experiments/STT-tensorflow
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Remove unnecessary uses of use_gpu for tests

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PiperOrigin-RevId: 236034198
This commit is contained in:
Gaurav Jain 2019-02-27 18:27:39 -08:00 committed by TensorFlower Gardener
parent 67a91b18b5
commit 64b4879c93
8 changed files with 269 additions and 290 deletions

2
tensorflow/python/kernel_tests/compare_and_bitpack_op_test.py
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@ -20,7 +20,6 @@ from __future__ import print_function
import numpy as np import numpy as np
from tensorflow.python.framework import test_util
from tensorflow.python.ops import math_ops from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test from tensorflow.python.platform import test
@ -31,7 +30,6 @@ class CompareAndBitpackTest(test.TestCase):
x, threshold, x, threshold,
truth, truth,
expected_err_re=None): expected_err_re=None):
with test_util.use_gpu():
ans = math_ops.compare_and_bitpack(x, threshold) ans = math_ops.compare_and_bitpack(x, threshold)
if expected_err_re is None: if expected_err_re is None:
tf_ans = self.evaluate(ans) tf_ans = self.evaluate(ans)

14
tensorflow/python/kernel_tests/dynamic_stitch_op_test.py
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@ -63,7 +63,6 @@ class DynamicStitchTestBase(object):
self.assertEqual([None], stitched_t.get_shape().as_list()) self.assertEqual([None], stitched_t.get_shape().as_list())
def testSimpleOneDimensional(self): def testSimpleOneDimensional(self):
with test_util.use_gpu():
# Test various datatypes in the simple case to ensure that the op was # Test various datatypes in the simple case to ensure that the op was
# registered under those types. # registered under those types.
dtypes_to_test = [ dtypes_to_test = [
@ -86,7 +85,6 @@ class DynamicStitchTestBase(object):
self.assertEqual([8], stitched_t.get_shape().as_list()) self.assertEqual([8], stitched_t.get_shape().as_list())
def testOneListOneDimensional(self): def testOneListOneDimensional(self):
with test_util.use_gpu():
indices = [constant_op.constant([1, 6, 2, 3, 5, 0, 4, 7])] indices = [constant_op.constant([1, 6, 2, 3, 5, 0, 4, 7])]
data = [constant_op.constant([10, 60, 20, 30, 50, 0, 40, 70])] data = [constant_op.constant([10, 60, 20, 30, 50, 0, 40, 70])]
stitched_t = self.stitch_op(indices, data) stitched_t = self.stitch_op(indices, data)
@ -96,7 +94,6 @@ class DynamicStitchTestBase(object):
self.assertEqual([8], stitched_t.get_shape().as_list()) self.assertEqual([8], stitched_t.get_shape().as_list())
def testSimpleTwoDimensional(self): def testSimpleTwoDimensional(self):
with test_util.use_gpu():
indices = [ indices = [
constant_op.constant([0, 4, 7]), constant_op.constant([0, 4, 7]),
constant_op.constant([1, 6]), constant_op.constant([1, 6]),
@ -115,7 +112,6 @@ class DynamicStitchTestBase(object):
self.assertEqual([8, 2], stitched_t.get_shape().as_list()) self.assertEqual([8, 2], stitched_t.get_shape().as_list())
def testZeroSizeTensor(self): def testZeroSizeTensor(self):
with test_util.use_gpu():
indices = [ indices = [
constant_op.constant([0, 4, 7]), constant_op.constant([0, 4, 7]),
constant_op.constant([1, 6]), constant_op.constant([1, 6]),
@ -137,7 +133,6 @@ class DynamicStitchTestBase(object):
@test_util.run_deprecated_v1 @test_util.run_deprecated_v1
def testHigherRank(self): def testHigherRank(self):
with self.session(use_gpu=True) as sess:
indices = [ indices = [
constant_op.constant(6), constant_op.constant(6),
constant_op.constant([4, 1]), constant_op.constant([4, 1]),
@ -159,7 +154,7 @@ class DynamicStitchTestBase(object):
grads = gradients_impl.gradients(stitched_t, indices + data, grads = gradients_impl.gradients(stitched_t, indices + data,
stitched_grad) stitched_grad)
self.assertEqual(grads[:3], [None] * 3) # Indices have no gradients self.assertEqual(grads[:3], [None] * 3) # Indices have no gradients
for datum, grad in zip(data, sess.run(grads[3:])): for datum, grad in zip(data, self.evaluate(grads[3:])):
self.assertAllEqual(7. * self.evaluate(datum), grad) self.assertAllEqual(7. * self.evaluate(datum), grad)
@test_util.run_deprecated_v1 @test_util.run_deprecated_v1
@ -241,7 +236,6 @@ class ParallelDynamicStitchTest(DynamicStitchTestBase, test.TestCase):
@test_util.run_deprecated_v1 @test_util.run_deprecated_v1
def testHigherRank(self): def testHigherRank(self):
with self.session(use_gpu=True) as sess:
indices = [ indices = [
constant_op.constant(6), constant_op.constant(6),
constant_op.constant([4, 1]), constant_op.constant([4, 1]),
@ -263,12 +257,11 @@ class ParallelDynamicStitchTest(DynamicStitchTestBase, test.TestCase):
grads = gradients_impl.gradients(stitched_t, indices + data, grads = gradients_impl.gradients(stitched_t, indices + data,
stitched_grad) stitched_grad)
self.assertEqual(grads[:3], [None] * 3) # Indices have no gradients self.assertEqual(grads[:3], [None] * 3) # Indices have no gradients
for datum, grad in zip(data, sess.run(grads[3:])): for datum, grad in zip(data, self.evaluate(grads[3:])):
self.assertAllEqual(7.0 * self.evaluate(datum), grad) self.assertAllEqual(7.0 * self.evaluate(datum), grad)
# GPU version unit tests # GPU version unit tests
def testScalarGPU(self): def testScalarGPU(self):
with self.cached_session():
indices = [constant_op.constant(0), constant_op.constant(1)] indices = [constant_op.constant(0), constant_op.constant(1)]
data = [constant_op.constant(40.0), constant_op.constant(60.0)] data = [constant_op.constant(40.0), constant_op.constant(60.0)]
for step in -1, 1: for step in -1, 1:
@ -280,7 +273,6 @@ class ParallelDynamicStitchTest(DynamicStitchTestBase, test.TestCase):
@test_util.run_deprecated_v1 @test_util.run_deprecated_v1
def testHigherRankGPU(self): def testHigherRankGPU(self):
with self.cached_session() as sess:
indices = [ indices = [
constant_op.constant(6), constant_op.constant(6),
constant_op.constant([4, 1]), constant_op.constant([4, 1]),
@ -302,7 +294,7 @@ class ParallelDynamicStitchTest(DynamicStitchTestBase, test.TestCase):
grads = gradients_impl.gradients(stitched_t, indices + data, grads = gradients_impl.gradients(stitched_t, indices + data,
stitched_grad) stitched_grad)
self.assertEqual(grads[:3], [None] * 3) # Indices have no gradients self.assertEqual(grads[:3], [None] * 3) # Indices have no gradients
for datum, grad in zip(data, sess.run(grads[3:])): for datum, grad in zip(data, self.evaluate(grads[3:])):
self.assertAllEqual(7.0 * self.evaluate(datum), grad) self.assertAllEqual(7.0 * self.evaluate(datum), grad)

