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Port test pad in test_transforms to pytest #3954

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merged 5 commits into from
Jun 3, 2021
Merged

Port test pad in test_transforms to pytest #3954

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port test_pad in test_transforms to pytest
Dbhasin1 Jun 3, 2021
f662667
port test_pad in test_transforms to pytest
Dbhasin1 Jun 3, 2021
06511f2
port test_pad in test_transforms to pytest
Dbhasin1 Jun 3, 2021
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code formatted
Dbhasin1 Jun 3, 2021
2aaa47e
unecessary formatting reverted
Dbhasin1 Jun 3, 2021
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211 changes: 107 additions & 104 deletions test/test_transforms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -403,110 +403,6 @@ def test_random_crop(self):
with self.assertRaisesRegex(ValueError, r"Required crop size .+ is larger then input image size .+"):
t(img)

def test_pad(self):
height = random.randint(10, 32) * 2
width = random.randint(10, 32) * 2
img = torch.ones(3, height, width)
padding = random.randint(1, 20)
fill = random.randint(1, 50)
result = transforms.Compose([
transforms.ToPILImage(),
transforms.Pad(padding, fill=fill),
transforms.ToTensor(),
])(img)
self.assertEqual(result.size(1), height + 2 * padding)
self.assertEqual(result.size(2), width + 2 * padding)
# check that all elements in the padded region correspond
# to the pad value
fill_v = fill / 255
eps = 1e-5
h_padded = result[:, :padding, :]
w_padded = result[:, :, :padding]
torch.testing.assert_close(
h_padded, torch.full_like(h_padded, fill_value=fill_v), check_stride=False, rtol=0.0, atol=eps
)
torch.testing.assert_close(
w_padded, torch.full_like(w_padded, fill_value=fill_v), check_stride=False, rtol=0.0, atol=eps
)
self.assertRaises(ValueError, transforms.Pad(padding, fill=(1, 2)),
transforms.ToPILImage()(img))

def test_pad_with_tuple_of_pad_values(self):
height = random.randint(10, 32) * 2
width = random.randint(10, 32) * 2
img = transforms.ToPILImage()(torch.ones(3, height, width))

padding = tuple([random.randint(1, 20) for _ in range(2)])
output = transforms.Pad(padding)(img)
self.assertEqual(output.size, (width + padding[0] * 2, height + padding[1] * 2))

padding = tuple([random.randint(1, 20) for _ in range(4)])
output = transforms.Pad(padding)(img)
self.assertEqual(output.size[0], width + padding[0] + padding[2])
self.assertEqual(output.size[1], height + padding[1] + padding[3])

# Checking if Padding can be printed as string
transforms.Pad(padding).__repr__()

def test_pad_with_non_constant_padding_modes(self):
"""Unit tests for edge, reflect, symmetric padding"""
img = torch.zeros(3, 27, 27).byte()
img[:, :, 0] = 1 # Constant value added to leftmost edge
img = transforms.ToPILImage()(img)
img = F.pad(img, 1, (200, 200, 200))

# pad 3 to all sidess
edge_padded_img = F.pad(img, 3, padding_mode='edge')
# First 6 elements of leftmost edge in the middle of the image, values are in order:
# edge_pad, edge_pad, edge_pad, constant_pad, constant value added to leftmost edge, 0
edge_middle_slice = np.asarray(edge_padded_img).transpose(2, 0, 1)[0][17][:6]
assert_equal(edge_middle_slice, np.asarray([200, 200, 200, 200, 1, 0], dtype=np.uint8), check_stride=False)
self.assertEqual(transforms.ToTensor()(edge_padded_img).size(), (3, 35, 35))

# Pad 3 to left/right, 2 to top/bottom
reflect_padded_img = F.pad(img, (3, 2), padding_mode='reflect')
# First 6 elements of leftmost edge in the middle of the image, values are in order:
# reflect_pad, reflect_pad, reflect_pad, constant_pad, constant value added to leftmost edge, 0
reflect_middle_slice = np.asarray(reflect_padded_img).transpose(2, 0, 1)[0][17][:6]
assert_equal(reflect_middle_slice, np.asarray([0, 0, 1, 200, 1, 0], dtype=np.uint8), check_stride=False)
self.assertEqual(transforms.ToTensor()(reflect_padded_img).size(), (3, 33, 35))

