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1254 lines (1143 loc) · 55.2 KB
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# 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.
# ==============================================================================
"""Functional tests for convolutional operations."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import test_util
def GetShrunkInceptionShapes(shrink=10):
"""Iterator for smaller versions of convolution shapes in 2015 Inception.
Relative to inception, each depth value is `depth // shrink`.
Args:
shrink: Factor to shrink each depth value by relative to Inception.
Yields:
Tuple (input_size, filter_size, out_size, stride, padding), the convolution
parameters of Inception layers.
"""
input_sizes = [[4, 5, 5, 1248], [4, 8, 8, 384], [4, 8, 8, 384],
[4, 8, 8, 2048], [4, 8, 8, 448], [4, 8, 8, 2048],
[4, 8, 8, 2048], [4, 8, 8, 2048], [4, 8, 8, 1760],
[4, 8, 8, 1760], [4, 8, 8, 1760], [4, 8, 8, 1760],
[4, 17, 17, 192], [4, 17, 17, 192], [4, 17, 17, 1248],
[4, 17, 17, 128], [4, 17, 17, 1248], [4, 17, 17, 224],
[4, 17, 17, 192], [4, 17, 17, 192], [4, 17, 17, 1216],
[4, 17, 17, 1216], [4, 17, 17, 224], [4, 17, 17, 192],
[4, 17, 17, 192], [4, 17, 17, 1152], [4, 17, 17, 1152],
[4, 17, 17, 192], [4, 17, 17, 160], [4, 17, 17, 1152],
[4, 17, 17, 1024], [4, 17, 17, 128], [4, 17, 17, 1024],
[4, 17, 17, 128], [4, 17, 17, 1024], [4, 17, 17, 128],
[4, 17, 17, 768], [4, 17, 17, 128], [4, 17, 17, 128],
[4, 17, 17, 768], [4, 17, 17, 768], [4, 35, 35, 96],
[4, 35, 35, 288], [4, 35, 35, 64], [4, 35, 35, 288],
[4, 35, 35, 256], [4, 35, 35, 48], [4, 35, 35, 256],
[4, 35, 35, 96], [4, 35, 35, 192], [4, 35, 35, 192],
[4, 35, 35, 192], [4, 73, 73, 64], [4, 73, 73, 64],
[4, 147, 147, 24]]
filter_sizes = [[1, 1, 1248, 128], [1, 3, 384, 384], [3, 1, 384, 384],
[1, 1, 2048, 192], [3, 3, 448, 384], [1, 1, 2048, 320],
[1, 1, 2048, 448], [1, 1, 2048, 384], [1, 1, 1760, 384],
[1, 1, 1760, 192], [1, 1, 1760, 448], [1, 1, 1760, 320],
[3, 3, 192, 192], [3, 3, 192, 192], [1, 1, 1248, 192],
[3, 3, 128, 320], [1, 1, 1248, 128], [1, 3, 224, 224],
[3, 1, 192, 256], [1, 3, 192, 256], [1, 1, 1216, 192],
[1, 1, 1216, 96], [3, 1, 224, 224], [3, 3, 192, 224],
[1, 3, 192, 192], [1, 1, 1152, 192], [1, 1, 1152, 128],
[3, 1, 192, 192], [3, 3, 160, 192], [1, 1, 1152, 160],
[1, 1, 1024, 128], [1, 3, 128, 192], [1, 1, 1024, 160],
[3, 1, 128, 192], [1, 1, 1024, 256], [3, 1, 128, 128],
[1, 1, 768, 192], [1, 3, 128, 128], [3, 3, 128, 128],
[1, 1, 768, 128], [1, 1, 768, 320], [3, 3, 96, 96],
[3, 3, 288, 384], [3, 3, 64, 96], [1, 1, 288, 64],
[1, 1, 256, 64], [5, 5, 48, 64], [1, 1, 256, 48],
[3, 3, 96, 96], [1, 1, 192, 32], [1, 1, 192, 64],
[1, 1, 192, 48], [3, 3, 64, 192], [1, 1, 64, 64],
[1, 1, 24, 64]]
out_sizes = [[4, 5, 5, 128], [4, 8, 8, 384], [4, 8, 8, 384],
[4, 8, 8, 192], [4, 8, 8, 384], [4, 8, 8, 320],
[4, 8, 8, 448], [4, 8, 8, 384], [4, 8, 8, 384],
[4, 8, 8, 192], [4, 8, 8, 448], [4, 8, 8, 320],
[4, 8, 8, 192], [4, 17, 17, 192], [4, 17, 17, 192],
[4, 8, 8, 320], [4, 17, 17, 128], [4, 17, 17, 224],
