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Merge branch 'main' into models/replace_se
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@datumbox
datumbox authored Sep 27, 2021
2 parents e269817 + f483e71 commit 8d86c45
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Showing 8 changed files with 96 additions and 53 deletions.
2 changes: 1 addition & 1 deletion test/common_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -148,7 +148,7 @@ def _create_data_batch(height=3, width=3, channels=3, num_samples=4, device="cpu
return batch_tensor


assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0)
assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=1e-6)


def get_list_of_videos(tmpdir, num_videos=5, sizes=None, fps=None):
Expand Down
48 changes: 35 additions & 13 deletions test/test_functional_tensor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -681,57 +681,65 @@ def check_functional_vs_PIL_vs_scripted(
@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('dtype', (None, torch.float32, torch.float64))
@pytest.mark.parametrize('config', [{"brightness_factor": f} for f in (0.1, 0.5, 1.0, 1.34, 2.5)])
def test_adjust_brightness(device, dtype, config):
@pytest.mark.parametrize('channels', [1, 3])
def test_adjust_brightness(device, dtype, config, channels):
check_functional_vs_PIL_vs_scripted(
F.adjust_brightness,
F_pil.adjust_brightness,
F_t.adjust_brightness,
config,
device,
dtype,
channels,
)


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('dtype', (None, torch.float32, torch.float64))
def test_invert(device, dtype):
@pytest.mark.parametrize('channels', [1, 3])
def test_invert(device, dtype, channels):
check_functional_vs_PIL_vs_scripted(
F.invert,
F_pil.invert,
F_t.invert,
{},
device,
dtype,
channels,
tol=1.0,
agg_method="max"
)


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('config', [{"bits": bits} for bits in range(0, 8)])
def test_posterize(device, config):
@pytest.mark.parametrize('channels', [1, 3])
def test_posterize(device, config, channels):
check_functional_vs_PIL_vs_scripted(
F.posterize,
F_pil.posterize,
F_t.posterize,
config,
device,
dtype=None,
channels=channels,
tol=1.0,
agg_method="max",
)


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('config', [{"threshold": threshold} for threshold in [0, 64, 128, 192, 255]])
def test_solarize1(device, config):
@pytest.mark.parametrize('channels', [1, 3])
def test_solarize1(device, config, channels):
check_functional_vs_PIL_vs_scripted(
F.solarize,
F_pil.solarize,
F_t.solarize,
config,
device,
dtype=None,
channels=channels,
tol=1.0,
agg_method="max",
)
Expand All @@ -740,14 +748,16 @@ def test_solarize1(device, config):
@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('dtype', (torch.float32, torch.float64))
@pytest.mark.parametrize('config', [{"threshold": threshold} for threshold in [0.0, 0.25, 0.5, 0.75, 1.0]])
def test_solarize2(device, dtype, config):
@pytest.mark.parametrize('channels', [1, 3])
def test_solarize2(device, dtype, config, channels):
check_functional_vs_PIL_vs_scripted(
F.solarize,
lambda img, threshold: F_pil.solarize(img, 255 * threshold),
F_t.solarize,
config,
device,
dtype,
channels,
tol=1.0,
agg_method="max",
)
Expand All @@ -756,34 +766,39 @@ def test_solarize2(device, dtype, config):
@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('dtype', (None, torch.float32, torch.float64))
@pytest.mark.parametrize('config', [{"sharpness_factor": f} for f in [0.2, 0.5, 1.0, 1.5, 2.0]])
def test_adjust_sharpness(device, dtype, config):
@pytest.mark.parametrize('channels', [1, 3])
def test_adjust_sharpness(device, dtype, config, channels):
check_functional_vs_PIL_vs_scripted(
F.adjust_sharpness,
F_pil.adjust_sharpness,
F_t.adjust_sharpness,
config,
device,
dtype,
channels,
)


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('dtype', (None, torch.float32, torch.float64))
def test_autocontrast(device, dtype):
@pytest.mark.parametrize('channels', [1, 3])
def test_autocontrast(device, dtype, channels):
check_functional_vs_PIL_vs_scripted(
F.autocontrast,
F_pil.autocontrast,
F_t.autocontrast,
{},
device,
dtype,
channels,
tol=1.0,
agg_method="max"
)


@pytest.mark.parametrize('device', cpu_and_gpu())
def test_equalize(device):
@pytest.mark.parametrize('channels', [1, 3])
def test_equalize(device, channels):
torch.use_deterministic_algorithms(False)
check_functional_vs_PIL_vs_scripted(
F.equalize,
Expand All @@ -792,6 +807,7 @@ def test_equalize(device):
{},
device,
dtype=None,
channels=channels,
tol=1.0,
agg_method="max",
)
Expand All @@ -809,35 +825,39 @@ def test_adjust_contrast(device, dtype, config, channels):
config,
device,
dtype,
channels=channels
channels
)


