num_train_steps=100, dataset = dataset,\n",
             "                                 grad_accum_every=grad_accum_every,\n",
-            "                                 learning_rate = 1e-4,\n",
+            "                                 learning_rate = learning_rate,\n",
             "                                 batch_size=batch_size,\n",
             "                                 checkpoint_every_epoch = 1)\n",
             "loss = trainer.train(300, stop_at_loss = 0.005)  "
@@ -664,7 +589,6 @@
          "metadata": {},
          "outputs": [],
          "source": [
-            " \n",
             "trainer.save(f'{working_dir}\\mesh-transformer_{project_name}.pt')   "
          ]
       },
@@ -697,14 +621,10 @@
             "obj_file_path.mkdir(exist_ok = True, parents = True)  \n",
             " \n",
             "text_coords = [] \n",
-            "for text in labels:\n",
+            "for text in labels[:10]:\n",
             "    print(f\"Generating {text}\")\n",
             "    faces_coordinates = transformer.generate(texts = [text],  temperature = 0.0) \n",
-            "    text_coords.append(faces_coordinates)\n",
-            "    \n",
-            "    mesh_render.write_mesh_output(f'{folder}/3d_output_{text}.obj', faces_coordinates)  \n",
-            "     \n",
-            " \n",
+            "    text_coords.append(faces_coordinates)   \n",
             "mesh_render.combind_mesh(f'{folder}/3d_models_all.obj', text_coords)"
          ]
       },
@@ -715,22 +635,6 @@
             "**Text + prompt of tokens**"
          ]
       },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "Grab fresh copy of dataset"
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": null,
-         "metadata": {},
-         "outputs": [],
-         "source": [
-            "dataset = MeshDataset.load(dataset_path) "
-         ]
-      },
       {
          "cell_type": "markdown",
          "metadata": {},
@@ -746,6 +650,10 @@
          "source": [
             "from pathlib import Path \n",
             "from meshgpt_pytorch import mesh_render \n",
+            "folder = working_dir / f'renders/text+codes'\n",
+            "obj_file_path = Path(folder)\n",
+            "obj_file_path.mkdir(exist_ok = True, parents = True)  \n",
+            "\n",
             "token_length_procent = 0.10 \n",
             "codes = []\n",
             "texts = []\n",
@@ -757,29 +665,19 @@
             "                faces = item['faces'],\n",
             "                face_edges = item['face_edges']\n",
             "            ) \n",
-            "            num_tokens = int(tokens.shape[0] * token_length_procent) \n",
-            "            \n",
+            "            num_tokens = int(tokens.shape[0] * token_length_procent)  \n",
             "            texts.append(item['texts']) \n",
             "            codes.append(tokens.flatten()[:num_tokens].unsqueeze(0))  \n",
             "            break\n",
             "        \n",
-            "folder = working_dir / f'renders/text+codes'\n",
-            "obj_file_path = Path(folder)\n",
-            "obj_file_path.mkdir(exist_ok = True, parents = True)  \n",
-            "\n",
             "coords = []  \n",
             "for text, prompt in zip(texts, codes): \n",
             "    print(f\"Generating {text} with {prompt.shape[1]} tokens\")\n",
             "    faces_coordinates = transformer.generate(texts = [text],  prompt = prompt, temperature = 0) \n",
-            "    coords.append(faces_coordinates)  \n",
-            "        \n",
+            "    coords.append(faces_coordinates)   \n",
             "    print(obj_file_path)\n",
-            "     \n",
-            " \n",
-            "mesh_render.combind_mesh(f'{folder}/text+prompt_all.obj', coords)\n",
-            "\n",
-            "if text_coords is not None: \n",
-            "    mesh_render.combind_mesh_with_rows(f'{folder}/both_verisons.obj', [text_coords , coords])"
+            "      \n",
+            "mesh_render.combind_mesh(f'{folder}/text+prompt_{token_length_procent*100}.obj', coords)"
          ]
       },
       {
@@ -825,10 +723,9 @@
             "        print(f\"Generating {text} with {prompt.shape[1]} tokens\")\n",
             "        faces_coordinates = transformer.generate(texts = [text],  prompt = prompt, temperature = 0) \n",
             "        coords.append(faces_coordinates) \n",
-            "        \n",
-            "        \n",
+            "         \n",
             "    mesh_rows.append(coords) \n",
-            "    mesh_render.combind_mesh(f'{folder}/text+prompt_all_{token_length_procent}.obj', coords)\n",
+            "    mesh_render.combind_mesh(f'{folder}/text+prompt_all_{token_length_procent*100}.obj', coords)\n",
             "    \n",
             "mesh_render.combind_mesh_with_rows(f'{folder}/all.obj', mesh_rows)\n",
             " "

From 989faa9db0c76b28a274525af65bc14ab742905a Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Tue, 7 May 2024 23:31:58 +0200
Subject: [PATCH 69/84] added fp16 training

---
 MeshGPT_demo.ipynb | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/MeshGPT_demo.ipynb b/MeshGPT_demo.ipynb
index 7391d1bf..6f10e869 100644
--- a/MeshGPT_demo.ipynb
+++ b/MeshGPT_demo.ipynb
@@ -421,6 +421,7 @@
             "\n",
             "for item in tqdm(dataset.data[:sample_size]):\n",
             "    codes = autoencoder.tokenize(vertices=item['vertices'], faces=item['faces'], face_edges=item['face_edges']) \n",
+            "    \n",