# Pad 3 to left, 2 to top, 2 to right, 1 to bottom
symmetric_padded_img = F.pad(img, (3, 2, 2, 1), padding_mode='symmetric')
# First 6 elements of leftmost edge in the middle of the image, values are in order:
# sym_pad, sym_pad, sym_pad, constant_pad, constant value added to leftmost edge, 0
symmetric_middle_slice = np.asarray(symmetric_padded_img).transpose(2, 0, 1)[0][17][:6]
assert_equal(symmetric_middle_slice, np.asarray([0, 1, 200, 200, 1, 0], dtype=np.uint8), check_stride=False)
self.assertEqual(transforms.ToTensor()(symmetric_padded_img).size(), (3, 32, 34))

# Check negative padding explicitly for symmetric case, since it is not
# implemented for tensor case to compare to
# Crop 1 to left, pad 2 to top, pad 3 to right, crop 3 to bottom
symmetric_padded_img_neg = F.pad(img, (-1, 2, 3, -3), padding_mode='symmetric')
symmetric_neg_middle_left = np.asarray(symmetric_padded_img_neg).transpose(2, 0, 1)[0][17][:3]
symmetric_neg_middle_right = np.asarray(symmetric_padded_img_neg).transpose(2, 0, 1)[0][17][-4:]
assert_equal(symmetric_neg_middle_left, np.asarray([1, 0, 0], dtype=np.uint8), check_stride=False)
assert_equal(symmetric_neg_middle_right, np.asarray([200, 200, 0, 0], dtype=np.uint8), check_stride=False)
self.assertEqual(transforms.ToTensor()(symmetric_padded_img_neg).size(), (3, 28, 31))

def test_pad_raises_with_invalid_pad_sequence_len(self):
with self.assertRaises(ValueError):
transforms.Pad(())

with self.assertRaises(ValueError):
transforms.Pad((1, 2, 3))

with self.assertRaises(ValueError):
transforms.Pad((1, 2, 3, 4, 5))

def test_pad_with_mode_F_images(self):
pad = 2
transform = transforms.Pad(pad)

img = Image.new("F", (10, 10))
padded_img = transform(img)
self.assertSequenceEqual(padded_img.size, [edge_size + 2 * pad for edge_size in img.size])

def test_lambda(self):
trans = transforms.Lambda(lambda x: x.add(10))
x = torch.randn(10)
Expand Down Expand Up @@ -1831,6 +1727,113 @@ def test_random_erasing(self):
t.__repr__()


class TestPad:

def test_pad(self):
height = random.randint(10, 32) * 2
width = random.randint(10, 32) * 2
img = torch.ones(3, height, width)
padding = random.randint(1, 20)
fill = random.randint(1, 50)
result = transforms.Compose([
transforms.ToPILImage(),
transforms.Pad(padding, fill=fill),
transforms.ToTensor(),
])(img)
assert result.size(1) == height + 2 * padding
assert result.size(2) == width + 2 * padding
# check that all elements in the padded region correspond
# to the pad value
fill_v = fill / 255
eps = 1e-5
h_padded = result[:, :padding, :]
w_padded = result[:, :, :padding]
torch.testing.assert_close(
h_padded, torch.full_like(h_padded, fill_value=fill_v), check_stride=False, rtol=0.0, atol=eps
)
torch.testing.assert_close(
w_padded, torch.full_like(w_padded, fill_value=fill_v), check_stride=False, rtol=0.0, atol=eps
)
pytest.raises(ValueError, transforms.Pad(padding, fill=(1, 2)),
transforms.ToPILImage()(img))

def test_pad_with_tuple_of_pad_values(self):
height = random.randint(10, 32) * 2
width = random.randint(10, 32) * 2
img = transforms.ToPILImage()(torch.ones(3, height, width))

padding = tuple([random.randint(1, 20) for _ in range(2)])
output = transforms.Pad(padding)(img)
assert output.size == (width + padding[0] * 2, height + padding[1] * 2)

padding = tuple([random.randint(1, 20) for _ in range(4)])
output = transforms.Pad(padding)(img)
assert output.size[0] == width + padding[0] + padding[2]
assert output.size[1] == height + padding[1] + padding[3]

