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# Copyright 2015 Google Inc. 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 tensorflow.ops.nn_ops.Pad."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
from tensorflow.python.kernel_tests import gradient_checker as gc
class PadOpTest(tf.test.TestCase):
def _npPad(self, inp, paddings):
return np.pad(inp, paddings, mode="constant")
def testNpPad(self):
self.assertAllClose(
np.array([[0, 0, 0, 0, 0, 0],
[0, 3, 3, 0, 0, 0],
[0, 4, 4, 0, 0, 0],
[0, 5, 5, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]]),
self._npPad(np.array([[3, 3], [4, 4], [5, 5]]), [[1, 2], [1, 3]]))
def _testPad(self, np_inputs, paddings, use_gpu=False):
np_val = self._npPad(np_inputs, paddings)
with self.test_session(use_gpu=use_gpu):
tf_val = tf.pad(np_inputs, paddings)
out = tf_val.eval()
self.assertAllClose(np_val, out)
self.assertShapeEqual(np_val, tf_val)
def _testGradient(self, x, a):
with self.test_session():
inx = tf.convert_to_tensor(x)
xs = list(x.shape)
ina = tf.convert_to_tensor(a)
y = tf.pad(inx, ina)
# Expected y's shape to be:
ys = list(np.array(x.shape) + np.sum(np.array(a), axis=1))
jacob_t, jacob_n = gc.ComputeGradient(inx, xs, y, ys, x_init_value=x)
self.assertAllClose(jacob_t, jacob_n, rtol=1e-5, atol=1e-5)
def _testAll(self, np_inputs, paddings):
self._testPad(np_inputs, paddings, use_gpu=False)
self._testPad(np_inputs, paddings, use_gpu=True)
if np_inputs.dtype == np.float32:
self._testGradient(np_inputs, paddings)
def testInputDims(self):
with self.test_session():
with self.assertRaises(ValueError):
tf.pad(
tf.reshape([1, 2], shape=[1, 2, 1, 1, 1, 1]),
tf.reshape([1, 2], shape=[1, 2]))
def testPaddingsDim(self):
with self.test_session():
with self.assertRaises(ValueError):
tf.pad(
tf.reshape([1, 2], shape=[1, 2]),
tf.reshape([1, 2], shape=[2]))
def testPaddingsDim2(self):
with self.test_session():
with self.assertRaises(ValueError):
tf.pad(
tf.reshape([1, 2], shape=[1, 2]),
tf.reshape([1, 2], shape=[2, 1]))
def testPaddingsDim3(self):
with self.test_session():
with self.assertRaises(ValueError):
tf.pad(
tf.reshape([1, 2], shape=[1, 2]),
tf.reshape([1, 2], shape=[1, 2]))
def testPaddingsDim4(self):
with self.test_session():
with self.assertRaises(ValueError):
tf.pad(
tf.reshape([1, 2], shape=[1, 2]),
tf.reshape([1, 2, 3, 4, 5, 6], shape=[3, 2]))
def testPaddingsNonNegative(self):
with self.test_session():
with self.assertRaisesRegexp(ValueError, "must be non-negative"):
tf.pad(
tf.constant([1], shape=[1]),
tf.constant([-1, 0], shape=[1, 2]))
def testPaddingsNonNegative2(self):
with self.test_session():
with self.assertRaisesRegexp(ValueError, "must be non-negative"):
tf.pad(
tf.constant([1], shape=[1]),
tf.constant([-1, 0], shape=[1, 2]))
def testIntTypes(self):
# TODO(touts): Figure out why the padding tests do not work on GPU
# for int types and rank > 2.
for t in [np.int32, np.int64]:
self._testPad((np.random.rand(4, 3, 3) * 100).astype(t),
[[1, 0], [2, 3], [0, 2]])
def testFloatTypes(self):
for t in [np.float32, np.float64]:
self._testAll(np.random.rand(2, 5).astype(t),
[[1, 0], [2, 0]])
def testShapeFunctionEdgeCases(self):
# Unknown paddings shape.
inp = tf.constant(0.0, shape=[4, 4, 4, 4])
padded = tf.pad(inp, tf.placeholder(tf.int32))
self.assertEqual([None, None, None, None], padded.get_shape().as_list())
# Unknown input shape.
inp = tf.placeholder(tf.float32)
padded = tf.pad(inp, [[2, 2], [2, 2]])
self.assertEqual([None, None], padded.get_shape().as_list())
# Unknown input and paddings shape.
inp = tf.placeholder(tf.float32)
padded = tf.pad(inp, tf.placeholder(tf.int32))
self.assertAllEqual(None, padded.get_shape().ndims)
def testScalars(self):
paddings = np.zeros((0, 2), dtype=np.int32)
inp = np.asarray(7)
for use_gpu in False, True:
with self.test_session(use_gpu=use_gpu):
tf_val = tf.pad(inp, paddings)
out = tf_val.eval()
self.assertAllClose(inp, out)
self.assertShapeEqual(inp, tf_val)
if __name__ == "__main__":
tf.test.main()
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