             "    codes = codes.flatten().unsqueeze(0)\n",
             "    codes = codes[:, :codes.shape[-1] // autoencoder.num_quantizers * autoencoder.num_quantizers] \n",
             " \n",
@@ -428,7 +429,7 @@
             "    orgs = item['vertices'][item['faces']].unsqueeze(0)\n",
             "\n",
             "    mse = torch.mean((orgs.view(-1, 3).cpu() - coords.view(-1, 3).cpu())**2)\n",
-            "    total_mse += mse\n",
+            "    total_mse += mse \n",
             "\n",
             "    if mse < min_mse: min_mse, min_coords, min_orgs = mse, coords, orgs\n",
             "    if mse > max_mse: max_mse, max_coords, max_orgs = mse, coords, orgs\n",
@@ -579,7 +580,11 @@
             "                                 grad_accum_every=grad_accum_every,\n",
             "                                 learning_rate = learning_rate,\n",
             "                                 batch_size=batch_size,\n",
-            "                                 checkpoint_every_epoch = 1)\n",
+            "                                 checkpoint_every_epoch = 1,\n",
+            "                                 # FP16 training, it doesn't speed up very much but can increase the batch size which will in turn speed up the training.\n",
+            "                                 # However it might cause nan after a while.\n",
+            "                                 # accelerator_kwargs = {\"mixed_precision\" : \"fp16\"}, optimizer_kwargs = { \"eps\": 1e-7} \n",
+            "                                 )\n",
             "loss = trainer.train(300, stop_at_loss = 0.005)  "
          ]
       },

From 19f616dc9d371e75517d835ee6ab01473474e425 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Fri, 10 May 2024 20:40:58 +0200
Subject: [PATCH 70/84] remove unused testing things

---
 meshgpt_pytorch/trainer.py | 15 +++++----------
 1 file changed, 5 insertions(+), 10 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 119217cf..f486dc91 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -75,8 +75,7 @@ def __init__(
         batch_size: int,
         grad_accum_every: int,
         val_dataset: Dataset | None = None,
-        val_every: int = 100,
-        shuffle: bool = True,
+        val_every: int = 100, 
         val_num_batches: int = 5,
         learning_rate: float = 1e-4,
         weight_decay: float = 0.,
@@ -125,8 +124,7 @@ def __init__(
 
         self.dataloader = DataLoader(
             dataset,
-            batch_size = batch_size,
-            shuffle = shuffle,
+            batch_size = batch_size, 
             drop_last = True,
             collate_fn = partial(custom_collate, pad_id = model.pad_id)
         )
@@ -141,8 +139,7 @@ def __init__(
 
             self.val_dataloader = DataLoader(
                 val_dataset,
-                batch_size = batch_size,
-                shuffle = shuffle,
+                batch_size = batch_size, 
                 drop_last = True,
                 collate_fn = partial(custom_collate, pad_id = model.pad_id)
             )
@@ -482,8 +479,7 @@ def __init__(
 
         self.dataloader = DataLoader(
             dataset,
-            batch_size = batch_size,
-            shuffle = shuffle,
+            batch_size = batch_size, 
             drop_last = True,
             collate_fn = partial(custom_collate, pad_id = model.pad_id)
         )
@@ -498,8 +494,7 @@ def __init__(
 
             self.val_dataloader = DataLoader(
                 val_dataset,
-                batch_size = batch_size,
-                shuffle = shuffle,
+                batch_size = batch_size, 
                 drop_last = True,
                 collate_fn = partial(custom_collate, pad_id = model.pad_id)
             )

From 6e3909b3db4b8324258d42c0d996cc8366bada3a Mon Sep 17 00:00:00 2001
From: lucidrains <lucidrains@gmail.com>
Date: Fri, 10 May 2024 16:59:28 -0700
Subject: [PATCH 71/84] small comment

---
 README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index b9615575..82ce14eb 100644
--- a/README.md
+++ b/README.md
@@ -109,7 +109,9 @@ loss.backward()
 
 # after much training of transformer, you can now sample novel 3d assets conditioned on text
 
-faces_coordinates, face_mask = transformer.generate(texts = ['a long table'])
+faces_coordinates, face_mask = transformer.generate(texts = ['a long table'],
+    cond_scale = 3.  # a cond_scale > 1. will enable classifier free guidance - can be placed anywhere from 3. - 10.
+)
 
 ```
 

From 0d5cb51e7e316c8018101d721c38849ee7f21249 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Fri, 7 Jun 2024 23:57:14 +0200
Subject: [PATCH 72/84] huggingface implementation & updated rendering

---
 .github/workflows/python-publish.yml |  38 ----------
 meshgpt_pytorch/mesh_render.py       | 107 ++++++++++++++-------------
 meshgpt_pytorch/meshgpt_pytorch.py   |  76 ++++++++++++++++++-
 setup.py                             |   1 +
 4 files changed, 131 insertions(+), 91 deletions(-)
 delete mode 100644 .github/workflows/python-publish.yml

diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml
deleted file mode 100644
index 5f38eed3..00000000
--- a/.github/workflows/python-publish.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-
-  
-# This workflow will upload a Python Package using Twine when a release is created
-# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
-
-# This workflow uses actions that are not certified by GitHub.
-# They are provided by a third-party and are governed by
-# separate terms of service, privacy policy, and support
-# documentation.
