![]() ![]() There are 7 classes in total so the final outout is a tensor like batch, 7, height, width which is a softmax output. Input_torch = torch. Very effective when training classification problems with C classes. Channel wise CrossEntropyLoss for image segmentation in pytorch Ask Question Asked 5 years, 1 month ago Modified 4 years, 4 months ago Viewed 13k times 4 I am doing an image segmentation task. If you start with a one-hot encoded matrix, you will have to convert it with np.argmax().Įxample with three classes and minibatch size of 1: import pytorch ![]() The target matrix is in the shape (Minibatch, H, W) with numbers ranging from 0 to (Classes-1). The input matrix is in the shape: (Minibatch, Classes, H, W). PyTorch Loss Functions Follow this guide to learn about the various loss functions available to use with PyTorch neural networks, and see how you can directly implement a custom loss function in their stead. In the 3D case, the torch.nn.CrossEntropy() functions expects two arguments: a 4D input matrix and a 3D target matrix. The built-in functions do indeed already support KD cross-entropy loss. So how can I fix my code to calculate channel wise CrossEntropy loss ?Īs Shai's answer already states, the documentation on the torch.nn.CrossEntropy() function can be found here and the code can be found here. So the targets and labels are (excluding the batch parameter for simplification ! ) \src\TH\THStorage.c:41įor example purpose I was trying to make it work on a 3 class problem. torch.nn.functional.crossentropy(input, target, weightNone, sizeaverageNone, ignoreindex- 100, reduceNone, reduction'mean', labelsmoothing0.0) source This criterion computes the cross entropy loss between input logits and target. The 2nd one says the following RuntimeError: invalid argument 2: size '' is invalid for input with 3840 elements at. This loss combines Dice loss with the standard binary cross-entropy (BCE) loss that is generally the default for segmentation models. One is mentioned on the code itself, where it expects one-hot vector. Labels = Variable(torch.LongTensor(5, 3, 4, 4).random_(3)) Images = Variable(torch.randn(5, 3, 4, 4)) Loss = F.nll_loss(log_p, target.view(-1), weight=weight, size_average=False) Target.view(n, w, z, 1).repeat(0, 0, 0, c) >= 0] # this looks wrong -> Should rather be a one-hot vector Log_p = log_p.permute(0, 3, 2, 1).contiguous().view(-1, c) # make class dimension last dimension ![]() With a help from some stackoverflow, My code so far looks like this from tograd import Variableĭef cross_entropy2d(input, target, weight=None, size_average=True): So I was planning to make a function on my own. Now intuitively I wanted to use CrossEntropy loss but the pytorch implementation doesn't work on channel wise one-hot encoded vector There are 7 classes in total so the final outout is a tensor like which is a softmax output. ![]()
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