[4, 17, 17, 256], [4, 17, 17, 256], [4, 17, 17, 192],
[4, 17, 17, 96], [4, 17, 17, 224], [4, 17, 17, 224],
[4, 17, 17, 192], [4, 17, 17, 192], [4, 17, 17, 128],
[4, 17, 17, 192], [4, 17, 17, 192], [4, 17, 17, 160],
[4, 17, 17, 128], [4, 17, 17, 192], [4, 17, 17, 160],
[4, 17, 17, 192], [4, 17, 17, 256], [4, 17, 17, 128],
[4, 17, 17, 192], [4, 17, 17, 128], [4, 17, 17, 128],
[4, 17, 17, 128], [4, 17, 17, 320], [4, 17, 17, 96],
[4, 17, 17, 384], [4, 35, 35, 96], [4, 35, 35, 64],
[4, 35, 35, 64], [4, 35, 35, 64], [4, 35, 35, 48],
[4, 35, 35, 96], [4, 35, 35, 32], [4, 35, 35, 64],
[4, 35, 35, 48], [4, 71, 71, 192], [4, 73, 73, 64],
[4, 147, 147, 64]]
strides = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
# Shrink sizes to make the test faster
for i in input_sizes:
i[3] //= shrink
for f in filter_sizes:
f[2] //= shrink
f[3] //= shrink
for o in out_sizes:
o[3] //= shrink
# pylint: disable=invalid-name
VALID = "VALID"
SAME = "SAME"
# pylint: enable=invalid-name
paddings = [SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME,
SAME, SAME, SAME, SAME, VALID, SAME, SAME, VALID,
SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME,
SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME,
SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME,
SAME, VALID, VALID, SAME, SAME, SAME, SAME, SAME,
SAME, SAME, SAME, SAME, VALID, VALID, VALID]
for i, f, o, s, p in zip(input_sizes, filter_sizes, out_sizes, strides,
paddings):
yield i, f, o, s, p
def NHWCToNCHW(input_tensor):
"""Convert the input from NHWC format to NCHW.
Args:
input_tensor: a 4-D tensor, or a 4-element array representing the same.
Returns:
the converted tensor or a shape array
"""
if isinstance(input_tensor, tf.Tensor):
return tf.transpose(input_tensor, [0, 3, 1, 2])
else:
return [input_tensor[0], input_tensor[3], input_tensor[1], input_tensor[2]]
def NCHWToNHWC(input_tensor):
"""Convert the input from NCHW format to NHWC.
Args:
input_tensor: a 4-D tensor, or a 4-element array representing the same.
Returns:
the converted tensor or a shape array
"""
if isinstance(input_tensor, tf.Tensor):
return tf.transpose(input_tensor, [0, 2, 3, 1])
else:
return [input_tensor[0], input_tensor[2], input_tensor[3], input_tensor[1]]
def GetTestConfigs():
"""Get all the valid tests configs to run.
Returns:
all the valid test configs as tuples of data_format and use_gpu.
"""
test_configs = [("NHWC", False), ("NHWC", True)]
if tf.test.is_gpu_available():
# "NCHW" format is not currently supported on CPU.
test_configs += [("NCHW", True)]
return test_configs
class Conv2DTest(tf.test.TestCase):
def _DtypesToTest(self, use_gpu):
if use_gpu and not test_util.CudaSupportsHalfMatMulAndConv():
return [tf.float32]
else:
# It is important that float32 comes before float16 here,
# as we will be using its gradients as reference for fp16 gradients.
return [tf.float32, tf.float16]
def _SetupValuesForDevice(self, tensor_in_sizes, filter_in_sizes, strides,
padding, data_format, dtype, use_gpu):
"""Verifies the output values of the convolution function.
