4ac9bda9d0
Some test methods are disabled, but all tests now have a new "_xla" version of the test for XLA:GPU testing. This will run 2 different tests. One with XLA and one without. PiperOrigin-RevId: 229149574
564 lines
22 KiB
Python
564 lines
22 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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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 tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes as dtypes_lib
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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 gradient_checker
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from tensorflow.python.ops import gradients_impl
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from tensorflow.python.platform import test
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from tensorflow.python.platform import tf_logging
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class MatrixDiagTest(test.TestCase):
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@test_util.run_deprecated_v1
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def testVector(self):
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with self.session(use_gpu=True):
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v = np.array([1.0, 2.0, 3.0])
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mat = np.diag(v)
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v_diag = array_ops.matrix_diag(v)
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self.assertEqual((3, 3), v_diag.get_shape())
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self.assertAllEqual(v_diag.eval(), mat)
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def _testBatchVector(self, dtype):
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with self.cached_session(use_gpu=True):
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v_batch = np.array([[1.0, 0.0, 3.0], [4.0, 5.0, 6.0]]).astype(dtype)
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mat_batch = np.array([[[1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 3.0]],
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[[4.0, 0.0, 0.0], [0.0, 5.0, 0.0],
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[0.0, 0.0, 6.0]]]).astype(dtype)
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v_batch_diag = array_ops.matrix_diag(v_batch)
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self.assertEqual((2, 3, 3), v_batch_diag.get_shape())
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self.assertAllEqual(v_batch_diag.eval(), mat_batch)
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@test_util.run_deprecated_v1
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def testBatchVector(self):
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self._testBatchVector(np.float32)
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self._testBatchVector(np.float64)
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self._testBatchVector(np.int32)
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self._testBatchVector(np.int64)
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self._testBatchVector(np.bool)
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@test_util.run_deprecated_v1
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def testInvalidShape(self):
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with self.assertRaisesRegexp(ValueError, "must be at least rank 1"):
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array_ops.matrix_diag(0)
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@test_util.run_deprecated_v1
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@test_util.disable_xla("This test never passed for XLA")
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def testInvalidShapeAtEval(self):
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with self.session(use_gpu=True):
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v = array_ops.placeholder(dtype=dtypes_lib.float32)
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with self.assertRaisesOpError("input must be at least 1-dim"):
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array_ops.matrix_diag(v).eval(feed_dict={v: 0.0})
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@test_util.run_deprecated_v1
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def testGrad(self):
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shapes = ((3,), (7, 4))
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with self.session(use_gpu=True):
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for shape in shapes:
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x = constant_op.constant(np.random.rand(*shape), np.float32)
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y = array_ops.matrix_diag(x)
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error = gradient_checker.compute_gradient_error(x,
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x.get_shape().as_list(),
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y,
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y.get_shape().as_list())
