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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.tf.scatter."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
def _AsType(v, vtype):
return v.astype(vtype) if isinstance(v, np.ndarray) else vtype(v)
def _NumpyAdd(ref, indices, updates):
# Since numpy advanced assignment does not support repeated indices,
# we run a simple loop to perform scatter_add.
for i, indx in np.ndenumerate(indices):
ref[indx] += updates[i]
def _NumpySub(ref, indices, updates):
for i, indx in np.ndenumerate(indices):
ref[indx] -= updates[i]
class ScatterTest(tf.test.TestCase):
def _VariableRankTest(self, np_scatter, tf_scatter, vtype, itype, use_gpu,
repeat_indices=False):
np.random.seed(8)
with self.test_session(use_gpu=use_gpu):
for indices_shape in (), (2,), (3, 7), (3, 4, 7):
for extra_shape in (), (5,), (5, 9):
# Generate random indices with no duplicates for easy numpy comparison
size = np.prod(indices_shape, dtype=itype)
first_dim = 3 * size
indices = np.arange(first_dim)
np.random.shuffle(indices)
indices = indices[:size]
if size > 1 and repeat_indices:
# Add some random repeats.
indices = indices[:size//2]
for _ in range(size-size//2):
# Randomly append some repeats.
indices = np.append(indices, indices[np.random.randint(size//2)])
np.random.shuffle(indices)
indices = indices.reshape(indices_shape)
updates = _AsType(np.random.randn(*(indices_shape + extra_shape)),
vtype)
old = _AsType(np.random.randn(*((first_dim,) + extra_shape)), vtype)
# Scatter via numpy
new = old.copy()
np_scatter(new, indices, updates)
# Scatter via tensorflow
ref = tf.Variable(old)
ref.initializer.run()
tf_scatter(ref, indices, updates).eval()
# Compare
self.assertAllClose(ref.eval(), new)
def _VariableRankTests(self, np_scatter, tf_scatter):
for vtype in (np.float32, np.float64):
for itype in (np.int32, np.int64):
for use_gpu in (False, True):
self._VariableRankTest(np_scatter, tf_scatter, vtype, itype, use_gpu)
def testVariableRankUpdate(self):
def update(ref, indices, updates):
ref[indices] = updates
self._VariableRankTests(update, tf.scatter_update)
def testVariableRankAdd(self):
self._VariableRankTests(_NumpyAdd, tf.scatter_add)
def testVariableRankSub(self):
self._VariableRankTests(_NumpySub, tf.scatter_sub)
def _ScatterRepeatIndicesTest(self, np_scatter, tf_scatter):
for vtype in (np.float32, np.float64):
for itype in (np.int32, np.int64):
for use_gpu in (False, True):
self._VariableRankTest(np_scatter, tf_scatter, vtype, itype, use_gpu,
repeat_indices=True)
def testScatterRepeatIndices(self):
"""This tests scatter_add using indices that repeat."""
self._ScatterRepeatIndicesTest(_NumpyAdd, tf.scatter_add)
self._ScatterRepeatIndicesTest(_NumpySub, tf.scatter_sub)
def testBooleanScatterUpdate(self):
with self.test_session(use_gpu=False) as session:
var = tf.Variable([True, False])
update0 = tf.scatter_update(var, 1, True)
update1 = tf.scatter_update(var, tf.constant(0, dtype=tf.int64), False)
var.initializer.run()
session.run([update0, update1])
self.assertAllEqual([False, True], var.eval())
def testScatterOutOfRangeCpu(self):
for op in (tf.scatter_add, tf.scatter_sub, tf.scatter_update):
params = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32)
updates = np.array([-3, -4, -5]).astype(np.float32)
with self.test_session(use_gpu=False):
ref = tf.Variable(params)
ref.initializer.run()
# Indices all in range, no problem.
indices = np.array([2, 0, 5])
op(ref, indices, updates).eval()
# Test some out of range errors.
indices = np.array([-1, 0, 5])
with self.assertRaisesOpError(r'indices\[0\] = -1 is not in \[0, 6\)'):
op(ref, indices, updates).eval()
indices = np.array([2, 0, 6])
with self.assertRaisesOpError(r'indices\[2\] = 6 is not in \[0, 6\)'):
op(ref, indices, updates).eval()
# TODO(fpmc): Re-enable this test when gpu_pip test actually runs on a GPU.
def _disabledTestScatterOutOfRangeGpu(self):
if not tf.test.IsBuiltWithCuda():
return
for op in (tf.scatter_add, tf.scatter_sub, tf.scatter_update):
params = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32)
updates = np.array([-3, -4, -5]).astype(np.float32)
# With GPU, the code ignores indices that are out of range.
# We don't test the implementation; just test there's no failures.
with self.test_session(force_gpu=True):
ref = tf.Variable(params)
ref.initializer.run()
# Indices all in range, no problem.
indices = np.array([2, 0, 5])
op(ref, indices, updates).eval()
# Indicies out of range should not fail.
indices = np.array([-1, 0, 5])
op(ref, indices, updates).eval()
indices = np.array([2, 0, 6])
op(ref, indices, updates).eval()
if __name__ == "__main__":
tf.test.main()