367f164359
Auto tuning on the GPU caused this error tolerance to be exceeded very often with XLA. PiperOrigin-RevId: 231248158
238 lines
9.9 KiB
Python
238 lines
9.9 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 tensorflow.python.framework import constant_op
|
|
from tensorflow.python.framework import dtypes
|
|
from tensorflow.python.framework import test_util
|
|
from tensorflow.python.ops import array_ops
|
|
from tensorflow.python.ops import gradient_checker
|
|
from tensorflow.python.ops import nn_impl
|
|
from tensorflow.python.ops import nn_ops
|
|
import tensorflow.python.ops.nn_grad # pylint: disable=unused-import
|
|
from tensorflow.python.platform import test
|
|
|
|
|
|
def _upsample_filters(filters, rate):
|
|
"""Upsamples the filters by a factor of rate along the spatial dimensions.
|
|
|
|
Args:
|
|
filters: [h, w, in_depth, out_depth]. Original filters.
|
|
rate: An int, specifying the upsampling rate.
|
|
|
|
Returns:
|
|
filters_up: [h_up, w_up, in_depth, out_depth]. Upsampled filters with
|
|
h_up = h + (h - 1) * (rate - 1)
|
|
w_up = w + (w - 1) * (rate - 1)
|
|
containing (rate - 1) zeros between consecutive filter values along
|
|
the filters' spatial dimensions.
|
|
"""
|
|
if rate == 1:
|
|
return filters
|
|
# [h, w, in_depth, out_depth] -> [in_depth, out_depth, h, w]
|
|
filters_up = np.transpose(filters, [2, 3, 0, 1])
|
|
ker = np.zeros([rate, rate], dtype=np.float32)
|
|
ker[0, 0] = 1
|
|
filters_up = np.kron(filters_up, ker)[:, :, :-(rate - 1), :-(rate - 1)]
|
|
# [in_depth, out_depth, h_up, w_up] -> [h_up, w_up, in_depth, out_depth]
|
|
filters_up = np.transpose(filters_up, [2, 3, 0, 1])
|
|
return filters_up
|
|
|
|
|
|
class AtrousConv2DTest(test.TestCase):
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testAtrousConv2DForward(self):
|
|
with self.session(use_gpu=True):
|
|
# Input: [batch, height, width, input_depth]
|
|
height = 9
|
|
for width in [9, 10]: # Test both odd and even width.
|
|
x_shape = [2, height, width, 2]
|
|
x = np.arange(np.prod(x_shape), dtype=np.float32).reshape(x_shape)
|
|
|
|
# Filter: [kernel_height, kernel_width, input_depth, output_depth]
|
|
for kernel_height in range(1, 4):
|
|
for kernel_width in range(1, 4):
|
|
f_shape = [kernel_height, kernel_width, 2, 2]
|
|
f = np.arange(np.prod(f_shape), dtype=np.float32).reshape(f_shape)
|
|
|
|
for rate in range(1, 4):
|
|
f_up = _upsample_filters(f, rate)
|
|
|
|
for padding in ["SAME", "VALID"]:
|
|
y1 = nn_ops.atrous_conv2d(x, f, rate, padding=padding)
|
|
y2 = nn_ops.conv2d(
|
|
x, f_up, strides=[1, 1, 1, 1], padding=padding)
|
|
self.assertAllClose(
|
|
y1.eval(), self.evaluate(y2), rtol=1e-3, atol=1e-3)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testAtrousSequence(self):
|
|
"""Tests optimization of sequence of atrous convolutions.
|
|
|
|
Verifies that a sequence of `atrous_conv2d` operations with identical `rate`
|
|
parameters, 'SAME' `padding`, and `filters` with odd heights/ widths:
|
|
|
|
net = atrous_conv2d(net, filters1, rate, padding="SAME")
|
|
net = atrous_conv2d(net, filters2, rate, padding="SAME")
|
|
...
|
|
net = atrous_conv2d(net, filtersK, rate, padding="SAME")
|
|
|
|
is equivalent to:
|
|
|
|
pad = ... # padding so that the input dims are multiples of rate
|
|
net = space_to_batch(net, paddings=pad, block_size=rate)
|
|
net = conv2d(net, filters1, strides=[1, 1, 1, 1], padding="SAME")
|
|
net = conv2d(net, filters2, strides=[1, 1, 1, 1], padding="SAME")
|
|
...
|
|
net = conv2d(net, filtersK, strides=[1, 1, 1, 1], padding="SAME")
|
|
net = batch_to_space(net, crops=pad, block_size=rate)
|
|
"""
|
|
padding = "SAME" # The padding needs to be "SAME"
|
|
np.random.seed(1) # Make it reproducible.
|
|
|
|
with self.session(use_gpu=True):
|
|
# Input: [batch, height, width, input_depth]
|
|
for height in range(15, 17):
|
|
for width in range(15, 17):
|
|
x_shape = [3, height, width, 2]
|
|
x = np.random.random_sample(x_shape).astype(np.float32)
|
|
|
|
for kernel in [1, 3, 5]: # The kernel size needs to be odd.