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self.assertLess(error, 1e-4)
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class MatrixSetDiagTest(test.TestCase):
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@test_util.run_deprecated_v1
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def testSquare(self):
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with self.session(use_gpu=True):
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v = np.array([1.0, 2.0, 3.0])
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mat = np.array([[0.0, 1.0, 0.0], [1.0, 0.0, 1.0], [1.0, 1.0, 1.0]])
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mat_set_diag = np.array([[1.0, 1.0, 0.0], [1.0, 2.0, 1.0],
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[1.0, 1.0, 3.0]])
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output = array_ops.matrix_set_diag(mat, v)
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self.assertEqual((3, 3), output.get_shape())
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self.assertAllEqual(mat_set_diag, self.evaluate(output))
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@test_util.run_deprecated_v1
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def testRectangular(self):
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with self.session(use_gpu=True):
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v = np.array([3.0, 4.0])
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mat = np.array([[0.0, 1.0, 0.0], [1.0, 0.0, 1.0]])
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expected = np.array([[3.0, 1.0, 0.0], [1.0, 4.0, 1.0]])
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output = array_ops.matrix_set_diag(mat, v)
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self.assertEqual((2, 3), output.get_shape())
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self.assertAllEqual(expected, self.evaluate(output))
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v = np.array([3.0, 4.0])
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mat = np.array([[0.0, 1.0], [1.0, 0.0], [1.0, 1.0]])
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expected = np.array([[3.0, 1.0], [1.0, 4.0], [1.0, 1.0]])
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output = array_ops.matrix_set_diag(mat, v)
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self.assertEqual((3, 2), output.get_shape())
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self.assertAllEqual(expected, self.evaluate(output))
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def _testSquareBatch(self, dtype):
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with self.cached_session(use_gpu=True):
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v_batch = np.array([[-1.0, 0.0, -3.0], [-4.0, -5.0, -6.0]]).astype(dtype)
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mat_batch = np.array([[[1.0, 0.0, 3.0], [0.0, 2.0, 0.0], [1.0, 0.0, 3.0]],
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[[4.0, 0.0, 4.0], [0.0, 5.0, 0.0],
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[2.0, 0.0, 6.0]]]).astype(dtype)
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mat_set_diag_batch = np.array([[[-1.0, 0.0, 3.0], [0.0, 0.0, 0.0],
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[1.0, 0.0, -3.0]],
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[[-4.0, 0.0, 4.0], [0.0, -5.0, 0.0],
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[2.0, 0.0, -6.0]]]).astype(dtype)
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output = array_ops.matrix_set_diag(mat_batch, v_batch)
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self.assertEqual((2, 3, 3), output.get_shape())
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self.assertAllEqual(mat_set_diag_batch, self.evaluate(output))
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@test_util.run_deprecated_v1
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def testSquareBatch(self):
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self._testSquareBatch(np.float32)
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self._testSquareBatch(np.float64)
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self._testSquareBatch(np.int32)
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self._testSquareBatch(np.int64)
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self._testSquareBatch(np.bool)
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@test_util.run_deprecated_v1
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def testRectangularBatch(self):
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with self.session(use_gpu=True):
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v_batch = np.array([[-1.0, -2.0], [-4.0, -5.0]])
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mat_batch = np.array([[[1.0, 0.0, 3.0], [0.0, 2.0, 0.0]],
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[[4.0, 0.0, 4.0], [0.0, 5.0, 0.0]]])
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mat_set_diag_batch = np.array([[[-1.0, 0.0, 3.0], [0.0, -2.0, 0.0]],
