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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 image.extract_glimpse()."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import image_ops
from tensorflow.python.platform import test
class ExtractGlimpseTest(test.TestCase):
def _VerifyValues(self, tensor_in_sizes, glimpse_sizes, offsets,
expected_rows, expected_cols):
"""Verifies the output values of the glimpse extraction kernel.
Args:
tensor_in_sizes: Input tensor dimensions in [input_rows, input_cols].
glimpse_sizes: Dimensions of the glimpse in [glimpse_rows, glimpse_cols].
offsets: Relative location of the center of the glimpse in the input
image expressed as [row_offset, col_offset].
expected_rows: A list containing the expected row numbers (None for
out of bound entries that are expected to be replaced by uniform
random entries in [0,1) ).
expected_cols: Same as expected_rows, but for column numbers.
"""
rows = tensor_in_sizes[0]
cols = tensor_in_sizes[1]
# Row Tensor with entries by row.
# [[ 1 1 1 ... ]
# [ 2 2 2 ... ]
# [ 3 3 3 ... ]
# [ ...
# ]
t_rows = array_ops.tile(
[[1.0 * r] for r in range(1, rows + 1)], [1, cols], name='tile_rows')
# Shuffle to switch to a convention of (batch_size, height, width, depth).
t_rows_4d = array_ops.transpose(
array_ops.expand_dims(array_ops.expand_dims(t_rows, 0), 3),
[0, 2, 1, 3])
# Column Tensor with entries by column.
# [[ 1 2 3 4 ... ]
# [ 1 2 3 4 ... ]
# [ 1 2 3 4 ... ]
# [ ... ]
# ]
t_cols = array_ops.tile(
[[1.0 * r for r in range(1, cols + 1)]], [rows, 1], name='tile_cols')
# Shuffle to switch to a convention of (batch_size, height, width, depth).
t_cols_4d = array_ops.transpose(
array_ops.expand_dims(array_ops.expand_dims(t_cols, 0), 3),
[0, 2, 1, 3])
# extract_glimpses from Row and Column Tensor, respectively.
# Switch order for glimpse_sizes and offsets to switch from (row, col)
# convention to tensorflows (height, width) convention.
t1 = constant_op.constant([glimpse_sizes[1], glimpse_sizes[0]], shape=[2])
t2 = constant_op.constant([offsets[1], offsets[0]], shape=[1, 2])
glimpse_rows = (array_ops.transpose(
image_ops.extract_glimpse(t_rows_4d, t1, t2), [0, 2, 1, 3]))
glimpse_cols = (array_ops.transpose(
image_ops.extract_glimpse(t_cols_4d, t1, t2), [0, 2, 1, 3]))
# Evaluate the TensorFlow Graph.
with self.cached_session() as sess:
value_rows, value_cols = sess.run([glimpse_rows, glimpse_cols])
# Check dimensions of returned glimpse.
self.assertEqual(value_rows.shape[1], glimpse_sizes[0])
self.assertEqual(value_rows.shape[2], glimpse_sizes[1])
self.assertEqual(value_cols.shape[1], glimpse_sizes[0])
self.assertEqual(value_cols.shape[2], glimpse_sizes[1])
# Check entries.
min_random_val = 0
max_random_val = max(rows, cols)
for i in range(glimpse_sizes[0]):
for j in range(glimpse_sizes[1]):
if expected_rows[i] is None or expected_cols[j] is None:
self.assertGreaterEqual(value_rows[0][i][j][0], min_random_val)
self.assertLessEqual(value_rows[0][i][j][0], max_random_val)
self.assertGreaterEqual(value_cols[0][i][j][0], min_random_val)
self.assertLessEqual(value_cols[0][i][j][0], max_random_val)
else:
self.assertEqual(value_rows[0][i][j][0], expected_rows[i])
self.assertEqual(value_cols[0][i][j][0], expected_cols[j])
def testCenterGlimpse(self):
self._VerifyValues(
tensor_in_sizes=[41, 61],
glimpse_sizes=[3, 5],
offsets=[0.0, 0.0],
expected_rows=[20, 21, 22],
expected_cols=[29, 30, 31, 32, 33])
def testEmptyTensor(self):
empty_image = np.zeros((0, 4, 3, 0))
offsets = np.zeros((0, 2))
with self.cached_session():
result = image_ops.extract_glimpse(empty_image, [1, 1], offsets)
self.assertAllEqual(
np.zeros(
(0, 1, 1, 0), dtype=np.float32), result.eval())
def testLargeCenterGlimpse(self):
self._VerifyValues(
tensor_in_sizes=[41, 61],
glimpse_sizes=[41, 61],
offsets=[0.0, 0.0],
expected_rows=list(range(1, 42)),
expected_cols=list(range(1, 62)))
def testTooLargeCenterGlimpse(self):
self._VerifyValues(
tensor_in_sizes=[41, 61],
glimpse_sizes=[43, 63],
offsets=[0.0, 0.0],
expected_rows=[None] + list(range(1, 42)) + [None],
expected_cols=[None] + list(range(1, 62)) + [None])
def testGlimpseFullOverlap(self):
self._VerifyValues(
tensor_in_sizes=[41, 61],
glimpse_sizes=[3, 5],
offsets=[0.1, 0.3],
expected_rows=[22, 23, 24],
expected_cols=[38, 39, 40, 41, 42])
def testGlimpseFullOverlap2(self):
self._VerifyValues(
tensor_in_sizes=[41, 61],
glimpse_sizes=[11, 3],
offsets=[-0.7, -0.7],
expected_rows=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
expected_cols=[8, 9, 10])
def testGlimpseBeforeLeftMargin(self):
self._VerifyValues(
tensor_in_sizes=[41, 61],
glimpse_sizes=[11, 5],
offsets=[-0.7, -0.9],
expected_rows=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
expected_cols=[1, 2, 3, 4, 5])
def testGlimpseLowerRightCorner(self):
self._VerifyValues(
tensor_in_sizes=[41, 61],
glimpse_sizes=[7, 5],
offsets=[1.0, 1.0],
expected_rows=[38, 39, 40, 41, None, None, None],
expected_cols=[59, 60, 61, None, None])
def testGlimpseNoOverlap(self):
self._VerifyValues(
tensor_in_sizes=[20, 30],
glimpse_sizes=[3, 3],
offsets=[-2.0, 2.0],
expected_rows=[None, None, None],
expected_cols=[None, None, None])
def testGlimpseOnLeftMargin(self):
self._VerifyValues(
tensor_in_sizes=[41, 61],
glimpse_sizes=[11, 7],
offsets=[-0.7, -1.0],
expected_rows=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
expected_cols=[None, None, None, 1, 2, 3, 4])
def testGlimpseUpperMargin(self):
self._VerifyValues(
tensor_in_sizes=[41, 61],
glimpse_sizes=[7, 5],
offsets=[-1, 0.9],
expected_rows=[None, None, None, 1, 2, 3, 4],
expected_cols=[56, 57, 58, 59, 60])
if __name__ == '__main__':
test.main()
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