RuntimeError: output with shape [1, 300, 200] doesn't match the broadcast shape [3, 300, 200]

[raitmoon]

Hello, I'm following the instruction but faced the error while training.
I ran the script on Macbook Pro.
Do you have any idea to fix this issue?

$ python train.py --dataroot ./datasets/jeans2skirt_ccp --model insta_gan --name jeans2skirt_ccp_instagan --loadSizeH 330 --loadSizeW 220 --fineSizeH 300 --fineSizeW 200 --niter 400 --niter_decay 200 --gpu_ids -1
----------------- Options ---------------
               batch_size: 1                             
                    beta1: 0.5                           
          checkpoints_dir: ./checkpoints                 
           continue_train: False                         
                 dataroot: ./datasets/jeans2skirt_ccp    	[default: None]
             dataset_mode: unaligned_seg                 
                direction: AtoB                          
              display_env: main                          
             display_freq: 400                           
               display_id: 1                             
            display_ncols: 6                             
             display_port: 8097                          
           display_server: http://localhost              
          display_winsize: 256                           
                    epoch: latest                        
              epoch_count: 1                             
                fineSizeH: 300                           	[default: 200]
                fineSizeW: 200                           
                  gpu_ids: -1                            	[default: 0]
                init_gain: 0.02                          
                init_type: normal                        
                 input_nc: 3                             
                  ins_max: 4                             
                  ins_per: 2                             
                  isTrain: True                          	[default: None]
                 lambda_A: 10.0                          
                 lambda_B: 10.0                          
               lambda_ctx: 1.0                           
               lambda_idt: 1.0                           
                loadSizeH: 330                           	[default: 220]
                loadSizeW: 220                           
                load_iter: 0                             	[default: 0]
                       lr: 0.0002                        
           lr_decay_iters: 50                            
                lr_policy: lambda                        
         max_dataset_size: inf                           
                    model: insta_gan                     
               n_layers_D: 3                             
                     name: jeans2skirt_ccp_instagan      	[default: experiment_name]
                      ndf: 64                            
                     netD: set                           
                     netG: set                           
                      ngf: 64                            
                    niter: 400                           	[default: 100]
              niter_decay: 200                           	[default: 100]
               no_dropout: True                          
                  no_flip: False                         
                  no_html: False                         
                 no_lsgan: False                         
                     norm: instance                      
              num_threads: 4                             
                output_nc: 3                             
                    phase: train                         
                pool_size: 50                            
               print_freq: 100                           
           resize_or_crop: resize_and_crop               
             save_by_iter: False                         
          save_epoch_freq: 5                             
         save_latest_freq: 5000                          
           serial_batches: False                         
                set_order: decreasing                    
                   suffix:                               
         update_html_freq: 1000                          
                  verbose: False                         
----------------- End -------------------
dataset [UnalignedSegDataset] was created
#training images = 167
initialize network with normal
initialize network with normal
initialize network with normal
initialize network with normal
model [InstaGANModel] was created
---------- Networks initialized -------------
[Network G_A] Total number of parameters : 26.815 M
[Network G_B] Total number of parameters : 26.815 M
[Network D_A] Total number of parameters : 9.754 M
[Network D_B] Total number of parameters : 9.754 M
-----------------------------------------------
Setting up a new session...
create web directory ./checkpoints/jeans2skirt_ccp_instagan/web...
Traceback (most recent call last):
  File "train.py", line 24, in <module>
    for i, data in enumerate(dataset):
  File "/Users/tomo/Documents/GitHub/instagan/data/__init__.py", line 72, in __iter__
    for i, data in enumerate(self.dataloader):
  File "/Users/tomo/.pyenv/versions/3.7.4/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 819, in __next__
    return self._process_data(data)
  File "/Users/tomo/.pyenv/versions/3.7.4/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 846, in _process_data
    data.reraise()
  File "/Users/tomo/.pyenv/versions/3.7.4/lib/python3.7/site-packages/torch/_utils.py", line 385, in reraise
    raise self.exc_type(msg)
RuntimeError: Caught RuntimeError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/Users/tomo/.pyenv/versions/3.7.4/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
    data = fetcher.fetch(index)
  File "/Users/tomo/.pyenv/versions/3.7.4/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/Users/tomo/.pyenv/versions/3.7.4/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/Users/tomo/Documents/GitHub/instagan/data/unaligned_seg_dataset.py", line 71, in __getitem__
    A_segs = self.read_segs(A_seg_path, seed)
  File "/Users/tomo/Documents/GitHub/instagan/data/unaligned_seg_dataset.py", line 42, in read_segs
    seg = self.fixed_transform(seg, seed)
  File "/Users/tomo/Documents/GitHub/instagan/data/unaligned_seg_dataset.py", line 34, in fixed_transform
    return self.transform(image)
  File "/Users/tomo/.pyenv/versions/3.7.4/lib/python3.7/site-packages/torchvision/transforms/transforms.py", line 61, in __call__
    img = t(img)
  File "/Users/tomo/.pyenv/versions/3.7.4/lib/python3.7/site-packages/torchvision/transforms/transforms.py", line 166, in __call__
    return F.normalize(tensor, self.mean, self.std, self.inplace)
  File "/Users/tomo/.pyenv/versions/3.7.4/lib/python3.7/site-packages/torchvision/transforms/functional.py", line 217, in normalize
    tensor.sub_(mean[:, None, None]).div_(std[:, None, None])
RuntimeError: output with shape [1, 300, 200] doesn't match the broadcast shape [3, 300, 200]

I tried the other one, pants2skirt_mhp_instagan but almost same result.
RuntimeError: output with shape [1, 240, 160] doesn't match the broadcast shape [3, 240, 160]
I'm sorry if this is not the right place to ask.
Thanks in advance.

[sangwoomo]

Hi, it seems that the image type (RGB vs black/white) does not match in some reason.
Please see ./data/unaligned_seg_dataset.py to check if the loaded image is 1-channel and 3-channel, and also check the required channel for models.

[raitmoon]

Thank you for your suggestion.

I searched on the internet and finally got solved by downgrading torchvision from 0.4.1 to 0.2.1.

pip3 uninstall torchvision
pip3 install torchvision=0.2.1

Many of them suggest transforms.Normalize((0.5,), (0.5,)) but didn't help.
https://stackoverflow.com/questions/55124407/output-and-broadcast-shape-mismatch-in-mnist-torchvision
I'm not sure what was the original problem though..

[bboynton97]

I ran into the same issue. The torchvision version should be specified in requirements.txt and the conda script.

[sangwoomo]

Hi, thank you for your suggestion. I modified requirements.txt to use == instead of >=.

[bboynton97]

Thanks for the super quick response! Great job on the paper btw. I'm using it for a class project.

[sangwoomo]

Thanks for your interest! :)

[VicZlq]

Hi, thank you for your suggestion.