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Port to_tensor tests in test_transforms to pytest #3966

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merged 8 commits into from
Jun 5, 2021
Merged

Port to_tensor tests in test_transforms to pytest #3966

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ca374b3
Port test to tensor in test_transforms to pytest
DevPranjal Jun 3, 2021
f2d52b4
Port test to tensor in test_transforms to pytest with parametrize
DevPranjal Jun 4, 2021
654aa9b
Add code formatting
DevPranjal Jun 4, 2021
cdf3e6c
Merge branch 'pytorch:master' into refactor-test-ToTensor
DevPranjal Jun 4, 2021
ed42460
Fix issue: Extract errors into seperate functions
DevPranjal Jun 5, 2021
9b7fba8
Merge branch 'refactor-test-ToTensor' of https://github.com/DevPranja…
DevPranjal Jun 5, 2021
e3deff2
Merge branch 'master' into refactor-test-ToTensor
DevPranjal Jun 5, 2021
bcd4777
Merge branch 'master' of github.com:pytorch/vision into refactor-test…
NicolasHug Jun 5, 2021
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163 changes: 82 additions & 81 deletions test/test_transforms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -491,54 +491,6 @@ def test_random_order(self):
# Checking if RandomOrder can be printed as string
random_order_transform.__repr__()

def test_to_tensor(self):
test_channels = [1, 3, 4]
height, width = 4, 4
trans = transforms.ToTensor()

with self.assertRaises(TypeError):
trans(np.random.rand(1, height, width).tolist())

with self.assertRaises(ValueError):
trans(np.random.rand(height))
trans(np.random.rand(1, 1, height, width))

for channels in test_channels:
input_data = torch.ByteTensor(channels, height, width).random_(0, 255).float().div_(255)
img = transforms.ToPILImage()(input_data)
output = trans(img)
torch.testing.assert_close(output, input_data, check_stride=False)

ndarray = np.random.randint(low=0, high=255, size=(height, width, channels)).astype(np.uint8)
output = trans(ndarray)
expected_output = ndarray.transpose((2, 0, 1)) / 255.0
torch.testing.assert_close(output.numpy(), expected_output, check_stride=False, check_dtype=False)

ndarray = np.random.rand(height, width, channels).astype(np.float32)
output = trans(ndarray)
expected_output = ndarray.transpose((2, 0, 1))
torch.testing.assert_close(output.numpy(), expected_output, check_stride=False, check_dtype=False)

# separate test for mode '1' PIL images
input_data = torch.ByteTensor(1, height, width).bernoulli_()
img = transforms.ToPILImage()(input_data.mul(255)).convert('1')
output = trans(img)
torch.testing.assert_close(input_data, output, check_dtype=False, check_stride=False)

def test_to_tensor_with_other_default_dtypes(self):
current_def_dtype = torch.get_default_dtype()

t = transforms.ToTensor()
np_arr = np.random.randint(0, 255, (32, 32, 3), dtype=np.uint8)
img = Image.fromarray(np_arr)

for dtype in [torch.float16, torch.float, torch.double]:
torch.set_default_dtype(dtype)
res = t(img)
self.assertTrue(res.dtype == dtype, msg=f"{res.dtype} vs {dtype}")

torch.set_default_dtype(current_def_dtype)

def test_max_value(self):
for dtype in int_dtypes():
self.assertEqual(F_t._max_value(dtype), torch.iinfo(dtype).max)
Expand Down Expand Up @@ -676,39 +628,6 @@ def test_accimage_to_tensor(self):

torch.testing.assert_close(output, expected_output)

def test_pil_to_tensor(self):
test_channels = [1, 3, 4]
height, width = 4, 4
trans = transforms.PILToTensor()

with self.assertRaises(TypeError):
trans(np.random.rand(1, height, width).tolist())
trans(np.random.rand(1, height, width))

for channels in test_channels:
input_data = torch.ByteTensor(channels, height, width).random_(0, 255)
img = transforms.ToPILImage()(input_data)
output = trans(img)
torch.testing.assert_close(input_data, output, check_stride=False)

input_data = np.random.randint(low=0, high=255, size=(height, width, channels)).astype(np.uint8)
img = transforms.ToPILImage()(input_data)
output = trans(img)
expected_output = input_data.transpose((2, 0, 1))
torch.testing.assert_close(output.numpy(), expected_output)

input_data = torch.as_tensor(np.random.rand(channels, height, width).astype(np.float32))
img = transforms.ToPILImage()(input_data) # CHW -> HWC and (* 255).byte()
output = trans(img) # HWC -> CHW
expected_output = (input_data * 255).byte()
torch.testing.assert_close(output, expected_output, check_stride=False)

