Why class 1 and 8 have all zeros?
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nb_classes = 9


confusion_matrix = torch.zeros(nb_classes, nb_classes)


with torch.no_grad():
    for i, (inputs, classes) in enumerate(dataloaders['test']):
        inputs = inputs.to(device)
        classes = classes.to(device)
        outputs = model_ft(inputs)
        _, preds = torch.max(outputs, 1)
        for t, p in zip(classes.view(-1), preds.view(-1)):
                confusion_matrix[t.long(), p.long()] += 1

print(confusion_matrix)

with torch.no_grad():
    for i, (inputs, classes) in enumerate(dataloaders['test']):
        print("i: ", i)
        #print("inputs is: ", inputs)
        print('classes is: ', classes)

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(labels_test, pred)

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tensor([[ 0.,  2.,  0.,  1.,  0.,  1.,  0.,  0.,  0.],
        [ 0.,  9., 11.,  0.,  0.,  0.,  4.,  0.,  0.],
        [ 0.,  3., 31.,  0.,  0.,  0.,  7.,  0.,  1.],
        [ 0.,  0.,  3.,  1.,  1.,  0.,  2.,  0.,  0.],
        [ 0.,  4.,  3.,  3.,  1.,  0.,  5.,  0.,  0.],
        [ 0.,  0.,  2.,  1.,  0.,  3.,  2.,  0.,  1.],
        [ 0.,  2.,  3.,  1.,  2.,  0., 13.,  0.,  3.],
        [ 0.,  1.,  0.,  1.,  1.,  0.,  1.,  0.,  0.],
        [ 0.,  1.,  3.,  0.,  1.,  0.,  3.,  0.,  2.]])
i:  0
classes is:  tensor([6, 8, 2, 0])
i:  1
classes is:  tensor([8, 2, 2, 2])
i:  2
classes is:  tensor([2, 4, 6, 1])
i:  3
classes is:  tensor([1, 1, 1, 1])
i:  4
classes is:  tensor([1, 7, 2, 6])
i:  5
classes is:  tensor([6, 4, 2, 2])
i:  6
classes is:  tensor([5, 1, 2, 6])
i:  7
classes is:  tensor([8, 4, 5, 6])
i:  8
classes is:  tensor([1, 1, 4, 2])
i:  9
classes is:  tensor([1, 3, 8, 7])
i:  10
classes is:  tensor([8, 4, 1, 0])
i:  11
classes is:  tensor([3, 2, 1, 2])
i:  12
classes is:  tensor([8, 2, 4, 6])
i:  13
classes is:  tensor([1, 5, 2, 6])
i:  14
classes is:  tensor([1, 1, 6, 3])
i:  15
classes is:  tensor([2, 4, 2, 8])
i:  16
classes is:  tensor([4, 7, 2, 5])
i:  17
classes is:  tensor([2, 2, 3, 6])
i:  18
classes is:  tensor([6, 6, 1, 1])
i:  19
classes is:  tensor([8, 2, 8, 7])
i:  20
classes is:  tensor([1, 6, 6, 1])
i:  21
classes is:  tensor([5, 2, 2, 2])
i:  22
classes is:  tensor([1, 3, 2, 6])
i:  23
classes is:  tensor([5, 1, 6, 5])
i:  24
classes is:  tensor([2, 2, 4, 2])
i:  25
classes is:  tensor([2, 3, 2, 4])
i:  26
classes is:  tensor([6, 2, 2, 4])
i:  27
classes is:  tensor([3, 6, 1, 5])
i:  28
classes is:  tensor([2, 0, 2, 2])
i:  29
classes is:  tensor([1, 2, 6, 6])
i:  30
classes is:  tensor([1, 2, 8, 6])
i:  31
classes is:  tensor([2, 4, 5, 2])
i:  32
classes is:  tensor([6, 4, 2, 6])
i:  33
classes is:  tensor([2, 2, 4, 6])
i:  34
classes is:  tensor([4, 2, 0, 4])