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adopt torch.testing.assert_close #1031

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merged 5 commits into from Jun 27, 2021
Merged

adopt torch.testing.assert_close #1031

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9041c42
adopt torch.testing.assert_close
pmeier May 24, 2021
62df5f8
Merge branch 'master' into adopt-assert-close
pmeier Jun 25, 2021
cf1e9e5
use torch.testing.assert_close
pmeier Jun 25, 2021
dc4cfb5
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Jun 25, 2021
d6ab476
add TODO
pmeier Jun 27, 2021
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45 changes: 44 additions & 1 deletion kornia/testing/__init__.py
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@@ -1,14 +1,16 @@
"""
The testing package contains testing-specific utilities.
"""
import contextlib
import importlib
from abc import ABC, abstractmethod
from copy import deepcopy
from itertools import product
from typing import Any, Optional

import torch

__all__ = ['tensor_to_gradcheck_var', 'create_eye_batch', 'xla_is_available']
__all__ = ['tensor_to_gradcheck_var', 'create_eye_batch', 'xla_is_available', 'assert_close']


def xla_is_available() -> bool:
Expand Down Expand Up @@ -128,3 +130,44 @@ def _get_precision_by_name(
return tol_val

return tol_val_default


try:
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# torch.testing.assert_close is only available for torch>=1.9
from torch.testing import assert_close as _assert_close
from torch.testing._core import _get_default_tolerance

def assert_close(
actual: torch.Tensor,
expected: torch.Tensor,
*,
rtol: Optional[float] = None,
atol: Optional[float] = None,
**kwargs: Any,
) -> None:
if rtol is None and atol is None:
with contextlib.suppress(Exception):
rtol, atol = _get_default_tolerance(actual, expected)

return _assert_close(actual, expected, rtol=rtol, atol=atol, check_stride=False, equal_nan=True, **kwargs)


except ImportError:
# Partial backport of torch.testing.assert_close for torch<1.9
from torch.testing import assert_allclose as _assert_allclose

class UsageError(Exception):
pass

def assert_close(
actual: torch.Tensor,
expected: torch.Tensor,
*,
rtol: Optional[float] = None,
atol: Optional[float] = None,
**kwargs: Any,
) -> None:
try:
return _assert_allclose(actual, expected, rtol=rtol, atol=atol, **kwargs)
except ValueError as error:
raise UsageError(str(error)) from error
303 changes: 151 additions & 152 deletions test/augmentation/test_augmentation.py
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108 changes: 54 additions & 54 deletions test/augmentation/test_augmentation_3d.py
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Expand Up @@ -4,7 +4,6 @@
import torch
import torch.nn as nn
from torch.autograd import gradcheck
from torch.testing import assert_allclose

import kornia
import kornia.testing as utils # test utils
Expand All @@ -16,6 +15,7 @@
RandomRotation3D,
RandomVerticalFlip3D,
)
from kornia.testing import assert_close


class TestRandomHorizontalFlip3D:
Expand Down Expand Up @@ -192,12 +192,12 @@ def test_random_vflip(self, device, dtype):
dtype=dtype,
) # 1 x 4 x 4

assert_allclose(f(input)[0], expected)
assert_allclose(f(input)[1], expected_transform)
assert_allclose(f1(input)[0], input)
assert_allclose(f1(input)[1], identity)
assert_allclose(f2(input), expected)
assert_allclose(f3(input), input)
assert_close(f(input)[0], expected)
assert_close(f(input)[1], expected_transform)
assert_close(f1(input)[0], input)
assert_close(f1(input)[1], identity)
assert_close(f2(input), expected)
assert_close(f3(input), input)

def test_batch_random_vflip(self, device):

Expand Down Expand Up @@ -225,10 +225,10 @@ def test_batch_random_vflip(self, device):
expected_transform = expected_transform.repeat(5, 1, 1) # 5 x 4 x 4
identity = identity.repeat(5, 1, 1) # 5 x 4 x 4

assert_allclose(f(input)[0], expected)
assert_allclose(f(input)[1], expected_transform)
assert_allclose(f1(input)[0], input)
assert_allclose(f1(input)[1], identity)
assert_close(f(input)[0], expected)
assert_close(f(input)[1], expected_transform)
assert_close(f1(input)[0], input)
assert_close(f1(input)[1], identity)

def test_same_on_batch(self, device):
f = RandomVerticalFlip3D(p=0.5, same_on_batch=True)
Expand All @@ -253,10 +253,10 @@ def test_sequential(self, device):

expected_transform_1 = expected_transform @ expected_transform

assert_allclose(f(input)[0], input)
assert_allclose(f(input)[1], expected_transform_1)
assert_allclose(f1(input)[0], input)
assert_allclose(f1(input)[1], expected_transform)
assert_close(f(input)[0], input)
assert_close(f(input)[1], expected_transform_1)
assert_close(f1(input)[0], input)
assert_close(f1(input)[1], expected_transform)

