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Fixes F.affine and F.rotate to support rectangular tensor images #2553

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merged 14 commits into from Aug 6, 2020
Merged

Fixes F.affine and F.rotate to support rectangular tensor images #2553

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36fef0d
Added code for F_t.rotate with test
vfdev-5 Jul 21, 2020
2b98bdc
Rotate test tolerance to 2%
vfdev-5 Jul 21, 2020
c7231bd
Fixes failing test
vfdev-5 Jul 21, 2020
44da86e
Merge branch 'master' of https://github.com/pytorch/vision into vfdev…
vfdev-5 Aug 3, 2020
d72cb3d
Optimized _expanded_affine_grid with a single matmul op
vfdev-5 Aug 3, 2020
a249f77
Recoded _compute_output_size
vfdev-5 Aug 3, 2020
a2c6dd1
[WIP] recoded F_t.rotate internal methods
vfdev-5 Aug 4, 2020
407e9c4
[WIP] Fixed F.affine to support rectangular images
vfdev-5 Aug 4, 2020
4325f67
Recoded _gen_affine_grid to optimized version ~ affine_grid
vfdev-5 Aug 6, 2020
978c4c0
Merge branch 'master' of https://github.com/pytorch/vision into fix-a…
vfdev-5 Aug 6, 2020
0e2a3c7
[WIP] Use _gen_affine_grid for affine and rotate
vfdev-5 Aug 6, 2020
6f05c3e
Merge branch 'vfdev-6/issue-2292-rotate' into fix-affine-rect-imgs
vfdev-5 Aug 6, 2020
781747c
Fixed tests on square / rectangular images for affine and rotate ops
vfdev-5 Aug 6, 2020
c862126
Removed redefinition of F.rotate
vfdev-5 Aug 6, 2020
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263 changes: 147 additions & 116 deletions test/test_functional_tensor.py
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Expand Up @@ -385,134 +385,165 @@ def test_resized_crop(self):
)

def test_affine(self):
# Tests on square image
tensor, pil_img = self._create_data(26, 26)

# Tests on square and rectangular images
scripted_affine = torch.jit.script(F.affine)
# 1) identity map
out_tensor = F.affine(tensor, angle=0, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=0)
self.assertTrue(
tensor.equal(out_tensor), msg="{} vs {}".format(out_tensor[0, :5, :5], tensor[0, :5, :5])
)
out_tensor = scripted_affine(tensor, angle=0, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=0)
self.assertTrue(
tensor.equal(out_tensor), msg="{} vs {}".format(out_tensor[0, :5, :5], tensor[0, :5, :5])
)

# 2) Test rotation
test_configs = [
(90, torch.rot90(tensor, k=1, dims=(-1, -2))),
(45, None),
(30, None),
(-30, None),
(-45, None),
(-90, torch.rot90(tensor, k=-1, dims=(-1, -2))),
(180, torch.rot90(tensor, k=2, dims=(-1, -2))),
]
for a, true_tensor in test_configs:
for fn in [F.affine, scripted_affine]:
out_tensor = fn(tensor, angle=a, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=0)
if true_tensor is not None:
self.assertTrue(
true_tensor.equal(out_tensor),
msg="{}\n{} vs \n{}".format(a, out_tensor[0, :5, :5], true_tensor[0, :5, :5])
)
else:
true_tensor = out_tensor

out_pil_img = F.affine(pil_img, angle=a, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=0)
out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))

num_diff_pixels = (true_tensor != out_pil_tensor).sum().item() / 3.0
ratio_diff_pixels = num_diff_pixels / true_tensor.shape[-1] / true_tensor.shape[-2]
# Tolerance : less than 6% of different pixels
self.assertLess(
ratio_diff_pixels,
0.06,
msg="{}\n{} vs \n{}".format(
ratio_diff_pixels, true_tensor[0, :7, :7], out_pil_tensor[0, :7, :7]
)
)
# 3) Test translation
test_configs = [
[10, 12], (-12, -13)
]
for t in test_configs:
for fn in [F.affine, scripted_affine]:
out_tensor = fn(tensor, angle=0, translate=t, scale=1.0, shear=[0.0, 0.0], resample=0)
out_pil_img = F.affine(pil_img, angle=0, translate=t, scale=1.0, shear=[0.0, 0.0], resample=0)
self.compareTensorToPIL(out_tensor, out_pil_img)