# Checking if Padding can be printed as string
transforms.Pad(padding).__repr__()

def test_pad_with_non_constant_padding_modes(self):
"""Unit tests for edge, reflect, symmetric padding"""
img = torch.zeros(3, 27, 27).byte()
img[:, :, 0] = 1 # Constant value added to leftmost edge
img = transforms.ToPILImage()(img)
img = F.pad(img, 1, (200, 200, 200))

# pad 3 to all sidess
edge_padded_img = F.pad(img, 3, padding_mode='edge')
# First 6 elements of leftmost edge in the middle of the image, values are in order:
# edge_pad, edge_pad, edge_pad, constant_pad, constant value added to leftmost edge, 0
edge_middle_slice = np.asarray(edge_padded_img).transpose(2, 0, 1)[0][17][:6]
assert_equal(edge_middle_slice, np.asarray([200, 200, 200, 200, 1, 0], dtype=np.uint8), check_stride=False)
assert transforms.ToTensor()(edge_padded_img).size() == (3, 35, 35)

# Pad 3 to left/right, 2 to top/bottom
reflect_padded_img = F.pad(img, (3, 2), padding_mode='reflect')
# First 6 elements of leftmost edge in the middle of the image, values are in order:
# reflect_pad, reflect_pad, reflect_pad, constant_pad, constant value added to leftmost edge, 0
reflect_middle_slice = np.asarray(reflect_padded_img).transpose(2, 0, 1)[0][17][:6]
assert_equal(reflect_middle_slice, np.asarray([0, 0, 1, 200, 1, 0], dtype=np.uint8), check_stride=False)
assert transforms.ToTensor()(reflect_padded_img).size() == (3, 33, 35)

# Pad 3 to left, 2 to top, 2 to right, 1 to bottom
symmetric_padded_img = F.pad(img, (3, 2, 2, 1), padding_mode='symmetric')
# First 6 elements of leftmost edge in the middle of the image, values are in order:
# sym_pad, sym_pad, sym_pad, constant_pad, constant value added to leftmost edge, 0
symmetric_middle_slice = np.asarray(symmetric_padded_img).transpose(2, 0, 1)[0][17][:6]
assert_equal(symmetric_middle_slice, np.asarray([0, 1, 200, 200, 1, 0], dtype=np.uint8), check_stride=False)
assert transforms.ToTensor()(symmetric_padded_img).size() == (3, 32, 34)

# Check negative padding explicitly for symmetric case, since it is not
# implemented for tensor case to compare to
# Crop 1 to left, pad 2 to top, pad 3 to right, crop 3 to bottom
symmetric_padded_img_neg = F.pad(img, (-1, 2, 3, -3), padding_mode='symmetric')
symmetric_neg_middle_left = np.asarray(symmetric_padded_img_neg).transpose(2, 0, 1)[0][17][:3]
symmetric_neg_middle_right = np.asarray(symmetric_padded_img_neg).transpose(2, 0, 1)[0][17][-4:]
assert_equal(symmetric_neg_middle_left, np.asarray([1, 0, 0], dtype=np.uint8), check_stride=False)
assert_equal(symmetric_neg_middle_right, np.asarray([200, 200, 0, 0], dtype=np.uint8), check_stride=False)
assert transforms.ToTensor()(symmetric_padded_img_neg).size() == (3, 28, 31)

def test_pad_raises_with_invalid_pad_sequence_len(self):
with pytest.raises(ValueError):
transforms.Pad(())

with pytest.raises(ValueError):
transforms.Pad((1, 2, 3))

with pytest.raises(ValueError):
transforms.Pad((1, 2, 3, 4, 5))

def test_pad_with_mode_F_images(self):
pad = 2
transform = transforms.Pad(pad)

img = Image.new("F", (10, 10))
padded_img = transform(img)
assert_equal(padded_img.size, [edge_size + 2 * pad for edge_size in img.size], check_stride=False)


def test_adjust_brightness():
x_shape = [2, 2, 3]
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
Expand Down