218 lines
8.4 KiB
Python
218 lines
8.4 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for inplace_ops."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from six.moves import xrange # pylint: disable=redefined-builtin
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import errors
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import inplace_ops
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from tensorflow.python.platform import test as test_lib
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class InplaceOpsTest(test_util.TensorFlowTestCase):
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def testBasicUpdate(self):
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for dtype in [dtypes.float32, dtypes.int32, dtypes.int64]:
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with test_util.use_gpu():
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x = array_ops.ones([7, 3], dtype)
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y = np.ones([7, 3], dtype.as_numpy_dtype)
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self.assertAllClose(x, y)
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x = inplace_ops.inplace_update(x, [3], array_ops.ones([1, 3], dtype))
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y[3, :] = 1
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self.assertAllClose(x, y)
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x = inplace_ops.inplace_update(x, [-1],
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array_ops.ones([1, 3], dtype) * 2)
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y[-1, :] = 2
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self.assertAllClose(x, y)
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x = inplace_ops.inplace_update(x, 5, array_ops.ones([3], dtype) * 7)
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y[5, :] = 7
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self.assertAllClose(x, y)
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def testBasicUpdateBool(self):
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with test_util.use_gpu():
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x = array_ops.ones([7, 3], dtypes.bool)
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y = np.ones([7, 3], dtypes.bool.as_numpy_dtype)
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self.assertAllClose(x, y)
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x = inplace_ops.inplace_update(x, [3], array_ops.ones([1, 3],
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dtypes.bool))
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y[3, :] = True
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self.assertAllClose(x, y)
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x = inplace_ops.inplace_update(x, [-1],
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array_ops.zeros([1, 3], dtypes.bool))
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y[-1, :] = False
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self.assertAllClose(x, y)
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x = inplace_ops.inplace_update(x, 5, array_ops.zeros([3], dtypes.bool))
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y[5, :] = False
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self.assertAllClose(x, y)
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def testBasicAdd(self):
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for dtype in [dtypes.float32, dtypes.int32, dtypes.int64]:
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with test_util.use_gpu():
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x = array_ops.ones([7, 3], dtype)
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y = np.ones([7, 3], dtype.as_numpy_dtype)
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self.assertAllClose(x, y)
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x = array_ops.inplace_add(x, [3], array_ops.ones([1, 3], dtype))
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y[3, :] += 1
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self.assertAllClose(x, y)
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x = inplace_ops.inplace_add(x, [-1], array_ops.ones([1, 3], dtype) * 2)
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y[-1, :] += 2
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self.assertAllClose(x, y)
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x = inplace_ops.inplace_add(x, 5, array_ops.ones([3], dtype) * 7)
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y[5, :] += 7
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self.assertAllClose(x, y)
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x = inplace_ops.inplace_add(x, None, array_ops.ones([7, 3], dtype) * 99)
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y[:, :] += 99
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self.assertAllClose(x, y)
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def testBasicSub(self):
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for dtype in [dtypes.float32, dtypes.int32, dtypes.int64]:
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with test_util.use_gpu():
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x = array_ops.ones([7, 3], dtype)
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y = np.ones([7, 3], dtype.as_numpy_dtype)
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self.assertAllClose(x, y)
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x = inplace_ops.inplace_sub(x, [3], array_ops.ones([1, 3], dtype))
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y[3, :] -= 1
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self.assertAllClose(x, y)
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x = inplace_ops.inplace_sub(x, [-1], array_ops.ones([1, 3], dtype) * 2)
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y[-1, :] -= 2
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self.assertAllClose(x, y)
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x = inplace_ops.inplace_sub(x, 5, array_ops.ones([3], dtype) * 7)
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y[5, :] -= 7
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self.assertAllClose(x, y)
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x = inplace_ops.inplace_sub(x, None, array_ops.ones([7, 3], dtype) * 99)
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y[:, :] -= 99
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self.assertAllClose(x, y)
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def testRandom(self):
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with test_util.use_gpu():
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d0, d1, d2 = 100, 3, 5
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x = array_ops.zeros([d0, d1, d2])
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y = np.zeros([d0, d1, d2])
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for _ in xrange(20):
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idx = np.random.choice(d0, d0 // 10, replace=False)
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val = np.random.randint(10, size=(d0 // 10, d1, d2))
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op = np.random.randint(3)
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if op == 0:
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x = inplace_ops.inplace_update(x, idx, val)
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y[idx, :] = val
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elif op == 1:
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x = inplace_ops.inplace_add(x, idx, val)
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y[idx, :] += val
