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# Copyright 2019 The TensorFlow Authors. 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 object_detection.utils.patch_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import parameterized
import numpy as np
import tensorflow.compat.v1 as tf
from object_detection.utils import patch_ops
from object_detection.utils import test_case
class GetPatchMaskTest(test_case.TestCase, parameterized.TestCase):
def testMaskShape(self):
image_shape = [15, 10]
mask = patch_ops.get_patch_mask(
10, 5, patch_size=3, image_shape=image_shape)
self.assertListEqual(mask.shape.as_list(), image_shape)
def testHandleImageShapeWithChannels(self):
image_shape = [15, 10, 3]
mask = patch_ops.get_patch_mask(
10, 5, patch_size=3, image_shape=image_shape)
self.assertListEqual(mask.shape.as_list(), image_shape[:2])
def testMaskDType(self):
mask = patch_ops.get_patch_mask(2, 3, patch_size=2, image_shape=[6, 7])
self.assertDTypeEqual(mask, bool)
def testMaskAreaWithEvenPatchSize(self):
image_shape = [6, 7]
mask = patch_ops.get_patch_mask(2, 3, patch_size=2, image_shape=image_shape)
expected_mask = np.array([
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
]).reshape(image_shape).astype(bool)
self.assertAllEqual(mask, expected_mask)
def testMaskAreaWithEvenPatchSize4(self):
image_shape = [6, 7]
mask = patch_ops.get_patch_mask(2, 3, patch_size=4, image_shape=image_shape)
expected_mask = np.array([
[0, 1, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
]).reshape(image_shape).astype(bool)
self.assertAllEqual(mask, expected_mask)
def testMaskAreaWithOddPatchSize(self):
image_shape = [6, 7]
mask = patch_ops.get_patch_mask(2, 3, patch_size=3, image_shape=image_shape)
expected_mask = np.array([
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
]).reshape(image_shape).astype(bool)
self.assertAllEqual(mask, expected_mask)
def testMaskAreaPartiallyOutsideImage(self):
image_shape = [6, 7]
mask = patch_ops.get_patch_mask(5, 6, patch_size=5, image_shape=image_shape)
expected_mask = np.array([
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1],
[0, 0, 0, 0, 1, 1, 1],
[0, 0, 0, 0, 1, 1, 1],
]).reshape(image_shape).astype(bool)
self.assertAllEqual(mask, expected_mask)
@parameterized.parameters(
{'y': 0, 'x': -1},
{'y': -1, 'x': 0},
{'y': 0, 'x': 11},
{'y': 16, 'x': 0},
)
def testStaticCoordinatesOutsideImageRaisesError(self, y, x):
image_shape = [15, 10]
with self.assertRaises(tf.errors.InvalidArgumentError):
patch_ops.get_patch_mask(y, x, patch_size=3, image_shape=image_shape)
def testDynamicCoordinatesOutsideImageRaisesError(self):
def graph_fn():
image_shape = [15, 10]
x = tf.random_uniform([], minval=-2, maxval=-1, dtype=tf.int32)
y = tf.random_uniform([], minval=0, maxval=1, dtype=tf.int32)
mask = patch_ops.get_patch_mask(
y, x, patch_size=3, image_shape=image_shape)
return mask
with self.assertRaises(tf.errors.InvalidArgumentError):
self.execute(graph_fn, [])
@parameterized.parameters(
{'patch_size': 0},
{'patch_size': -1},
)
def testStaticNonPositivePatchSizeRaisesError(self, patch_size):
image_shape = [6, 7]
with self.assertRaises(tf.errors.InvalidArgumentError):
patch_ops.get_patch_mask(
0, 0, patch_size=patch_size, image_shape=image_shape)
def testDynamicNonPositivePatchSizeRaisesError(self):
def graph_fn():
image_shape = [6, 7]
patch_size = -1 * tf.random_uniform([], minval=0, maxval=3,
dtype=tf.int32)
mask = patch_ops.get_patch_mask(
0, 0, patch_size=patch_size, image_shape=image_shape)
return mask
with self.assertRaises(tf.errors.InvalidArgumentError):
self.execute(graph_fn, [])
if __name__ == '__main__':
tf.test.main()
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