Error assigning Loss Function to Segmentation unet_learner

Part 1 (2019) UTC+0 UK / EU West
1 
=== Software === 
python        : 3.7.6
fastai        : 1.0.57
fastprogress  : 0.1.21
torch         : 1.4.0
nvidia driver : 441.22
torch cuda    : 10.1 / is available
torch cudnn   : 7501 / is enabled

=== Hardware === 
nvidia gpus   : 2
torch devices : 2
  - gpu0      : 8129MB | Tesla M60
  - gpu1      : 8129MB | Tesla M60

=== Environment === 
platform      : Windows-10-10.0.14393-SP0
conda env     : ml
python        : C:\Users\-sysop-dur7an-z\.conda\envs\ml\python.exe

Describe the bug

When I train a learner learn = unet_learner(data ,models.resnet50, metrics=metrics) with the default Loss function (FlattenedLoss), I have no error and the model trains fine. However, when I try to use another loss function, (e.g. learn.loss_func = nn.BCEWithLogitsLoss() ), I get the following error:

ValueError: Target size (torch.Size([2, 1, 64, 128])) must be the same as input size (torch.Size([2, 2, 64, 128]))

Do you know why this is?

Provide your installation details

To Reproduce

learn = unet_learner(data ,models.resnet50, metrics=metrics)
learn.loss_fn = nn.BCEWithLogitsLoss()

lr_find(learn)

ValueError: Target size (torch.Size([2, 1, 64, 128])) must be the same as input size (torch.Size([2, 2, 64, 128]))

2

BCEWithLogitsLoss is expecting a channel for each class, in this case 2 while your target has a single channel.

1 Like
3

@rpdunne did you find a solution for this?@juvian i’m in a similar position, i think its because im trying to use the original camvid notebook modified for a binary classification problem. It’s working ok in terms of initial results however i wanted to change my loss function and now im running into a similar headache.

Should i be trying to change the "target = " part of the code or addressing this elsewhere?

4

You should either set classes to 2 so that it works out of the box or use a custom loss function to get just first channel and apply the loss you want

5

Thanks @juvian - out of the box sounds much more in line with what i want to do. My next stupid question is where are you setting the classes to 2? I thought i already had done that implicitly by only having two classes in the code

i.e

Screenshot 2020-03-02 at 14.11.53
Screenshot 2020-03-02 at 14.11.532506×1264 352 KB

6

Check what’s the value of data.c

7

data.c

2 @juvian

I also found another function to try and debug from below, now i’m wondering is this a case that i’m not passing the masks in too? Or that my masks are greyscaled and it’s expecting RGB?

Screenshot 2020-03-02 at 18.49.36
Screenshot 2020-03-02 at 18.49.361482×220 25.6 KB

8

Make a custom loss function that does something like this:

Def customloss(input, target)
Input = input[:, 0,:,:]
Return nn.bce()(input, target)

9

if you are using pretrained model (3 channel) it should be rgb! examples available if you search the forums.

10

Actually, it is possible to load gray valued images as 3-channel images by duplicating the one channel. This way pre-trained image net can be used (and also enhances results).