2
tensorflow/python/kernel_tests/extract_image_patches_op_test.py
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@ -21,7 +21,6 @@ from __future__ import print_function
import numpy as np import numpy as np
from tensorflow.python.framework import constant_op from tensorflow.python.framework import constant_op
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test from tensorflow.python.platform import test
@ -44,7 +43,6 @@ class ExtractImagePatches(test.TestCase):
strides = [1] + strides + [1] strides = [1] + strides + [1]
rates = [1] + rates + [1] rates = [1] + rates + [1]
with test_util.use_gpu():
out_tensor = array_ops.extract_image_patches( out_tensor = array_ops.extract_image_patches(
constant_op.constant(image), constant_op.constant(image),
ksizes=ksizes, ksizes=ksizes,

2
tensorflow/python/kernel_tests/extract_volume_patches_op_test.py
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@ -21,7 +21,6 @@ from __future__ import print_function
import numpy as np import numpy as np
from tensorflow.python.framework import constant_op from tensorflow.python.framework import constant_op
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test from tensorflow.python.platform import test
@ -46,7 +45,6 @@ class ExtractVolumePatches(test.TestCase):
ksizes = [1] + ksizes + [1] ksizes = [1] + ksizes + [1]
strides = [1] + strides + [1] strides = [1] + strides + [1]
with test_util.use_gpu():
out_tensor = array_ops.extract_volume_patches( out_tensor = array_ops.extract_volume_patches(
constant_op.constant(image), constant_op.constant(image),
ksizes=ksizes, ksizes=ksizes,

5
tensorflow/python/kernel_tests/lu_op_test.py
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@ -66,8 +66,6 @@ class LuOpTest(test.TestCase):
def _verifyLu(self, x, output_idx_type=dtypes.int64): def _verifyLu(self, x, output_idx_type=dtypes.int64):
# Verify that Px = LU. # Verify that Px = LU.
with test_util.use_gpu():
lu, perm = linalg_ops.lu(x, output_idx_type=output_idx_type) lu, perm = linalg_ops.lu(x, output_idx_type=output_idx_type)
# Prepare the lower factor of shape num_rows x num_rows # Prepare the lower factor of shape num_rows x num_rows
@ -140,7 +138,6 @@ class LuOpTest(test.TestCase):
self._verifyLu(complex_data, output_idx_type=output_idx_type) self._verifyLu(complex_data, output_idx_type=output_idx_type)
def testPivoting(self): def testPivoting(self):
with test_util.use_gpu():
# This matrix triggers partial pivoting because the first diagonal entry # This matrix triggers partial pivoting because the first diagonal entry
# is small. # is small.
data = np.array([[1e-9, 1., 0.], [1., 0., 0], [0., 1., 5]]) data = np.array([[1e-9, 1., 0.], [1., 0., 0], [0., 1., 5]])
@ -167,7 +164,6 @@ class LuOpTest(test.TestCase):
# LU factorization gives an error when the input is singular. # LU factorization gives an error when the input is singular.
# Note: A singular matrix may return without error but it won't be a valid # Note: A singular matrix may return without error but it won't be a valid
# factorization. # factorization.
with test_util.use_gpu():
for dtype in self.float_types: for dtype in self.float_types:
with self.assertRaises(errors.InvalidArgumentError): with self.assertRaises(errors.InvalidArgumentError):
self.evaluate( self.evaluate(
@ -220,7 +216,6 @@ class LuOpTest(test.TestCase):
@test_util.run_deprecated_v1 @test_util.run_deprecated_v1
def testConcurrentExecutesWithoutError(self): def testConcurrentExecutesWithoutError(self):
with test_util.use_gpu():
matrix1 = random_ops.random_normal([5, 5], seed=42) matrix1 = random_ops.random_normal([5, 5], seed=42)
matrix2 = random_ops.random_normal([5, 5], seed=42) matrix2 = random_ops.random_normal([5, 5], seed=42)
lu1, p1 = linalg_ops.lu(matrix1) lu1, p1 = linalg_ops.lu(matrix1)

