Hi @bodokaiser Do you know how to adapt this code for binary masks? (background = black, foreground = white) I changed the number of classes to 2: #NUM_CLASSES = 22 NUM_CLASSES = 2 But some erros still occurs:

Traceback (most recent call last):
  File "main2.py", line 164, in <module>
    main(parser.parse_args())
  File "main2.py", line 139, in main
    train(args, model)
  File "main2.py", line 74, in train
    loss = criterion(outputs, targets[:, 0])
  File "/home/ubuntu/src/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/module.py", line 224, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/ubuntu/pytorch/piwise/piwise/criterion.py", line 13, in forward
    return self.loss(F.log_softmax(outputs), targets)
  File "/home/ubuntu/src/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/module.py", line 224, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/ubuntu/src/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/loss.py", line 132, in forward
    self.ignore_index)
  File "/home/ubuntu/src/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py", line 674, in nll_loss
    return _functions.thnn.NLLLoss2d.apply(input, target, weight, size_average, ignore_index)
  File "/home/ubuntu/src/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/nn/_functions/thnn/auto.py", line 47, in forward
    output, *ctx.additional_args)
RuntimeError: weight tensor should be defined either for all or no classes at /pytorch/torch/lib/THCUNN/generic/SpatialClassNLLCriterion.cu:28
(pytorch) ubuntu@ip-172-31-85-122:~/pytorch/piwise$

In the assessment there was a mistake, I was not familiar with image segmentation, the problem is here RuntimeError: inconsistent tensor size, expected tensor [256 x 256] and mask [256] to have the same number of elements, but got 65536 and 256 elements respectively at d:\projects\pytorch\torch\lib\th\generic/THTensorMath.c:138 I feel that there is a problem here for label in range(1, len(self.cmap)): mask = gray_image[0] == label color_image[0][mask] = self.cmap[label][0] color_image[1][mask] = self.cmap[label][1] color_image[2][mask] = self.cmap[label][2]

Hello I read the the relization of the Segnet in network.py and I have a question. In the paper of Segnet,the last layer boxed in the picture below is (one Upsampling + one Conv +one Conv)

but in the relization of the Segnet in network.py (https://github.com/bodokaiser/piwise/blob/50f8be658693dd27bfa463cb00d1acec7e869fc4/piwise/network.py#L275),the layer metioned above is (one Upsampling + one Conv) ,it lose one Conv layer.

Thank you

Throws a Syntax Error when running the main.py file

File "main.py", line 84
    f'input (epoch: {epoch}, step: {step})')
                                          ^
SyntaxError: invalid syntax

New to pytorch, after data download, I occured this issue:

root@2d5d0934e049:/home/yuanshuai/code/piwise# python main.py 
  File "main.py", line 84
    f'input (epoch: {epoch}, step: {step})')
                                          ^
SyntaxError: invalid syntax

Hello, In the evaluation phase, your code outpus binary segmentation maps (for a binary segmentation task), while there cases in which a the real-value probability map is desired. How can I change your code to get the probability map?

Thanks Saeed

We want to implement following models (ordered after priority):

1. FCN
2. SqueezeNet
3. SegNet
4. PSPNet
5. RefineNet

Removed models (because too big, too complicated or not much information):

• ~~DeepLab~~

I think the most contribution in segnet is the idx-maxpooling. you can use the F.max_unpool2d(idx, x) to replace the nn.upsample(). The original upsample can't fit the loss of location information.

I ran this program on my own computer very well, but when I ran it on another computer, such a error happened: RuntimeError: cuda runtime error(59): device-side assert triggered when running transfer_learning_tutorial This error seems to be caused by a problem with the labels. Can you tell me how to solve this bug?

Every label of image have a white margin , I want to ignore the margin when I train net ,But I do not know how to do,So i ask you this question Could you help me?

The code need input size (256 ,256) ,and output size is (256,256).This limit the ability of the practice.So I want to extend the net ability to arbitrary input size .If I do ,the code need adjustment? I can not understand the code in depth. Please help def colormap(n): cmap=np.zeros([n, 3]).astype(np.uint8)

for i in np.arange(n):
    r, g, b = np.zeros(3)

    for j in np.arange(8):
        r = r + (1<<(7-j))*((i&(1<<(3*j))) >> (3*j))
        g = g + (1<<(7-j))*((i&(1<<(3*j+1))) >> (3*j+1))
        b = b + (1<<(7-j))*((i&(1<<(3*j+2))) >> (3*j+2))

    cmap[i,:] = np.array([r, g, b])

return cmap