Raise Exception("unsupported type! "+str(img.size())) Raise Exception("unsupported type! " + str(img.size())) We can use a hash set to mark which elements have been at the 1st index of current recursion. 1 has already been at the 1st index of current recursion level, so the last possibility is redundant. This method takes a list as an input and returns an object list of tuples that contain all permutations in a list form. At recursion level 0, we have 1 + permute2,3,4,1, 2 + permute1,3,4,1, 3 + permute2,1,4,1, 4 + permute2,3,1,1, 1 + permute2,3,4,1. Print('warning: more than 3 channels! only channels 0,1,2 are preserved!')Įlif bz > 1 and c = 1: # multiple grayscale imagesĮlif bz > 1 and c = 3: # multiple RGB imagesĮlif bz > 1 and c > 3: # multiple feature maps First import itertools package to implement the permutations method in python. If bz=1 and c=1: # single grayscale imageĮlif bz=1 and c > 3: # multiple feature maps desired cell2mat (Acell) EDIT: the solution by the cyclist is nicer for this particular problem, but be careful about generalizing it. Acell reshape (Acell,size (A,3),1) make the cell array a vector. Show(x,y,z) produces three windows, displaying x, y, z respectively, where x,y,z can be in any form described above. Acell num2cell (A, 1 2) put each page of A into a cell. If x is a 2D tensor, it will be shown as grayscale map If x is a 3D tensor, this function shows first 3 channels at most (in RGB format) There are plenty of examples of permutations in the. If x is a 4D tensor (like image batch with the size of b(atch)*c(hannel)*h(eight)*w(eight), this function splits x in batch dimension, showing b subplots in total, where each subplot displays first 3 channels (3*h*w) at most. Permutation is a mathematical calculation of the number of ways a particular. Show(x) gives the visualization of x, where x should be a torch.Tensor
Input imgs can be single or multiple tensor(s), this function uses matplotlib to visualize. How many different ways are there of selecting the three balls 10C3 1010 × 9 × 8 120 3 (10 3)3 × 2 × 1. I've written a simple function to visualize the pytorch tensor using matplotlib. # If you try to plot image with shape (C, H, W) Tensor_image = tensor_image.view(tensor_image.shape, tensor_image.shape, tensor_image.shape) Print(type(tensor_image), tensor_image.shape) But PyTorch Tensors ("Image tensors") are channel first, so to use them with matplotlib you need to reshape it: As you can see matplotlib works fine even without conversion to numpy array.
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