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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.reverse_sequence_op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
import sys
import numpy as np
from tensorflow.python.client import session
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 random_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.platform import benchmark
from tensorflow.python.platform import test
class WhereOpTest(test.TestCase):
def _testWhere(self, x, truth, expected_err_re=None):
with self.test_session(use_gpu=True):
ans = array_ops.where(x)
self.assertEqual([None, x.ndim], ans.get_shape().as_list())
if expected_err_re is None:
tf_ans = ans.eval()
self.assertAllClose(tf_ans, truth, atol=1e-10)
else:
with self.assertRaisesOpError(expected_err_re):
ans.eval()
def testWrongNumbers(self):
with self.test_session(use_gpu=True):
with self.assertRaises(ValueError):
array_ops.where([False, True], [1, 2], None)
with self.assertRaises(ValueError):
array_ops.where([False, True], None, [1, 2])
def testBasicVec(self):
x = np.asarray([True, False])
truth = np.asarray([[0]], dtype=np.int64)
self._testWhere(x, truth)
x = np.asarray([False, True, False])
truth = np.asarray([[1]], dtype=np.int64)
self._testWhere(x, truth)
x = np.asarray([False, False, True, False, True])
truth = np.asarray([[2], [4]], dtype=np.int64)
self._testWhere(x, truth)
def testRandomVec(self):
x = np.random.rand(1000000) > 0.5
truth = np.vstack([np.where(x)[0].astype(np.int64)]).T
self._testWhere(x, truth)
def testBasicMat(self):
x = np.asarray([[True, False], [True, False]])
# Ensure RowMajor mode
truth = np.asarray([[0, 0], [1, 0]], dtype=np.int64)
self._testWhere(x, truth)
def testBasic3Tensor(self):
x = np.asarray([[[True, False], [True, False]],
[[False, True], [False, True]],
[[False, False], [False, True]]])
# Ensure RowMajor mode
truth = np.asarray(
[[0, 0, 0], [0, 1, 0], [1, 0, 1], [1, 1, 1], [2, 1, 1]], dtype=np.int64)
self._testWhere(x, truth)
def _testRandom(self, dtype, expected_err_re=None):
shape = [127, 33, 53]
x = np.random.randn(*shape) + 1j * np.random.randn(*shape)
x = (np.random.randn(*shape) > 0).astype(dtype)
truth = np.where(np.abs(x) > 0) # Tuples of indices by axis.
truth = np.vstack(truth).T # Convert to [num_true, indices].
self._testWhere(x, truth, expected_err_re)
def testRandomBool(self):
self._testRandom(np.bool)
def testRandomInt32(self):
self._testRandom(np.int32)
def testRandomInt64(self):
self._testRandom(np.int64)
def testRandomFloat(self):
self._testRandom(np.float32)
def testRandomDouble(self):
self._testRandom(np.float64)
def testRandomComplex64(self):
self._testRandom(np.complex64)
def testRandomComplex128(self):
self._testRandom(np.complex128)
def testRandomUint8(self):
self._testRandom(np.uint8)
def testRandomInt8(self):
self._testRandom(np.int8)
def testRandomInt16(self):
self._testRandom(np.int16)
def testThreeArgument(self):
x = np.array([[-2, 3, -1], [1, -3, -3]])
np_val = np.where(x > 0, x * x, -x)
with self.test_session(use_gpu=True):
tf_val = array_ops.where(constant_op.constant(x) > 0, x * x, -x).eval()
self.assertAllEqual(tf_val, np_val)
def testBatchSelect(self):
x = np.array([[-2, 3, -1] * 64, [1, -3, -3] * 64] * 8192) # [16384, 192]
c_mat = np.array([[False] * 192, [True] * 192] * 8192) # [16384, 192]
c_vec = np.array([False, True] * 8192) # [16384]
np_val = np.where(c_mat, x * x, -x)
with self.test_session(use_gpu=True):
tf_val = array_ops.where(c_vec, x * x, -x).eval()
self.assertAllEqual(tf_val, np_val)
class WhereBenchmark(test.Benchmark):
def benchmarkWhere(self):
for (m, n, p, use_gpu) in itertools.product(
[10],
[10, 100, 1000, 10000, 100000, 1000000],
[0.01, 0.5, 0.99],
[False, True]):
name = "m_%d_n_%d_p_%g_use_gpu_%s" % (m, n, p, use_gpu)
device = "/%s:0" % ("gpu" if use_gpu else "cpu")
with ops.Graph().as_default():
with ops.device(device):
x = random_ops.random_uniform((m, n), dtype=dtypes.float32) <= p
v = resource_variable_ops.ResourceVariable(x)
op = array_ops.where(v)
with session.Session(config=benchmark.benchmark_config()) as sess:
v.initializer.run()
r = self.run_op_benchmark(sess, op, min_iters=100, name=name)
gb_processed_input = m * n / 1.0e9
# approximate size of output: m*n*p int64s for each axis.
gb_processed_output = 2 * 8 * m * n * p / 1.0e9
gb_processed = gb_processed_input + gb_processed_output
throughput = gb_processed / r["wall_time"]
print("Benchmark: %s \t wall_time: %0.03g s \t "
"Throughput: %0.03g GB/s" % (name, r["wall_time"], throughput))
sys.stdout.flush()
def benchmarkBatchSelect(self):
for (m, n, use_gpu) in itertools.product([1000, 10000, 100000],
[10, 100, 1000], [False, True]):
name = "m_%d_n_%d_use_gpu_%s" % (m, n, use_gpu)
device = "/%s:0" % ("gpu" if use_gpu else "cpu")
with ops.Graph().as_default():
with ops.device(device):
x_gen = random_ops.random_uniform([m, n], dtype=dtypes.float32)
y_gen = random_ops.random_uniform([m, n], dtype=dtypes.float32)
c_gen = random_ops.random_uniform([m], dtype=dtypes.float32) <= 0.5
x = resource_variable_ops.ResourceVariable(x_gen)
y = resource_variable_ops.ResourceVariable(y_gen)
c = resource_variable_ops.ResourceVariable(c_gen)
op = array_ops.where(c, x, y)
with session.Session(config=benchmark.benchmark_config()) as sess:
x.initializer.run()
y.initializer.run()
c.initializer.run()
r = self.run_op_benchmark(sess, op, min_iters=100, name=name)
# approximate size of output: m*n*2 floats for each axis.
gb_processed = m * n * 8 / 1.0e9
throughput = gb_processed / r["wall_time"]
print("Benchmark: %s \t wall_time: %0.03g s \t "
"Throughput: %0.03g GB/s" % (name, r["wall_time"], throughput))
sys.stdout.flush()
if __name__ == "__main__":
test.main()
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