5bfc37ef25
use_gpu is True by default in test utils starting CL 356906251 I will wait a bit before checking this in since once this is checked in, it would be harder to roll back CL 356906251 PiperOrigin-RevId: 357322055 Change-Id: Ibbeb900d93f9fb43c2dc61285ee38e582b29dcfc
261 lines
9.1 KiB
Python
261 lines
9.1 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 convolution related functionality in tensorflow.ops.nn."""
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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 dtypes
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import gradient_checker
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from tensorflow.python.ops import nn_ops
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import tensorflow.python.ops.nn_grad # pylint: disable=unused-import
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from tensorflow.python.platform import test
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class Conv1DTransposeTest(test.TestCase):
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def testConv1DTransposeSingleStride(self):
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with self.cached_session():
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strides = [1, 1, 1]
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# Input, output: [batch, width, depth]
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x_shape = [2, 6, 3]
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y_shape = [2, 6, 2]
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# Filter: [kernel_width, output_depth, input_depth]
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f_shape = [3, 2, 3]
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x = constant_op.constant(
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1.0, shape=x_shape, name="x", dtype=dtypes.float32)
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f = constant_op.constant(
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1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
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output = nn_ops.conv1d_transpose(
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x, f, y_shape, strides=strides, padding="SAME")
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value = self.evaluate(output)
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for n in xrange(y_shape[0]):
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for w in xrange(y_shape[1]):
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for c in xrange(y_shape[2]):
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target = 2 * 3.0
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w_in = w > 0 and w < y_shape[1] - 1
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if w_in:
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target += 3.0
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self.assertAllClose(target, value[n, w, c])
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def testConv1DTransposeSame(self):
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with self.cached_session():
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strides = [1, 2, 1]
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# Input, output: [batch, width, depth]
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x_shape = [2, 4, 3]
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y_shape = [2, 8, 2]
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# Filter: [kernel_width, output_depth, input_depth]
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f_shape = [3, 2, 3]
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x = constant_op.constant(
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1.0, shape=x_shape, name="x", dtype=dtypes.float32)
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f = constant_op.constant(
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1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
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output = nn_ops.conv1d_transpose(
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x, f, y_shape, strides=strides, padding="SAME")
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value = self.evaluate(output)
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for n in xrange(x_shape[0]):
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for k in xrange(f_shape[1]):
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for w in xrange(y_shape[1]):
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target = 3.0
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# We add a case for locations divisible by the stride.
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w_in = w % strides[1] == 0 and w > 0 and w < y_shape[1] - 1
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if w_in:
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target += 3.0
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self.assertAllClose(target, value[n, w, k])
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def testConv1DTransposeValid(self):
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with self.cached_session():
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strides = [1, 2, 1]
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# Input, output: [batch, width, depth]
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x_shape = [2, 4, 3]
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y_shape = [2, 9, 2]
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# Filter: [kernel_width, output_depth, input_depth]
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f_shape = [3, 2, 3]
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x = constant_op.constant(
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1.0, shape=x_shape, name="x", dtype=dtypes.float32)
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f = constant_op.constant(
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1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
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output = nn_ops.conv1d_transpose(
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x, f, y_shape, strides=strides, padding="VALID")
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value = self.evaluate(output)
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cache_values = np.zeros(y_shape, dtype=np.float32)
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# The amount of padding added
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pad = 1
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for n in xrange(x_shape[0]):
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for k in xrange(f_shape[1]):
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for w in xrange(pad, y_shape[1] - pad):
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target = 3.0
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# We add a case for locations divisible by the stride.
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w_in = w % strides[1] == 0 and w > pad and w < y_shape[1] - 1 - pad
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if w_in:
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target += 3.0
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cache_values[n, w, k] = target
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# copy values in the border
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cache_values[n, 0, k] = cache_values[n, 1, k]
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cache_values[n, -1, k] = cache_values[n, -2, k]
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cache_values[n, :, k] = cache_values[n, :, k]
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self.assertAllClose(cache_values, value)
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@test_util.run_deprecated_v1
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def testGradient(self):
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x_shape = [2, 4, 3]
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f_shape = [3, 2, 3]
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y_shape = [2, 8, 2]
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strides = [1, 2, 1]
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np.random.seed(1) # Make it reproducible.