-
-name: Upload Python Package
-
-on:
-  release:
-    types: [published]
-
-jobs:
-  deploy:
-
-    runs-on: ubuntu-latest
-
-    steps:
-    - uses: actions/checkout@v2
-    - name: Set up Python
-      uses: actions/setup-python@v2
-      with:
-        python-version: '3.x'
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install build
-    - name: Build package
-      run: python -m build
-    - name: Publish package
-      uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
-      with:
-        user: __token__
-        password: ${{ secrets.PYPI_API_TOKEN }}
diff --git a/meshgpt_pytorch/mesh_render.py b/meshgpt_pytorch/mesh_render.py
index af982534..121e1ee6 100644
--- a/meshgpt_pytorch/mesh_render.py
+++ b/meshgpt_pytorch/mesh_render.py
@@ -1,63 +1,70 @@
-def combind_mesh(path, mesh):
-    all_vertices = []
-    all_faces = []
-    vertex_offset = 0
-    translation_distance = 0.5  
+import numpy as np
+import math
 
-    for r, faces_coordinates in enumerate(mesh):  
-        numpy_data = faces_coordinates[0].cpu().numpy().reshape(-1, 3)   
-        numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  
-    
-        for vertex in numpy_data:
-            all_vertices.append(f"v {vertex[0]} {vertex[1]} {vertex[2]}\n")
-    
-        for i in range(1, len(numpy_data), 3):
-            all_faces.append(f"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\n")
-    
-        vertex_offset += len(numpy_data)
-    
-    obj_file_content = "".join(all_vertices) + "".join(all_faces)
+def orient_triangle_upward(v1, v2, v3): 
+    edge1 = v2 - v1
+    edge2 = v3 - v1
+    normal = np.cross(edge1, edge2) 
+    normal = normal / np.linalg.norm(normal)
      
-    with open(path , "w") as file:
-        file.write(obj_file_content)   
+    up = np.array([0, 1, 0])
+    if np.dot(normal, up) < 0: 
+        v1, v3 = v3, v1 
+    return v1, v2, v3
+
+def get_angle(v1, v2, v3):
+    v1, v2, v3 = orient_triangle_upward(v1, v2, v3) 
+    vec1 = v2 - v1
+    vec2 = v3 - v1 
+    angle_rad = np.arccos(np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2)))
+    return math.degrees(angle_rad)  
  
-def combind_mesh_with_rows(path, meshes):
-    all_vertices = []
-    all_faces = []
+def save_rendering(path, input_meshes):
+    all_vertices,all_faces = [],[]
     vertex_offset = 0
     translation_distance = 0.5  
     obj_file_content = ""
+    meshes = input_meshes if isinstance(input_meshes, list) else [input_meshes] 
     
     for row, mesh in enumerate(meshes): 
-        for r, faces_coordinates in enumerate(mesh): 
-            numpy_data = faces_coordinates[0].cpu().numpy().reshape(-1, 3)  
-            numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  
-            numpy_data[:, 2] += translation_distance * (row / 0.2 - 1)  
-        
-            for vertex in numpy_data:
-                all_vertices.append(f"v {vertex[0]} {vertex[1]} {vertex[2]}\n")
-        
-            for i in range(1, len(numpy_data), 3):
-                all_faces.append(f"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\n")
-        
-            vertex_offset += len(numpy_data)
-        
+        mesh = mesh if isinstance(mesh, list) else [mesh]
+        cell_offset = 0
+        for tensor, mask in mesh:   
+            for tensor_batch, mask_batch in zip(tensor,mask):  
+                numpy_data = tensor_batch[mask_batch].cpu().numpy().reshape(-1, 3)  
+                numpy_data[:, 0] += translation_distance * (cell_offset / 0.2 - 1)  
+                numpy_data[:, 2] += translation_distance * (row / 0.2 - 1)  
+                cell_offset += 1
+                for vertex in numpy_data:
+                    all_vertices.append(f"v {vertex[0]} {vertex[1]} {vertex[2]}\n")
+                
+                mesh_center = np.mean(numpy_data, axis=0)
+                for i in range(0, len(numpy_data), 3):
+                    v1 = numpy_data[i]
+                    v2 = numpy_data[i + 1]
+                    v3 = numpy_data[i + 2]
+  
+                    normal = np.cross(v2 - v1, v3 - v1)   
+                    if get_angle(v1, v2, v3) > 60:  
+                        direction_vector = mesh_center - np.mean([v1, v2, v3], axis=0)
+                        direction_vector = -direction_vector
+                    else:
+                        direction_vector = [0, 1, 0]  
+                         
+                    if np.dot(normal, direction_vector) > 0: 
+                        order =  [0, 1, 2]
+                    else:
+                        order = [0, 2, 1]
+
+                    reordered_vertices = [v1, v2, v3][order[0]], [v1, v2, v3][order[1]], [v1, v2, v3][order[2]] 
+                    indices = [np.where((numpy_data == vertex).all(axis=1))[0][0] + 1 + vertex_offset for vertex in reordered_vertices] 
+                    all_faces.append(f"f {indices[0]} {indices[1]} {indices[2]}\n")
+
+                vertex_offset += len(numpy_data)   
         obj_file_content = "".join(all_vertices) + "".join(all_faces)
      
     with open(path , "w") as file:
-        file.write(obj_file_content)   
-        
-        
-def write_mesh_output(path, coords):
-    numpy_data = coords[0].cpu().numpy().reshape(-1, 3)  
-    obj_file_content = ""
-    
-    for vertex in numpy_data:
-        obj_file_content += f"v {vertex[0]} {vertex[1]} {vertex[2]}\n"
-
-    for i in range(1, len(numpy_data), 3):
-        obj_file_content += f"f {i} {i + 1} {i + 2}\n"
- 
-    with open(path, "w") as file:
         file.write(obj_file_content) 
+        
+    print(f"[Save_rendering] Saved at {path}") 
          
\ No newline at end of file
diff --git a/meshgpt_pytorch/meshgpt_pytorch.py b/meshgpt_pytorch/meshgpt_pytorch.py
