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-rw-r--r--tensorflow/python/kernel_tests/scan_ops_test.py229
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+# Copyright 2016 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.
+# ==============================================================================
+
+"""Functional tests for scan ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from itertools import combinations
+
+import numpy as np
+import tensorflow as tf
+
+
+def numpy_reverse(x, axis):
+ ix = [slice(None, None, -1)
+ if i == axis else slice(None) for i in range(len(x.shape))]
+ return x[ix]
+
+def handle_options(func, x, axis, exclusive, reverse):
+ """Adds tf options to numpy scan ops"""
+ if reverse:
+ x = numpy_reverse(x, axis)
+
+ if exclusive:
+ ix_head = [slice(0, 1) if i == axis else slice(None)
+ for i in range(len(x.shape))]
+ ix_init = [slice(0, -1) if i == axis else slice(None)
+ for i in range(len(x.shape))]
+ if func == np.cumsum:
+ init = np.zeros_like(x[ix_head])
+ elif func == np.cumprod:
+ init = np.ones_like(x[ix_head])
+ else:
+ raise ValueError("Unknown scan function")
+ x = np.concatenate([init, func(x[ix_init], axis)], axis=axis)
+ else:
+ x = func(x, axis=axis)
+
+ if reverse:
+ x = numpy_reverse(x, axis)
+ return x
+
+class CumsumTest(tf.test.TestCase):
+
+ valid_dtypes = [np.int32, np.int64, np.float16, np.float32,
+ np.float64, np.complex64, np.complex128]
+
+ def _compare(self, x, axis, exclusive, reverse, use_gpu=False):
+ np_out = handle_options(np.cumsum, x, axis, exclusive, reverse)
+ with self.test_session(use_gpu=use_gpu):
+ tf_out = tf.cumsum(x, axis, exclusive, reverse).eval()
+
+ self.assertAllClose(np_out, tf_out)
+
+ def _compareAll(self, x, axis):
+ for exclusive in [True, False]:
+ for reverse in [True, False]:
+ for use_gpu in [True, False]:
+ self._compare(x, axis, exclusive, reverse, use_gpu)
+
+ def test1D(self):
+ for dtype in self.valid_dtypes:
+ x = np.arange(1, 6).reshape([5]).astype(dtype)
+ self._compareAll(x, 0)
+
+ def test2D(self):
+ for dtype in self.valid_dtypes:
+ x = np.arange(0, 10).reshape([2, 5]).astype(dtype)
+ self._compareAll(x, 0)
+ self._compareAll(x, 1)
+
+ def test3D(self):
+ for dtype in self.valid_dtypes:
+ x = np.arange(0, 20).reshape([2, 2, 5]).astype(dtype)
+ self._compareAll(x, 0)
+ self._compareAll(x, 1)
+ self._compareAll(x, 2)
+
+ def testInvalidAxis(self):
+ x = np.arange(0, 10).reshape([2, 5]).astype(np.float32)
+ input_tensor = tf.convert_to_tensor(x)
+ with self.test_session():
+ with self.assertRaisesWithPredicateMatch(
+ tf.errors.InvalidArgumentError,
+ lambda e: "Expected scan axis in the range" in str(e)):
+ tf.cumsum(input_tensor, -1).eval()
+ with self.assertRaisesWithPredicateMatch(
+ tf.errors.InvalidArgumentError,
+ lambda e: "Expected scan axis in the range" in str(e)):
+ tf.cumsum(input_tensor, 2).eval()
+ with self.assertRaisesWithPredicateMatch(
+ tf.errors.InvalidArgumentError,
+ lambda e: "axis must be a scalar" in str(e)):
+ tf.cumsum(input_tensor, [0]).eval()
+
+ def _compareGradient(self, shape, axis, exclusive, reverse):
+ x = np.arange(0, 50).reshape(shape).astype(np.float64)
+ with self.test_session():
+ t = tf.convert_to_tensor(x)
+ result = tf.cumsum(t, axis, exclusive, reverse)
+ jacob_t, jacob_n = tf.test.compute_gradient(t,
+ shape,
+ result,
+ shape,
