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')