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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.math_ops.matmul."""
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 errors_impl
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test as test_lib
_MAXDIM = 5
def _add_test(test, test_name, fn):
test_name = "_".join(["test", test_name])
if hasattr(test, test_name):
raise RuntimeError("Test %s defined more than once" % test_name)
setattr(test, test_name, fn)
class TensordotTest(test_lib.TestCase):
def test_invalid_shape(self):
a = [[1, 2], [3, 4]]
b = [[1, 2], [3, 4], [5, 6]]
a_axes = [1]
b_axes = [0]
# Invalid static shapes.
with self.assertRaises(ValueError):
math_ops.tensordot(a, b, (a_axes, b_axes))
# Invalid dynamic shapes.
with self.test_session() as sess:
with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
"Matrix size-incompatible"):
a_ph = array_ops.placeholder(dtypes.float32)
b_ph = array_ops.placeholder(dtypes.float32)
axes_ph = array_ops.placeholder(dtypes.int32)
output = math_ops.tensordot(a_ph, b_ph, axes_ph)
_ = sess.run(
[output], feed_dict={
a_ph: a,
b_ph: b,
axes_ph: (a_axes, b_axes)
})
def test_invalid_axes(self):
a = [[1, 2], [3, 4]]
b = [[1, 2], [3, 4]]
# Invalid static axes.
for axes_value in -1, 3, [1], [[1]], [[1], [0, 1]]:
with self.assertRaises(ValueError):
math_ops.tensordot(a, b, axes_value)
with self.assertRaises(IndexError):
math_ops.tensordot(a, b, [[0], [7]])
# Invalid dynamic axes.
a_ph = array_ops.placeholder(dtypes.float32)
b_ph = array_ops.placeholder(dtypes.float32)
axes_ph = array_ops.placeholder(dtypes.int32)
output = math_ops.tensordot(a_ph, b_ph, axes_ph)
# Note: We don't support scalar Tensor values for axes.
for axes_value in 1, [1], [0, 1], [[1]], [[0, 1]], [[0], [7]]:
with self.test_session() as sess:
with self.assertRaises(errors_impl.InvalidArgumentError):
_ = sess.run(
[output], feed_dict={
a_ph: a,
b_ph: b,
axes_ph: axes_value
})
# Test case for 11950
def test_valid_axis(self):
for axes_value in [1, 2], [[1], [2]], [[], []], 0:
with self.test_session() as sess:
np_a = np.ones((3, 3))
np_b = np.array([2, 3, 1])[None, None]
np_ans = np.tensordot(np_a, np_b, axes_value)
tf_a = array_ops.ones((3, 3), dtype=dtypes.float32)
tf_b = constant_op.constant([2, 3, 1], dtype=dtypes.float32)[None, None]
tf_ans = math_ops.tensordot(tf_a, tf_b, axes_value).eval()
self.assertAllEqual(tf_ans.shape, np_ans.shape)
self.assertAllEqual(tf_ans, np_ans)
def test_partial_shape_inference(self):
for axes in ([1], [0]), 1:
a = array_ops.placeholder(dtypes.float32)
b = array_ops.placeholder(dtypes.float32)
output = math_ops.tensordot(a, b, axes)
self.assertEqual(output.get_shape().ndims, None)
a.set_shape([None, 2])
b.set_shape([2, 3])
output = math_ops.tensordot(a, b, axes)
output_shape = output.get_shape()
self.assertEqual(output_shape.ndims, 2)
output_shape = output_shape.as_list()
self.assertEqual(output_shape[0], None)
self.assertEqual(output_shape[1], 3)
a = array_ops.placeholder(dtypes.float32)
b = array_ops.placeholder(dtypes.float32)
a.set_shape([2, 2])
b.set_shape([2, None])
output = math_ops.tensordot(a, b, axes)
output_shape = output.get_shape()
self.assertEqual(output_shape.ndims, 2)
output_shape = output_shape.as_list()
self.assertEqual(output_shape[0], 2)
self.assertEqual(output_shape[1], None)
def _get_tensordot_tests(dtype_, rank_a_, rank_b_, num_dims_, dynamic_shape_):