+ x_init_value=x,
+ delta=1)
+ self.assertAllClose(jacob_t, jacob_n, rtol=1e-8, atol=1e-8)
+
+ def testGradient(self):
+ self._compareGradient([50], 0, False, False)
+
+ def testGradientReverse(self):
+ self._compareGradient([50], 0, False, True)
+
+ def testGradientExclusive(self):
+ self._compareGradient([50], 0, True, False)
+
+ def testGradientExclusiveReverse(self):
+ self._compareGradient([50], 0, True, True)
+
+ def testGradient2D(self):
+ for axis in [0, 1]:
+ for exclusive in [True, False]:
+ for reverse in [True, False]:
+ self._compareGradient([5, 10], axis, exclusive, reverse)
+
+
+class CumprodTest(tf.test.TestCase):
+
+ valid_dtypes = [np.int32, np.int64, np.float16, np.float32,
+ np.float64, np.complex64, np.complex128]
+
+ def _compare(self, x, axis, exclusive, reverse, use_gpu=False):
+ np_out = handle_options(np.cumprod, x, axis, exclusive, reverse)
+ with self.test_session(use_gpu=use_gpu):
+ tf_out = tf.cumprod(x, axis, exclusive, reverse).eval()
+
+ self.assertAllClose(np_out, tf_out)
+
+ def _compareAll(self, x, axis):
+ for exclusive in [True, False]:
+ for reverse in [True, False]:
+ for use_gpu in [True, False]:
+ self._compare(x, axis, exclusive, reverse, use_gpu)
+
+
+ def test1D(self):
+ for dtype in self.valid_dtypes:
+ x = np.arange(1, 6).reshape([5]).astype(dtype)
+ self._compareAll(x, 0)
+
+ def test2D(self):
+ for dtype in self.valid_dtypes:
+ x = np.arange(1, 11).reshape([2, 5]).astype(dtype)
+ self._compareAll(x, 0)
+ self._compareAll(x, 1)
+
+ def test3D(self):
+ for dtype in self.valid_dtypes:
+ x = np.arange(1, 21).reshape([2, 2, 5]).astype(dtype)
+ self._compareAll(x, 0)
+ self._compareAll(x, 1)
+ self._compareAll(x, 2)
+
+ def testInvalidAxis(self):
+ x = np.arange(0, 10).reshape([2, 5]).astype(np.float32)
+ input_tensor = tf.convert_to_tensor(x)
+ with self.test_session():
+ with self.assertRaisesWithPredicateMatch(
+ tf.errors.InvalidArgumentError,
+ lambda e: "Expected scan axis in the range" in str(e)):
+ tf.cumprod(input_tensor, -1).eval()
+ with self.assertRaisesWithPredicateMatch(
+ tf.errors.InvalidArgumentError,
+ lambda e: "Expected scan axis in the range" in str(e)):
+ tf.cumprod(input_tensor, 2).eval()
+ with self.assertRaisesWithPredicateMatch(
+ tf.errors.InvalidArgumentError,
+ lambda e: "axis must be a scalar" in str(e)):
+ tf.cumprod(input_tensor, [0]).eval()
+
+ def _compareGradient(self, shape, axis, exclusive, reverse):
+ x = np.arange(1, 9).reshape(shape).astype(np.float64)
+ with self.test_session():
+ t = tf.convert_to_tensor(x)
+ result = tf.cumprod(t, axis, exclusive, reverse)
+ jacob_t, jacob_n = tf.test.compute_gradient(t,
+ shape,
+ result,
+ shape,
+ x_init_value=x,
+ delta=1)
+ self.assertAllClose(jacob_t, jacob_n, rtol=1e-8, atol=1e-8)
+
+ def testGradient(self):
+ self._compareGradient([8], 0, False, False)
+
+ def testGradientReverse(self):
+ self._compareGradient([8], 0, False, True)
+
+ def testGradientExclusive(self):
+ self._compareGradient([8], 0, True, False)
+
+ def testGradientExclusiveReverse(self):
+ self._compareGradient([8], 0, True, True)
+
+ def testGradient2D(self):
+ for axis in [0, 1]:
+ for exclusive in [True, False]:
+ for reverse in [True, False]:
+ self._compareGradient([2, 4], axis, exclusive, reverse)
+
+
+if __name__ == "__main__":
+ tf.test.main()
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