Custom Loaders Support (#27)

* support for custom loaders and datasets * updated configs * custom loaders in inspect command * updated inspect for multi-task labels * removed custom loader from test config * deleted comment * deleted comment * removed custom dataset * removed comment * skipping archiver test untill fixed in luxonis-ml * [Automated] Updated coverage badge --------- Co-authored-by: GitHub Actions <[email protected]>
luxonis · Oct 9, 2024 · 0a5d3f2 · 0a5d3f2
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Showing 27 changed files with 239 additions and 227 deletions.
diff --git a/configs/classification_model.yaml b/configs/classification_model.yaml
@@ -15,8 +15,9 @@ model:
         thickness: 2
         include_plot: True
 
-dataset:
-  name: cifar10_test
+loader:
+  params:
+    dataset_name: cifar10_test
 
 trainer:
   preprocessing:

diff --git a/configs/coco_model.yaml b/configs/coco_model.yaml
@@ -95,12 +95,14 @@ tracker:
   wandb_entity: luxonis
   is_mlflow: False
 
-dataset:
-  name: coco_test
+loader:
   train_view: train
   val_view: val
   test_view: test
 
+  params:
+    dataset_name: coco_test
+
 trainer:
   accelerator: auto
   devices: auto

diff --git a/configs/detection_model.yaml b/configs/detection_model.yaml
@@ -10,8 +10,9 @@ model:
     params:
       use_neck: True
 
-dataset:
-  name: coco_test
+loader:
+  params:
+    dataset_name: coco_test
 
 trainer:
   preprocessing:

diff --git a/configs/example_export.yaml b/configs/example_export.yaml
@@ -12,8 +12,9 @@ model:
       backbone: MicroNet
       task: binary
 
-dataset:
-  name: coco_test
+loader:
+  params:
+    dataset_name: coco_test
 
 trainer:
   preprocessing:

diff --git a/configs/example_tuning.yaml b/configs/example_tuning.yaml
@@ -11,8 +11,9 @@ model:
       backbone: MicroNet
       task: binary
 
-dataset:
-  name: coco_test
+loader:
+  params:
+    dataset_name: coco_test
 
 trainer:
   preprocessing:

diff --git a/configs/keypoint_bbox_model.yaml b/configs/keypoint_bbox_model.yaml
@@ -8,8 +8,9 @@ model:
   predefined_model:
     name: KeypointDetectionModel
 
-dataset:
-  name: coco_test
+loader:
+  params:
+    dataset_name: coco_test
 
 trainer:
   preprocessing:

diff --git a/configs/resnet_model.yaml b/configs/resnet_model.yaml
@@ -29,8 +29,9 @@ model:
         thickness: 2
         include_plot: True
 
-dataset:
-  name: cifar10_test
+loader:
+  params:
+    dataset_name: cifar10_test
 
 trainer:
   batch_size: 4

diff --git a/configs/segmentation_model.yaml b/configs/segmentation_model.yaml
@@ -11,8 +11,9 @@ model:
       backbone: MicroNet
       task: binary
 
-dataset:
-  name: coco_test
+loader:
+  params:
+    dataset_name: coco_test
 
 trainer:
   preprocessing:

diff --git a/luxonis_train/__init__.py b/luxonis_train/__init__.py
@@ -1,4 +1,5 @@
 from .attached_modules import *
+from .core import *
 from .models import *
 from .utils import *
 

diff --git a/luxonis_train/__main__.py b/luxonis_train/__main__.py
@@ -5,8 +5,10 @@
 from typing import Annotated, Optional
 
 import cv2
-import torch
 import typer
+from torch.utils.data import DataLoader
+
+from luxonis_train.utils.registry import LOADERS
 
 app = typer.Typer(help="Luxonis Train CLI", add_completion=False)
 