index 4c4cd89d..e13cdf0d 100644
--- a/meshgpt_pytorch/meshgpt_pytorch.py
+++ b/meshgpt_pytorch/meshgpt_pytorch.py
@@ -16,7 +16,8 @@
 from pytorch_custom_utils import save_load
 
 from beartype import beartype
-from beartype.typing import Tuple, Callable, List, Dict, Any
+from beartype.typing import Tuple, Callable, List, Dict, Any, Optional, Union
+from huggingface_hub import PyTorchModelHubMixin, hf_hub_download
 
 from einops import rearrange, repeat, reduce, pack, unpack
 from einops.layers.torch import Rearrange
@@ -418,7 +419,7 @@ def forward(
 # main classes
 
 @save_load(version = __version__)
-class MeshAutoencoder(Module):
+class MeshAutoencoder(Module, PyTorchModelHubMixin):
     @beartype
     def __init__(
         self,
@@ -634,6 +635,41 @@ def __init__(
 
         self.commit_loss_weight = commit_loss_weight
         self.bin_smooth_blur_sigma = bin_smooth_blur_sigma
+        
+    @classmethod
+    def _from_pretrained(
+        cls,
+        *,
+        model_id: str,
+        revision: Optional[str],
+        cache_dir: Optional[Union[str, Path]],
+        force_download: bool,
+        proxies: Optional[Dict],
+        resume_download: bool,
+        local_files_only: bool,
+        token: Union[str, bool, None],
+        map_location: str = "cpu",
+        strict: bool = False,
+        **model_kwargs,
+    ): 
+        model_filename = "mesh-autoencoder.bin" 
+        model_file = Path(model_id) / model_filename 
+        if not model_file.exists(): 
+            model_file = hf_hub_download(
+                repo_id=model_id,
+                filename=model_filename,
+                revision=revision,
+                cache_dir=cache_dir,
+                force_download=force_download,
+                proxies=proxies,
+                resume_download=resume_download,
+                token=token,
+                local_files_only=local_files_only,
+            )    
+        model = cls.init_and_load(model_file,strict=strict) 
+        model.to(map_location)
+        return model
+    
 
     @beartype
     def encode(
@@ -1042,7 +1078,7 @@ def forward(
         return recon_faces, total_loss, loss_breakdown
 
 @save_load(version = __version__)
-class MeshTransformer(Module):
+class MeshTransformer(Module, PyTorchModelHubMixin):
     @beartype
     def __init__(
         self,
@@ -1194,6 +1230,40 @@ def __init__(
         self.pad_id = pad_id
         autoencoder.pad_id = pad_id
 
+    @classmethod
+    def _from_pretrained(
+        cls,
+        *,
+        model_id: str,
+        revision: Optional[str],
+        cache_dir: Optional[Union[str, Path]],
+        force_download: bool,
+        proxies: Optional[Dict],
+        resume_download: bool,
+        local_files_only: bool,
+        token: Union[str, bool, None],
+        map_location: str = "cpu",
+        strict: bool = False,
+        **model_kwargs,
+    ): 
+        model_filename = "mesh-transformer.bin" 
+        model_file = Path(model_id) / model_filename 
+        if not model_file.exists(): 
+            model_file = hf_hub_download(
+                repo_id=model_id,
+                filename=model_filename,
+                revision=revision,
+                cache_dir=cache_dir,
+                force_download=force_download,
+                proxies=proxies,
+                resume_download=resume_download,
+                token=token,
+                local_files_only=local_files_only,
+            )    
+        model = cls.init_and_load(model_file,strict=strict) 
+        model.to(map_location)
+        return model
+
     @property
     def device(self):
         return next(self.parameters()).device
diff --git a/setup.py b/setup.py
index 64acb286..9ac13f08 100644
--- a/setup.py
+++ b/setup.py
@@ -21,6 +21,7 @@
   ],
   install_requires=[
     'accelerate>=0.25.0',
+    'huggingface_hub>=0.21.4',
     'beartype',
     'classifier-free-guidance-pytorch>=0.6.2',
     'einops>=0.7.0',

From b5e74674973de4a97431561d7723d9b29629a695 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Sat, 8 Jun 2024 00:28:07 +0200
Subject: [PATCH 73/84] init file

---
 meshgpt_pytorch/__init__.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/meshgpt_pytorch/__init__.py b/meshgpt_pytorch/__init__.py
index 3ecc9bca..8d7fd34c 100644
--- a/meshgpt_pytorch/__init__.py
+++ b/meshgpt_pytorch/__init__.py
@@ -18,8 +18,6 @@
     MeshDataset
 )
 from meshgpt_pytorch.mesh_render import (
-    combind_mesh_with_rows,
-    write_mesh_output,
-    combind_mesh
+    save_rendering
 )
  

From 01ac2bbb16e9bf516065c338bbef6413ef4230bd Mon Sep 17 00:00:00 2001
From: "K. S. Ernest (iFire) Lee" <fire@users.noreply.github.com>
Date: Wed, 12 Jun 2024 00:36:38 -0700
Subject: [PATCH 74/84] Update mesh_dataset.py from entrys to entries

---
 meshgpt_pytorch/mesh_dataset.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/meshgpt_pytorch/mesh_dataset.py b/meshgpt_pytorch/mesh_dataset.py
index 34d6c47c..13601710 100644
--- a/meshgpt_pytorch/mesh_dataset.py
+++ b/meshgpt_pytorch/mesh_dataset.py
@@ -48,7 +48,7 @@ class MeshDataset(Dataset):
     """
     def __init__(self, data): 
         self.data = data 
-        print(f"[MeshDataset] Created from {len(self.data)} entrys")
+        print(f"[MeshDataset] Created from {len(self.data)} entries")
 
     def __len__(self):
         return len(self.data)
@@ -59,7 +59,7 @@ def __getitem__(self, idx):
     
     def save(self, path):  
         np.savez_compressed(path, self.data, allow_pickle=True) 
-        print(f"[MeshDataset] Saved {len(self.data)} entrys at {path}")
+        print(f"[MeshDataset] Saved {len(self.data)} entries at {path}")
         
     @classmethod