Args:
tensor_in_sizes: Input tensor dimensions in
[batch, input_rows, input_cols, input_depth].
filter_in_sizes: Filter tensor dimensions in
[kernel_rows, kernel_cols, input_depth, output_depth].
strides: Stride: [col_stride, row_stride]
padding: Padding type.
data_format: Format of the data tensors.
dtype: Data type for inputs and outputs.
use_gpu: True if the operations should be run on GPU
Returns:
Symbolic tensor value that can be used to execute the computation
"""
total_size_1 = 1
total_size_2 = 1
for s in tensor_in_sizes:
total_size_1 *= s
for s in filter_in_sizes:
total_size_2 *= s
# Initializes the input tensor with array containing incrementing
# numbers from 1.
x1 = [f * 1.0 for f in range(1, total_size_1 + 1)]
x2 = [f * 1.0 for f in range(1, total_size_2 + 1)]
with self.test_session(use_gpu=use_gpu) as sess:
t1 = tf.constant(x1, shape=tensor_in_sizes, dtype=dtype)
t2 = tf.constant(x2, shape=filter_in_sizes, dtype=dtype)
strides = [1] + strides + [1]
if data_format == "NCHW":
t1 = NHWCToNCHW(t1)
strides = NHWCToNCHW(strides)
conv = tf.nn.conv2d(t1,
t2,
strides=strides,
padding=padding,
data_format=data_format)
if data_format == "NCHW":
conv = NCHWToNHWC(conv)
return conv
def _CompareFwdValues(self, tensor_in_sizes, filter_in_sizes,
conv_strides, padding):
"""Verifies that CPU and GPU produce the same values.
Args:
tensor_in_sizes: Input tensor dimensions in
[batch, input_rows, input_cols, input_depth].
filter_in_sizes: Filter tensor dimensions in
[kernel_rows, kernel_cols, input_depth, output_depth].
conv_strides: [row_stride, col_stride] for the convolution;
padding: Padding type.
"""
x1 = np.random.rand(*tensor_in_sizes).astype(np.float32)
x2 = np.random.rand(*filter_in_sizes).astype(np.float32)
def _SetupVal(data_format, use_gpu):
with self.test_session(use_gpu=use_gpu):
t1 = tf.constant(x1, shape=tensor_in_sizes)
t2 = tf.constant(x2, shape=filter_in_sizes)
strides = [1] + conv_strides + [1]
if data_format == "NCHW":
t1 = NHWCToNCHW(t1)
strides = NHWCToNCHW(strides)
conv = tf.nn.conv2d(t1,
t2,
strides=strides,
padding=padding,
data_format=data_format)
if data_format == "NCHW":
conv = NCHWToNHWC(conv)
return conv
tensors = []
for (data_format, use_gpu) in GetTestConfigs():
tensors.append(_SetupVal(data_format, use_gpu))
with self.test_session() as sess:
values = sess.run(tensors)
for i in range(1, len(values)):
self.assertAllClose(values[0], values[i], rtol=1e-5, atol=1e-5)
def _VerifyValues(self, tensor_in_sizes, filter_in_sizes, strides,
padding, expected):
tensors = []
for (data_format, use_gpu) in GetTestConfigs():
for dtype in self._DtypesToTest(use_gpu):
result = self._SetupValuesForDevice(tensor_in_sizes,
filter_in_sizes,
strides,
padding,
data_format,
dtype,
use_gpu=use_gpu)
tensors.append(result)
with self.test_session() as sess:
values = sess.run(tensors)
for i in range(len(tensors)):
conv = tensors[i]
value = values[i]
print("expected = ", expected)
print("actual = ", value)
tol = 1e-5
if value.dtype == np.float16:
tol = 1e-3
self.assertAllClose(expected, np.ravel(value), atol=tol, rtol=tol)
self.assertShapeEqual(value, conv)
def testConv2D1x1Filter(self):
expected_output = [30.0, 36.0, 42.0, 66.0, 81.0, 96.0, 102.0, 126.0, 150.0,
138.0, 171.0, 204.0, 174.0, 216.0, 258.0, 210.0, 261.0,
312.0]
self._VerifyValues(tensor_in_sizes=[1, 2, 3, 3],
filter_in_sizes=[1, 1, 3, 3],
strides=[1, 1], padding="VALID",
expected=expected_output)
def testConv2DEmpty(self):
expected_output = []
self._VerifyValues(tensor_in_sizes=[0, 2, 3, 3],
filter_in_sizes=[1, 1, 3, 3],
strides=[1, 1], padding="VALID",
expected=expected_output)
def testConv2D2x2Filter(self):
