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# Copyright 2018 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 tensorflow.ops.tf.gather_nd."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import errors
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
class GatherNdTest(xla_test.XLATestCase):
def _runGather(self, params, indices):
with self.test_session():
paramsp = array_ops.placeholder(params.dtype)
indicesp = array_ops.placeholder(indices.dtype)
with self.test_scope():
gather_nd_t = array_ops.gather_nd(paramsp, indicesp)
feed_dict = {paramsp: params, indicesp: indices}
return gather_nd_t.eval(feed_dict=feed_dict)
def testSimpleDtype(self):
for dtype in self.numeric_types:
self.assertAllEqual(
np.array([7, 7, 8], dtype=dtype),
self._runGather(
np.array([8, 1, 2, 3, 7, 5], dtype=dtype),
np.array([[4], [4], [0]], np.int32)))
def testEmptyIndicesAndParamsOKButJustEmptyParamsFails(self):
with self.test_session():
params = np.ones((3, 3), dtype=np.float32)
indices_empty = np.empty((0, 2), dtype=np.int32)
gather_nd_ok_val = self._runGather(params, indices_empty)
self.assertAllClose(np.empty((0,), dtype=np.float32), gather_nd_ok_val)
indices_empty = np.empty((0, 1), dtype=np.int32)
gather_nd_ok_val = self._runGather(params, indices_empty)
self.assertAllClose(np.empty((0, 3), dtype=np.float32), gather_nd_ok_val)
params_empty = np.empty((0, 3), dtype=np.float32)
indices_empty = np.empty((0, 2), dtype=np.int32)
gather_nd_ok_val = self._runGather(params_empty, indices_empty)
self.assertAllClose(np.empty((0,), dtype=np.float32), gather_nd_ok_val)
params_empty = np.empty((0, 3), dtype=np.float32)
indices_nonempty = np.zeros((1, 2), dtype=np.int32)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError, r"Gather dimension 0 is of size zero"):
self._runGather(params_empty, indices_nonempty)
def testIndexScalar(self):
params = np.array(
[[-8, -1, -2, -3, -7, -5], [8, 1, 2, 3, 7, 5]], dtype=np.float32).T
indices = np.array([4, 1], dtype=np.int32)
gather_nd_val = self._runGather(params, indices)
self.assertAllEqual(np.array(7), gather_nd_val)
def testParamsRankLargerThanIndexIndexScalarSlices(self):
params = np.array(
[[-8, -1, -2, -3, -7, -5], [8, 1, 2, 3, 7, 5]], dtype=np.float32).T
indices = np.array(
[
4,
], dtype=np.int32)
gather_nd_val = self._runGather(params, indices)
self.assertAllEqual(np.array([-7, 7]), gather_nd_val)
def testParamsRankLargerThanIndexSlices(self):
params = np.array(
[[-8, -1, -2, -3, -7, -5], [8, 1, 2, 3, 7, 5]], dtype=np.float32).T
indices = np.array([[4], [4], [0]], np.int32)
gather_nd_val = self._runGather(params, indices)
self.assertAllEqual(np.array([[-7, 7], [-7, 7], [-8, 8]]), gather_nd_val)
def testHigherRankParamsLargerThanIndexSlices(self):
params = np.array(
[[[-8, -1, -2, -3, -7, -5], [8, 1, 2, 3, 7, 5]],
[[-80, -10, -20, -30, -70, -50], [80, 10, 20, 30, 70, 50]]],
dtype=np.float32).T
indices = np.array([[4], [4], [0]], np.int32)
gather_nd_val = self._runGather(params, indices)
self.assertAllEqual(params[[4, 4, 0]], gather_nd_val)
def testEmptyIndicesLastRankMeansCopyEntireTensor(self):
params = np.array(
[[[-8, -1, -2, -3, -7, -5], [8, 1, 2, 3, 7, 5]],
[[-80, -10, -20, -30, -70, -50], [80, 10, 20, 30, 70, 50]]],
dtype=np.float32).T
indices = np.array([[], []], dtype=np.int32) # Size (2, 0)
gather_nd_val = self._runGather(params, indices)
self.assertAllEqual(
np.vstack((params[np.newaxis, :], params[np.newaxis, :])),
gather_nd_val)
def testHigherRankParamsAndIndicesLargerThanIndexSlices(self):
params = np.array(
[[[-8, -1, -2, -3, -7, -5], [8, 1, 2, 3, 7, 5]],
[[-80, -10, -20, -30, -70, -50], [80, 10, 20, 30, 70, 50]]],
dtype=np.float32).T
indices = np.array([[[3], [2], [1]], [[4], [4], [0]]], np.int32)
gather_nd_val = self._runGather(params, indices)
self.assertAllEqual(params[[3, 2, 1, 4, 4, 0]].reshape(2, 3, 2, 2),
gather_nd_val)
def testHigherRankParams(self):
shape = (10, 20, 5, 1, 17)
params = np.random.rand(*shape).astype(np.float32)
indices = np.vstack(
[np.random.randint(0, s, size=2000, dtype=np.int32) for s in shape]).T
gather_nd_val = self._runGather(params, indices)
expected = params[tuple(indices.T)]
self.assertAllEqual(expected, gather_nd_val)
def testHigherRankParamsAndIndices(self):
shape = (10, 20, 5, 1, 17)
params = np.random.rand(*shape).astype(np.float32)
indices = np.vstack(
[np.random.randint(0, s, size=2000, dtype=np.int32) for s in shape]).T
indices_reshaped = indices.reshape([10, 10, 20, 5])
gather_nd_val = self._runGather(params, indices_reshaped)
expected = params[tuple(indices.T)]
self.assertAllEqual(expected.reshape([10, 10, 20]), gather_nd_val)
if __name__ == "__main__":
test.main()
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