def test_to_HWC(self):
test_image = np.random.randint(0, 256, size=(3, 32, 32), dtype=np.uint8)
converted = convert_to_HWC(test_image, 'chw')
self.assertEqual(converted.shape, (32, 32, 3))
test_image = np.random.randint(0, 256, size=(16, 3, 32, 32), dtype=np.uint8)
converted = convert_to_HWC(test_image, 'nchw')
self.assertEqual(converted.shape, (64, 256, 3))
test_image = np.random.randint(0, 256, size=(32, 32), dtype=np.uint8)
converted = convert_to_HWC(test_image, 'hw')
self.assertEqual(converted.shape, (32, 32, 3))
def test_convert_to_HWC_dtype_remains_same(self):
# test to ensure convert_to_HWC restores the dtype of input np array and
# thus the scale_factor calculated for the image is 1
test_image = torch.tensor([[[[1, 2, 3], [4, 5, 6]]]], dtype=torch.uint8)
tensor = make_np(test_image)
tensor = convert_to_HWC(tensor, 'NCHW')
scale_factor = summary._calc_scale_factor(tensor)
self.assertEqual(scale_factor, 1, msg='Values are already in [0, 255], scale factor should be 1')
