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I was calling w_pred = net(V.float) instead of w_pred = net(V.float()). It’s written right there in the error that I kept looking at but I kept missing it until I posted it here

If I open a label image on Photoshop, I can see the image mode is set to Grayscale and not RGB, so the shape should be [3, 1, 256, 256], right?

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If your labels are indeed (batches of) images of shape [3, 3, 256, 256], then you have to figure out how they are “encoded” to give binary class labels. Could they be pure black-and-white images that happen to be encoded as three-channel RGB images?

Clearly, the model returns a size of 65536, which is 256*256, converting the image to a line. But how do I convert it back to a square? And how do I get rid of the batch size’s 3?

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I’m new to machine learning and PyTorch, and I’m stuck on this error which seems really simple but I can’t find where to fix it:

The images are of size torch.Size([3, 3, 256, 256]) and not torch.Size([3, 256, 256]). The first 3 is the batch size, the second one is for the 3 RGB channels, and the 256s are the image dimensions.

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The input to your model should have shape [nBatch, nChannels, H, W], where in your case nChannels = 3 and presumably correspond to the RGB channels of a color image, and have type float (or double).

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So, yes, get rid of the final x = x.view(x.size(0), -1) (and the commented-out x = self.linear(x)), and have your model output the result of the final Conv2d (self.end) layer. And, yes, out = 1 is correct.

If your labels are indeed (batches of) images of shape [3, 3, 256, 256], then you have to figure out how they are “encoded” to give binary class labels. Could they be pure black-and-white images that happen to be encoded as three-channel RGB images?

Note, UNet does not (typically) have H and W wired into it – the same UNet can be trained on, and perform inference on images of differing shapes – but any given batch has to consist of images of the same shape.

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Edit 1: Getting rid of the line x = x.view(x.size(0), -1) … Edit 2: Using sizes (3, 256, 256) for images and (1, 256, 256) for labels, and removing .astype(int) from the __getitem__ method

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As a general rule (and as a requirement for convolutional networks), pytorch networks work with batches of inputs (and outputs and labels). If you want to process a single image, you still need to package it as a batch with a batch size of one.

In any event, you have to process your labels “images” to be single-channel binary labels (of type float). (Your labels don’t actually have to be pure binary, that is, exactly zero or one – they can be probabilistic labels that run from zero to one.)

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Based on the name TissueDataset, the name UNet, and the use of BCEWithLogitsLoss as your loss criterion, I assume that you are performing binary semantic segmentation. That is, you wish to classify each pixel in your input as being in either “class 0” (background or healthy tissue or whatever) or “class 1” (foreground or diseased tissue or whatever).

Edit 2: Using sizes (3, 256, 256) for images and (1, 256, 256) for labels, and removing .astype(int) from the __getitem__ method gives this error:

Thank you so much for your detailed answer! I am indeed trying to solve a segmentation problem. Here is an example of an image and its label:

Edit 1: Getting rid of the line x = x.view(x.size(0), -1) and using a batch size of 4 instead of 3 for clarity, the error becomes: