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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 tensorflow.ops.tf.gather."""
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.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradients_impl
from tensorflow.python.platform import test
_TEST_TYPES = (dtypes.int64, dtypes.float32,
dtypes.complex64, dtypes.complex128)
class GatherTest(test.TestCase):
def _buildParams(self, data, dtype):
data = data.astype(dtype.as_numpy_dtype)
# For complex types, add an index-dependent imaginary component so we can
# tell we got the right value.
if dtype.is_complex:
return data + 10j * data
return data
def testScalar1D(self):
with self.test_session(use_gpu=True):
data = np.array([0, 1, 2, 3, 7, 5])
for dtype in _TEST_TYPES:
for indices in 4, [1, 2, 2, 4, 5]:
params_np = self._buildParams(data, dtype)
params = constant_op.constant(params_np)
indices_tf = constant_op.constant(indices)
gather_t = array_ops.gather(params, indices_tf)
gather_val = gather_t.eval()
np_val = params_np[indices]
self.assertAllEqual(np_val, gather_val)
self.assertEqual(np_val.shape, gather_t.get_shape())
def testScalar2D(self):
with self.test_session(use_gpu=True):
data = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8],
[9, 10, 11], [12, 13, 14]])
for dtype in _TEST_TYPES:
for axis in range(data.ndim):
params_np = self._buildParams(data, dtype)
params = constant_op.constant(params_np)
indices = constant_op.constant(2)
gather_t = array_ops.gather(params, indices, axis=axis)
gather_val = gather_t.eval()
self.assertAllEqual(np.take(params_np, 2, axis=axis), gather_val)
expected_shape = data.shape[:axis] + data.shape[axis + 1:]
self.assertEqual(expected_shape, gather_t.get_shape())
def testSimpleTwoD32(self):
with self.test_session(use_gpu=True):
data = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8],
[9, 10, 11], [12, 13, 14]])
for dtype in _TEST_TYPES:
for axis in range(data.ndim):
params_np = self._buildParams(data, dtype)
params = constant_op.constant(params_np)
# The indices must be in bounds for any axis.
indices = constant_op.constant([0, 1, 0, 2])
gather_t = array_ops.gather(params, indices, axis=axis)
gather_val = gather_t.eval()
self.assertAllEqual(np.take(params_np, [0, 1, 0, 2], axis=axis),
gather_val)
expected_shape = data.shape[:axis] + (4,) + data.shape[axis + 1:]
self.assertEqual(expected_shape, gather_t.get_shape())
def testHigherRank(self):
# We check that scalar and empty indices shapes work as well
shape = (2, 1, 3, 2)
for indices_shape in (), (0,), (2, 0), (2, 3):
for dtype in _TEST_TYPES:
for axis in range(len(shape)):
params = self._buildParams(np.random.randn(*shape), dtype)
indices = np.random.randint(shape[axis], size=indices_shape)
with self.test_session(use_gpu=True) as sess:
tf_params = constant_op.constant(params)
tf_indices = constant_op.constant(indices)
# Check that both positive and negative indices for axis work.
tf_axis = constant_op.constant(axis)
tf_negative_axis = constant_op.constant(-len(shape) + axis)
gather = array_ops.gather(tf_params, tf_indices, axis=tf_axis)
gather_negative_axis = array_ops.gather(
tf_params, tf_indices, axis=tf_negative_axis)
gather_value, gather_negative_axis_value = sess.run(
[gather, gather_negative_axis])
gather_np = np.take(params, indices, axis)
self.assertAllEqual(gather_np, gather_value)
self.assertAllEqual(gather_np, gather_negative_axis_value)
expected_shape = (params.shape[:axis] + indices.shape +
params.shape[axis + 1:])
self.assertEqual(expected_shape, gather.shape)
self.assertEqual(expected_shape, gather_negative_axis.shape)
# Test gradients
gather_grad = np.random.randn(
*gather.get_shape().as_list()).astype(dtype.as_numpy_dtype)
if dtype.is_complex:
gather_grad -= 1j * gather_grad
params_grad, indices_grad, axis_grad = gradients_impl.gradients(
gather, [tf_params, tf_indices, tf_axis], gather_grad)
self.assertEqual(indices_grad, None)
self.assertEqual(axis_grad, None)
if dtype.is_integer:
self.assertEqual(params_grad, None)