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('dtype', (None, torch.float32, torch.float64))
@pytest.mark.parametrize('config', [{"saturation_factor": f} for f in [0.5, 0.75, 1.0, 1.5, 2.0]])
def test_adjust_saturation(device, dtype, config):
@pytest.mark.parametrize('channels', [1, 3])
def test_adjust_saturation(device, dtype, config, channels):
check_functional_vs_PIL_vs_scripted(
F.adjust_saturation,
F_pil.adjust_saturation,
F_t.adjust_saturation,
config,
device,
dtype
dtype,
channels
)


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('dtype', (None, torch.float32, torch.float64))
@pytest.mark.parametrize('config', [{"hue_factor": f} for f in [-0.45, -0.25, 0.0, 0.25, 0.45]])
def test_adjust_hue(device, dtype, config):
@pytest.mark.parametrize('channels', [1, 3])
def test_adjust_hue(device, dtype, config, channels):
check_functional_vs_PIL_vs_scripted(
F.adjust_hue,
F_pil.adjust_hue,
F_t.adjust_hue,
config,
device,
dtype,
channels,
tol=16.1,
agg_method="max"
)
Expand All @@ -846,14 +866,16 @@ def test_adjust_hue(device, dtype, config):
@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('dtype', (None, torch.float32, torch.float64))
@pytest.mark.parametrize('config', [{"gamma": g1, "gain": g2} for g1, g2 in zip([0.8, 1.0, 1.2], [0.7, 1.0, 1.3])])
def test_adjust_gamma(device, dtype, config):
@pytest.mark.parametrize('channels', [1, 3])
def test_adjust_gamma(device, dtype, config, channels):
check_functional_vs_PIL_vs_scripted(
F.adjust_gamma,
F_pil.adjust_gamma,
F_t.adjust_gamma,
config,
device,
dtype,
channels,
)


Expand Down
21 changes: 18 additions & 3 deletions test/test_transforms.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,12 @@
os.path.dirname(os.path.abspath(__file__)), 'assets', 'encode_jpeg', 'grace_hopper_517x606.jpg')


def _get_grayscale_test_image(img, fill=None):
img = img.convert('L')
fill = (fill[0], ) if isinstance(fill, tuple) else fill
return img, fill


class TestConvertImageDtype:
@pytest.mark.parametrize('input_dtype, output_dtype', cycle_over(float_dtypes()))
def test_float_to_float(self, input_dtype, output_dtype):
Expand Down Expand Up @@ -1482,9 +1488,12 @@ def test_five_crop(single_dim):

@pytest.mark.parametrize('policy', transforms.AutoAugmentPolicy)
@pytest.mark.parametrize('fill', [None, 85, (128, 128, 128)])
def test_autoaugment(policy, fill):
@pytest.mark.parametrize('grayscale', [True, False])
def test_autoaugment(policy, fill, grayscale):
random.seed(42)
img = Image.open(GRACE_HOPPER)
if grayscale:
img, fill = _get_grayscale_test_image(img, fill)
transform = transforms.AutoAugment(policy=policy, fill=fill)
for _ in range(100):
img = transform(img)
Expand All @@ -1494,9 +1503,12 @@ def test_autoaugment(policy, fill):
@pytest.mark.parametrize('num_ops', [1, 2, 3])
@pytest.mark.parametrize('magnitude', [7, 9, 11])
@pytest.mark.parametrize('fill', [None, 85, (128, 128, 128)])
def test_randaugment(num_ops, magnitude, fill):
@pytest.mark.parametrize('grayscale', [True, False])
def test_randaugment(num_ops, magnitude, fill, grayscale):
random.seed(42)
img = Image.open(GRACE_HOPPER)
if grayscale:
img, fill = _get_grayscale_test_image(img, fill)
transform = transforms.RandAugment(num_ops=num_ops, magnitude=magnitude, fill=fill)
for _ in range(100):
img = transform(img)
Expand All @@ -1505,9 +1517,12 @@ def test_randaugment(num_ops, magnitude, fill):

@pytest.mark.parametrize('fill', [None, 85, (128, 128, 128)])
@pytest.mark.parametrize('num_magnitude_bins', [10, 13, 30])
def test_trivialaugmentwide(fill, num_magnitude_bins):
@pytest.mark.parametrize('grayscale', [True, False])
def test_trivialaugmentwide(fill, num_magnitude_bins, grayscale):
random.seed(42)
img = Image.open(GRACE_HOPPER)
if grayscale:
img, fill = _get_grayscale_test_image(img, fill)
transform = transforms.TrivialAugmentWide(fill=fill, num_magnitude_bins=num_magnitude_bins)
for _ in range(100):
img = transform(img)
Expand Down
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