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elif op == 2:
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x = inplace_ops.inplace_sub(x, idx, val)
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y[idx, :] -= val
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self.assertAllClose(x, y)
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def testRandom1D(self):
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with test_util.use_gpu():
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d0 = 100
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x = array_ops.zeros([d0])
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y = np.zeros([d0])
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for _ in xrange(20):
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idx = np.random.choice(d0, d0 // 10, replace=False)
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val = np.random.randint(10, size=(d0 // 10))
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op = np.random.randint(3)
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if op == 0:
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x = inplace_ops.inplace_update(x, idx, val)
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y[idx] = val
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elif op == 1:
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x = inplace_ops.inplace_add(x, idx, val)
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y[idx] += val
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elif op == 2:
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x = inplace_ops.inplace_sub(x, idx, val)
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y[idx] -= val
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self.assertAllClose(x, y)
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def testAlias(self):
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with test_util.use_gpu():
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x = array_ops.ones([2, 3])
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y = inplace_ops.alias_inplace_add(x, [0], [[1, 2, 3]])
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with ops.control_dependencies([y]):
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z = array_ops.identity(x)
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_, vy, vz = self.evaluate([x, y, z])
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self.assertAllClose(vy, vz)
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def testError(self):
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with self.assertRaisesRegex(errors.InvalidArgumentError,
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"must be a vector"):
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_ = self.evaluate(inplace_ops.inplace_update([[1.]], [[0]], [[10]]))
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with self.assertRaisesRegex(errors.InvalidArgumentError,
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"x and v shape doesn't match"):
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_ = self.evaluate(inplace_ops.inplace_update([[1.]], [0], [10]))
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with self.assertRaisesRegex(errors.InvalidArgumentError,
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"i and x shape doesn't match"):
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_ = self.evaluate(inplace_ops.inplace_update([[1.]], [0, 1], [[10]]))
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def testEmpty(self):
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for dtype in [
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dtypes.float32, dtypes.float64, dtypes.int32, dtypes.int64, dtypes.bool,
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dtypes.uint8
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]:
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with test_util.use_gpu():
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test_shapes = [(), (1,), (2, 3), (0, 2), (2, 3, 5), (2, 0, 5)]
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for shape in test_shapes:
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val = self.evaluate(inplace_ops.empty(shape, dtype))
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self.assertEqual(val.shape, shape)
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self.assertEqual(val.dtype, dtype.as_numpy_dtype)
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val = self.evaluate(inplace_ops.empty(shape, dtype, init=True))
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self.assertEqual(val.shape, shape)
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self.assertEqual(val.dtype, dtype.as_numpy_dtype)
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self.assertAllEqual(val, np.zeros(shape, dtype.as_numpy_dtype))
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val = self.evaluate(
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inplace_ops.empty_like(array_ops.zeros(shape, dtype)))
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self.assertEqual(val.shape, shape)
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self.assertEqual(val.dtype, dtype.as_numpy_dtype)
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val = self.evaluate(inplace_ops.empty_like(
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array_ops.zeros(shape, dtype), init=True))
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self.assertEqual(val.shape, shape)
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self.assertEqual(val.dtype, dtype.as_numpy_dtype)
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self.assertAllEqual(val, np.zeros(shape, dtype.as_numpy_dtype))
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with test_util.use_gpu():
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val = self.evaluate(inplace_ops.empty((1, 2), dtypes.string, init=True))
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self.assertEqual(val.tolist(), [[b"", b""]])
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val = self.evaluate(inplace_ops.empty((1, 2), dtypes.string, init=False))
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self.assertEqual(val.tolist(), [[b"", b""]])
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def testInplaceOpOnEmptyTensors(self):
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op_fns = [
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inplace_ops.inplace_add,
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inplace_ops.inplace_sub,
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inplace_ops.inplace_update,
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]
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for dtype in [dtypes.float32, dtypes.int32, dtypes.int64]:
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for op_fn in op_fns:
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with test_util.use_gpu():
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x = array_ops.zeros([7, 0], dtype)
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y = np.zeros([7, 0], dtype.as_numpy_dtype)
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self.assertAllClose(x, y)
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x = op_fn(x, [3], array_ops.ones([1, 0], dtype))
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self.assertAllClose(x, y)
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x = op_fn(x, None, array_ops.ones([1, 0], dtype))
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self.assertAllClose(x, y)
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if __name__ == "__main__":
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test_lib.main()
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