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slice_op_test.py
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257 lines (214 loc) · 9.46 KB
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# Copyright 2015 Google Inc. 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 slice op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.python.platform
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
class SliceTest(tf.test.TestCase):
def _testEmpty(self, use_gpu):
inp = np.random.rand(4, 4).astype("f")
for k in xrange(4):
with self.test_session(use_gpu=use_gpu):
a = tf.constant(inp, shape=[4, 4], dtype=tf.float32)
slice_t = a[2, k:k]
slice_val = slice_t.eval()
self.assertAllEqual(slice_val, inp[2, k:k])
def testEmptyAll(self):
self._testEmpty(use_gpu=False)
self._testEmpty(use_gpu=True)
def _testInt32(self, use_gpu):
inp = np.random.rand(4, 4).astype("i")
for k in xrange(4):
with self.test_session(use_gpu=use_gpu):
a = tf.constant(inp, shape=[4, 4], dtype=tf.int32)
slice_t = a[2, k:k]
slice_val = slice_t.eval()
self.assertAllEqual(slice_val, inp[2, k:k])
def testInt32(self):
self._testEmpty(use_gpu=False)
self._testEmpty(use_gpu=True)
def _testSelectAll(self, use_gpu):
with self.test_session(use_gpu=use_gpu):
inp = np.random.rand(4, 4, 4, 4).astype("f")
a = tf.constant(inp, shape=[4, 4, 4, 4],
dtype=tf.float32)
slice_explicit_t = tf.slice(a, [0, 0, 0, 0], [-1, -1, -1, -1])
slice_implicit_t = a[:, :, :, :]
self.assertAllEqual(inp, slice_explicit_t.eval())
self.assertAllEqual(inp, slice_implicit_t.eval())
self.assertEqual(inp.shape, slice_explicit_t.get_shape())
self.assertEqual(inp.shape, slice_implicit_t.get_shape())
def testSelectAll(self):
for _ in range(10):
self._testSelectAll(use_gpu=False)
self._testSelectAll(use_gpu=True)
def _testSingleDimension(self, use_gpu):
with self.test_session(use_gpu=use_gpu):
inp = np.random.rand(10).astype("f")
a = tf.constant(inp, shape=[10], dtype=tf.float32)
hi = np.random.random_integers(0, 9)
scalar_t = a[hi]
scalar_val = scalar_t.eval()
self.assertAllEqual(scalar_val, inp[hi])
lo = np.random.random_integers(0, hi)
slice_t = a[lo:hi]
slice_val = slice_t.eval()
self.assertAllEqual(slice_val, inp[lo:hi])
def testSingleDimension(self):
for _ in range(10):
self._testSingleDimension(use_gpu=False)
self._testSingleDimension(use_gpu=True)
def _testSliceMatrixDim0(self, x, begin, size, use_gpu):
with self.test_session(use_gpu=use_gpu):
tf_ans = tf.slice(x, [begin, 0], [size, x.shape[1]]).eval()
np_ans = x[begin:begin+size, :]
self.assertAllEqual(tf_ans, np_ans)
def testSliceMatrixDim0(self):
for use_gpu in [False, True]:
x = np.random.rand(8, 4).astype("f")
self._testSliceMatrixDim0(x, 1, 2, use_gpu)
self._testSliceMatrixDim0(x, 3, 3, use_gpu)
y = np.random.rand(8, 7).astype("f") # 7 * sizeof(float) is not aligned
self._testSliceMatrixDim0(y, 1, 2, use_gpu)
self._testSliceMatrixDim0(y, 3, 3, use_gpu)
def _testIndexAndSlice(self, use_gpu):
with self.test_session(use_gpu=use_gpu):
inp = np.random.rand(4, 4).astype("f")
a = tf.constant(inp, shape=[4, 4], dtype=tf.float32)
x, y = np.random.random_integers(0, 3, size=2).tolist()
slice_t = a[x, 0:y]
slice_val = slice_t.eval()
self.assertAllEqual(slice_val, inp[x, 0:y])
def testSingleElementAll(self):
for _ in range(10):
self._testIndexAndSlice(use_gpu=False)
self._testIndexAndSlice(use_gpu=True)
def _testSimple(self, use_gpu):
with self.test_session(use_gpu=use_gpu) as sess:
inp = np.random.rand(4, 4).astype("f")
a = tf.constant([float(x) for x in inp.ravel(order="C")],
shape=[4, 4], dtype=tf.float32)
slice_t = tf.slice(a, [0, 0], [2, 2])
slice2_t = a[:2, :2]
slice_val, slice2_val = sess.run([slice_t, slice2_t])
self.assertAllEqual(slice_val, inp[:2, :2])
self.assertAllEqual(slice2_val, inp[:2, :2])
self.assertEqual(slice_val.shape, slice_t.get_shape())
self.assertEqual(slice2_val.shape, slice2_t.get_shape())
def testSimpleAll(self):
self._testSimple(use_gpu=False)
self._testSimple(use_gpu=True)