@@ -105,7 +107,6 @@ def inspect(
     """Inspect dataset."""
     from lightning.pytorch import seed_everything
     from luxonis_ml.data import (
-        LuxonisDataset,
         TrainAugmentations,
         ValAugmentations,
     )
@@ -117,7 +118,7 @@ def inspect(
         get_unnormalized_images,
     )
     from luxonis_train.utils.config import Config
-    from luxonis_train.utils.loaders import LuxonisLoaderTorch, collate_fn
+    from luxonis_train.utils.loaders import collate_fn
     from luxonis_train.utils.types import LabelType
 
     overrides = {}
@@ -134,79 +135,63 @@ def inspect(
 
     image_size = cfg.trainer.preprocessing.train_image_size
 
-    dataset = LuxonisDataset(
-        dataset_name=cfg.dataset.name,
-        team_id=cfg.dataset.team_id,
-        dataset_id=cfg.dataset.id,
-        bucket_type=cfg.dataset.bucket_type,
-        bucket_storage=cfg.dataset.bucket_storage,
-    )
-    augmentations = (
-        TrainAugmentations(
-            image_size=image_size,
-            augmentations=[
-                i.model_dump() for i in cfg.trainer.preprocessing.augmentations
-            ],
-            train_rgb=cfg.trainer.preprocessing.train_rgb,
-            keep_aspect_ratio=cfg.trainer.preprocessing.keep_aspect_ratio,
-        )
-        if view == "train"
-        else ValAugmentations(
-            image_size=image_size,
-            augmentations=[
-                i.model_dump() for i in cfg.trainer.preprocessing.augmentations
-            ],
-            train_rgb=cfg.trainer.preprocessing.train_rgb,
-            keep_aspect_ratio=cfg.trainer.preprocessing.keep_aspect_ratio,
-        )
+    augmentations = (TrainAugmentations if view == "train" else ValAugmentations)(
+        image_size=image_size,
+        augmentations=[i.model_dump() for i in cfg.trainer.preprocessing.augmentations],
+        train_rgb=cfg.trainer.preprocessing.train_rgb,
+        keep_aspect_ratio=cfg.trainer.preprocessing.keep_aspect_ratio,
     )
 
-    loader_train = LuxonisLoaderTorch(
-        dataset,
-        view=view,
-        augmentations=augmentations,
+    loader = LOADERS.get(cfg.loader.name)(
+        view=view, augmentations=augmentations, **cfg.loader.params
     )
 
-    pytorch_loader_train = torch.utils.data.DataLoader(
-        loader_train,
-        batch_size=4,
-        num_workers=1,
+    pytorch_loader = DataLoader(
+        loader,
+        batch_size=1,
+        num_workers=0,
         collate_fn=collate_fn,
     )
 
     if save_dir is not None:
         os.makedirs(save_dir, exist_ok=True)
 
     counter = 0
-    for data in pytorch_loader_train:
-        imgs, label_dict = data
-        images = get_unnormalized_images(cfg, imgs)
-        for i, img in enumerate(images):
-            for label_type, labels in label_dict.items():
-                if label_type == LabelType.CLASSIFICATION:
-                    continue
-                elif label_type == LabelType.BOUNDINGBOX:
-                    img = draw_bounding_box_labels(
-                        img, labels[labels[:, 0] == i][:, 2:], colors="yellow", width=1
-                    )
-                elif label_type == LabelType.KEYPOINT:
-                    img = draw_keypoint_labels(
-                        img, labels[labels[:, 0] == i][:, 1:], colors="red"
+    for data in pytorch_loader:
+        imgs, task_dict = data
+        for task, label_dict in task_dict.items():
+            images = get_unnormalized_images(cfg, imgs)
+            for i, img in enumerate(images):
+                for label_type, labels in label_dict.items():
+                    if label_type == LabelType.CLASSIFICATION:
+                        continue
+                    elif label_type == LabelType.BOUNDINGBOX:
+                        img = draw_bounding_box_labels(
+                            img,
+                            labels[labels[:, 0] == i][:, 2:],
+                            colors="yellow",
+                            width=1,
+                        )
+                    elif label_type == LabelType.KEYPOINT:
+                        img = draw_keypoint_labels(
+                            img, labels[labels[:, 0] == i][:, 1:], colors="red"
+                        )
+                    elif label_type == LabelType.SEGMENTATION:
+                        img = draw_segmentation_labels(
+                            img, labels[i], alpha=0.8, colors="#5050FF"
+                        )
+
+                img_arr = img.permute(1, 2, 0).numpy()
+                img_arr = cv2.cvtColor(img_arr, cv2.COLOR_RGB2BGR)
+                if save_dir is not None:
+                    counter += 1
+                    cv2.imwrite(
+                        os.path.join(save_dir, f"{counter}_{task}.png"), img_arr
                     )
-                elif label_type == LabelType.SEGMENTATION:
-                    img = draw_segmentation_labels(
-                        img, labels[i], alpha=0.8, colors="#5050FF"
-                    )
-
-            img_arr = img.permute(1, 2, 0).numpy()
-            img_arr = cv2.cvtColor(img_arr, cv2.COLOR_RGB2BGR)
-            if save_dir is not None:
-                counter += 1
-                cv2.imwrite(os.path.join(save_dir, f"{counter}.png"), img_arr)
-            else:
-                cv2.imshow("img", img_arr)
-                if cv2.waitKey() == ord("q"):
-                    exit()
+                else:
+                    cv2.imshow(task, img_arr)
+        if save_dir is None and cv2.waitKey() == ord("q"):
+            exit()
 
