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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 broadcast_to ops."""
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.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.platform import test as test_lib
class BroadcastToTest(test_util.TensorFlowTestCase):
def testBroadcastToBasic(self):
for dtype in [np.uint8, np.uint16, np.int8, np.int16, np.int32, np.int64]:
with self.test_session(use_gpu=True):
x = np.array([1, 2, 3], dtype=dtype)
v_tf = array_ops.broadcast_to(constant_op.constant(x), [3, 3])
v_np = np.broadcast_to(x, [3, 3])
self.assertAllEqual(v_tf.eval(), v_np)
def testBroadcastToString(self):
with self.test_session(use_gpu=True):
x = np.array([b"1", b"2", b"3"])
v_tf = array_ops.broadcast_to(constant_op.constant(x), [3, 3])
v_np = np.broadcast_to(x, [3, 3])
self.assertAllEqual(v_tf.eval(), v_np)
def testBroadcastToBool(self):
with self.test_session(use_gpu=True):
x = np.array([True, False, True], dtype=np.bool)
v_tf = array_ops.broadcast_to(constant_op.constant(x), [3, 3])
v_np = np.broadcast_to(x, [3, 3])
self.assertAllEqual(v_tf.eval(), v_np)
def testBroadcastToShape(self):
for input_dim in range(1, 6):
for output_dim in range(input_dim, 6):
with self.test_session(use_gpu=True):
input_shape = [2] * input_dim
output_shape = [2] * output_dim
x = np.array(np.random.randint(5, size=input_shape), dtype=np.int32)
v_tf = array_ops.broadcast_to(constant_op.constant(x), output_shape)
v_np = np.broadcast_to(x, output_shape)
self.assertAllEqual(v_tf.eval(), v_np)
def testBroadcastToScalar(self):
with self.test_session(use_gpu=True):
x = np.array(1, dtype=np.int32)
v_tf = array_ops.broadcast_to(constant_op.constant(x), [3, 3])
v_np = np.broadcast_to(x, [3, 3])
self.assertAllEqual(v_tf.eval(), v_np)
def testBroadcastToShapeTypeAndInference(self):
for dtype in [dtypes.int32, dtypes.int64]:
with self.test_session(use_gpu=True):
x = np.array([1, 2, 3])
v_tf = array_ops.broadcast_to(
constant_op.constant(x),
constant_op.constant([3, 3], dtype=dtype))
shape = v_tf.get_shape().as_list()
v_np = np.broadcast_to(x, [3, 3])
self.assertAllEqual(v_tf.eval(), v_np)
# check shape inference when shape input is constant
self.assertAllEqual(shape, v_np.shape)
def testGradientForScalar(self):
# TODO(alextp): There is a bug with broadcast_to on GPU from scalars,
# hence we make this test cpu-only.
with ops.device("cpu:0"):
x = constant_op.constant(1, dtype=dtypes.float32)
v = array_ops.broadcast_to(x, [2, 4, 3])
out = 2 * v
with self.test_session():
err = gradient_checker.compute_gradient_error(x, x.get_shape(),
out, out.get_shape())
self.assertLess(err, 1e-4)
def testGradientWithSameRank(self):
x = constant_op.constant(np.reshape(np.arange(6), (2, 1, 3)),
dtype=dtypes.float32)
v = array_ops.broadcast_to(x, [2, 5, 3])
out = 2 * v
with self.test_session():
err = gradient_checker.compute_gradient_error(x, x.get_shape(),
out, out.get_shape())
self.assertLess(err, 1e-4)
def testGradientWithIncreasingRank(self):
x = constant_op.constant([[1], [2]],
dtype=dtypes.float32)
v = array_ops.broadcast_to(x, [5, 2, 3])
out = 2 * v
with self.test_session():
err = gradient_checker.compute_gradient_error(x, x.get_shape(),
out, out.get_shape())
self.assertLess(err, 1e-4)
def testGradientWithBroadcastAllDimensions(self):
x = constant_op.constant([[1, 2, 3], [4, 5, 6]], dtype=dtypes.float32)
v = array_ops.broadcast_to(x, [5, 4, 6])
out = 2 * v
with self.test_session():
err = gradient_checker.compute_gradient_error(x, x.get_shape(),
out, out.get_shape())
self.assertLess(err, 1e-4)
if __name__ == "__main__":
test_lib.main()
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