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# Copyright 2015 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 random_crop."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.ops import random_ops
from tensorflow.python.platform import test
class RandomCropTest(test.TestCase):
def testNoOp(self):
# No random cropping is performed since the size is value.shape.
for shape in (2, 1, 1), (2, 1, 3), (4, 5, 3):
value = np.arange(0, np.prod(shape), dtype=np.int32).reshape(shape)
with self.cached_session():
crop = random_ops.random_crop(value, shape).eval()
self.assertAllEqual(crop, value)
def testContains(self):
with self.cached_session():
shape = (3, 5, 7)
target = (2, 3, 4)
value = np.random.randint(1000000, size=shape)
value_set = set(
tuple(value[i:i + 2, j:j + 3, k:k + 4].ravel())
for i in range(2) for j in range(3) for k in range(4))
crop = random_ops.random_crop(value, size=target)
for _ in range(20):
y = crop.eval()
self.assertAllEqual(y.shape, target)
self.assertTrue(tuple(y.ravel()) in value_set)
def testRandomization(self):
# Run 1x1 crop num_samples times in an image and ensure that one finds each
# pixel 1/size of the time.
num_samples = 1000
shape = [5, 4, 1]
size = np.prod(shape)
single = [1, 1, 1]
value = np.arange(size).reshape(shape)
with self.cached_session():
crop = random_ops.random_crop(value, single, seed=7)
counts = np.zeros(size, dtype=np.int32)
for _ in range(num_samples):
y = crop.eval()
self.assertAllEqual(y.shape, single)
counts[y] += 1
# Calculate the mean and 4 * standard deviation.
mean = np.repeat(num_samples / size, size)
four_stddev = 4.0 * np.sqrt(mean)
# Ensure that each entry is observed in 1/size of the samples
# within 4 standard deviations.
self.assertAllClose(counts, mean, atol=four_stddev)
if __name__ == '__main__':
test.main()
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