# 3) Test rotation + translation + scale + share
test_configs = [
(45, [5, 6], 1.0, [0.0, 0.0]),
(33, (5, -4), 1.0, [0.0, 0.0]),
(45, [-5, 4], 1.2, [0.0, 0.0]),
(33, (-4, -8), 2.0, [0.0, 0.0]),
(85, (10, -10), 0.7, [0.0, 0.0]),
(0, [0, 0], 1.0, [35.0, ]),
(25, [0, 0], 1.2, [0.0, 15.0]),
(45, [-10, 0], 0.7, [2.0, 5.0]),
(45, [-10, -10], 1.2, [4.0, 5.0]),
]
for r in [0, ]:
for a, t, s, sh in test_configs:
out_pil_img = F.affine(pil_img, angle=a, translate=t, scale=s, shear=sh, resample=r)
out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))
for tensor, pil_img in [self._create_data(26, 26), self._create_data(32, 26)]:

# 1) identity map
out_tensor = F.affine(tensor, angle=0, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=0)
self.assertTrue(
tensor.equal(out_tensor), msg="{} vs {}".format(out_tensor[0, :5, :5], tensor[0, :5, :5])
)
out_tensor = scripted_affine(tensor, angle=0, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=0)
self.assertTrue(
tensor.equal(out_tensor), msg="{} vs {}".format(out_tensor[0, :5, :5], tensor[0, :5, :5])
)

if pil_img.size[0] == pil_img.size[1]:
# 2) Test rotation
test_configs = [
(90, torch.rot90(tensor, k=1, dims=(-1, -2))),
(45, None),
(30, None),
(-30, None),
(-45, None),
(-90, torch.rot90(tensor, k=-1, dims=(-1, -2))),
(180, torch.rot90(tensor, k=2, dims=(-1, -2))),
]
for a, true_tensor in test_configs:
for fn in [F.affine, scripted_affine]:
out_tensor = fn(tensor, angle=a, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=0)
if true_tensor is not None:
self.assertTrue(
true_tensor.equal(out_tensor),
msg="{}\n{} vs \n{}".format(a, out_tensor[0, :5, :5], true_tensor[0, :5, :5])
)
else:
true_tensor = out_tensor

out_pil_img = F.affine(
pil_img, angle=a, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=0
)
out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))

num_diff_pixels = (true_tensor != out_pil_tensor).sum().item() / 3.0
ratio_diff_pixels = num_diff_pixels / true_tensor.shape[-1] / true_tensor.shape[-2]
# Tolerance : less than 6% of different pixels
self.assertLess(
ratio_diff_pixels,
0.06,
msg="{}\n{} vs \n{}".format(
ratio_diff_pixels, true_tensor[0, :7, :7], out_pil_tensor[0, :7, :7]
)
)
else:
test_configs = [
90, 45, 15, -30, -60, -120
]
for a in test_configs:
for fn in [F.affine, scripted_affine]:
out_tensor = fn(tensor, angle=a, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=0)
out_pil_img = F.affine(
pil_img, angle=a, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=0
)
out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))

num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
# Tolerance : less than 3% of different pixels
self.assertLess(
ratio_diff_pixels,
0.03,
msg="{}: {}\n{} vs \n{}".format(
a, ratio_diff_pixels, out_tensor[0, :7, :7], out_pil_tensor[0, :7, :7]
)
)

# 3) Test translation
test_configs = [
[10, 12], (-12, -13)
]
for t in test_configs:
for fn in [F.affine, scripted_affine]:
out_tensor = fn(tensor, angle=a, translate=t, scale=s, shear=sh, resample=r)
num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
# Tolerance : less than 5% of different pixels
self.assertLess(
ratio_diff_pixels,
0.05,
msg="{}: {}\n{} vs \n{}".format(
(r, a, t, s, sh), ratio_diff_pixels, out_tensor[0, :7, :7], out_pil_tensor[0, :7, :7]
out_tensor = fn(tensor, angle=0, translate=t, scale=1.0, shear=[0.0, 0.0], resample=0)
out_pil_img = F.affine(pil_img, angle=0, translate=t, scale=1.0, shear=[0.0, 0.0], resample=0)
self.compareTensorToPIL(out_tensor, out_pil_img)