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[[-4.0, 0.0, 4.0], [0.0, -5.0, 0.0]]])
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output = array_ops.matrix_set_diag(mat_batch, v_batch)
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self.assertEqual((2, 2, 3), output.get_shape())
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self.assertAllEqual(mat_set_diag_batch, self.evaluate(output))
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@test_util.run_deprecated_v1
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def testInvalidShape(self):
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with self.assertRaisesRegexp(ValueError, "must be at least rank 2"):
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array_ops.matrix_set_diag(0, [0])
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with self.assertRaisesRegexp(ValueError, "must be at least rank 1"):
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array_ops.matrix_set_diag([[0]], 0)
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@test_util.run_deprecated_v1
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def testInvalidShapeAtEval(self):
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with self.session(use_gpu=True):
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v = array_ops.placeholder(dtype=dtypes_lib.float32)
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with self.assertRaisesOpError("input must be at least 2-dim"):
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array_ops.matrix_set_diag(v, [v]).eval(feed_dict={v: 0.0})
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with self.assertRaisesOpError(
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r"but received input shape: \[1,1\] and diagonal shape: \[\]"):
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array_ops.matrix_set_diag([[v]], v).eval(feed_dict={v: 0.0})
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@test_util.run_deprecated_v1
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def testGrad(self):
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shapes = ((3, 4, 4), (3, 3, 4), (3, 4, 3), (7, 4, 8, 8))
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with self.session(use_gpu=True):
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for shape in shapes:
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x = constant_op.constant(
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np.random.rand(*shape), dtype=dtypes_lib.float32)
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diag_shape = shape[:-2] + (min(shape[-2:]),)
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x_diag = constant_op.constant(
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np.random.rand(*diag_shape), dtype=dtypes_lib.float32)
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y = array_ops.matrix_set_diag(x, x_diag)
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error_x = gradient_checker.compute_gradient_error(
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x,
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x.get_shape().as_list(), y,
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y.get_shape().as_list())
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self.assertLess(error_x, 1e-4)
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error_x_diag = gradient_checker.compute_gradient_error(
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x_diag,
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x_diag.get_shape().as_list(), y,
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y.get_shape().as_list())
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self.assertLess(error_x_diag, 1e-4)
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@test_util.run_deprecated_v1
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def testGradWithNoShapeInformation(self):
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with self.session(use_gpu=True) as sess:
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v = array_ops.placeholder(dtype=dtypes_lib.float32)
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mat = array_ops.placeholder(dtype=dtypes_lib.float32)
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grad_input = array_ops.placeholder(dtype=dtypes_lib.float32)
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output = array_ops.matrix_set_diag(mat, v)
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grads = gradients_impl.gradients(output, [mat, v], grad_ys=grad_input)
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grad_input_val = np.random.rand(3, 3).astype(np.float32)
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grad_vals = sess.run(
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grads,
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feed_dict={
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v: 2 * np.ones(3),
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mat: np.ones((3, 3)),
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grad_input: grad_input_val