def _testComplex(self, use_gpu):
with self.test_session(use_gpu=use_gpu):
inp = np.random.rand(4, 10, 10, 4).astype("f")
a = tf.constant(inp, dtype=tf.float32)
x = np.random.random_integers(0, 9)
z = np.random.random_integers(0, 9)
y = np.random.random_integers(0, z)
slice_t = a[:, x, y:z, :]
self.assertAllEqual(slice_t.eval(), inp[:, x, y:z, :])
def testComplex(self):
for _ in range(10):
self._testComplex(use_gpu=False)
self._testComplex(use_gpu=True)
def _RunAndVerifyResult(self, use_gpu):
# Random dims of rank 6
input_shape = np.random.randint(0, 20, size=6)
inp = np.random.rand(*input_shape).astype("f")
with self.test_session(use_gpu=use_gpu) as sess:
a = tf.constant([float(x) for x in inp.ravel(order="C")],
shape=input_shape, dtype=tf.float32)
indices = [0 if x == 0 else np.random.randint(x) for x in input_shape]
sizes = [np.random.randint(0, input_shape[i] - indices[i] + 1)
for i in range(6)]
slice_t = tf.slice(a, indices, sizes)
slice2_t = a[indices[0]:indices[0]+sizes[0],
indices[1]:indices[1]+sizes[1],
indices[2]:indices[2]+sizes[2],
indices[3]:indices[3]+sizes[3],
indices[4]:indices[4]+sizes[4],
indices[5]:indices[5]+sizes[5]]
slice_val, slice2_val = sess.run([slice_t, slice2_t])
expected_val = inp[indices[0]:indices[0]+sizes[0],
indices[1]:indices[1]+sizes[1],
indices[2]:indices[2]+sizes[2],
indices[3]:indices[3]+sizes[3],
indices[4]:indices[4]+sizes[4],
indices[5]:indices[5]+sizes[5]]
self.assertAllEqual(slice_val, expected_val)
self.assertAllEqual(slice2_val, expected_val)
self.assertEqual(expected_val.shape, slice_t.get_shape())
self.assertEqual(expected_val.shape, slice2_t.get_shape())
def testRandom(self):
for _ in range(10):
self._RunAndVerifyResult(use_gpu=False)
self._RunAndVerifyResult(use_gpu=True)
def _testGradientSlice(self, input_shape, slice_begin, slice_size, use_gpu):
with self.test_session(use_gpu=use_gpu):
num_inputs = np.prod(input_shape)
num_grads = np.prod(slice_size)
inp = np.random.rand(num_inputs).astype("f").reshape(input_shape)
a = tf.constant([float(x) for x in inp.ravel(order="C")],
shape=input_shape, dtype=tf.float32)
slice_t = tf.slice(a, slice_begin, slice_size)
grads = np.random.rand(num_grads).astype("f").reshape(slice_size)
grad_tensor = tf.constant(grads)
grad = tf.gradients(slice_t, [a], grad_tensor)[0]
result = grad.eval()
# Create a zero tensor of the input shape ane place
# the grads into the right location to compare against TensorFlow.
np_ans = np.zeros(input_shape)
slices = []
for i in xrange(len(input_shape)):
slices.append(slice(slice_begin[i], slice_begin[i] + slice_size[i]))
np_ans[slices] = grads
self.assertAllClose(np_ans, result)
def _testGradientVariableSize(self, use_gpu):
with self.test_session(use_gpu=use_gpu):
inp = tf.constant([1.0, 2.0, 3.0], name="in")
out = tf.slice(inp, [1], [-1])
grad_actual = tf.gradients(out, inp)[0].eval()
self.assertAllClose([0., 1., 1.], grad_actual)
def _testGradientsSimple(self, use_gpu):
# Slice the middle square out of a 4x4 input
self._testGradientSlice([4, 4], [1, 1], [2, 2], use_gpu)
# Slice the upper left square out of a 4x4 input
self._testGradientSlice([4, 4], [0, 0], [2, 2], use_gpu)
# Slice a non-square input starting from (2,1)
self._testGradientSlice([4, 4], [2, 1], [1, 2], use_gpu)
# Slice a 3D tensor
self._testGradientSlice([3, 3, 3], [0, 1, 0], [2, 1, 1], use_gpu)
# Use -1 as a slice dimension.
self._testGradientVariableSize(use_gpu)
def testGradientsAll(self):
self._testGradientsSimple(use_gpu=False)
self._testGradientsSimple(use_gpu=True)
def testNotIterable(self):
# NOTE(mrry): If we register __getitem__ as an overloaded
# operator, Python will valiantly attempt to iterate over the
# Tensor from 0 to infinity. This test ensures that this
# unintended behavior is prevented.
c = tf.constant(5.0)
with self.assertRaisesWithPredicateMatch(
TypeError,
lambda e: "'Tensor' object is not iterable" in str(e)):
for _ in c:
pass
if __name__ == "__main__":
tf.test.main()