# 3) Test rotation + translation + scale + share
test_configs = [
(45, [5, 6], 1.0, [0.0, 0.0]),
(33, (5, -4), 1.0, [0.0, 0.0]),
(45, [-5, 4], 1.2, [0.0, 0.0]),
(33, (-4, -8), 2.0, [0.0, 0.0]),
(85, (10, -10), 0.7, [0.0, 0.0]),
(0, [0, 0], 1.0, [35.0, ]),
(-25, [0, 0], 1.2, [0.0, 15.0]),
(-45, [-10, 0], 0.7, [2.0, 5.0]),
(-45, [-10, -10], 1.2, [4.0, 5.0]),
(-90, [0, 0], 1.0, [0.0, 0.0]),
]
for r in [0, ]:
for a, t, s, sh in test_configs:
out_pil_img = F.affine(pil_img, angle=a, translate=t, scale=s, shear=sh, resample=r)
out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))

for fn in [F.affine, scripted_affine]:
out_tensor = fn(tensor, angle=a, translate=t, scale=s, shear=sh, resample=r)
num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
# Tolerance : less than 5% of different pixels
self.assertLess(
ratio_diff_pixels,
0.05,
msg="{}: {}\n{} vs \n{}".format(
(r, a, t, s, sh), ratio_diff_pixels, out_tensor[0, :7, :7], out_pil_tensor[0, :7, :7]
)
)
)

def test_rotate(self):
# Tests on square image
tensor, pil_img = self._create_data(26, 26)
scripted_rotate = torch.jit.script(F.rotate)

img_size = pil_img.size

centers = [
None,
(int(img_size[0] * 0.3), int(img_size[0] * 0.4)),
[int(img_size[0] * 0.5), int(img_size[0] * 0.6)]
]

for r in [0, ]:
for a in range(-120, 120, 23):
for e in [True, False]:
for c in centers:

out_pil_img = F.rotate(pil_img, angle=a, resample=r, expand=e, center=c)
out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))
for fn in [F.rotate, scripted_rotate]:
out_tensor = fn(tensor, angle=a, resample=r, expand=e, center=c)

self.assertEqual(
out_tensor.shape,
out_pil_tensor.shape,
msg="{}: {} vs {}".format(
(r, a, e, c), out_tensor.shape, out_pil_tensor.shape
for tensor, pil_img in [self._create_data(26, 26), self._create_data(32, 26)]:

img_size = pil_img.size
centers = [
None,
(int(img_size[0] * 0.3), int(img_size[0] * 0.4)),
[int(img_size[0] * 0.5), int(img_size[0] * 0.6)]
]

for r in [0, ]:
for a in range(-180, 180, 17):
for e in [True, False]:
for c in centers:

out_pil_img = F.rotate(pil_img, angle=a, resample=r, expand=e, center=c)
out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))
for fn in [F.rotate, scripted_rotate]:
out_tensor = fn(tensor, angle=a, resample=r, expand=e, center=c)

self.assertEqual(
out_tensor.shape,
out_pil_tensor.shape,
msg="{}: {} vs {}".format(
(img_size, r, a, e, c), out_tensor.shape, out_pil_tensor.shape
)
)
)
num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
# Tolerance : less than 2% of different pixels
self.assertLess(
ratio_diff_pixels,
0.02,
msg="{}: {}\n{} vs \n{}".format(
(r, a, e, c), ratio_diff_pixels, out_tensor[0, :7, :7], out_pil_tensor[0, :7, :7]
num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
# Tolerance : less than 2% of different pixels
self.assertLess(
ratio_diff_pixels,
0.02,
msg="{}: {}\n{} vs \n{}".format(
(img_size, r, a, e, c),
ratio_diff_pixels,
out_tensor[0, :7, :7],
out_pil_tensor[0, :7, :7]
)
)
)


if __name__ == '__main__':
Expand Down
11 changes: 5 additions & 6 deletions torchvision/transforms/functional.py
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Expand Up @@ -848,8 +848,9 @@ def rotate(
center_f = [0.0, 0.0]
if center is not None:
img_size = _get_image_size(img)
# Center is normalized to [-1, +1]
center_f = [2.0 * t / s - 1.0 for s, t in zip(img_size, center)]
# Center values should be in pixel coordinates but translated such that (0, 0) corresponds to image center.
center_f = [1.0 * (c - s * 0.5) for c, s in zip(center, img_size)]

# due to current incoherence of rotation angle direction between affine and rotate implementations
# we need to set -angle.
matrix = _get_inverse_affine_matrix(center_f, -angle, [0.0, 0.0], 1.0, [0.0, 0.0])
Expand Down Expand Up @@ -926,10 +927,8 @@ def affine(

return F_pil.affine(img, matrix=matrix, resample=resample, fillcolor=fillcolor)

# we need to rescale translate by image size / 2 as its values can be between -1 and 1
translate = [2.0 * t / s for s, t in zip(img_size, translate)]

matrix = _get_inverse_affine_matrix([0.0, 0.0], angle, translate, scale, shear)
translate_f = [1.0 * t for t in translate]
matrix = _get_inverse_affine_matrix([0.0, 0.0], angle, translate_f, scale, shear)
return F_t.affine(img, matrix=matrix, resample=resample, fillcolor=fillcolor)


Expand Down