# separate test for mode '1' PIL images
input_data = torch.ByteTensor(1, height, width).bernoulli_()
img = transforms.ToPILImage()(input_data.mul(255)).convert('1')
output = trans(img).view(torch.uint8).bool().to(torch.uint8)
torch.testing.assert_close(input_data, output, check_stride=False)

@unittest.skipIf(accimage is None, 'accimage not available')
def test_accimage_pil_to_tensor(self):
trans = transforms.PILToTensor()
Expand Down Expand Up @@ -1403,6 +1322,88 @@ def test_random_erasing(self):
t.__repr__()


@pytest.mark.parametrize('channels', [1, 3, 4])
def test_pil_to_tensor(channels):
height, width = 4, 4
trans = transforms.PILToTensor()

with pytest.raises(TypeError):
trans(np.random.rand(1, height, width).tolist())
trans(np.random.rand(1, height, width))
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I think we should have 2 raises blocks here, one per line. This is because we want to make sure that both lines below raise a TypeError. With a single block, we're only asserting that one of them raises the error. In fact I think the original test code was incorrect.

Also, we don't need those assertions to be part of the parametrization, it's a bit overkill as they don't need the channel parameter. So I'd suggest to extract them into a separate test_pil_to_tensor_errors() test function


input_data = torch.ByteTensor(channels, height, width).random_(0, 255)
img = transforms.ToPILImage()(input_data)
output = trans(img)
torch.testing.assert_close(input_data, output, check_stride=False)

input_data = np.random.randint(low=0, high=255, size=(height, width, channels)).astype(np.uint8)
img = transforms.ToPILImage()(input_data)
output = trans(img)
expected_output = input_data.transpose((2, 0, 1))
torch.testing.assert_close(output.numpy(), expected_output)

input_data = torch.as_tensor(np.random.rand(channels, height, width).astype(np.float32))
img = transforms.ToPILImage()(input_data) # CHW -> HWC and (* 255).byte()
output = trans(img) # HWC -> CHW
expected_output = (input_data * 255).byte()
torch.testing.assert_close(output, expected_output, check_stride=False)

# separate test for mode '1' PIL images
input_data = torch.ByteTensor(1, height, width).bernoulli_()
img = transforms.ToPILImage()(input_data.mul(255)).convert('1')
output = trans(img).view(torch.uint8).bool().to(torch.uint8)
torch.testing.assert_close(input_data, output, check_stride=False)


@pytest.mark.parametrize('channels', [1, 3, 4])
def test_to_tensor(channels):
height, width = 4, 4
trans = transforms.ToTensor()

with pytest.raises(TypeError):
trans(np.random.rand(1, height, width).tolist())

with pytest.raises(ValueError):
trans(np.random.rand(height))
trans(np.random.rand(1, 1, height, width))
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same here about having 1 raises block per line, and we should also move these out in a separate test function


input_data = torch.ByteTensor(channels, height, width).random_(0, 255).float().div_(255)
img = transforms.ToPILImage()(input_data)
output = trans(img)
torch.testing.assert_close(output, input_data, check_stride=False)

ndarray = np.random.randint(low=0, high=255, size=(height, width, channels)).astype(np.uint8)
output = trans(ndarray)
expected_output = ndarray.transpose((2, 0, 1)) / 255.0
torch.testing.assert_close(output.numpy(), expected_output, check_stride=False, check_dtype=False)

ndarray = np.random.rand(height, width, channels).astype(np.float32)
output = trans(ndarray)
expected_output = ndarray.transpose((2, 0, 1))
torch.testing.assert_close(output.numpy(), expected_output, check_stride=False, check_dtype=False)

# separate test for mode '1' PIL images
input_data = torch.ByteTensor(1, height, width).bernoulli_()
img = transforms.ToPILImage()(input_data.mul(255)).convert('1')
output = trans(img)
torch.testing.assert_close(input_data, output, check_dtype=False, check_stride=False)


@pytest.mark.parametrize('dtype', [torch.float16, torch.float, torch.double])
def test_to_tensor_with_other_default_dtypes(dtype):
current_def_dtype = torch.get_default_dtype()

t = transforms.ToTensor()
np_arr = np.random.randint(0, 255, (32, 32, 3), dtype=np.uint8)
img = Image.fromarray(np_arr)

torch.set_default_dtype(dtype)
res = t(img)
assert res.dtype == dtype, f"{res.dtype} vs {dtype}"

torch.set_default_dtype(current_def_dtype)


class TestPad:

def test_pad(self):
Expand Down