def test_gradcheck(self, device):
input = torch.rand((1, 3, 3)).to(device) # 4 x 4
Expand Down Expand Up @@ -319,12 +319,12 @@ def test_random_dflip(self, device, dtype):
dtype=dtype,
) # 4 x 4

assert_allclose(f(input)[0], expected)
assert_allclose(f(input)[1], expected_transform)
assert_allclose(f1(input)[0], input)
assert_allclose(f1(input)[1], identity)
assert_allclose(f2(input), expected)
assert_allclose(f3(input), input)
assert_close(f(input)[0], expected)
assert_close(f(input)[1], expected_transform)
assert_close(f1(input)[0], input)
assert_close(f1(input)[1], identity)
assert_close(f2(input), expected)
assert_close(f3(input), input)

def test_batch_random_dflip(self, device):

Expand Down Expand Up @@ -363,10 +363,10 @@ def test_batch_random_dflip(self, device):
expected_transform = expected_transform.repeat(5, 1, 1) # 5 x 4 x 4
identity = identity.repeat(5, 1, 1) # 5 x 4 x 4

assert_allclose(f(input)[0], expected)
assert_allclose(f(input)[1], expected_transform)
assert_allclose(f1(input)[0], input)
assert_allclose(f1(input)[1], identity)
assert_close(f(input)[0], expected)
assert_close(f(input)[1], expected_transform)
assert_close(f1(input)[0], input)
assert_close(f1(input)[1], identity)

def test_same_on_batch(self, device):
f = RandomDepthicalFlip3D(p=0.5, same_on_batch=True)
Expand Down Expand Up @@ -400,10 +400,10 @@ def test_sequential(self, device):

expected_transform_1 = expected_transform @ expected_transform

assert_allclose(f(input)[0], input)
assert_allclose(f(input)[1], expected_transform_1)
assert_allclose(f1(input)[0], input)
assert_allclose(f1(input)[1], expected_transform)
assert_close(f(input)[0], input)
assert_close(f(input)[1], expected_transform_1)
assert_close(f1(input)[0], input)
assert_close(f1(input)[1], expected_transform)

def test_gradcheck(self, device):
input = torch.rand((1, 3, 3)).to(device) # 4 x 4
Expand Down Expand Up @@ -488,11 +488,11 @@ def test_random_rotation(self, device, dtype):
)

out, mat = f(input)
assert_allclose(out, expected, rtol=1e-6, atol=1e-4)
assert_allclose(mat, expected_transform, rtol=1e-6, atol=1e-4)
assert_close(out, expected, rtol=1e-6, atol=1e-4)
assert_close(mat, expected_transform, rtol=1e-6, atol=1e-4)

torch.manual_seed(0) # for random reproductibility
assert_allclose(f1(input), expected, rtol=1e-6, atol=1e-4)
assert_close(f1(input), expected, rtol=1e-6, atol=1e-4)

def test_batch_random_rotation(self, device, dtype):

Expand Down Expand Up @@ -585,8 +585,8 @@ def test_batch_random_rotation(self, device, dtype):
input = input.repeat(2, 1, 1, 1, 1) # 5 x 4 x 4 x 3

out, mat = f(input)
assert_allclose(out, expected, rtol=1e-6, atol=1e-4)
assert_allclose(mat, expected_transform, rtol=1e-6, atol=1e-4)
assert_close(out, expected, rtol=1e-6, atol=1e-4)
assert_close(mat, expected_transform, rtol=1e-6, atol=1e-4)

def test_same_on_batch(self, device, dtype):
f = RandomRotation3D(degrees=40, same_on_batch=True)
Expand Down Expand Up @@ -671,9 +671,9 @@ def test_sequential(self, device, dtype):

out, mat = f(input)
_, mat_2 = f1(input)
assert_allclose(out, expected, rtol=1e-6, atol=1e-4)
assert_allclose(mat, expected_transform, rtol=1e-6, atol=1e-4)
assert_allclose(mat_2, expected_transform_2, rtol=1e-6, atol=1e-4)
assert_close(out, expected, rtol=1e-6, atol=1e-4)
assert_close(mat, expected_transform, rtol=1e-6, atol=1e-4)
assert_close(mat_2, expected_transform_2, rtol=1e-6, atol=1e-4)

def test_gradcheck(self, device):

