![]() Minimum = per_channel_op(data, op=torch.min) # only one value cause MNISTĪnd finally, to apply normalization on MNIST (watch out, as those will only have -1, 1 values as all pixels are black and white, will act differently on datasets like CIFAR etc. Maximum = per_channel_op(data) # value per channel, here (when I resize to 1800x1800 from 512x512 it does so without problems) I get this error: OutOfMemoryError: CUDA out of memory. But I have a problem when doing the operation described in the title. # Divide cause they are uint8 type by defaultĭata = (1).float() / 255 img2img can't resize to 2048 from 512 with RTX 4090 Hello guys, I'm trying to learn how to use stable diffusion. # Unsqueeze to add superficial channel for MNIST You could calculate those from data, for MNIST you could calculate them like this: def per_channel_op(data, op=torch.max): You would have to provide Tuple of minimum values and Tuple of maximum values (one value per channel for both) just like for standard PyTorch's torchvision normalization though. ![]() (tensor - minimum) * (self.high - self.low) Minimum = torch.as_tensor(self.minimum, dtype=dtype, device=vice) Maximum = torch.as_tensor(self.maximum, dtype=dtype, device=vice) It depends whether you want it per-channel or in another form, but something along those lines should work (see wikipedia for formula of the normalization, here it's applied per-channel): import Normalize: When it comes to normalization, you can see PyTorch's per-channel normalization source here. Print(dataset.shape) # 1, 32, 32 (channels, width, height) Note that for the validation and test data, we do not do the RandomResizedCrop, RandomRotation and RandomHorizontalFlip transformations. # Simply put the size you want in Resize (can be tuple for height, width) It can also be used in Fully Convolutional Networks to emulate different scales for an input image, this is data-augmentation. Resizing MNIST to 32x32 height x width can be done like so: import tempfile Resize is used in Convolutional Neural Networks to adapt the input image to the network input shape, in this case this is not data-augmentation but just pre-processing.
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