Reporting errors while training one's own dataset

[lipenghuai]

Validation sanity check: 0%| | 0/16 [00:00<?, ?it/s][2025-01-21 13:44:30,094][main][CRITICAL] - Training failed due to Given groups=1, weight of size [64, 4, 7, 7], expected input[2, 3, 262, 262] to have 4 channels, but got 3 channels instead:
Traceback (most recent call last):
File "/home/dell/nfs/lp/lama/bin/train.py", line 64, in main
trainer.fit(training_model)
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 499, in fit
self.dispatch()
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 546, in dispatch
self.accelerator.start_training(self)
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/pytorch_lightning/accelerators/accelerator.py", line 73, in start_training
self.training_type_plugin.start_training(trainer)
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 114, in start_training
self._results = trainer.run_train()
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 607, in run_train
self.run_sanity_check(self.lightning_module)
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 864, in run_sanity_check
_, eval_results = self.run_evaluation(max_batches=self.num_sanity_val_batches)
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 726, in run_evaluation
output = self.evaluation_loop.evaluation_step(batch, batch_idx, dataloader_idx)
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/pytorch_lightning/trainer/evaluation_loop.py", line 166, in evaluation_step
output = self.trainer.accelerator.validation_step(args)
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/pytorch_lightning/accelerators/accelerator.py", line 177, in validation_step
return self.training_type_plugin.validation_step(*args)
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/pytorch_lightning/plugins/training_type/ddp.py", line 315, in validation_step
return self.model(*args, **kwargs)
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/torch/nn/parallel/distributed.py", line 886, in forward
output = self.module(*inputs[0], **kwargs[0])
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/pytorch_lightning/overrides/base.py", line 63, in forward
output = self.module.validation_step(*inputs, **kwargs)
File "/home/dell/nfs/lp/lama/saicinpainting/training/trainers/base.py", line 161, in validation_step
return self._do_step(batch, batch_idx, mode=mode, extra_val_key=extra_val_key)
File "/home/dell/nfs/lp/lama/saicinpainting/training/trainers/base.py", line 232, in _do_step
batch = self(batch)
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/dell/nfs/lp/lama/saicinpainting/training/trainers/default.py", line 70, in forward
batch['predicted_image'] = self.generator(masked_img)
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/dell/nfs/lp/lama/saicinpainting/training/modules/ffc.py", line 367, in forward
return self.model(input)
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/torch/nn/modules/container.py", line 141, in forward
input = module(input)
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/dell/nfs/lp/lama/saicinpainting/training/modules/ffc.py", line 252, in forward
x_l, x_g = self.ffc(x)
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/dell/nfs/lp/lama/saicinpainting/training/modules/ffc.py", line 221, in forward
out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 446, in forward
return self._conv_forward(input, self.weight, self.bias)
File "/home/dell/anaconda3/envs/lama/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 441, in _conv_forward
_pair(0), self.dilation, self.groups)
RuntimeError: Given groups=1, weight of size [64, 4, 7, 7], expected input[2, 3, 262, 262] to have 4 channels, but got 3 channels instead
Thank you very much

[Angericky]

Encountered the same problem.