2
tensorflow/python/kernel_tests/matmul_op_test.py
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@ -127,7 +127,7 @@ def _GetMatMulGradientTest(a_np_, b_np_, use_static_shape_, **kwargs_):
epsilon = np.finfo(a_np_.dtype).eps epsilon = np.finfo(a_np_.dtype).eps
delta = epsilon**(1.0 / 3.0) delta = epsilon**(1.0 / 3.0)
tol = 20 * delta tol = 20 * delta
with self.session(), test_util.use_gpu(): with self.session():
theoretical, numerical = gradient_checker_v2.compute_gradient( theoretical, numerical = gradient_checker_v2.compute_gradient(
lambda x: math_ops.matmul(x, effective_b_np, **kwargs_), lambda x: math_ops.matmul(x, effective_b_np, **kwargs_),
[effective_a_np], [effective_a_np],

2
tensorflow/python/kernel_tests/matrix_square_root_op_test.py
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@ -32,7 +32,7 @@ class SquareRootOpTest(test.TestCase):
def _verifySquareRoot(self, matrix, np_type): def _verifySquareRoot(self, matrix, np_type):
matrix = matrix.astype(np_type) matrix = matrix.astype(np_type)
with test_util.use_gpu():
# Verify that matmul(sqrtm(A), sqrtm(A)) = A # Verify that matmul(sqrtm(A), sqrtm(A)) = A
sqrt = gen_linalg_ops.matrix_square_root(matrix) sqrt = gen_linalg_ops.matrix_square_root(matrix)
square = math_ops.matmul(sqrt, sqrt) square = math_ops.matmul(sqrt, sqrt)

6
tensorflow/python/kernel_tests/unstack_op_test.py
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@ -41,7 +41,6 @@ class UnstackOpTest(test.TestCase):
def testSimple(self): def testSimple(self):
np.random.seed(7) np.random.seed(7)
with test_util.use_gpu():
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2): for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
for dtype in [ for dtype in [
np.bool, np.float16, np.float32, np.float64, np.int32, np.int64 np.bool, np.float16, np.float32, np.float64, np.int32, np.int64
@ -80,7 +79,7 @@ class UnstackOpTest(test.TestCase):
data = np.random.randn(*shape) data = np.random.randn(*shape)
shapes = [shape[1:]] * shape[0] shapes = [shape[1:]] * shape[0]
for i in xrange(shape[0]): for i in xrange(shape[0]):
with self.cached_session(use_gpu=True): with self.cached_session():
x = constant_op.constant(data) x = constant_op.constant(data)
cs = array_ops.unstack(x, num=shape[0]) cs = array_ops.unstack(x, num=shape[0])
err = gradient_checker.compute_gradient_error(x, shape, cs[i], err = gradient_checker.compute_gradient_error(x, shape, cs[i],
@ -94,7 +93,7 @@ class UnstackOpTest(test.TestCase):
out_shape = list(shape) out_shape = list(shape)
del out_shape[1] del out_shape[1]
for i in xrange(shape[1]): for i in xrange(shape[1]):
with self.cached_session(use_gpu=True): with self.cached_session():
x = constant_op.constant(data) x = constant_op.constant(data)
cs = array_ops.unstack(x, num=shape[1], axis=1) cs = array_ops.unstack(x, num=shape[1], axis=1)
err = gradient_checker.compute_gradient_error(x, shape, cs[i], err = gradient_checker.compute_gradient_error(x, shape, cs[i],
@ -103,7 +102,6 @@ class UnstackOpTest(test.TestCase):
@test_util.run_deprecated_v1 @test_util.run_deprecated_v1
def testInferNum(self): def testInferNum(self):
with self.cached_session():
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2): for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
x = array_ops.placeholder(np.float32, shape=shape) x = array_ops.placeholder(np.float32, shape=shape)
cs = array_ops.unstack(x) cs = array_ops.unstack(x)