U-Net can't perfectly segment image

vision
25

The docs state:

  • pos_weight (Tensor, optional) – a weight of positive examples. Must be a vector with length equal to the number of classes.

https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html

That seems to suggest you won’t need the torch.sum, and should only add on the batch dimension.

# Calculate pos_weight
        negatives = img_y == 0
        positives = img_y == 1
...

negs = torch.zeros((1, 256, 256)) #make sure the size here matches the dims and sizes for 1 image
posv = torch.zeros((1, 256, 256))
for i in range(len(train_y)):
    image, mask, negatives, positives = dataset[i]
    negs += negatives #if this has a batch dim, just append negatives.sum(dim=1)
    posv += positives #see above note

pos_weight = negs/posv

See the comments, too.