 
 @app.command()

diff --git a/luxonis_train/callbacks/test_on_train_end.py b/luxonis_train/callbacks/test_on_train_end.py
@@ -1,43 +1,14 @@
 import lightning.pytorch as pl
-from luxonis_ml.data import LuxonisDataset, ValAugmentations
-from torch.utils.data import DataLoader
 
-from luxonis_train.utils.config import Config
-from luxonis_train.utils.loaders import LuxonisLoaderTorch, collate_fn
+import luxonis_train
 from luxonis_train.utils.registry import CALLBACKS
 
 
 @CALLBACKS.register_module()
 class TestOnTrainEnd(pl.Callback):
     """Callback to perform a test run at the end of the training."""
 
-    def on_train_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
-        cfg: Config = pl_module.cfg
-
-        dataset = LuxonisDataset(
-            dataset_name=cfg.dataset.name,
-            team_id=cfg.dataset.team_id,
-            dataset_id=cfg.dataset.id,
-            bucket_type=cfg.dataset.bucket_type,
-            bucket_storage=cfg.dataset.bucket_storage,
-        )
-
-        loader_test = LuxonisLoaderTorch(
-            dataset,
-            view=cfg.dataset.test_view,
-            augmentations=ValAugmentations(
-                image_size=cfg.trainer.preprocessing.train_image_size,
-                augmentations=[
-                    i.model_dump() for i in cfg.trainer.preprocessing.augmentations
-                ],
-                train_rgb=cfg.trainer.preprocessing.train_rgb,
-                keep_aspect_ratio=cfg.trainer.preprocessing.keep_aspect_ratio,
-            ),
-        )
-        pytorch_loader_test = DataLoader(
-            loader_test,
-            batch_size=cfg.trainer.batch_size,
-            num_workers=cfg.trainer.num_workers,
-            collate_fn=collate_fn,
-        )
-        trainer.test(pl_module, pytorch_loader_test)
+    def on_train_end(
+        self, trainer: pl.Trainer, pl_module: "luxonis_train.models.LuxonisModel"
+    ) -> None:
+        trainer.test(pl_module, pl_module._core.pytorch_loaders["test"])
diff --git a/luxonis_train/core/__init__.py b/luxonis_train/core/__init__.py
@@ -1,7 +1,8 @@
 from .archiver import Archiver
+from .core import Core
 from .exporter import Exporter
 from .inferer import Inferer
 from .trainer import Trainer
 from .tuner import Tuner
 
-__all__ = ["Exporter", "Trainer", "Tuner", "Inferer", "Archiver"]
+__all__ = ["Exporter", "Trainer", "Tuner", "Inferer", "Archiver", "Core"]
diff --git a/luxonis_train/core/archiver.py b/luxonis_train/core/archiver.py
@@ -45,7 +45,7 @@ def __init__(
             cfg=self.cfg,
             dataset_metadata=self.dataset_metadata,
             save_dir=self.run_save_dir,
-            input_shape=self.loader_train.input_shape,
+            input_shape=self.loaders["train"].input_shape,
         )
 
         self.model_name = self.cfg.model.name