Comments (12)
yassouali
commented on May 28, 2024
Hi @Byronnar, thank you for your interest in our work.
This error is at the end of the first epoch, where in the last batch, you have only one image and BN gives an error, to solve this, please simply set drop_last = True in the dataloaders (or change the batch size / do an if statement to skip the last step if BZ = 1).
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Byronnar
commented on May 28, 2024
Thank for your reply~ I have set batch_size=2 have solved this problem. But I cannot find where is the dataloaders(Except in train_cam.py), you mean in dataloader in voc.py?
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Byronnar
commented on May 28, 2024
Hi @Byronnar, thank you for your interest in our work.
This error is at the end of the first epoch, where in the last batch, you have only one image and BN gives an error, to solve this, please simply set drop_last = True in the dataloaders (or change the batch size / do an if statement to skip the last step if BZ = 1).
The error below:
self.init_kwargs: 88888888 {'dataset': Dataset: VOCDataset
# data: 9118
Split: train_unsupervised
Root: /home/byronnar/bigfile/datasets/img_detection/VOCdevkit/VOC2012, 'batch_size': 1, 'shuffle': True, 'num_workers': 8, 'pin_memory': True, 'drop_last': True}
self.init_kwargs: 88888888 {'dataset': Dataset: VOCDataset
# data: 1449
Split: val
Root: /home/byronnar/bigfile/datasets/img_detection/VOCdevkit/VOC2012, 'batch_size': 1, 'shuffle': False, 'num_workers': 4, 'pin_memory': True, 'drop_last': True}
Loading pretrained model:models/backbones/pretrained/3x3resnet50-imagenet.pth
Nbr of trainable parameters: 47210776
Detected GPUs: 1 Requested: 1
0%| | 0/9118 [00:00<?, ?it/s]Traceback (most recent call last):
File "train.py", line 99, in <module>
main(config, args.resume)
File "train.py", line 83, in main
trainer.train()
File "/home/byronnar/bigfile/projects/CCT/base/base_trainer.py", line 91, in train
results = self._train_epoch(epoch)
File "/home/byronnar/bigfile/projects/CCT/trainer.py", line 75, in _train_epoch
total_loss, cur_losses, outputs = self.model(x_l=input_l, target_l=target_l, x_ul=input_ul, curr_iter=batch_idx, target_ul=target_ul, epoch=epoch-1)
File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py", line 547, in __call__
result = self.forward(*input, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 150, in forward
return self.module(*inputs[0], **kwargs[0])
File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py", line 547, in __call__
result = self.forward(*input, **kwargs)
File "/home/byronnar/bigfile/projects/CCT/models/model.py", line 93, in forward
output_l = self.main_decoder(self.encoder(x_l))
File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py", line 547, in __call__
result = self.forward(*input, **kwargs)
File "/home/byronnar/bigfile/projects/CCT/models/encoder.py", line 61, in forward
x = self.psp(x)
File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py", line 547, in __call__
result = self.forward(*input, **kwargs)
File "/home/byronnar/bigfile/projects/CCT/models/encoder.py", line 36, in forward
align_corners=False) for stage in self.stages])
File "/home/byronnar/bigfile/projects/CCT/models/encoder.py", line 36, in <listcomp>
align_corners=False) for stage in self.stages])
File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py", line 547, in __call__
result = self.forward(*input, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/container.py", line 92, in forward
input = module(input)
File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py", line 547, in __call__
result = self.forward(*input, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/batchnorm.py", line 81, in forward
exponential_average_factor, self.eps)
File "/opt/conda/lib/python3.6/site-packages/torch/nn/functional.py", line 1652, in batch_norm
raise ValueError('Expected more than 1 value per channel when training, got input size {}'.format(size))
ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 512, 1, 1])
0%| | 0/9118 [00:02<?, ?it/s]
I have added the 'drop_last': True.
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yassouali
commented on May 28, 2024
Sorry, I thought you had a problem only at the end of the first epochs (multi gpu training). In your case, I think you are training with only one batch, but the nn.BatchNorm layers expect more than one image per batch to compute the running mean. You'll need to set the batch size to be > 1.
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Byronnar
commented on May 28, 2024
Sorry, I thought you had a problem only at the end of the first epochs (multi gpu training). In your case, I think you are training with only one batch, but the
nn.BatchNormlayers expect more than one image per batch to compute the running mean. You'll need to set the batch size to be > 1.
Thanks for your reply.
I can train on VOC2012, but I don't know how to generate images in the SegmentationClassAug? I mean that unsupervised images.
Looking forward to your reply.
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Byronnar
commented on May 28, 2024
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yassouali
commented on May 28, 2024
Are you trying to use it on your dataset? SegmentationClassAug is only specific for VOC2012.
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Byronnar
commented on May 28, 2024
Yeah, I AM trying to use it on my dataset, but I don't know how to get the unlabeled data, it can be original RGB colorful image?
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Byronnar
commented on May 28, 2024
Are you trying to use it on your dataset? SegmentationClassAug is only specific for VOC2012.
whether the custom datasets should as same as SegmentationClassAug?
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yassouali
commented on May 28, 2024
No need for SegmentationClassAug, simply use your own dataloader, you'll need to create two dataloaders, one for the supervised dataset and one for the unsupervised dataset. You can create it them any way you like (see closed issues, there is a lot of discussion on the usage of custom datasets).
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Byronnar
commented on May 28, 2024
No need for SegmentationClassAug, simply use your own dataloader, you'll need to create two dataloaders, one for the supervised dataset and one for the unsupervised dataset. You can create it them any way you like (see closed issues, there is a lot of discussion on the usage of custom datasets).
Thank you! You mean don't need for image-level data? (in the paper, there arre 9k image-level data in unsupervised section.)
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SuzannaLin
commented on May 28, 2024
I used https://github.com/wkentaro/labelme to annotate my own dataset
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Related Issues (20)
- Error in training the model
- ModuleNotFoundError: No module named 'tensorboard' HOT 1
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