634c902f4a
Previously randn()'s results were cast to np.bool resuling in True being the only value often. Also, check both the 0th and the -1th axis. PiperOrigin-RevId: 308109624 Change-Id: I2b1b8bc6e6111874ad6f33c634d4d8a85c444882
223 lines
8.0 KiB
Python
223 lines
8.0 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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"""Functional tests for Unstack Op."""
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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 constant_op
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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.platform import test
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def np_split_squeeze(array, axis):
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axis_len = array.shape[axis]
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return [
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np.squeeze(
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arr, axis=(axis,)) for arr in np.split(
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array, axis_len, axis=axis)
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]
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class UnstackOpTest(test.TestCase):
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def randn(self, shape, dtype):
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data = np.random.randn(*shape)
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if dtype == np.bool:
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return data < 0 # Naive casting yields True with P(1)!
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else:
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return data.astype(dtype)
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def unstackReference(self, data, axis):
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"""Use numpy primitives to implement unstack equivalent."""
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result = []
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rank = len(data.shape)
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axis = axis + rank if axis < 0 else axis
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for k in range(data.shape[axis]):
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axis = rank + axis if axis < 0 else axis
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# Slice in axis dimension of k'th slice.
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# e.g. if rank=4 k=2, axis=2 then equivalent of data[:,:,2,:]
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# Give error with loop context
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slice_spec = tuple(
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slice(None) if i != axis else k for i in range(rank))
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result.append(data.__getitem__(slice_spec))
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return result
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def testSimple(self):
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np.random.seed(7)
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for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
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rank = len(shape)
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for axis in range(-rank, rank):
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for dtype in [
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np.bool, np.float16, np.float32, np.float64, np.uint8, np.int32,
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np.int64
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]:
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data = self.randn(shape, dtype)
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# Convert data to a single tensorflow tensor
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x = constant_op.constant(data)
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# Unstack into a list of tensors
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ref = self.unstackReference(data, axis)
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cs = array_ops.unstack(x, axis=axis)
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self.assertEqual(type(cs), list)
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self.assertEqual(len(cs), shape[axis])
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for k, c in enumerate(cs):
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with self.subTest(shape=shape, k=k, axis=axis, dtype=dtype):
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self.assertAllEqual(ref[k], self.evaluate(c))
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def testSimpleGpu(self):
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if not test_util.is_gpu_available():
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self.skipTest('No GPU available')
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np.random.seed(7)
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with test_util.force_gpu():
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for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
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rank = len(shape)
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for axis in range(-rank, rank):
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for dtype in [
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np.bool, np.float16, np.float32, np.float64, np.uint8, np.int32,
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np.int64
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]:
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data = self.randn(shape, dtype)
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# Convert data to a single tensorflow tensor
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x = constant_op.constant(data)
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# Unstack into a list of tensors
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ref = self.unstackReference(data, axis)
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cs = array_ops.unstack(x, axis=axis)
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self.assertEqual(type(cs), list)
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self.assertEqual(len(cs), shape[axis])
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for k, c in enumerate(cs):
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# Give error with loop context
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with self.subTest(shape=shape, k=k, axis=axis, dtype=dtype):
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self.assertAllEqual(ref[k], self.evaluate(c))
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@test_util.run_deprecated_v1
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def testGradientsAxis0(self):
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for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
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data = np.random.randn(*shape)
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shapes = [shape[1:]] * shape[0]
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for i in xrange(shape[0]):
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with self.cached_session():
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x = constant_op.constant(data)
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cs = array_ops.unstack(x, num=shape[0])
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err = gradient_checker.compute_gradient_error(x, shape, cs[i],
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shapes[i])
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self.assertLess(err, 1e-6)
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@test_util.run_deprecated_v1
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def testGradientsAxis1(self):
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for shape in (2, 3), (3, 2), (4, 3, 2):
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data = np.random.randn(*shape)
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out_shape = list(shape)
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del out_shape[1]
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for i in xrange(shape[1]):
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with self.cached_session():
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x = constant_op.constant(data)
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cs = array_ops.unstack(x, num=shape[1], axis=1)
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err = gradient_checker.compute_gradient_error(x, shape, cs[i],
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out_shape)
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self.assertLess(err, 1e-6)
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@test_util.run_deprecated_v1
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def testInferNum(self):
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for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
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x = array_ops.placeholder(np.float32, shape=shape)
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cs = array_ops.unstack(x)
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self.assertEqual(type(cs), list)
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self.assertEqual(len(cs), shape[0])
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@test_util.run_deprecated_v1
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def testCannotInferNumFromUnknownShape(self):
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x = array_ops.placeholder(np.float32)
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with self.assertRaisesRegexp(ValueError,
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r'Cannot infer num from shape <unknown>'):
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array_ops.unstack(x)
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@test_util.run_deprecated_v1
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def testUnknownShapeOkWithNum(self):
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x = array_ops.placeholder(np.float32)
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array_ops.unstack(x, num=2)
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@test_util.run_deprecated_v1
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def testCannotInferNumFromNoneShape(self):
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x = array_ops.placeholder(np.float32, shape=(None,))
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with self.assertRaisesRegexp(ValueError,
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r'Cannot infer num from shape \((\?|None),\)'):
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array_ops.unstack(x)
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def testAgainstNumpy(self):
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# For 1 to 5 dimensions.
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for i in range(1, 6):
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a = np.random.random(np.random.permutation(i) + 1)
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# For all the possible axis to split it, including negative indices.
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for j in range(-i, i):
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expected = np_split_squeeze(a, j)
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actual_unstack = self.evaluate(array_ops.unstack(a, axis=j))
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self.assertAllEqual(expected, actual_unstack)
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def testAxis0Default(self):
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a = constant_op.constant([[1, 2, 3], [4, 5, 6]], name='a')
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unstacked = self.evaluate(array_ops.unstack(a))
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self.assertEqual(len(unstacked), 2)
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self.assertAllEqual(unstacked[0], [1, 2, 3])
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self.assertAllEqual(unstacked[1], [4, 5, 6])
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def testAxisOutOfRange(self):
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a = constant_op.constant([[1, 2, 3], [4, 5, 6]], name='a')
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with self.assertRaisesRegexp(ValueError, r'axis = 2 not in \[-2, 2\)'):
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array_ops.unstack(a, axis=2)
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def testAxisOutOfNegativeRange(self):
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a = constant_op.constant([[1, 2, 3], [4, 5, 6]], name='a')
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with self.assertRaisesRegexp(ValueError, r'axis = -3 not in \[-2, 2\)'):
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array_ops.unstack(a, axis=-3)
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def testZeroLengthDim(self):
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x = array_ops.zeros(shape=(0, 1, 2))
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y = self.evaluate(array_ops.unstack(x, axis=1)[0])
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self.assertEqual(y.shape, (0, 2))
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def testComplexGpu(self):
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if not test_util.is_gpu_available():
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self.skipTest('No GPU available')
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np.random.seed(7)
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with test_util.force_gpu():
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for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
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for dtype in [np.complex64, np.complex128]:
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data = np.random.randn(*shape).astype(dtype)
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# Convert data to a single tensorflow tensor
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x = constant_op.constant(data)
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# Unstack into a list of tensors
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cs = array_ops.unstack(x, num=shape[0])
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self.assertEqual(type(cs), list)
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self.assertEqual(len(cs), shape[0])
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cs = [self.evaluate(c) for c in cs]
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self.assertAllEqual(cs, data)
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if __name__ == '__main__':
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test.main()
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