b6d4bc7fb6
Arithmetic operations are much faster than tf.where. Also add unit tests for tf.clip_by_global_norm. PiperOrigin-RevId: 306217896 Change-Id: Ib07da0581550a03fb329480b85ac55349b88ea98
136 lines
5.1 KiB
Python
136 lines
5.1 KiB
Python
# Copyright 2017 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 Clip Operations."""
|
|
|
|
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 ops
|
|
from tensorflow.python.framework import test_util
|
|
from tensorflow.python.ops import clip_ops
|
|
from tensorflow.python.ops import numerics
|
|
from tensorflow.python.platform import test
|
|
|
|
|
|
class ClipOpsTest(test.TestCase):
|
|
|
|
def __init__(self, method_name="runTest"):
|
|
super(ClipOpsTest, self).__init__(method_name)
|
|
|
|
def _testClipTensorByNorm(self, inputs, max_norm, expected):
|
|
input_op = constant_op.constant(inputs)
|
|
clipped = clip_ops.clip_by_norm(input_op, max_norm)
|
|
check_op = numerics.add_check_numerics_ops()
|
|
result, _ = self.evaluate([clipped, check_op])
|
|
self.assertAllClose(result, expected)
|
|
|
|
def _testClipTensorByGlobalNorm(self, inputs, max_norm, expected):
|
|
clipped = clip_ops.clip_by_global_norm(inputs, max_norm)
|
|
result, _ = self.evaluate(clipped)
|
|
self.assertAllClose(result, expected)
|
|
|
|
def _testNonFiniteClippingByGlobalNorm(self, inputs, max_norm):
|
|
clipped = clip_ops.clip_by_global_norm(inputs, max_norm)
|
|
result, _ = self.evaluate(clipped)
|
|
self.assertTrue(np.all(np.isnan(result)))
|
|
|
|
def _testClipIndexedSlicesByNorm(self, values, indices, shape, max_norm,
|
|
axes):
|
|
values = constant_op.constant(values)
|
|
indices = constant_op.constant(indices)
|
|
shape = constant_op.constant(shape)
|
|
# IndexedSlices mode
|
|
indexed_slices = ops.IndexedSlices(values, indices, shape)
|
|
clipped = clip_ops.clip_by_norm(indexed_slices, max_norm, axes)
|
|
# clipped should be IndexedSlices
|
|
self.assertIsInstance(clipped, ops.IndexedSlices)
|
|
clipped = ops.convert_to_tensor(clipped)
|
|
|
|
# Tensor mode
|
|
dense_tensor = ops.convert_to_tensor(indexed_slices)
|
|
dense_clipped = clip_ops.clip_by_norm(dense_tensor, max_norm, axes)
|
|
result, expected = self.evaluate([clipped, dense_clipped])
|
|
self.assertAllClose(result, expected)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testClipTensorByNorm(self):
|
|
# Simple example
|
|
self._testClipTensorByNorm([[-3.0, 0.0, 0.0], [4.0, 0.0, 0.0]], 4.0,
|
|
[[-2.4, 0.0, 0.0], [3.2, 0.0, 0.0]])
|
|
# No clipping.
|
|
self._testClipTensorByNorm([[1.0, 0.0, 0.0], [1.0, 0.0, 0.0]], 4.0,
|
|
[[1.0, 0.0, 0.0], [1.0, 0.0, 0.0]])
|
|
# Zero norm
|
|
self._testClipTensorByNorm([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], 4.0,
|
|
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testClipTensorByGlobalNorm(self):
|
|
# Simple example
|
|
self._testClipTensorByGlobalNorm([[-3.0, 0.0, 0.0], [4.0, 0.0, 0.0]], 4.0,
|
|
[[-2.4, 0.0, 0.0], [3.2, 0.0, 0.0]])
|
|
# No clipping.
|
|
self._testClipTensorByGlobalNorm([[1.0, 0.0, 0.0], [1.0, 0.0, 0.0]], 4.0,
|
|
[[1.0, 0.0, 0.0], [1.0, 0.0, 0.0]])
|
|
# Zero norm.
|
|
self._testClipTensorByGlobalNorm([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], 4.0,
|
|
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testGlobalClipWithNonfinite(self):
|
|
self._testNonFiniteClippingByGlobalNorm(
|
|
[[-3.0, 0.0, 0.0], [float("inf"), 0.0, 0.0]], 4.0)
|
|
self._testNonFiniteClippingByGlobalNorm(
|
|
[[-3.0, 0.0, 0.0], [float("-inf"), 0.0, 0.0]], 4.0)
|
|
self._testNonFiniteClippingByGlobalNorm(
|
|
[[-3.0, 0.0, 0.0], [float("nan"), 0.0, 0.0]], 4.0)
|
|
|
|
def testClipIndexedSlicesByNorm(self):
|
|
values = [[[-3.0, 0.0, 0.0], [4.0, 0.0, 0.0]],
|
|
[[0.0, 2.0, 0.0], [0.0, 0.0, -1.0]]]
|
|
indices = [2, 6]
|
|
shape = [10, 2, 3]
|
|
# Axes == None
|
|
self._testClipIndexedSlicesByNorm(values, indices, shape, 4.0, None)
|
|
|
|
# Axes == 0
|
|
self._testClipIndexedSlicesByNorm(values, indices, shape, 4.0, 0)
|
|
|
|
# Axes == 1
|
|
self._testClipIndexedSlicesByNorm(values, indices, shape, 4.0, 1)
|
|
|
|
# Axes == 2
|
|
self._testClipIndexedSlicesByNorm(values, indices, shape, 4.0, 1)
|
|
|
|
# Axes == [0, 1]
|
|
self._testClipIndexedSlicesByNorm(values, indices, shape, 4.0, [0, 1])
|
|
|
|
# Axes == [0, 1]
|
|
self._testClipIndexedSlicesByNorm(values, indices, shape, 4.0, [0, 2])
|
|
|
|
# Axes == [0, 1]
|
|
self._testClipIndexedSlicesByNorm(values, indices, shape, 4.0, [1, 2])
|
|
|
|
# Axes == [0, 1]
|
|
self._testClipIndexedSlicesByNorm(values, indices, shape, 4.0, [0, 1, 2])
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test.main()
|