RuntimeError with train_real_fixed.py

[ObeidaZakzak]

System :

Ubuntu 20.04 Nvidia GTX 1070

Everything works fine with background matting (all steps) until I try to make a training on my own dataset. I used prepare_real.py to create the .csv file after doing the segmentation manually (I used the segmentation I got while making my tests)

When I use the command :

CUDA_VISIBLE_DEVICES=0,1 python train_real_fixed.py -n Real_fixed -bs 4 -res 512 -init_model Models/syn-comp-adobe-trainset/net_epoch_64.pth

I get this RuntimeError :

RuntimeError: Given groups=1, weight of size [64, 3, 7, 7], expected input[4, 4, 518, 518] to have 3 channels, but got 4 channels instead

For more details, this is the whole execution log :

CUDA_VISIBLE_DEVICES=0,1 python train_real_fixed.py -n Real_fixed -bs 4 -res 512 -init_model Models/syn-comp-adobe-trainset/net_epoch_64.pth CUDA Device: 0,1

[Phase 1] : Data Preparation

[Phase 2] : Initialization /home/pample/Bureau/Stage_Keying/Background-Matting/Background-Matting/networks.py:120: UserWarning: nn.init.xavier_uniform is now deprecated in favor of nn.init.xavieruniform. init.xavier_uniform(m.weight, gain=np.sqrt(2)) /home/pample/Bureau/StageKeying/Background-Matting/Background-Matting/networks.py:123: UserWarning: nn.init.constant is now deprecated in favor of nn.init.constant. init.constant(m.bias, 0) /home/pample/Bureau/StageKeying/Background-Matting/Background-Matting/networks.py:130: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal. init.normal(m.weight.data, 1.0, 0.2) /home/pample/Bureau/StageKeying/Background-Matting/Background-Matting/networks.py:131: UserWarning: nn.init.constant is now deprecated in favor of nn.init.constant. init.constant(m.bias.data, 0.0) Starting Training Traceback (most recent call last): File "train_real_fixed.py", line 126, in alpha_pred_sup,fg_pred_sup=netB(image,bg,seg,multi_fr) File "/home/pample/anaconda3/envs/back-matting/lib/python3.6/site-packages/torch/nn/modules/module.py", line 550, in call result = self.forward(*input, kwargs) File "/home/pample/anaconda3/envs/back-matting/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 153, in forward return self.module(*inputs[0], *kwargs[0]) File "/home/pample/anaconda3/envs/back-matting/lib/python3.6/site-packages/torch/nn/modules/module.py", line 550, in call result = self.forward(input, kwargs) File "/home/pample/Bureau/Stage_Keying/Background-Matting/Background-Matting/networks.py", line 95, in forward back_feat=self.model_enc_back(back) File "/home/pample/anaconda3/envs/back-matting/lib/python3.6/site-packages/torch/nn/modules/module.py", line 550, in call result = self.forward(*input, *kwargs) File "/home/pample/anaconda3/envs/back-matting/lib/python3.6/site-packages/torch/nn/modules/container.py", line 100, in forward input = module(input) File "/home/pample/anaconda3/envs/back-matting/lib/python3.6/site-packages/torch/nn/modules/module.py", line 550, in call result = self.forward(input, **kwargs) File "/home/pample/anaconda3/envs/back-matting/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 349, in forward return self._conv_forward(input, self.weight) File "/home/pample/anaconda3/envs/back-matting/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 346, in _conv_forward self.padding, self.dilation, self.groups) RuntimeError: Given groups=1, weight of size [64, 3, 7, 7], expected input[4, 4, 518, 518] to have 3 channels, but got 4 channels instead

[CocoRLin]

The code in dataloader :io.imread()may read img with RGBA. Just use cv2 and set color in RGB not BGR.

[ObeidaZakzak]

set color in RGB not BGR.

@CocoRLin what do you mean by setting color in RGB (for which images in the code exactly) ? I replaced io.imread() by cv2.imread() then converted from BGR to RGB for all images where cv2.imread is used, for instance :

img = cv2.imread(self.frames.iloc[idx, 0])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

It gives me the following error (it's due to changing io.imread into cv2.imread :

Execution log :

[Phase 1] : Data Preparation

[Phase 2] : Initialization
Starting Training
Traceback (most recent call last):
  File "train_real_fixed.py", line 114, in <module>
    for i,data in enumerate(train_loader):
  File "/home/pample/anaconda3/envs/back-matting/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 345, in __next__
    data = self._next_data()
  File "/home/pample/anaconda3/envs/back-matting/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 856, in _next_data
    return self._process_data(data)
  File "/home/pample/anaconda3/envs/back-matting/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 881, in _process_data
    data.reraise()
  File "/home/pample/anaconda3/envs/back-matting/lib/python3.6/site-packages/torch/_utils.py", line 395, in reraise
    raise self.exc_type(msg)
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/home/pample/anaconda3/envs/back-matting/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
    data = fetcher.fetch(index)
  File "/home/pample/anaconda3/envs/back-matting/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/pample/anaconda3/envs/back-matting/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/pample/Bureau/Stage_Keying/Background-Matting/Background-Matting/data_loader.py", line 57, in __getitem__
    bbox=create_bbox(seg,seg.shape[0],seg.shape[1])
  File "/home/pample/Bureau/Stage_Keying/Background-Matting/Background-Matting/data_loader.py", line 230, in create_bbox
    x1, y1 = np.amin(where, axis=1)
ValueError: too many values to unpack (expected 2)

[senguptaumd]

Make sure your image tensor has 3 channels and not 4. This can happen if the input images has an alpha channel. This should have been resolved in using cv2.imread(). The error happens in getting a bounding box for segmentation. Make sure the Segmentation image seg has a single channel.