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})
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self.assertAllEqual(np.diag(grad_input_val), grad_vals[1])
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self.assertAllEqual(grad_input_val - np.diag(np.diag(grad_input_val)),
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grad_vals[0])
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class MatrixDiagPartTest(test.TestCase):
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@test_util.run_deprecated_v1
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def testSquare(self):
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with self.session(use_gpu=True):
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v = np.array([1.0, 2.0, 3.0])
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mat = np.diag(v)
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mat_diag = array_ops.matrix_diag_part(mat)
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self.assertEqual((3,), mat_diag.get_shape())
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self.assertAllEqual(mat_diag.eval(), v)
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@test_util.run_deprecated_v1
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def testRectangular(self):
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with self.session(use_gpu=True):
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mat = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
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mat_diag = array_ops.matrix_diag_part(mat)
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self.assertAllEqual(mat_diag.eval(), np.array([1.0, 5.0]))
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mat = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
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mat_diag = array_ops.matrix_diag_part(mat)
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self.assertAllEqual(mat_diag.eval(), np.array([1.0, 4.0]))
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def _testSquareBatch(self, dtype):
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with self.cached_session(use_gpu=True):
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v_batch = np.array([[1.0, 0.0, 3.0], [4.0, 5.0, 6.0]]).astype(dtype)
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mat_batch = np.array([[[1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 3.0]],
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[[4.0, 0.0, 0.0], [0.0, 5.0, 0.0],
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[0.0, 0.0, 6.0]]]).astype(dtype)
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self.assertEqual(mat_batch.shape, (2, 3, 3))
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mat_batch_diag = array_ops.matrix_diag_part(mat_batch)
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self.assertEqual((2, 3), mat_batch_diag.get_shape())
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self.assertAllEqual(mat_batch_diag.eval(), v_batch)
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@test_util.run_deprecated_v1
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def testSquareBatch(self):
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self._testSquareBatch(np.float32)
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self._testSquareBatch(np.float64)
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self._testSquareBatch(np.int32)
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self._testSquareBatch(np.int64)
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self._testSquareBatch(np.bool)
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@test_util.run_deprecated_v1
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def testRectangularBatch(self):
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with self.session(use_gpu=True):
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v_batch = np.array([[1.0, 2.0], [4.0, 5.0]])
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mat_batch = np.array([[[1.0, 0.0, 0.0], [0.0, 2.0, 0.0]],
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[[4.0, 0.0, 0.0], [0.0, 5.0, 0.0]]])
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self.assertEqual(mat_batch.shape, (2, 2, 3))
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mat_batch_diag = array_ops.matrix_diag_part(mat_batch)
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self.assertEqual((2, 2), mat_batch_diag.get_shape())
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self.assertAllEqual(mat_batch_diag.eval(), v_batch)
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@test_util.run_deprecated_v1
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def testInvalidShape(self):
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with self.assertRaisesRegexp(ValueError, "must be at least rank 2"):
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array_ops.matrix_diag_part(0)
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@test_util.run_deprecated_v1