     def load(cls, path):   
@@ -67,7 +67,7 @@ def load(cls, path):
         data = []
         for item in loaded_data["arr_0"]:
             data.append(item)  
-        print(f"[MeshDataset] Loaded {len(data)} entrys")
+        print(f"[MeshDataset] Loaded {len(data)} entries")
         return cls(data) 
     def sort_dataset_keys(self):
         desired_order = ['vertices', 'faces', 'face_edges', 'texts','text_embeds','codes'] 
@@ -83,7 +83,7 @@ def generate_face_edges(self):
                 i += 1
             
         self.sort_dataset_keys()
-        print(f"[MeshDataset] Generated face_edges for {i}/{len(self.data)} entrys")
+        print(f"[MeshDataset] Generated face_edges for {i}/{len(self.data)} entries")
 
     def generate_codes(self, autoencoder : MeshAutoencoder, batch_size = 25): 
         total_batches = (len(self.data) + batch_size - 1) // batch_size
@@ -109,7 +109,7 @@ def generate_codes(self, autoencoder : MeshAutoencoder, batch_size = 25):
                 item['codes'] = item_codes_masked 
                 
         self.sort_dataset_keys()
-        print(f"[MeshDataset] Generated codes for {len(self.data)} entrys")
+        print(f"[MeshDataset] Generated codes for {len(self.data)} entries")
     
     def embed_texts(self, transformer : MeshTransformer, batch_size = 50): 
         unique_texts = set(item['texts'] for item in self.data)
@@ -125,4 +125,4 @@ def embed_texts(self, transformer : MeshTransformer, batch_size = 50):
                 item['text_embeds'] = text_embedding_dict.get(item['texts'], None)
                 del item['texts'] 
         self.sort_dataset_keys()
-        print(f"[MeshDataset] Generated {len(embeddings)} text_embeddings") 
\ No newline at end of file
+        print(f"[MeshDataset] Generated {len(embeddings)} text_embeddings") 

From 11bff2b7f81458e7f6f78e9b688f46ce6edff25d Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Mon, 17 Jun 2024 03:11:38 +0200
Subject: [PATCH 75/84] notebook updates

---
 MeshGPT_demo.ipynb             | 26 +++++++++++---------------
 meshgpt_pytorch/__init__.py    |  3 ++-
 meshgpt_pytorch/mesh_render.py | 27 +++++++++++++++++++++++++++
 3 files changed, 40 insertions(+), 16 deletions(-)

diff --git a/MeshGPT_demo.ipynb b/MeshGPT_demo.ipynb
index 6f10e869..49a3d71d 100644
--- a/MeshGPT_demo.ipynb
+++ b/MeshGPT_demo.ipynb
@@ -627,10 +627,10 @@
             " \n",
             "text_coords = [] \n",
             "for text in labels[:10]:\n",
-            "    print(f\"Generating {text}\")\n",
-            "    faces_coordinates = transformer.generate(texts = [text],  temperature = 0.0) \n",
-            "    text_coords.append(faces_coordinates)   \n",
-            "mesh_render.combind_mesh(f'{folder}/3d_models_all.obj', text_coords)"
+            "    print(f\"Generating {text}\") \n",
+            "    text_coords.append(transformer.generate(texts = [text],  temperature = 0.0))   \n",
+            "    \n",
+            "mesh_render.save_rendering(f'{folder}/3d_models_all.obj', text_coords)"
          ]
       },
       {
@@ -677,12 +677,10 @@
             "        \n",
             "coords = []  \n",
             "for text, prompt in zip(texts, codes): \n",
-            "    print(f\"Generating {text} with {prompt.shape[1]} tokens\")\n",
-            "    faces_coordinates = transformer.generate(texts = [text],  prompt = prompt, temperature = 0) \n",
-            "    coords.append(faces_coordinates)   \n",
-            "    print(obj_file_path)\n",
+            "    print(f\"Generating {text} with {prompt.shape[1]} tokens\") \n",
+            "    coords.append(transformer.generate(texts = [text],  prompt = prompt, temperature = 0) )    \n",
             "      \n",
-            "mesh_render.combind_mesh(f'{folder}/text+prompt_{token_length_procent*100}.obj', coords)"
+            "mesh_render.save_rendering(f'{folder}/text+prompt_{token_length_procent*100}.obj', coords)"
          ]
       },
       {
@@ -725,14 +723,12 @@
             "            \n",
             "    coords = []   \n",
             "    for text, prompt in zip(texts, codes):  \n",
-            "        print(f\"Generating {text} with {prompt.shape[1]} tokens\")\n",
-            "        faces_coordinates = transformer.generate(texts = [text],  prompt = prompt, temperature = 0) \n",
-            "        coords.append(faces_coordinates) \n",
+            "        print(f\"Generating {text} with {prompt.shape[1]} tokens\") \n",
+            "        coords.append(transformer.generate(texts = [text],  prompt = prompt, temperature = 0)) \n",
             "         \n",
-            "    mesh_rows.append(coords) \n",
-            "    mesh_render.combind_mesh(f'{folder}/text+prompt_all_{token_length_procent*100}.obj', coords)\n",
+            "    mesh_rows.append(coords)  \n",
             "    \n",
-            "mesh_render.combind_mesh_with_rows(f'{folder}/all.obj', mesh_rows)\n",
+            "mesh_render.save_rendering(f'{folder}/all.obj', mesh_rows)\n",
             " "
          ]
       },
diff --git a/meshgpt_pytorch/__init__.py b/meshgpt_pytorch/__init__.py
index 8d7fd34c..be549b33 100644
--- a/meshgpt_pytorch/__init__.py
+++ b/meshgpt_pytorch/__init__.py
@@ -18,6 +18,7 @@
     MeshDataset
 )
 from meshgpt_pytorch.mesh_render import (
-    save_rendering
+    save_rendering,
+    combind_mesh_with_rows
 )
  
diff --git a/meshgpt_pytorch/mesh_render.py b/meshgpt_pytorch/mesh_render.py
index 121e1ee6..98f5e25d 100644
--- a/meshgpt_pytorch/mesh_render.py
+++ b/meshgpt_pytorch/mesh_render.py
@@ -18,7 +18,34 @@ def get_angle(v1, v2, v3):
     vec2 = v3 - v1 
     angle_rad = np.arccos(np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2)))