Expand Down Expand Up @@ -759,7 +759,7 @@ def test_no_padding(self, batch_size, device, dtype):
dtype=dtype,
)

assert_allclose(out, expected, atol=1e-4, rtol=1e-4)
assert_close(out, expected, atol=1e-4, rtol=1e-4)

def test_same_on_batch(self, device, dtype):
f = RandomCrop3D(size=(2, 3, 4), padding=None, align_corners=True, p=1.0, same_on_batch=True)
Expand Down Expand Up @@ -795,7 +795,7 @@ def test_padding_batch(self, padding, device, dtype):
f = RandomCrop3D(size=(2, 3, 4), fill=10.0, padding=padding, align_corners=True, p=1.0)
out = f(inp)

assert_allclose(out, expected, atol=1e-4, rtol=1e-4)
assert_close(out, expected, atol=1e-4, rtol=1e-4)

def test_pad_if_needed(self, device, dtype):
torch.manual_seed(42)
Expand All @@ -815,7 +815,7 @@ def test_pad_if_needed(self, device, dtype):
rc = RandomCrop3D(size=(2, 3, 4), pad_if_needed=True, fill=9, align_corners=True, p=1.0)
out = rc(inp)

assert_allclose(out, expected, atol=1e-4, rtol=1e-4)
assert_close(out, expected, atol=1e-4, rtol=1e-4)

def test_gradcheck(self, device, dtype):
torch.manual_seed(0) # for random reproductibility
Expand All @@ -832,7 +832,7 @@ def test_jit(self, device, dtype):

actual = op_script(img)
expected = kornia.center_crop3d(img)
assert_allclose(actual, expected)
assert_close(actual, expected)

@pytest.mark.skip("Need to fix Union type")
def test_jit_trace(self, device, dtype):
Expand All @@ -850,7 +850,7 @@ def test_jit_trace(self, device, dtype):
# 3. Evaluate
actual = op_trace(img)
expected = op(img)
assert_allclose(actual, expected)
assert_close(actual, expected)


class TestCenterCrop3D:
Expand Down Expand Up @@ -905,12 +905,12 @@ def test_random_equalize(self, device, dtype):

identity = kornia.eye_like(4, expected)

assert_allclose(f(inputs3d)[0], expected, rtol=1e-4, atol=1e-4)
assert_allclose(f(inputs3d)[1], identity, rtol=1e-4, atol=1e-4)
assert_allclose(f1(inputs3d)[0], inputs3d, rtol=1e-4, atol=1e-4)
assert_allclose(f1(inputs3d)[1], identity, rtol=1e-4, atol=1e-4)
assert_allclose(f2(inputs3d), expected, rtol=1e-4, atol=1e-4)
assert_allclose(f3(inputs3d), inputs3d, rtol=1e-4, atol=1e-4)
assert_close(f(inputs3d)[0], expected, rtol=1e-4, atol=1e-4)
assert_close(f(inputs3d)[1], identity, rtol=1e-4, atol=1e-4)
assert_close(f1(inputs3d)[0], inputs3d, rtol=1e-4, atol=1e-4)
assert_close(f1(inputs3d)[1], identity, rtol=1e-4, atol=1e-4)
assert_close(f2(inputs3d), expected, rtol=1e-4, atol=1e-4)
assert_close(f3(inputs3d), inputs3d, rtol=1e-4, atol=1e-4)

def test_batch_random_equalize(self, device, dtype):
f = RandomEqualize3D(p=1.0, return_transform=True)
Expand All @@ -927,12 +927,12 @@ def test_batch_random_equalize(self, device, dtype):

identity = kornia.eye_like(4, expected) # 2 x 4 x 4

assert_allclose(f(inputs3d)[0], expected, rtol=1e-4, atol=1e-4)
assert_allclose(f(inputs3d)[1], identity, rtol=1e-4, atol=1e-4)
assert_allclose(f1(inputs3d)[0], inputs3d, rtol=1e-4, atol=1e-4)
assert_allclose(f1(inputs3d)[1], identity, rtol=1e-4, atol=1e-4)
assert_allclose(f2(inputs3d), expected, rtol=1e-4, atol=1e-4)
assert_allclose(f3(inputs3d), inputs3d, rtol=1e-4, atol=1e-4)
assert_close(f(inputs3d)[0], expected, rtol=1e-4, atol=1e-4)
assert_close(f(inputs3d)[1], identity, rtol=1e-4, atol=1e-4)
assert_close(f1(inputs3d)[0], inputs3d, rtol=1e-4, atol=1e-4)
assert_close(f1(inputs3d)[1], identity, rtol=1e-4, atol=1e-4)
assert_close(f2(inputs3d), expected, rtol=1e-4, atol=1e-4)
assert_close(f3(inputs3d), inputs3d, rtol=1e-4, atol=1e-4)

def test_same_on_batch(self, device, dtype):
f = RandomEqualize3D(p=0.5, same_on_batch=True)
Expand Down