|
|
# Filter: [kernel_height, kernel_width, input_depth, output_depth]
|
|
f_shape = [kernel, kernel, 2, 2]
|
|
f = 1e-2 * np.random.random_sample(f_shape).astype(np.float32)
|
|
|
|
for rate in range(2, 4):
|
|
# y1: three atrous_conv2d in a row.
|
|
y1 = nn_ops.atrous_conv2d(x, f, rate, padding=padding)
|
|
y1 = nn_ops.atrous_conv2d(y1, f, rate, padding=padding)
|
|
y1 = nn_ops.atrous_conv2d(y1, f, rate, padding=padding)
|
|
# y2: space_to_batch, three conv2d in a row, batch_to_space
|
|
pad_bottom = 0 if height % rate == 0 else rate - height % rate
|
|
pad_right = 0 if width % rate == 0 else rate - width % rate
|
|
pad = [[0, pad_bottom], [0, pad_right]]
|
|
y2 = array_ops.space_to_batch(x, paddings=pad, block_size=rate)
|
|
y2 = nn_ops.conv2d(y2, f, strides=[1, 1, 1, 1], padding=padding)
|
|
y2 = nn_ops.conv2d(y2, f, strides=[1, 1, 1, 1], padding=padding)
|
|
y2 = nn_ops.conv2d(y2, f, strides=[1, 1, 1, 1], padding=padding)
|
|
y2 = array_ops.batch_to_space(y2, crops=pad, block_size=rate)
|
|
self.assertAllClose(
|
|
y1.eval(), self.evaluate(y2), rtol=1e-2, atol=1e-2)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testGradient(self):
|
|
with self.session(use_gpu=True):
|
|
# Input: [batch, height, width, input_depth]
|
|
x_shape = [2, 5, 6, 2]
|
|
# Filter: [kernel_height, kernel_width, input_depth, output_depth]
|
|
f_shape = [3, 3, 2, 2]
|
|
# Output: [batch, height, width, output_depth]
|
|
y_shape = [2, 5, 6, 2]
|
|
|
|
np.random.seed(1) # Make it reproducible.
|
|
x_val = np.random.random_sample(x_shape).astype(np.float32)
|
|
f_val = np.random.random_sample(f_shape).astype(np.float32)
|
|
x = constant_op.constant(x_val, name="x", dtype=dtypes.float32)
|
|
f = constant_op.constant(f_val, name="f", dtype=dtypes.float32)
|
|
|
|
for rate in range(1, 4):
|
|
output = nn_ops.atrous_conv2d(x, f, rate=rate, padding="SAME")
|
|
err = gradient_checker.compute_gradient_error([x, f],
|
|
[x_shape, f_shape],
|
|
output, y_shape)
|
|
print("atrous_conv2d gradient err = %g " % err)
|
|
err_tolerance = 4e-3 if test_util.is_xla_enabled() else 1e-3
|
|
self.assertLess(err, err_tolerance)
|
|
|
|
|
|
class AtrousConv2DTransposeTest(test.TestCase):
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testAtrousConv2DTransposeForward(self):
|
|
with self.session(use_gpu=True):
|
|
# Input: [batch, height, width, input_depth]
|
|
height = 9
|
|
for width in [9, 10]: # Test both odd and even width.
|
|
x_shape = [2, height, width, 2]
|
|
x = np.arange(np.prod(x_shape), dtype=np.float32).reshape(x_shape)
|
|
|
|
# Filter: [kernel_height, kernel_width, input_depth, output_depth]
|
|
for kernel_height in range(1, 4):
|
|
for kernel_width in range(1, 4):
|
|
f_shape = [kernel_height, kernel_width, 2, 2]
|
|
f = np.arange(np.prod(f_shape), dtype=np.float32).reshape(f_shape)
|
|
|
|
for rate in range(1, 4):
|
|
f_up = _upsample_filters(f, rate)
|
|
kernel_height_up = (kernel_height + (kernel_height - 1) *
|
|
(rate - 1))
|
|
kernel_width_up = kernel_width + (kernel_width - 1) * (rate - 1)
|
|
|
|
for padding in ["SAME", "VALID"]:
|
|
if padding == "SAME":
|
|
y_shape = [2, height, width, 2]
|
|
else:
|
|
y_shape = [
|
|
2, height + kernel_height_up - 1,
|
|
width + kernel_width_up - 1, 2
|
|
]
|
|
|
|
y1 = nn_ops.atrous_conv2d_transpose(x, f, y_shape, rate,
|
|
padding)
|
|
y2 = nn_ops.conv2d_transpose(
|
|
x, f_up, y_shape, strides=[1, 1, 1, 1], padding=padding)
|
|
self.assertAllClose(
|
|
y1.eval(), self.evaluate(y2), rtol=1e-3, atol=1e-3)
|
|
|
|
|
|
class AtrousDepthwiseConv2DTest(test.TestCase):
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testAtrousDepthwiseConv2DForward(self):
|
|
strides = [1, 1, 1, 1]
|
|
with self.session(use_gpu=True):
|
|
# Input: [batch, height, width, input_depth]
|
|
height = 9
|
|
for width in [9, 10]: # Test both odd and even width.
|
|
x_shape = [2, height, width, 2]
|
|
x = np.arange(np.prod(x_shape), dtype=np.float32).reshape(x_shape)
|
|
|
|
# Filter: [kernel_height, kernel_width, input_depth, output_depth]
|
|
for kernel_height in range(1, 4):
|
|
for kernel_width in range(1, 4):
|
|
f_shape = [kernel_height, kernel_width, 2, 2]
|
|
f = np.arange(np.prod(f_shape), dtype=np.float32).reshape(f_shape)
|
|
|
|
for rate in range(1, 4):
|
|
f_up = _upsample_filters(f, rate)
|
|
|
|
for padding in ["SAME", "VALID"]:
|
|
y1 = nn_impl.depthwise_conv2d(
|
|
x, f, strides, padding, rate=[rate, rate])
|
|
y2 = nn_impl.depthwise_conv2d(x, f_up, strides, padding)
|
|
self.assertAllClose(
|
|
y1.eval(), self.evaluate(y2), rtol=1e-3, atol=1e-3)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test.main()
|