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@test_util.disable_xla("This test never passed for XLA")
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def testInvalidShapeAtEval(self):
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with self.session(use_gpu=True):
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v = array_ops.placeholder(dtype=dtypes_lib.float32)
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with self.assertRaisesOpError("input must be at least 2-dim"):
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array_ops.matrix_diag_part(v).eval(feed_dict={v: 0.0})
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@test_util.run_deprecated_v1
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def testGrad(self):
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shapes = ((3, 3), (2, 3), (3, 2), (5, 3, 3))
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with self.session(use_gpu=True):
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for shape in shapes:
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x = constant_op.constant(np.random.rand(*shape), dtype=np.float32)
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y = array_ops.matrix_diag_part(x)
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error = gradient_checker.compute_gradient_error(x,
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x.get_shape().as_list(),
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y,
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y.get_shape().as_list())
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self.assertLess(error, 1e-4)
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class DiagTest(test.TestCase):
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def _diagOp(self, diag, dtype, expected_ans, use_gpu):
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with self.cached_session(use_gpu=use_gpu):
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tf_ans = array_ops.diag(ops.convert_to_tensor(diag.astype(dtype)))
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out = self.evaluate(tf_ans)
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tf_ans_inv = array_ops.diag_part(expected_ans)
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inv_out = self.evaluate(tf_ans_inv)
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self.assertAllClose(out, expected_ans)
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self.assertAllClose(inv_out, diag)
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self.assertShapeEqual(expected_ans, tf_ans)
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self.assertShapeEqual(diag, tf_ans_inv)
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def diagOp(self, diag, dtype, expected_ans):
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self._diagOp(diag, dtype, expected_ans, False)
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self._diagOp(diag, dtype, expected_ans, True)
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def testEmptyTensor(self):
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x = np.array([])
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expected_ans = np.empty([0, 0])
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self.diagOp(x, np.int32, expected_ans)
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def testRankOneIntTensor(self):
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x = np.array([1, 2, 3])
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expected_ans = np.array([[1, 0, 0], [0, 2, 0], [0, 0, 3]])
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self.diagOp(x, np.int32, expected_ans)
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self.diagOp(x, np.int64, expected_ans)
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def testRankOneFloatTensor(self):
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x = np.array([1.1, 2.2, 3.3])
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expected_ans = np.array([[1.1, 0, 0], [0, 2.2, 0], [0, 0, 3.3]])
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self.diagOp(x, np.float32, expected_ans)
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self.diagOp(x, np.float64, expected_ans)
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def testRankOneComplexTensor(self):
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for dtype in [np.complex64, np.complex128]:
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x = np.array([1.1 + 1.1j, 2.2 + 2.2j, 3.3 + 3.3j], dtype=dtype)
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expected_ans = np.array(
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[[1.1 + 1.1j, 0 + 0j, 0 + 0j], [0 + 0j, 2.2 + 2.2j, 0 + 0j],
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[0 + 0j, 0 + 0j, 3.3 + 3.3j]],
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dtype=dtype)
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self.diagOp(x, dtype, expected_ans)
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def testRankTwoIntTensor(self):
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x = np.array([[1, 2, 3], [4, 5, 6]])
