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# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for dm_pix._src.patch."""
import functools
from absl.testing import absltest
from absl.testing import parameterized
import chex
from dm_pix._src import patch
import numpy as np
import tensorflow as tf
def _create_test_images(shape):
images = np.arange(np.prod(np.array(shape)), dtype=np.float32)
return np.reshape(images, shape)
class PatchTest(chex.TestCase, parameterized.TestCase):
@chex.all_variants
@parameterized.named_parameters(
('padding_valid', 'VALID'),
('padding_same', 'SAME'),
)
def test_extract_patches(self, padding):
image_shape = (2, 5, 7, 3)
images = _create_test_images(image_shape)
sizes = (1, 2, 3, 1)
strides = (1, 1, 2, 1)
rates = (1, 2, 1, 1)
extract_patches = self.variant(
functools.partial(patch.extract_patches, padding=padding),
static_argnums=(1, 2, 3))
jax_patches = extract_patches(
images,
sizes,
strides,
rates,
)
tf_patches = tf.image.extract_patches(
images,
sizes=sizes,
strides=strides,
rates=rates,
padding=padding,
)
np.testing.assert_array_equal(jax_patches, tf_patches.numpy())
@chex.all_variants
@parameterized.named_parameters(
('padding_valid', 'VALID'),
('padding_same', 'SAME'),
)
def test_extract_patches_0d(self, padding):
image_shape = (2, 3)
images = _create_test_images(image_shape)
sizes = (1, 1)
strides = (1, 1)
rates = (1, 1)
extract_patches = self.variant(
functools.partial(patch.extract_patches, padding=padding),
static_argnums=(1, 2, 3))
jax_patches = extract_patches(
images,
sizes,
strides,
rates,
)
# 0D patches is a no-op.
np.testing.assert_array_equal(jax_patches, images)
@chex.all_variants
@parameterized.named_parameters(
('padding_valid', 'VALID'),
('padding_same', 'SAME'),
)
def test_extract_patches_1d(self, padding):
image_shape = (2, 7, 3)
images = _create_test_images(image_shape)
sizes = (1, 2, 1)
strides = (1, 1, 1)
rates = (1, 2, 1)
extract_patches = self.variant(
functools.partial(patch.extract_patches, padding=padding),
static_argnums=(1, 2, 3))
jax_patches = extract_patches(
images,
sizes,
strides,
rates,
)
jax_patches = np.expand_dims(jax_patches, -2)
# Reference patches are computed over an image with an extra singleton dim.
tf_patches = tf.image.extract_patches(
np.expand_dims(images, 2),
sizes=sizes + (1,),
strides=strides + (1,),
rates=rates + (1,),
padding=padding,
)
np.testing.assert_array_equal(jax_patches, tf_patches.numpy())
@chex.all_variants
def test_extract_patches_3d(self):
image_shape = (2, 4, 9, 6, 3)
images = _create_test_images(image_shape)
sizes = (1, 2, 3, 2, 1)
strides = (1, 2, 3, 2, 1)
rates = (1, 1, 1, 1, 1)
extract_patches = self.variant(
functools.partial(patch.extract_patches, padding='VALID'),
static_argnums=(1, 2, 3))
jax_patches = extract_patches(
images,
sizes,
strides,
rates,
)
# Reconstructing the original from non-overlapping patches.
images_reconstructed = np.reshape(
jax_patches,
jax_patches.shape[:-1] + sizes[1:-1] + images.shape[-1:]
)
images_reconstructed = np.moveaxis(images_reconstructed,
(-4, -3, -2),
(2, 4, 6))
images_reconstructed = images_reconstructed.reshape(image_shape)
np.testing.assert_allclose(images_reconstructed, images, rtol=5e-3)
@chex.all_variants
@parameterized.product(
({
'sizes': (1, 2, 3),
'strides': (1, 1, 2, 1),
'rates': (1, 2, 1, 1),
}, {
'sizes': (1, 2, 3, 1),
'strides': (1, 1, 2),
'rates': (1, 2, 1, 1),
}, {
'sizes': (1, 2, 3, 1),
'strides': (1, 1, 2, 1),
'rates': (1, 2, 1),
}, {
'sizes': (1, 2, 1),
'strides': (1, 2, 1),
'rates': (1, 1),
}, {
'sizes': (1, 1),
'strides': (1, 2),
'rates': (1, 1),
}, {
'sizes': (1, 1),
'strides': (1,),
'rates': (1, 1),
}, {
'sizes': (1, 2, 3, 4, 1),
'strides': (1, 2, 3, 4, 2),
'rates': (1, 1, 1, 1, 1),
}, {
'sizes': (1, 2, 3, 1),
'strides': (1, 2, 3, 4, 1),
'rates': (1, 1, 1, 1, 1),
}),
padding=('VALID', 'SAME'),
)
def test_extract_patches_raises(self, sizes, strides, rates, padding):
image_shape = (2, 5, 7, 3)
images = _create_test_images(image_shape)
extract_patches = self.variant(
functools.partial(patch.extract_patches, padding=padding),
static_argnums=(1, 2, 3))
with self.assertRaises(ValueError):
extract_patches(
images,
sizes,
strides,
rates,
)
if __name__ == '__main__':
absltest.main()