# Select a random subset of size m from [0, 1, ..., n-1].
def _random_subset(m, n):
assert m <= n
return (np.random.permutation(n)[:m]).astype(np.int32)
def _generate_random_tensors_and_dims():
a_shape = np.random.random_integers(1, _MAXDIM, rank_a_)
b_shape = np.random.random_integers(1, _MAXDIM, rank_b_)
shared_shape = np.random.random_integers(1, _MAXDIM, num_dims_)
a_dims = _random_subset(num_dims_, rank_a_)
b_dims = _random_subset(num_dims_, rank_b_)
for i in range(num_dims_):
a_shape[a_dims[i]] = shared_shape[i]
b_shape[b_dims[i]] = shared_shape[i]
a = np.random.uniform(
low=-1.0, high=1.0,
size=np.prod(a_shape)).reshape(a_shape).astype(dtype_)
b = np.random.uniform(
low=-1.0, high=1.0,
size=np.prod(b_shape)).reshape(b_shape).astype(dtype_)
return a, b, a_dims, b_dims
def test_tensordot(self):
num_trials = min(30, num_dims_ * num_dims_)
if dtype_ == np.float16:
tol = 0.05
elif dtype_ == np.float32 or dtype_ == np.complex64:
tol = 1e-5
else:
tol = 1e-12
for _ in range(num_trials):
a_np, b_np, a_dims_np, b_dims_np = _generate_random_tensors_and_dims()
np_ans = np.tensordot(a_np, b_np, axes=(a_dims_np, b_dims_np))
with self.test_session(use_gpu=True) as sess:
if dynamic_shape_:
a = array_ops.placeholder(dtype_)
b = array_ops.placeholder(dtype_)
axes = array_ops.placeholder(dtypes.int32)
c = math_ops.tensordot(a, b, axes)
tf_ans = sess.run(
c, feed_dict={
a: a_np,
b: b_np,
axes: (a_dims_np, b_dims_np)
})
else:
tf_ans = math_ops.tensordot(a_np, b_np, (a_dims_np, b_dims_np)).eval()
self.assertAllClose(tf_ans, np_ans, rtol=tol, atol=tol)
self.assertAllEqual(tf_ans.shape, np_ans.shape)
def test_tensordot_scalar_axes(self):
if num_dims_ < 1:
self.skipTest("Not a test")
if dtype_ == np.float16:
tol = 0.05
elif dtype_ == np.float32 or dtype_ == np.complex64:
tol = 1e-5
else:
tol = 1e-12
shape = [5] * num_dims_
a_np = np.random.uniform(
low=-1.0, high=1.0, size=np.prod(shape)).reshape(shape).astype(dtype_)
b_np = np.random.uniform(
low=-1.0, high=1.0, size=np.prod(shape)).reshape(shape).astype(dtype_)
all_axes = [0, 1]
if a_np.ndim > 2:
all_axes.append(a_np.ndim - 1)
for axes in all_axes:
np_ans = np.tensordot(a_np, b_np, axes=axes)
with self.test_session(use_gpu=True) as sess:
if dynamic_shape_:
a = array_ops.placeholder(dtype_)
b = array_ops.placeholder(dtype_)
c = math_ops.tensordot(a, b, axes=axes)
tf_ans = sess.run(c, feed_dict={a: a_np, b: b_np})
else:
tf_ans = math_ops.tensordot(a_np, b_np, axes=axes).eval()
self.assertAllClose(tf_ans, np_ans, rtol=tol, atol=tol)
self.assertAllEqual(tf_ans.shape, np_ans.shape)
return [test_tensordot, test_tensordot_scalar_axes]
if __name__ == "__main__":
for dtype in np.float16, np.float32, np.float64, np.complex64, np.complex128:
for rank_a in 1, 2, 4, 5:
for rank_b in 1, 2, 4, 5:
for num_dims in range(0, min(rank_a, rank_b) + 1):
for dynamic_shape in False, True:
for testcase in _get_tensordot_tests(dtype, rank_a, rank_b,
num_dims, dynamic_shape):
name = "%s_%s_%s_%s_%s_%s" % (testcase.__name__, dtype.__name__,
rank_a, rank_b, num_dims,
dynamic_shape)
_add_test(TensordotTest, name, testcase)
test_lib.main()
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