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x_val = np.random.random_sample(x_shape).astype(np.float64)
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f_val = np.random.random_sample(f_shape).astype(np.float64)
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with self.cached_session():
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x = constant_op.constant(x_val, name="x", dtype=dtypes.float32)
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f = constant_op.constant(f_val, name="f", dtype=dtypes.float32)
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output = nn_ops.conv1d_transpose(
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x, f, y_shape, strides=strides, padding="SAME")
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err = gradient_checker.compute_gradient_error([x, f], [x_shape, f_shape],
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output, y_shape)
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print("conv1d_transpose gradient err = %g " % err)
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err_tolerance = 0.0005
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self.assertLess(err, err_tolerance)
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def testConv1DTransposeSingleStrideNCW(self):
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# `NCW` data format is only supported for CUDA device.
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if test.is_gpu_available(cuda_only=True):
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with self.session():
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strides = [1, 1, 1]
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# Input, output: [batch, depth, width]
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x_shape = [2, 3, 4]
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y_shape = [2, 2, 4]
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# Filter: [kernel_width, output_depth, input_depth]
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f_shape = [3, 2, 3]
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x = constant_op.constant(
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1.0, shape=x_shape, name="x", dtype=dtypes.float32)
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f = constant_op.constant(
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1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
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output = nn_ops.conv1d_transpose(
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x, f, y_shape, strides=strides, padding="SAME", data_format="NCW")
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value = self.evaluate(output)
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for n in xrange(x_shape[0]):
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for k in xrange(f_shape[1]):
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for w in xrange(y_shape[2]):
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target = 2 * 3.0
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w_in = w > 0 and w < y_shape[2] - 1
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if w_in:
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target += 3.0
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self.assertAllClose(target, value[n, k, w])
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def testConv1DTransposeSameNCW(self):
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# `NCW` data format is only supported for CUDA device.
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if test.is_gpu_available(cuda_only=True):
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with self.session():
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strides = [1, 1, 2]
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# Input, output: [batch, depth, width]
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x_shape = [2, 3, 4]
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y_shape = [2, 2, 8]
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# Filter: [kernel_width, output_depth, input_depth]
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f_shape = [3, 2, 3]
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x = constant_op.constant(
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1.0, shape=x_shape, name="x", dtype=dtypes.float32)
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f = constant_op.constant(
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1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
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output = nn_ops.conv1d_transpose(
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x, f, y_shape, strides=strides, padding="SAME", data_format="NCW")
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value = self.evaluate(output)
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for n in xrange(x_shape[0]):
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for k in xrange(f_shape[1]):
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for w in xrange(y_shape[2]):
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target = 3.0
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# We add a case for locations divisible by the stride.
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w_in = w % strides[2] == 0 and w > 0 and w < y_shape[2] - 1
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if w_in:
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target += 3.0
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self.assertAllClose(target, value[n, k, w])
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def testConv1DTransposeValidNCW(self):
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# `NCW` data format is only supported for CUDA device.
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if test.is_gpu_available(cuda_only=True):
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with self.session():
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strides = [1, 1, 2]
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# Input, output: [batch, depth, width]
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x_shape = [2, 3, 4]
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y_shape = [2, 2, 9]
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# Filter: [kernel_width, output_depth, input_depth]
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f_shape = [3, 2, 3]
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x = constant_op.constant(
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1.0, shape=x_shape, name="x", dtype=dtypes.float32)
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f = constant_op.constant(
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1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
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output = nn_ops.conv1d_transpose(
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x, f, y_shape, strides=strides, padding="VALID", data_format="NCW")
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value = self.evaluate(output)
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cache_values = np.zeros(y_shape, dtype=np.float32)
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# The amount of padding added
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pad = 1
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for n in xrange(x_shape[0]):
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for k in xrange(f_shape[1]):
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for w in xrange(pad, y_shape[2] - pad):
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target = 3.0
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# We add a case for locations divisible by the stride.
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w_in = w % strides[2] == 0 and w > pad and \
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w < y_shape[2] - 1 - pad
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if w_in:
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target += 3.0
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cache_values[n, k, w] = target
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# copy values in the border
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cache_values[n, k, 0] = cache_values[n, k, 1]
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cache_values[n, k, -1] = cache_values[n, k, -2]
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cache_values[n, k, :] = cache_values[n, k, :]
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self.assertAllClose(cache_values, value)
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if __name__ == "__main__":
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test.main()
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