Working with custom dataset, IndexError: index out of range in self

[varundesai10]

Forecaster
L2: 1e-06
Linear Window: 0
Linear Shared Weights: False
RevIN: False
Decomposition: False
/home/vdesai/spacetimeformer/spacetimeformer/spacetimeformer_model/nn/decoder.py:43: UserWarning: The implementation of Local Cross Attn with exogenous variables
makes an unintuitive assumption about variable order. Please see
spacetimeformer_model.nn.decoder.DecoderLayer source code and comments
warnings.warn(
GlobalSelfAttn: AttentionLayer(
(inner_attention): PerformerAttention(
(kernel_fn): ReLU()
)
(query_projection): Linear(in_features=200, out_features=800, bias=True)
(key_projection): Linear(in_features=200, out_features=800, bias=True)
(value_projection): Linear(in_features=200, out_features=800, bias=True)
(out_projection): Linear(in_features=800, out_features=200, bias=True)
(dropout_qkv): Dropout(p=0.0, inplace=False)
)
GlobalCrossAttn: AttentionLayer(
(inner_attention): PerformerAttention(
(kernel_fn): ReLU()
)
(query_projection): Linear(in_features=200, out_features=800, bias=True)
(key_projection): Linear(in_features=200, out_features=800, bias=True)
(value_projection): Linear(in_features=200, out_features=800, bias=True)
(out_projection): Linear(in_features=800, out_features=200, bias=True)
(dropout_qkv): Dropout(p=0.0, inplace=False)
)
LocalSelfAttn: AttentionLayer(
(inner_attention): PerformerAttention(
(kernel_fn): ReLU()
)
(query_projection): Linear(in_features=200, out_features=800, bias=True)
(key_projection): Linear(in_features=200, out_features=800, bias=True)
(value_projection): Linear(in_features=200, out_features=800, bias=True)
(out_projection): Linear(in_features=800, out_features=200, bias=True)
(dropout_qkv): Dropout(p=0.0, inplace=False)
)
LocalCrossAttn: AttentionLayer(
(inner_attention): PerformerAttention(
(kernel_fn): ReLU()
)
(query_projection): Linear(in_features=200, out_features=800, bias=True)
(key_projection): Linear(in_features=200, out_features=800, bias=True)
(value_projection): Linear(in_features=200, out_features=800, bias=True)
(out_projection): Linear(in_features=800, out_features=200, bias=True)
(dropout_qkv): Dropout(p=0.0, inplace=False)
)
Using Embedding: spatio-temporal
Time Emb Dim: 6
Space Embedding: True
Time Embedding: True
Val Embedding: True
Given Embedding: True
Null Value: -1
Pad Value: -1
Reconstruction Dropout: Timesteps 0.05, Standard 0.1, Seq (max len = 5) 0.2, Skip All Drop 1.0
*** Spacetimeformer (v1.5) Summary: ***
Model Dim: 200
FF Dim: 800
Enc Layers: 3
Dec Layers: 3
Embed Dropout: 0.2
FF Dropout: 0.3
Attn Out Dropout: 0.0
Attn Matrix Dropout: 0.0
QKV Dropout: 0.0
L2 Coeff: 1e-06
Warmup Steps: 0
Normalization Scheme: batch
Attention Time Windows: 1
Shifted Time Windows: False
Position Emb Type: abs
Recon Loss Imp: 0.0

---

/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/pytorch_lightning/loops/utilities.py:91: PossibleUserWarning: max_epochs was not set. Setting it to 1000 epochs. To train without an epoch limit, set max_epochs=-1.
rank_zero_warn(
GPU available: True, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:1823: PossibleUserWarning: GPU available but not used. Set accelerator and devices using Trainer(accelerator='gpu', devices=2).
rank_zero_warn(
Trainer(limit_val_batches=1.0) was configured so 100% of the batches will be used..

| Name | Type | Params

0 | spacetimeformer | Spacetimeformer | 13.5 M

13.5 M Trainable params
0 Non-trainable params
13.5 M Total params
54.080 Total estimated model params size (MB)
Sanity Checking DataLoader 0: 0%| | 0/2 [00:01<?, ?it/s]Traceback (most recent call last):
File "train.py", line 181, in
trainer.fit(model, datamodule=data_module)
File "/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 771, in fit
self._call_and_handle_interrupt(
File "/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 724, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 812, in _fit_impl
results = self._run(model, ckpt_path=self.ckpt_path)
File "/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1237, in _run
results = self._run_stage()
File "/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1324, in _run_stage
return self._run_train()
File "/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1346, in _run_train
self._run_sanity_check()
File "/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1414, in _run_sanity_check
val_loop.run()
File "/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 153, in advance
dl_outputs = self.epoch_loop.run(self._data_fetcher, dl_max_batches, kwargs)
File "/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 127, in advance
output = self._evaluation_step(**kwargs)
File "/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 222, in _evaluation_step
output = self.trainer._call_strategy_hook("validation_step", *kwargs.values())
File "/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1766, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/pytorch_lightning/strategies/strategy.py", line 344, in validation_step
return self.model.validation_step(*args, **kwargs)
File "/home/vdesai/spacetimeformer/spacetimeformer/forecaster.py", line 256, in validation_step
stats = self.step(batch, train=False)
File "/home/vdesai/spacetimeformer/spacetimeformer/spacetimeformer_model/spacetimeformer_model.py", line 183, in step
loss_dict = self.compute_loss(
File "/home/vdesai/spacetimeformer/spacetimeformer/spacetimeformer_model/spacetimeformer_model.py", line 228, in compute_loss
forecast_out, recon_out, (logits, labels) = self(
File "/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/home/vdesai/spacetimeformer/spacetimeformer/forecaster.py", line 204, in forward
preds, *extra = self.forward_model_pass(
File "/home/vdesai/spacetimeformer/spacetimeformer/spacetimeformer_model/spacetimeformer_model.py", line 286, in forward_model_pass
forecast_output, recon_output, (logits, labels), attn = self.spacetimeformer(
File "/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/home/vdesai/spacetimeformer/spacetimeformer/spacetimeformer_model/nn/model.py", line 266, in forward
enc_vt_emb, enc_s_emb, enc_var_idxs, enc_mask_seq = self.enc_embedding(
File "/home/vdesai/spacetimeformer/spacetimeformer/spacetimeformer_model/nn/embed.py", line 91, in call
return emb(y=y, x=x)
File "/home/vdesai/spacetimeformer/spacetimeformer/spacetimeformer_model/nn/embed.py", line 239, in spatio_temporal_embed
space_emb = self.space_emb(var_idx)
File "/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/torch/nn/modules/sparse.py", line 158, in forward
return F.embedding(
File "/home/vdesai/anaconda3/envs/bats/lib/python3.8/site-packages/torch/nn/functional.py", line 2183, in embedding
return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
IndexError: index out of range in self

Any help would be appreciated.

[pdy265]

can you help me how to use custom datasets?