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expected_ans = np.array([[[[1, 0, 0], [0, 0, 0]], [[0, 2, 0], [0, 0, 0]],
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[[0, 0, 3], [0, 0, 0]]],
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[[[0, 0, 0], [4, 0, 0]], [[0, 0, 0], [0, 5, 0]],
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[[0, 0, 0], [0, 0, 6]]]])
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self.diagOp(x, np.int32, expected_ans)
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self.diagOp(x, np.int64, expected_ans)
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def testRankTwoFloatTensor(self):
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x = np.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]])
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expected_ans = np.array(
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[[[[1.1, 0, 0], [0, 0, 0]], [[0, 2.2, 0], [0, 0, 0]],
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[[0, 0, 3.3], [0, 0, 0]]], [[[0, 0, 0], [4.4, 0, 0]],
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[[0, 0, 0], [0, 5.5, 0]], [[0, 0, 0],
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[0, 0, 6.6]]]])
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self.diagOp(x, np.float32, expected_ans)
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self.diagOp(x, np.float64, expected_ans)
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def testRankTwoComplexTensor(self):
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for dtype in [np.complex64, np.complex128]:
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x = np.array(
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[[1.1 + 1.1j, 2.2 + 2.2j, 3.3 + 3.3j],
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[4.4 + 4.4j, 5.5 + 5.5j, 6.6 + 6.6j]],
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dtype=dtype)
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expected_ans = np.array(
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[[[[1.1 + 1.1j, 0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j, 0 + 0j]], [
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[0 + 0j, 2.2 + 2.2j, 0 + 0j], [0 + 0j, 0 + 0j, 0 + 0j]
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], [[0 + 0j, 0 + 0j, 3.3 + 3.3j], [0 + 0j, 0 + 0j, 0 + 0j]]], [[
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[0 + 0j, 0 + 0j, 0 + 0j], [4.4 + 4.4j, 0 + 0j, 0 + 0j]
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], [[0 + 0j, 0 + 0j, 0 + 0j], [0 + 0j, 5.5 + 5.5j, 0 + 0j]
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], [[0 + 0j, 0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j, 6.6 + 6.6j]]]],
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dtype=dtype)
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self.diagOp(x, dtype, expected_ans)
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def testRankThreeFloatTensor(self):
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x = np.array([[[1.1, 2.2], [3.3, 4.4]], [[5.5, 6.6], [7.7, 8.8]]])
|
|
expected_ans = np.array([[[[[[1.1, 0], [0, 0]], [[0, 0], [0, 0]]],
|
|
[[[0, 2.2], [0, 0]], [[0, 0], [0, 0]]]],
|
|
[[[[0, 0], [3.3, 0]], [[0, 0], [0, 0]]],
|
|
[[[0, 0], [0, 4.4]], [[0, 0], [0, 0]]]]],
|
|
[[[[[0, 0], [0, 0]], [[5.5, 0], [0, 0]]],
|
|
[[[0, 0], [0, 0]], [[0, 6.6], [0, 0]]]],
|
|
[[[[0, 0], [0, 0]], [[0, 0], [7.7, 0]]],
|
|
[[[0, 0], [0, 0]], [[0, 0], [0, 8.8]]]]]])
|
|
self.diagOp(x, np.float32, expected_ans)
|
|
self.diagOp(x, np.float64, expected_ans)
|
|
|
|
def testRankThreeComplexTensor(self):
|
|
for dtype in [np.complex64, np.complex128]:
|
|
x = np.array(
|
|
[[[1.1 + 1.1j, 2.2 + 2.2j], [3.3 + 3.3j, 4.4 + 4.4j]],
|
|
[[5.5 + 5.5j, 6.6 + 6.6j], [7.7 + 7.7j, 8.8 + 8.8j]]],
|
|
dtype=dtype)
|
|
expected_ans = np.array(
|
|
[[[[[[1.1 + 1.1j, 0 + 0j], [0 + 0j, 0 + 0j]], [[0 + 0j, 0 + 0j], [
|
|
0 + 0j, 0 + 0j
|
|
]]], [[[0 + 0j, 2.2 + 2.2j], [0 + 0j, 0 + 0j]], [[0 + 0j, 0 + 0j], [
|
|
0 + 0j, 0 + 0j
|
|
]]]], [[[[0 + 0j, 0 + 0j], [3.3 + 3.3j, 0 + 0j]], [[0 + 0j, 0 + 0j], [
|
|
0 + 0j, 0 + 0j
|
|
]]], [[[0 + 0j, 0 + 0j], [0 + 0j, 4.4 + 4.4j]], [[0 + 0j, 0 + 0j], [
|
|
0 + 0j, 0 + 0j
|
|
]]]]], [[[[[0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j]], [
|
|
[5.5 + 5.5j, 0 + 0j], [0 + 0j, 0 + 0j]
|
|
]], [[[0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j]], [[0 + 0j, 6.6 + 6.6j], [
|
|
0 + 0j, 0 + 0j
|
|
]]]], [[[[0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j]], [[0 + 0j, 0 + 0j], [
|
|
7.7 + 7.7j, 0 + 0j
|
|
]]], [[[0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j]],
|
|
[[0 + 0j, 0 + 0j], [0 + 0j, 8.8 + 8.8j]]]]]],
|
|
dtype=dtype)
|
|
self.diagOp(x, dtype, expected_ans)
|
|
|
|
def testRankFourNumberTensor(self):
|
|
for dtype in [np.float32, np.float64, np.int64, np.int32]:
|
|
# Input with shape [2, 1, 2, 3]
|
|
x = np.array(
|
|
[[[[1, 2, 3], [4, 5, 6]]], [[[7, 8, 9], [10, 11, 12]]]], dtype=dtype)
|
|
# Output with shape [2, 1, 2, 3, 2, 1, 2, 3]
|
|
expected_ans = np.array(
|
|
[[[[[[[[1, 0, 0], [0, 0, 0]]], [[[0, 0, 0], [0, 0, 0]]]], [
