229 lines
9.1 KiB
Python
229 lines
9.1 KiB
Python
# Copyright 2015 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.
|
|
# ==============================================================================
|
|
"""Tests for convolution related functionality in tensorflow.ops.nn."""
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import numpy as np
|
|
from six.moves import xrange # pylint: disable=redefined-builtin
|
|
|
|
from tensorflow.python.framework import constant_op
|
|
from tensorflow.python.framework import dtypes
|
|
from tensorflow.python.framework import test_util
|
|
from tensorflow.python.ops import gradient_checker
|
|
from tensorflow.python.ops import nn_ops
|
|
import tensorflow.python.ops.nn_grad # pylint: disable=unused-import
|
|
from tensorflow.python.platform import test
|
|
|
|
|
|
class Conv3DTransposeTest(test.TestCase):
|
|
|
|
def testConv3DTransposeSingleStride(self):
|
|
with self.cached_session():
|
|
strides = [1, 1, 1, 1, 1]
|
|
|
|
# Input, output: [batch, depth, height, width, channel]
|
|
x_shape = [2, 5, 6, 4, 3]
|
|
y_shape = [2, 5, 6, 4, 2]
|
|
|
|
# Filter: [kernel_depth, kernel_height, kernel_width, out_depth, in_depth]
|
|
f_shape = [3, 3, 3, 2, 3]
|
|
|
|
x = constant_op.constant(
|
|
1.0, shape=x_shape, name="x", dtype=dtypes.float32)
|
|
f = constant_op.constant(
|
|
1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
|
|
output = nn_ops.conv3d_transpose(
|
|
x, f, y_shape, strides=strides, padding="SAME")
|
|
value = self.evaluate(output)
|
|
|
|
# We count the number of cells being added at the locations in the output.
|
|
# At the center, #cells = kernel_depth * kernel_height * kernel_width
|
|
# At the corners, #cells = ceil(kernel_depth/2) * ceil(kernel_height/2)
|
|
# * ceil(kernel_width/2)
|
|
# At the edges, #cells =
|
|
# kernel_depth * ceil(kernel_height/2) * ceil(kernel_width/2) or
|
|
# ceil(kernel_depth/2) * kernel_height * ceil(kernel_width/2) or
|
|
# ceil(kernel_depth/2) * ceil(kernel_height/2) * kernel_width
|
|
# At the borders, #cells =
|
|
# ceil(kernel_depth/2) * kernel_height * kernel_width or
|
|
# kernel_depth * ceil(kernel_height/2) * kernel_width or
|
|
# kernel_depth * kernel_height * ceil(kernel_width/2)
|
|
|
|
for n in xrange(x_shape[0]):
|
|
for k in xrange(f_shape[3]):
|
|
for w in xrange(y_shape[3]):
|
|
for h in xrange(y_shape[2]):
|
|
for d in xrange(y_shape[1]):
|
|
d_in = d > 0 and d < y_shape[1] - 1
|
|
h_in = h > 0 and h < y_shape[2] - 1
|
|
w_in = w > 0 and w < y_shape[3] - 1
|
|
if d_in + h_in + w_in == 3:
|
|
target = 27 * 3.0
|
|
elif d_in + h_in + w_in == 2:
|
|
target = 18 * 3.0
|
|
elif d_in or h_in or w_in:
|
|
target = 12 * 3.0
|
|
else:
|
|
target = 8 * 3.0
|
|
self.assertAllClose(target, value[n, d, h, w, k])
|
|
|
|
def testConv3DTransposeSame(self):
|
|
with self.cached_session():
|
|
strides = [1, 2, 2, 2, 1]
|
|
|
|
# Input, output: [batch, depth, height, width, depth]
|
|
x_shape = [2, 5, 6, 4, 3]
|
|
y_shape = [2, 10, 12, 8, 2]
|
|
|
|
# Filter: [kernel_depth, kernel_height, kernel_width, out_depth, in_depth]
|
|
f_shape = [3, 3, 3, 2, 3]
|
|
|
|
x = constant_op.constant(
|
|
1.0, shape=x_shape, name="x", dtype=dtypes.float32)
|
|
f = constant_op.constant(
|
|
1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
|
|
output = nn_ops.conv3d_transpose(
|
|
x, f, y_shape, strides=strides, padding="SAME")
|
|
value = self.evaluate(output)
|
|
|
|
for n in xrange(x_shape[0]):
|
|
for k in xrange(f_shape[3]):
|
|
for w in xrange(y_shape[3]):
|
|
for h in xrange(y_shape[2]):
|
|
for d in xrange(y_shape[1]):