     return math.degrees(angle_rad)  
+
+def combind_mesh_with_rows(path, meshes):
+    all_vertices = []
+    all_faces = []
+    vertex_offset = 0
+    translation_distance = 0.5  
+    obj_file_content = ""
+
+    for row, mesh in enumerate(meshes): 
+        for r, faces_coordinates in enumerate(mesh): 
+            numpy_data = faces_coordinates[0].cpu().numpy().reshape(-1, 3)  
+            numpy_data[:, 0] += translation_distance * (r / 0.2 - 1)  
+            numpy_data[:, 2] += translation_distance * (row / 0.2 - 1)  
+
+            for vertex in numpy_data:
+                all_vertices.append(f"v {vertex[0]} {vertex[1]} {vertex[2]}\n")
+
+            for i in range(1, len(numpy_data), 3):
+                all_faces.append(f"f {i + vertex_offset} {i + 1 + vertex_offset} {i + 2 + vertex_offset}\n")
+
+            vertex_offset += len(numpy_data)
+
+        obj_file_content = "".join(all_vertices) + "".join(all_faces)
+
+    with open(path , "w") as file:
+        file.write(obj_file_content)   
  
+
 def save_rendering(path, input_meshes):
     all_vertices,all_faces = [],[]
     vertex_offset = 0

From b7a36117b4b32b59de205b39f30398ab83354dba Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Mon, 17 Jun 2024 17:12:11 +0200
Subject: [PATCH 76/84] bug fix

---
 meshgpt_pytorch/trainer.py | 13 +++----------
 1 file changed, 3 insertions(+), 10 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 785a62cc..4aba9784 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -143,16 +143,13 @@ def __init__(
                 drop_last = True,
                 collate_fn = partial(custom_collate, pad_id = model.pad_id)
             )
-
-<<<<<<< HEAD
-        self.data_kwargs = data_kwargs
-=======
+            
         if hasattr(dataset, 'data_kwargs') and exists(dataset.data_kwargs):
             assert beartype_isinstance(dataset.data_kwargs, List[str])
             self.data_kwargs = dataset.data_kwargs
         else:
             self.data_kwargs = data_kwargs
->>>>>>> upstream/main
+ 
 
         (
             self.model,
@@ -506,16 +503,12 @@ def __init__(
                 drop_last = True,
                 collate_fn = partial(custom_collate, pad_id = model.pad_id)
             )
-
-<<<<<<< HEAD
-        self.data_kwargs = data_kwargs
-=======
+ 
         if hasattr(dataset, 'data_kwargs') and exists(dataset.data_kwargs):
             assert beartype_isinstance(dataset.data_kwargs, List[str])
             self.data_kwargs = dataset.data_kwargs
         else:
             self.data_kwargs = data_kwargs
->>>>>>> upstream/main
 
         (
             self.model,

From 481428d6dc33c94ad81a2541156b2cd6959e3666 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Mon, 17 Jun 2024 17:15:55 +0200
Subject: [PATCH 77/84] bug fix

---
 meshgpt_pytorch/trainer.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 4aba9784..5af82560 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -86,7 +86,7 @@ def __init__(
         accelerator_kwargs: dict = dict(),
         optimizer_kwargs: dict = dict(),
         checkpoint_every = 1000,
-        checkpoint_every_epoch: Optional[int] = None,
+        checkpoint_every_epoch: Type[int] | None = None,
         checkpoint_folder = './checkpoints',
         data_kwargs: Tuple[str, ...] = ('vertices', 'faces', 'face_edges'),
         warmup_steps = 1000,
@@ -436,14 +436,14 @@ def __init__(
         val_dataset: Dataset | None = None,
         val_every = 1,
         val_num_batches = 5,
-        scheduler: Optional[Type[_LRScheduler]] = None,
+        scheduler: Type[_LRScheduler] | None = None,
         scheduler_kwargs: dict = dict(),
         ema_kwargs: dict = dict(),
         accelerator_kwargs: dict = dict(),
         optimizer_kwargs: dict = dict(),
         
         checkpoint_every = 1000, 
-        checkpoint_every_epoch: Optional[int] = None,
+        checkpoint_every_epoch: Type[int] | None = None,
         checkpoint_folder = './checkpoints',
         data_kwargs: Tuple[str, ...] = ('vertices', 'faces', 'face_edges', 'text'),
         warmup_steps = 1000,

From bdfcade1396ea848ccb6d78e043d7ee62c3bed02 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Mon, 17 Jun 2024 17:17:58 +0200
Subject: [PATCH 78/84] bug fix

---
 meshgpt_pytorch/trainer.py | 36 ------------------------------------
 1 file changed, 36 deletions(-)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index 5af82560..a4327855 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -175,24 +175,6 @@ def ema_tokenizer(self):
     def tokenize(self, *args, **kwargs):
         return self.ema_tokenizer.tokenize(*args, **kwargs)
 
-    @contextmanager
-    @beartype
-    def trackers(
-        self,
-        project_name: str,
-        run_name: Optional[str] = None,
-        hps: Optional[dict] = None
-    ):
-        assert self.use_wandb_tracking
-
-        self.accelerator.init_trackers(project_name, config = hps)
-
-        if exists(run_name):
-            self.accelerator.trackers[0].run.name = run_name
-
-        yield
-        self.accelerator.end_training()
-
     def log(self, **data_kwargs):
         self.accelerator.log(data_kwargs, step = self.step.item())
 
@@ -527,24 +509,6 @@ def __init__(
         self.checkpoint_folder = Path(checkpoint_folder)
         self.checkpoint_folder.mkdir(exist_ok = True, parents = True)
 
-    @contextmanager
-    @beartype
-    def trackers(
-        self,
-        project_name: str,
-        run_name: Optional[str] = None,
-        hps: Optional[dict] = None
-    ):
-        assert self.use_wandb_tracking
-
-        self.accelerator.init_trackers(project_name, config = hps)
-
-        if exists(run_name):
