(gh2) root@autodl-container-7d95439be5-49723134:~/GaitGraph2# python gaitgraph_casia_b.py --config GaitGraph/configs/casia_b.yaml
|'''''''''''''╔╬╬╬╬╬╬╬╬ _____ ______ _____ ______ ___ __
| ╔╬╬╬╬╬╬╬╬╬ |\ _ \ _ \ |\ _ \ _ \ |\ |\
| ░░ ╬╬╬╬╬╬╬╬╬╬ \ \ \_\ \ \ \ \ \_\ \ \ \ \ / /|_
░░░░ ╬╬╬╬╬╬╬╬╬╬ \ \ \|| \ \ \ \ \|| \ \ \ \ ___
░░░░░╦╬╦ ╔╬╬╬╬╬╬╬╬╬╬ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \
░░░░░╬╬╬╬ ▓▓└╬╬╬╬╬╬╬╬╬╬╬ \ _\ \ _\ \ _\ \ _\ \ _\ _
░░░░░╔╬╬╬ ▓▓▓ ╓╬╬╬╬╬╬╬╬╬ || || || || || ||
░░░░░╠╬╬╬ ▓▓▓ └╬╬╬╬╬╬╬╬╬
░░░░└╬╬╬╬ ▓▓ ╬╬╬╬╬╬╬╬╬ Chair of Human-Machine Communication
░░░░░╙╬╬╬╩ ╬╬ TUM School of Computation, Information and Technology
░░░░░ ╚ ''''''''''''''' Technical University of Munich
░░░
Global seed set to 5318008
Processing...
load [train]: 100%|███████████████████████████████████████████████████████████████████████████████████| 1265749/1265749 [00:20<00:00, 62095.80it/s]
process [train]: 100%|██████████████████████████████████████████████████████████████████████████████████████| 8140/8140 [00:00<00:00, 14842.14it/s]
Done!
Processing...
load [test]: 100%|████████████████████████████████████████████████████████████████████████████████████| 1265749/1265749 [00:16<00:00, 75690.93it/s]
process [test]: 100%|███████████████████████████████████████████████████████████████████████████████████████| 5498/5498 [00:00<00:00, 23693.01it/s]
Done!
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
Missing logger folder: /root/GaitGraph2/lightning_logs
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
| Name | Type | Params
0 | backbone | ResGCN | 350 K
1 | distance | LpDistance | 0
2 | train_loss | SupConLoss | 0
3 | val_loss | ContrastiveLoss | 0
350 K Trainable params
0 Non-trainable params
350 K Total params
1.403 Total estimated model params size (MB)
Epoch 0: 0%| | 0/11 [00:00<?, ?it/s]Traceback (most recent call last):
File "gaitgraph_casia_b.py", line 306, in
cli_main()
File "gaitgraph_casia_b.py", line 300, in cli_main
cli.trainer.fit(cli.model, datamodule=cli.datamodule)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 771, in fit
self._call_and_handle_interrupt(
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 724, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/root/miniconda3/envs/gh2/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 "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1237, in _run
results = self._run_stage()
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1324, in _run_stage
return self._run_train()
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1354, in _run_train
self.fit_loop.run()
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/loops/fit_loop.py", line 269, in advance
self._outputs = self.epoch_loop.run(self._data_fetcher)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 208, in advance
batch_output = self.batch_loop.run(batch, batch_idx)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 88, in advance
outputs = self.optimizer_loop.run(split_batch, optimizers, batch_idx)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 203, in advance
result = self._run_optimization(
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 256, in _run_optimization
self._optimizer_step(optimizer, opt_idx, batch_idx, closure)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 369, in _optimizer_step
self.trainer._call_lightning_module_hook(
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1596, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/core/lightning.py", line 1625, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/core/optimizer.py", line 168, in step
step_output = self._strategy.optimizer_step(self._optimizer, self._optimizer_idx, closure, **kwargs)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/strategies/strategy.py", line 193, in optimizer_step
return self.precision_plugin.optimizer_step(model, optimizer, opt_idx, closure, **kwargs)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 155, in optimizer_step
return optimizer.step(closure=closure, **kwargs)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/torch/optim/lr_scheduler.py", line 68, in wrapper
return wrapped(*args, **kwargs)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/torch/optim/optimizer.py", line 373, in wrapper
out = func(*args, **kwargs)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/torch/optim/optimizer.py", line 76, in _use_grad
ret = func(self, *args, **kwargs)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/torch/optim/adamw.py", line 161, in step
loss = closure()
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 140, in _wrap_closure
closure_result = closure()
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 148, in call
self._result = self.closure(*args, **kwargs)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 134, in closure
step_output = self._step_fn()
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 427, in _training_step
training_step_output = self.trainer._call_strategy_hook("training_step", *step_kwargs.values())
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1766, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/pytorch_lightning/strategies/strategy.py", line 333, in training_step
return self.model.training_step(*args, **kwargs)
File "gaitgraph_casia_b.py", line 71, in training_step
y_hat = self(x)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "gaitgraph_casia_b.py", line 66, in forward
return self.backbone(x)[0]
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/root/miniconda3/envs/gh2/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/root/GaitGraph2/GaitGraph/models/ResGCNv1/nets.py", line 61, in forward
x = x.permute(0, 3, 4, 1, 2)
RuntimeError: permute(sparse_coo): number of dimensions in the tensor input does not match the length of the desired ordering of dimensions i.e. input.dim() = 4 is not equal to len(dims) = 5
Epoch 0: 0%| | 0/11 [00:08<?, ?it/s]