|
|
[[[0, 2, 0], [0, 0, 0]]], [[[0, 0, 0], [0, 0, 0]]]
|
|
], [[[[0, 0, 3], [0, 0, 0]]], [[[0, 0, 0], [0, 0, 0]]]]], [[
|
|
[[[0, 0, 0], [4, 0, 0]]], [[[0, 0, 0], [0, 0, 0]]]
|
|
], [[[[0, 0, 0], [0, 5, 0]]], [[[0, 0, 0], [0, 0, 0]]]], [
|
|
[[[0, 0, 0], [0, 0, 6]]], [[[0, 0, 0], [0, 0, 0]]]
|
|
]]]], [[[[[[[0, 0, 0], [0, 0, 0]]], [[[7, 0, 0], [0, 0, 0]]]], [
|
|
[[[0, 0, 0], [0, 0, 0]]], [[[0, 8, 0], [0, 0, 0]]]
|
|
], [[[[0, 0, 0], [0, 0, 0]]], [[[0, 0, 9], [0, 0, 0]]]]], [[
|
|
[[[0, 0, 0], [0, 0, 0]]], [[[0, 0, 0], [10, 0, 0]]]
|
|
], [[[[0, 0, 0], [0, 0, 0]]], [[[0, 0, 0], [0, 11, 0]]]
|
|
], [[[[0, 0, 0], [0, 0, 0]]], [[[0, 0, 0], [0, 0, 12]]]]]]]],
|
|
dtype=dtype)
|
|
self.diagOp(x, dtype, expected_ans)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testInvalidRank(self):
|
|
with self.assertRaisesRegexp(ValueError, "must be at least rank 1"):
|
|
array_ops.diag(0.0)
|
|
|
|
|
|
class DiagPartOpTest(test.TestCase):
|
|
|
|
def setUp(self):
|
|
np.random.seed(0)
|
|
|
|
def _diagPartOp(self, tensor, dtype, expected_ans, use_gpu):
|
|
with self.cached_session(use_gpu=use_gpu):
|
|
tensor = ops.convert_to_tensor(tensor.astype(dtype))
|
|
tf_ans_inv = array_ops.diag_part(tensor)
|
|
inv_out = self.evaluate(tf_ans_inv)
|
|
self.assertAllClose(inv_out, expected_ans)
|
|
self.assertShapeEqual(expected_ans, tf_ans_inv)
|
|
|
|
def diagPartOp(self, tensor, dtype, expected_ans):
|
|
self._diagPartOp(tensor, dtype, expected_ans, False)
|
|
self._diagPartOp(tensor, dtype, expected_ans, True)
|
|
|
|
def testRankTwoFloatTensor(self):
|
|
x = np.random.rand(3, 3)
|
|
i = np.arange(3)
|
|
expected_ans = x[i, i]
|
|
self.diagPartOp(x, np.float32, expected_ans)
|
|
self.diagPartOp(x, np.float64, expected_ans)
|
|
|
|
def testRankFourFloatTensorUnknownShape(self):
|
|
x = np.random.rand(3, 3)
|
|
i = np.arange(3)
|
|
expected_ans = x[i, i]
|
|
for shape in None, (None, 3), (3, None):
|
|
with self.cached_session(use_gpu=False):
|
|
t = ops.convert_to_tensor(x.astype(np.float32))
|
|
t.set_shape(shape)
|
|
tf_ans = array_ops.diag_part(t)
|
|
out = self.evaluate(tf_ans)
|
|
self.assertAllClose(out, expected_ans)
|
|
self.assertShapeEqual(expected_ans, tf_ans)
|
|
|
|
def testRankFourFloatTensor(self):
|
|
x = np.random.rand(2, 3, 2, 3)
|
|
i = np.arange(2)[:, None]
|
|
j = np.arange(3)
|
|
expected_ans = x[i, j, i, j]
|
|
self.diagPartOp(x, np.float32, expected_ans)
|
|
self.diagPartOp(x, np.float64, expected_ans)
|
|
|
|
def testRankSixFloatTensor(self):
|
|
x = np.random.rand(2, 2, 2, 2, 2, 2)
|
|
i = np.arange(2)[:, None, None]
|
|
j = np.arange(2)[:, None]
|
|
k = np.arange(2)
|
|
expected_ans = x[i, j, k, i, j, k]
|
|
self.diagPartOp(x, np.float32, expected_ans)
|
|
self.diagPartOp(x, np.float64, expected_ans)
|
|
|
|
def testRankEightComplexTensor(self):
|
|
x = np.random.rand(2, 2, 2, 3, 2, 2, 2, 3)
|
|
i = np.arange(2)[:, None, None, None]
|
|
j = np.arange(2)[:, None, None]
|
|
k = np.arange(2)[:, None]
|
|
l = np.arange(3)
|
|
expected_ans = x[i, j, k, l, i, j, k, l]
|
|
self.diagPartOp(x, np.complex64, expected_ans)
|
|
self.diagPartOp(x, np.complex128, expected_ans)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testOddRank(self):
|
|
w = np.random.rand(2)
|
|
x = np.random.rand(2, 2, 2)
|
|
self.assertRaises(ValueError, self.diagPartOp, w, np.float32, 0)
|
|
self.assertRaises(ValueError, self.diagPartOp, x, np.float32, 0)
|
|
with self.assertRaises(ValueError):
|
|
array_ops.diag_part(0.0)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testUnevenDimensions(self):
|
|
w = np.random.rand(2, 5)
|
|
x = np.random.rand(2, 1, 2, 3)
|
|
self.assertRaises(ValueError, self.diagPartOp, w, np.float32, 0)
|
|
self.assertRaises(ValueError, self.diagPartOp, x, np.float32, 0)
|
|
|
|
|
|
class DiagGradOpTest(test.TestCase):
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testDiagGrad(self):
|
|
np.random.seed(0)
|
|
shapes = ((3,), (3, 3), (3, 3, 3))
|
|
dtypes = (dtypes_lib.float32, dtypes_lib.float64)
|
|
with self.session(use_gpu=False):
|
|
errors = []
|
|
for shape in shapes:
|
|
for dtype in dtypes:
|
|
x1 = constant_op.constant(np.random.rand(*shape), dtype=dtype)
|
|
y = array_ops.diag(x1)
|
|
error = gradient_checker.compute_gradient_error(
|
|
x1,
|
|
x1.get_shape().as_list(), y,
|
|
y.get_shape().as_list())
|
|
tf_logging.info("error = %f", error)
|
|
self.assertLess(error, 1e-4)
|
|
|
|
|
|
class DiagGradPartOpTest(test.TestCase):
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testDiagPartGrad(self):
|
|
np.random.seed(0)
|
|
shapes = ((3, 3), (3, 3, 3, 3))
|
|
dtypes = (dtypes_lib.float32, dtypes_lib.float64)
|
|
with self.session(use_gpu=False):
|
|
errors = []
|
|
for shape in shapes:
|
|
for dtype in dtypes:
|
|
x1 = constant_op.constant(np.random.rand(*shape), dtype=dtype)
|
|
y = array_ops.diag_part(x1)
|
|
error = gradient_checker.compute_gradient_error(
|
|
x1,
|
|
x1.get_shape().as_list(), y,
|
|
y.get_shape().as_list())
|
|
tf_logging.info("error = %f", error)
|
|
self.assertLess(error, 1e-4)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test.main()
|