continue
# For axis 0, we are able to create an efficient IndexedSlices for
# the gradient.
if axis == 0:
self.assertEqual(type(params_grad), ops.IndexedSlices)
params_grad = ops.convert_to_tensor(params_grad)
correct_params_grad = np.zeros(shape).astype(dtype.as_numpy_dtype)
outer_dims = axis
inner_dims = len(shape) - axis - 1
gather_grad = gather_grad.reshape(
shape[:axis] + (indices.size,) + shape[axis + 1:])
for source_index, dest_index in enumerate(indices.flat):
dest_slice = ((slice(None),) * outer_dims + (dest_index,) +
(slice(None),) * inner_dims)
source_slice = ((slice(None),) * outer_dims + (source_index,) +
(slice(None),) * inner_dims)
correct_params_grad[dest_slice] += gather_grad[source_slice]
self.assertAllClose(correct_params_grad, params_grad.eval(),
atol=2e-6, rtol=2e-6)
def testString(self):
params = np.array([[b"asdf", b"zxcv"], [b"qwer", b"uiop"]])
with self.cached_session():
self.assertAllEqual([b"qwer", b"uiop"],
array_ops.gather(params, 1, axis=0).eval())
self.assertAllEqual([b"asdf", b"qwer"],
array_ops.gather(params, 0, axis=1).eval())
def testUInt32AndUInt64(self):
for unsigned_type in (dtypes.uint32, dtypes.uint64):
params = self._buildParams(
np.array([[1, 2, 3], [7, 8, 9]]), unsigned_type)
with self.cached_session():
self.assertAllEqual([7, 8, 9],
array_ops.gather(params, 1, axis=0).eval())
self.assertAllEqual([1, 7], array_ops.gather(params, 0, axis=1).eval())
def testUnknownIndices(self):
params = constant_op.constant([[0, 1, 2]])
indices = array_ops.placeholder(dtypes.int32)
gather_t = array_ops.gather(params, indices)
self.assertEqual(None, gather_t.get_shape())
def testUnknownAxis(self):
params = constant_op.constant([[0, 1, 2]])
indices = constant_op.constant([[0, 0], [0, 0]])
axis = array_ops.placeholder(dtypes.int32)
gather_t = array_ops.gather(params, indices, axis=axis)
# Rank 2 params with rank 2 indices results in a rank 3 shape.
self.assertEqual([None, None, None], gather_t.shape.as_list())
# If indices is also unknown the result rank is unknown.
indices = array_ops.placeholder(dtypes.int32)
gather_t = array_ops.gather(params, indices, axis=axis)
self.assertEqual(None, gather_t.shape)
def testBadIndicesCPU(self):
with self.test_session(use_gpu=False):
params = [[0, 1, 2], [3, 4, 5]]
with self.assertRaisesOpError(r"indices\[0,0\] = 7 is not in \[0, 2\)"):
array_ops.gather(params, [[7]], axis=0).eval()
with self.assertRaisesOpError(r"indices\[0,0\] = 7 is not in \[0, 3\)"):
array_ops.gather(params, [[7]], axis=1).eval()
def _disabledTestBadIndicesGPU(self):
# TODO disabled due to different behavior on GPU and CPU
# On GPU the bad indices do not raise error but fetch 0 values
if not test.is_gpu_available():
return
with self.test_session(use_gpu=True):
params = [[0, 1, 2], [3, 4, 5]]
with self.assertRaisesOpError(r"indices\[0,0\] = 7 is not in \[0, 2\)"):
array_ops.gather(params, [[7]], axis=0).eval()
with self.assertRaisesOpError(r"indices\[0,0\] = 7 is not in \[0, 3\)"):
array_ops.gather(params, [[7]], axis=1).eval()
def testBadAxis(self):
with self.test_session(use_gpu=True):
params = [0, 1, 2]
params_ph = array_ops.placeholder(dtypes.int32)
indices = 0
for bad_axis in (1, 2, -2):
# Shape inference can validate axis for known params rank.
with self.assertRaisesWithPredicateMatch(
ValueError, "Shape must be at least rank . but is rank 1"):
array_ops.gather(params, indices, axis=bad_axis)
# If params rank is unknown, an op error occurs.
with self.assertRaisesOpError(
r"Expected axis in the range \[-1, 1\), but got %s" % bad_axis):
array_ops.gather(params_ph, indices, axis=bad_axis).eval(
feed_dict={params_ph: params})
def testEmptySlices(self):
with self.test_session(use_gpu=True):
for dtype in _TEST_TYPES:
for itype in np.int32, np.int64:
# Leading axis gather.
params = np.zeros((7, 0, 0), dtype=dtype.as_numpy_dtype)
indices = np.array([3, 4], dtype=itype)
gather = array_ops.gather(params, indices, axis=0)
self.assertAllEqual(gather.eval(), np.zeros((2, 0, 0)))
# Middle axis gather.
params = np.zeros((0, 7, 0), dtype=dtype.as_numpy_dtype)
gather = array_ops.gather(params, indices, axis=1)
self.assertAllEqual(gather.eval(), np.zeros((0, 2, 0)))
# Trailing axis gather.
params = np.zeros((0, 0, 7), dtype=dtype.as_numpy_dtype)
gather = array_ops.gather(params, indices, axis=2)
self.assertAllEqual(gather.eval(), np.zeros((0, 0, 2)))
if __name__ == "__main__":
test.main()
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