-            self.accelerator.trackers[0].run.name = run_name
-
-        yield
-        self.accelerator.end_training()
-
     def log(self, **data_kwargs):
         self.accelerator.log(data_kwargs, step = self.step.item())
 

From d166f2f25053694cbba16e7d628a031aa1b83dfc Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Mon, 17 Jun 2024 18:11:08 +0200
Subject: [PATCH 79/84] dataset improvements

---
 meshgpt_pytorch/mesh_dataset.py    | 59 +++++++++++++++++++++---------
 meshgpt_pytorch/meshgpt_pytorch.py |  4 ++
 2 files changed, 46 insertions(+), 17 deletions(-)

diff --git a/meshgpt_pytorch/mesh_dataset.py b/meshgpt_pytorch/mesh_dataset.py
index 13601710..0a403977 100644
--- a/meshgpt_pytorch/mesh_dataset.py
+++ b/meshgpt_pytorch/mesh_dataset.py
@@ -2,6 +2,7 @@
 import numpy as np  
 from torch.nn.utils.rnn import pad_sequence
 from tqdm import tqdm
+import torch
 from meshgpt_pytorch import ( 
     MeshAutoencoder,
     MeshTransformer
@@ -75,15 +76,34 @@ def sort_dataset_keys(self):
             {key: d[key] for key in desired_order if key in d} for d in self.data
         ]
        
-    def generate_face_edges(self): 
-        i = 0
-        for item in self.data:
-            if 'face_edges' not in item:
-                item['face_edges'] =  derive_face_edges_from_faces(item['faces']) 
-                i += 1
+    def generate_face_edges(self, batch_size = 5):   
+        data_to_process = [item for item in self.data if 'faces_edges' not in item]
+        
+        total_batches = (len(data_to_process) + batch_size - 1) // batch_size 
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        
+        for i in tqdm(range(0, len(data_to_process), batch_size), total=total_batches):
+            batch_data = data_to_process[i:i+batch_size]  
+            
+            if not batch_data:  
+                continue
             
+            padded_batch_faces = pad_sequence(
+                [item['faces'] for item in batch_data], 
+                batch_first=True, 
+                padding_value=-1
+            ).to(device)
+            
+            batched_faces_edges = derive_face_edges_from_faces(padded_batch_faces, pad_id=-1)
+
+            mask = (batched_faces_edges != -1).all(dim=-1) 
+            for item_idx, (item_edges, item_mask) in enumerate(zip(batched_faces_edges, mask)):            
+                item_edges_masked = item_edges[item_mask]
+                item = batch_data[item_idx]
+                item['face_edges'] = item_edges_masked 
+
         self.sort_dataset_keys()
-        print(f"[MeshDataset] Generated face_edges for {i}/{len(self.data)} entries")
+        print(f"[MeshDataset] Generated face_edges for {len(data_to_process)} entries")
 
     def generate_codes(self, autoencoder : MeshAutoencoder, batch_size = 25): 
         total_batches = (len(self.data) + batch_size - 1) // batch_size
@@ -91,9 +111,9 @@ def generate_codes(self, autoencoder : MeshAutoencoder, batch_size = 25):
         for i in tqdm(range(0, len(self.data), batch_size), total=total_batches):
             batch_data = self.data[i:i+batch_size] 
             
-            padded_batch_vertices = pad_sequence([item['vertices'] for item in batch_data], batch_first=True, padding_value=autoencoder.pad_id)
-            padded_batch_faces = pad_sequence([item['faces'] for item in batch_data], batch_first=True, padding_value=autoencoder.pad_id)
-            padded_batch_face_edges = pad_sequence([item['face_edges'] for item in batch_data], batch_first=True, padding_value=autoencoder.pad_id)
+            padded_batch_vertices = pad_sequence([item['vertices'] for item in batch_data], batch_first=True, padding_value=autoencoder.pad_id).to(autoencoder.device)
+            padded_batch_faces = pad_sequence([item['faces'] for item in batch_data], batch_first=True, padding_value=autoencoder.pad_id).to(autoencoder.device)
+            padded_batch_face_edges = pad_sequence([item['face_edges'] for item in batch_data], batch_first=True, padding_value=autoencoder.pad_id).to(autoencoder.device)
             
             batch_codes = autoencoder.tokenize(
                 vertices=padded_batch_vertices,
@@ -112,17 +132,22 @@ def generate_codes(self, autoencoder : MeshAutoencoder, batch_size = 25):
         print(f"[MeshDataset] Generated codes for {len(self.data)} entries")
     
     def embed_texts(self, transformer : MeshTransformer, batch_size = 50): 
-        unique_texts = set(item['texts'] for item in self.data)
+        unique_texts = list(set(item['texts'] for item in self.data))
         embeddings = []
-        for i in range(0,len(unique_texts), batch_size):
-            text_embeddings = transformer.embed_texts(list(unique_texts)[i:i+batch_size])
-            embeddings.extend(text_embeddings)
+        text_embedding_dict = {}
+        for i in tqdm(range(0,len(unique_texts), batch_size)):
+            batch_texts = unique_texts[i:i+batch_size]
+            text_embeddings = transformer.embed_texts(batch_texts)
+            mask = (text_embeddings != transformer.conditioner.text_embed_pad_value).all(dim=-1)  
             
-        text_embedding_dict = dict(zip(unique_texts, embeddings))
-
-        for item in self.data:
+            for idx, text in enumerate(batch_texts): 
+                masked_embedding = text_embeddings[idx][mask[idx]]
+                text_embedding_dict[text] = masked_embedding
+                
+        for item in self.data: 
             if 'texts' in item:  
                 item['text_embeds'] = text_embedding_dict.get(item['texts'], None)
                 del item['texts'] 
+                
         self.sort_dataset_keys()
         print(f"[MeshDataset] Generated {len(embeddings)} text_embeddings") 