|
|
# We add a case for locations divisible by the stride.
|
|
d_in = d % strides[1] == 0 and 0 < d < y_shape[1] - 1
|
|
h_in = h % strides[2] == 0 and 0 < h < y_shape[2] - 1
|
|
w_in = w % strides[3] == 0 and 0 < w < y_shape[3] - 1
|
|
if d_in + h_in + w_in == 3:
|
|
target = 8 * 3.0
|
|
elif d_in + h_in + w_in == 2:
|
|
target = 4 * 3.0
|
|
elif d_in or h_in or w_in:
|
|
target = 2 * 3.0
|
|
else:
|
|
target = 3.0
|
|
self.assertAllClose(target, value[n, d, h, w, k])
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testConv3DTransposeShapeMismatch(self):
|
|
# Test case for GitHub issue 18460
|
|
x_shape = [2, 2, 3, 4, 3]
|
|
f_shape = [3, 3, 3, 2, 2]
|
|
y_shape = [2, 2, 6, 8, 6]
|
|
strides = [1, 1, 2, 2, 2]
|
|
np.random.seed(1)
|
|
x_value = np.random.random_sample(x_shape).astype(np.float64)
|
|
f_value = np.random.random_sample(f_shape).astype(np.float64)
|
|
nn_ops.conv3d_transpose(
|
|
x_value, f_value, y_shape, strides, data_format='NCDHW')
|
|
|
|
def testConv3DTransposeOutputShapeType(self):
|
|
# Test case for GitHub issue 18887
|
|
for dtype in [dtypes.int32, dtypes.int64]:
|
|
with self.cached_session():
|
|
x_shape = [2, 5, 6, 4, 3]
|
|
y_shape = [2, 5, 6, 4, 2]
|
|
f_shape = [3, 3, 3, 2, 3]
|
|
strides = [1, 1, 1, 1, 1]
|
|
x_value = constant_op.constant(
|
|
1.0, shape=x_shape, name="x", dtype=dtypes.float32)
|
|
f_value = constant_op.constant(
|
|
1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
|
|
output = nn_ops.conv3d_transpose(
|
|
x_value, f_value, constant_op.constant(y_shape, dtype=dtype),
|
|
strides=strides, padding="SAME")
|
|
self.evaluate(output)
|
|
|
|
def testConv3DTransposeValid(self):
|
|
with self.cached_session():
|
|
strides = [1, 2, 2, 2, 1]
|
|
|
|
# Input, output: [batch, depth, height, width, depth]
|
|
x_shape = [2, 5, 6, 4, 3]
|
|
y_shape = [2, 11, 13, 9, 2]
|
|
|
|
# Filter: [kernel_depth, kernel_height, kernel_width, out_depth, in_depth]
|
|
f_shape = [3, 3, 3, 2, 3]
|
|
|
|
x = constant_op.constant(
|
|
1.0, shape=x_shape, name="x", dtype=dtypes.float32)
|
|
f = constant_op.constant(
|
|
1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
|
|
output = nn_ops.conv3d_transpose(
|
|
x, f, y_shape, strides=strides, padding="VALID")
|
|
value = self.evaluate(output)
|
|
|
|
cache_values = np.zeros(y_shape, dtype=np.float32)
|
|
|
|
# The amount of padding added
|
|
pad = 1
|
|
|
|
for n in xrange(x_shape[0]):
|
|
for k in xrange(f_shape[3]):
|
|
for w in xrange(y_shape[3]):
|
|
for h in xrange(y_shape[2]):
|
|
for d in xrange(y_shape[1]):
|
|
# We add a case for locations divisible by the stride.
|
|
d_in = d % strides[1] == 0 and pad < d < y_shape[1] - 1 - pad
|
|
h_in = h % strides[2] == 0 and pad < h < y_shape[2] - 1 - pad
|
|
w_in = w % strides[3] == 0 and pad < w < y_shape[3] - 1 - pad
|
|
if d_in + h_in + w_in == 3:
|
|
target = 8 * 3.0
|
|
elif d_in + h_in + w_in == 2:
|
|
target = 4 * 3.0
|
|
elif d_in or h_in or w_in:
|
|
target = 2 * 3.0
|
|
else:
|
|
target = 3.0
|
|
cache_values[n, d, h, w, k] = target
|
|
|
|
# copy values in the border
|
|
cache_values[n, :, :, 0, k] = cache_values[n, :, :, 1, k]
|
|
cache_values[n, :, :, -1, k] = cache_values[n, :, :, -2, k]
|
|
cache_values[n, :, 0, :, k] = cache_values[n, :, 1, :, k]
|
|
cache_values[n, :, -1, :, k] = cache_values[n, :, -2, :, k]
|
|
cache_values[n, 0, :, :, k] = cache_values[n, 1, :, :, k]
|
|
cache_values[n, -1, :, :, k] = cache_values[n, -2, :, :, k]
|
|
|
|
self.assertAllClose(cache_values, value)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testGradient(self):
|
|
x_shape = [2, 3, 4, 3, 2]
|
|
f_shape = [3, 3, 3, 2, 2]
|
|
y_shape = [2, 6, 8, 6, 2]
|
|
strides = [1, 2, 2, 2, 1]
|
|
np.random.seed(1) # Make it reproducible.
|
|
x_val = np.random.random_sample(x_shape).astype(np.float64)
|
|
f_val = np.random.random_sample(f_shape).astype(np.float64)
|
|
with self.cached_session():
|
|
x = constant_op.constant(x_val, name="x", dtype=dtypes.float32)
|
|
f = constant_op.constant(f_val, name="f", dtype=dtypes.float32)
|
|
output = nn_ops.conv3d_transpose(
|
|
x, f, y_shape, strides=strides, padding="SAME")
|
|
err = gradient_checker.compute_gradient_error([x, f], [x_shape, f_shape],
|
|
output, y_shape)
|
|
print("conv3d_transpose gradient err = %g " % err)
|
|
err_tolerance = 0.0005
|
|
self.assertLess(err, err_tolerance)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test.main()
|