diff --git a/meshgpt_pytorch/meshgpt_pytorch.py b/meshgpt_pytorch/meshgpt_pytorch.py
index efb653d2..30943943 100644
--- a/meshgpt_pytorch/meshgpt_pytorch.py
+++ b/meshgpt_pytorch/meshgpt_pytorch.py
@@ -635,6 +635,10 @@ def __init__(
         self.commit_loss_weight = commit_loss_weight
         self.bin_smooth_blur_sigma = bin_smooth_blur_sigma
 
+    @property
+    def device(self):
+        return next(self.parameters()).device
+    
     @classmethod
     def _from_pretrained(
         cls,

From 6cad755164a45fe871d14ac6e47a758f37e6da74 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Tue, 18 Jun 2024 02:51:01 +0200
Subject: [PATCH 80/84] dataset embed_texts - output message

---
 meshgpt_pytorch/mesh_dataset.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/meshgpt_pytorch/mesh_dataset.py b/meshgpt_pytorch/mesh_dataset.py
index 0a403977..2361de05 100644
--- a/meshgpt_pytorch/mesh_dataset.py
+++ b/meshgpt_pytorch/mesh_dataset.py
@@ -70,6 +70,7 @@ def load(cls, path):
             data.append(item)  
         print(f"[MeshDataset] Loaded {len(data)} entries")
         return cls(data) 
+    
     def sort_dataset_keys(self):
         desired_order = ['vertices', 'faces', 'face_edges', 'texts','text_embeds','codes'] 
         self.data = [
@@ -132,8 +133,7 @@ def generate_codes(self, autoencoder : MeshAutoencoder, batch_size = 25):
         print(f"[MeshDataset] Generated codes for {len(self.data)} entries")
     
     def embed_texts(self, transformer : MeshTransformer, batch_size = 50): 
-        unique_texts = list(set(item['texts'] for item in self.data))
-        embeddings = []
+        unique_texts = list(set(item['texts'] for item in self.data)) 
         text_embedding_dict = {}
         for i in tqdm(range(0,len(unique_texts), batch_size)):
             batch_texts = unique_texts[i:i+batch_size]
@@ -150,4 +150,4 @@ def embed_texts(self, transformer : MeshTransformer, batch_size = 50):
                 del item['texts'] 
                 
         self.sort_dataset_keys()
-        print(f"[MeshDataset] Generated {len(embeddings)} text_embeddings") 
+        print(f"[MeshDataset] Generated {len(text_embedding_dict)} text_embeddings") 

From 5f6d670187ebcfbca2bf73e7e28bae39c55bb47a Mon Sep 17 00:00:00 2001
From: Marcus Loren <65302107+MarcusLoppe@users.noreply.github.com>
Date: Tue, 18 Jun 2024 21:58:56 +0200
Subject: [PATCH 81/84] Update setup.py

---
 setup.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/setup.py b/setup.py
index 7da9210a..0a016ea7 100644
--- a/setup.py
+++ b/setup.py
@@ -33,6 +33,7 @@
     'jaxtyping',
     'local-attention>=1.9.0',
     'numpy',
+    'matplotlib',
     'pytorch-custom-utils>=0.0.9',
     'taylor-series-linear-attention>=0.1.6',
     'torch>=2.1',

From 3e7ed57c0cbad9fca06fc685c0507f4e43666d2a Mon Sep 17 00:00:00 2001
From: Marcus Loren <65302107+MarcusLoppe@users.noreply.github.com>
Date: Fri, 21 Jun 2024 12:33:16 +0200
Subject: [PATCH 82/84] Update setup.py

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 0a016ea7..4f6aac0d 100644
--- a/setup.py
+++ b/setup.py
@@ -34,7 +34,7 @@
     'local-attention>=1.9.0',
     'numpy',
     'matplotlib',
-    'pytorch-custom-utils>=0.0.9',
+    'pytorch-custom-utils>=0.0.20',
     'taylor-series-linear-attention>=0.1.6',
     'torch>=2.1',
     'torch_geometric',

From 9e1bb0d2c70da090a5b390125e88b61dc7fd9752 Mon Sep 17 00:00:00 2001
From: MarcusLoppe <marcusloren1993@gmail.com>
Date: Sat, 22 Jun 2024 21:55:21 +0200
Subject: [PATCH 83/84] bug fix

---
 meshgpt_pytorch/trainer.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/meshgpt_pytorch/trainer.py b/meshgpt_pytorch/trainer.py
index c3e29a65..9ed3d8ef 100644
--- a/meshgpt_pytorch/trainer.py
+++ b/meshgpt_pytorch/trainer.py
@@ -126,6 +126,7 @@ def __init__(
 
         self.dataloader = DataLoader(
             dataset,
+            shuffle = True,
             batch_size = batch_size, 
             drop_last = True,
             collate_fn = partial(custom_collate, pad_id = model.pad_id)
@@ -141,6 +142,7 @@ def __init__(
 
             self.val_dataloader = DataLoader(
                 val_dataset,
+                shuffle = True,
                 batch_size = batch_size, 
                 drop_last = True,
                 collate_fn = partial(custom_collate, pad_id = model.pad_id)
@@ -468,6 +470,7 @@ def __init__(
 
         self.dataloader = DataLoader(
             dataset,
+            shuffle = True,
             batch_size = batch_size, 
             drop_last = True,
             collate_fn = partial(custom_collate, pad_id = model.pad_id)
@@ -483,6 +486,7 @@ def __init__(
 
             self.val_dataloader = DataLoader(
                 val_dataset,
+                shuffle = True,
                 batch_size = batch_size, 
                 drop_last = True,
                 collate_fn = partial(custom_collate, pad_id = model.pad_id)

From a8880add6dbc5bf90a64d18028b6a630f1f0dd9e Mon Sep 17 00:00:00 2001
From: Marcus Loren <65302107+MarcusLoppe@users.noreply.github.com>
Date: Thu, 4 Jul 2024 23:26:16 +0200
Subject: [PATCH 84/84] Update setup.py

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index c8f8f267..5da26ce6 100644
--- a/setup.py
+++ b/setup.py
@@ -22,7 +22,6 @@
   install_requires=[
     'matplotlib',
     'accelerate>=0.25.0',
-    'huggingface_hub>=0.21.4',
     'beartype',
     "huggingface_hub>=0.21.4",
     'classifier-free-guidance-pytorch>=0.6.10',
@@ -35,6 +34,7 @@
     'local-attention>=1.9.0',
     'numpy',
     'pytorch-custom-utils>=0.0.9',
+    'rotary-embedding-torch>=0.6.4',
     'taylor-series-linear-attention>=0.1.6',
     'torch>=2.1',
     'torch_geometric',