# The outputs are computed using third_party/py/IPython/notebook.
expected_output = [2271.0, 2367.0, 2463.0, 2901.0, 3033.0, 3165.0]
self._VerifyValues(tensor_in_sizes=[1, 2, 3, 3],
filter_in_sizes=[2, 2, 3, 3],
strides=[1, 1], padding="VALID",
expected=expected_output)
def testConv2D1x2Filter(self):
# The outputs are computed using third_party/py/IPython/notebook.
expected_output = [231.0, 252.0, 273.0, 384.0, 423.0, 462.0, 690.0,
765.0, 840.0, 843.0, 936.0, 1029.0]
self._VerifyValues(tensor_in_sizes=[1, 2, 3, 3],
filter_in_sizes=[1, 2, 3, 3],
strides=[1, 1], padding="VALID",
expected=expected_output)
def testConv2D2x2FilterStride2(self):
expected_output = [2271.0, 2367.0, 2463.0]
self._VerifyValues(tensor_in_sizes=[1, 2, 3, 3],
filter_in_sizes=[2, 2, 3, 3],
strides=[2, 2], padding="VALID",
expected=expected_output)
def testConv2D2x2FilterStride2Same(self):
expected_output = [2271.0, 2367.0, 2463.0, 1230.0, 1305.0, 1380.0]
self._VerifyValues(tensor_in_sizes=[1, 2, 3, 3],
filter_in_sizes=[2, 2, 3, 3],
strides=[2, 2], padding="SAME",
expected=expected_output)
def testConv2D2x2FilterStride1x2(self):
expected_output = [58.0, 78.0, 98.0, 118.0, 138.0, 158.0]
self._VerifyValues(tensor_in_sizes=[1, 3, 6, 1],
filter_in_sizes=[2, 2, 1, 1],
strides=[1, 2], padding="VALID",
expected=expected_output)
def testConv2DKernelSmallerThanStrideValid(self):
expected_output = [65, 95, 275, 305]
self._VerifyValues(tensor_in_sizes=[1, 7, 7, 1],
filter_in_sizes=[2, 2, 1, 1],
strides=[3, 3], padding="VALID",
expected=expected_output)
def testConv2DKernelSmallerThanStrideSame(self):
self._VerifyValues(tensor_in_sizes=[1, 3, 3, 1],
filter_in_sizes=[1, 1, 1, 1],
strides=[2, 2], padding="SAME",
expected=[1, 3, 7, 9])
self._VerifyValues(tensor_in_sizes=[1, 4, 4, 1],
filter_in_sizes=[1, 1, 1, 1],
strides=[2, 2], padding="SAME",
expected=[1, 3, 9, 11])
self._VerifyValues(tensor_in_sizes=[1, 4, 4, 1],
filter_in_sizes=[2, 2, 1, 1],
strides=[3, 3], padding="SAME",
expected=[44, 28, 41, 16])
# TODO this currently fails.
#self._VerifyValues(tensor_in_sizes=[1, 8, 8, 1],
# filter_in_sizes=[2, 2, 1, 1],
# strides=[4, 4], padding="SAME",
# expected=[72, 112, 392, 432])
# Testing for backprops
def _RunAndVerifyBackpropInput(self, input_sizes, filter_sizes, output_sizes,
strides, padding, expected, data_format,
use_gpu, err):
total_output_size = 1
total_filter_size = 1
for s in output_sizes:
total_output_size *= s
for s in filter_sizes:
total_filter_size *= s
# Initializes the input tensor with array containing incrementing
# numbers from 1.
x1 = [f * 1.0 for f in range(1, total_filter_size + 1)]
x2 = [f * 1.0 for f in range(1, total_output_size + 1)]
with self.test_session(use_gpu=use_gpu) as sess:
if data_format == "NCHW":
input_sizes = NHWCToNCHW(input_sizes)
t0 = tf.constant(input_sizes, shape=[len(input_sizes)])
t1 = tf.constant(x1, shape=filter_sizes)
t2 = tf.constant(x2, shape=output_sizes)
strides = [1] + strides + [1]
if data_format == "NCHW":
t2 = NHWCToNCHW(t2)
strides = NHWCToNCHW(strides)
conv = tf.nn.conv2d_backprop_input(t0,
t1,
t2,
strides=strides,
padding=padding,
data_format=data_format)
if data_format == "NCHW":
conv = NCHWToNHWC(conv)
# "values" consists of two tensors for two backprops
value = sess.run(conv)
self.assertShapeEqual(value, conv)
print("expected = ", expected)
print("actual = ", value)
self.assertArrayNear(expected, value.flatten(), err)
def _CompareBackpropInput(self, input_sizes, filter_sizes, output_sizes,
conv_strides, padding):
x1 = np.random.rand(*filter_sizes).astype(np.float32)
x2 = np.random.rand(*output_sizes).astype(np.float32)
def _GetVal(data_format, use_gpu):
with self.test_session(use_gpu=use_gpu) as sess:
if data_format == "NCHW":
new_input_sizes = NHWCToNCHW(input_sizes)
else:
new_input_sizes = input_sizes
t0 = tf.constant(new_input_sizes, shape=[len(new_input_sizes)])
t1 = tf.constant(x1, shape=filter_sizes)
t2 = tf.constant(x2, shape=output_sizes)
strides = [1] + conv_strides + [1]
if data_format == "NCHW":
t2 = NHWCToNCHW(t2)
strides = NHWCToNCHW(strides)
conv = tf.nn.conv2d_backprop_input(t0,
t1,
t2,
strides=strides,
padding=padding,
data_format=data_format)
if data_format == "NCHW":
conv = NCHWToNHWC(conv)
ret = conv.eval()
self.assertShapeEqual(ret, conv)
return ret
values = []
for (data_format, use_gpu) in GetTestConfigs():
values.append(_GetVal(data_format, use_gpu))
for i in range(1, len(values)):
self.assertAllClose(values[0], values[i], rtol=1e-4, atol=1e-4)
def testConv2D2x2Depth1ValidBackpropInput(self):
expected_output = [1.0, 4.0, 4.0, 3.0, 10.0, 8.0]
for (data_format, use_gpu) in GetTestConfigs():
self._RunAndVerifyBackpropInput(input_sizes=[1, 2, 3, 1],
filter_sizes=[2, 2, 1, 1],
output_sizes=[1, 1, 2, 1],
strides=[1, 1],
padding="VALID",
expected=expected_output,
data_format=data_format,
use_gpu=use_gpu,
err=1e-5)
def testConv2D2x2Depth3ValidBackpropInput(self):
expected_output = [14.0, 32.0, 50.0,
100.0, 163.0, 226.0,
167.0, 212.0, 257.0,
122.0, 140.0, 158.0,
478.0, 541.0, 604.0,
437.0, 482.0, 527.0]
for (data_format, use_gpu) in GetTestConfigs():
# The GPU version of this test is not very stable. So adjusting the
# error threshold to 1e-4.
self._RunAndVerifyBackpropInput(input_sizes=[1, 2, 3, 3],
filter_sizes=[2, 2, 3, 3],
output_sizes=[1, 1, 2, 3],
strides=[1, 1],
padding="VALID",
expected=expected_output,
data_format=data_format,
use_gpu=use_gpu,
err=1e-4)
def testConv2D2x2Depth3ValidBackpropInputStride1x2(self):
expected_output = [1.0, 2.0, 2.0, 4.0, 3.0, 6.0,
7.0, 12.0, 11.0, 18.0, 15.0, 24.0,
12.0, 16.0, 15.0, 20.0, 18.0, 24.0]
for (data_format, use_gpu) in GetTestConfigs():
self._RunAndVerifyBackpropInput(input_sizes=[1, 3, 6, 1],
filter_sizes=[2, 2, 1, 1],
output_sizes=[1, 2, 3, 1],
strides=[1, 2],
padding="VALID",
expected=expected_output,
data_format=data_format,
use_gpu=use_gpu,
err=1e-5)
def testConv2DStrideTwoFilterOneSameBackpropInput(self):
expected_output = [1.0, 0.0, 2.0, 0.0,
0.0, 0.0, 0.0, 0.0,
3.0, 0.0, 4.0, 0.0,
0.0, 0.0, 0.0, 0.0]
for (data_format, use_gpu) in GetTestConfigs():
self._RunAndVerifyBackpropInput(input_sizes=[1, 4, 4, 1],
filter_sizes=[1, 1, 1, 1],
output_sizes=[1, 2, 2, 1],
strides=[2, 2],
padding="SAME",
expected=expected_output,
data_format=data_format,
use_gpu=use_gpu,
err=1e-5)
# Testing for backprops
def _RunAndVerifyBackpropFilter(self, input_sizes, filter_sizes, output_sizes,
strides, padding, expected, data_format,
use_gpu):
total_input_size = 1
total_output_size = 1
for s in input_sizes:
total_input_size *= s
for s in output_sizes:
total_output_size *= s
# Initializes the input tensor with array containing incrementing
# numbers from 1.
x0 = [f * 1.0 for f in range(1, total_input_size + 1)]
x2 = [f * 1.0 for f in range(1, total_output_size + 1)]
for dtype in self._DtypesToTest(use_gpu=use_gpu):
with self.test_session(use_gpu=use_gpu) as sess:
t0 = tf.constant(x0, shape=input_sizes, dtype=dtype)
t1 = tf.constant(filter_sizes, shape=[len(filter_sizes)])
t2 = tf.constant(x2, shape=output_sizes, dtype=dtype)
explicit_strides = [1] + strides + [1]
if data_format == "NCHW":
t0 = NHWCToNCHW(t0)
t2 = NHWCToNCHW(t2)
explicit_strides = NHWCToNCHW(explicit_strides)
conv = tf.nn.conv2d_backprop_filter(t0,
t1,
t2,
strides=explicit_strides,
padding=padding,
data_format=data_format)
value = sess.run(conv)
self.assertShapeEqual(value, conv)
print("expected = ", expected)
print("actual = ", value)
self.assertArrayNear(expected, value.flatten(), 1e-5)
def _CompareBackFilter(self, input_sizes, filter_sizes, output_sizes,
conv_strides, padding):
x0 = np.random.rand(*input_sizes).astype(np.float32)
x2 = np.random.rand(*output_sizes).astype(np.float32)
def _GetVal(data_format, use_gpu):
with self.test_session(use_gpu=use_gpu) as sess:
t0 = tf.constant(x0, shape=input_sizes)
t1 = tf.constant(filter_sizes, shape=[len(filter_sizes)])
t2 = tf.constant(x2, shape=output_sizes)
strides = [1] + conv_strides + [1]
if data_format == "NCHW":
t0 = NHWCToNCHW(t0)
t2 = NHWCToNCHW(t2)
strides = NHWCToNCHW(strides)
conv = tf.nn.conv2d_backprop_filter(t0,
t1,
t2,
strides=strides,
padding=padding,
data_format=data_format)
ret = conv.eval()
self.assertShapeEqual(ret, conv)
return ret
values = []
for (data_format, use_gpu) in GetTestConfigs():
values.append(_GetVal(data_format, use_gpu))
for i in range(1, len(values)):
self.assertAllClose(values[0], values[i], rtol=1e-4, atol=1e-4)
def testConv2D2x2Depth1ValidBackpropFilter(self):
expected = [5.0, 8.0, 14.0, 17.0]
for (data_format, use_gpu) in GetTestConfigs():
self._RunAndVerifyBackpropFilter(input_sizes=[1, 2, 3, 1],
filter_sizes=[2, 2, 1, 1],
output_sizes=[1, 1, 2, 1],
strides=[1, 1],
padding="VALID",
expected=expected,
data_format=data_format,
use_gpu=use_gpu)
def testConv2D2x2Depth3ValidBackpropFilter(self):
expected = [17.0, 22.0, 27.0, 22.0, 29.0, 36.0, 27.0, 36.0, 45.0,
32.0, 43.0, 54.0, 37.0, 50.0, 63.0, 42.0, 57.0, 72.0,
62.0, 85.0, 108.0, 67.0, 92.0, 117.0, 72.0, 99.0, 126.0,
77.0, 106.0, 135.0, 82.0, 113.0, 144.0, 87.0, 120.0, 153.0]
for (data_format, use_gpu) in GetTestConfigs():
self._RunAndVerifyBackpropFilter(input_sizes=[1, 2, 3, 3],
filter_sizes=[2, 2, 3, 3],
output_sizes=[1, 1, 2, 3],
strides=[1, 1],
padding="VALID",
expected=expected,
data_format=data_format,
use_gpu=use_gpu)
def testConv2D2x2Depth3ValidBackpropFilterStride1x2(self):
expected = [161.0, 182.0, 287.0, 308.0]
for (data_format, use_gpu) in GetTestConfigs():
self._RunAndVerifyBackpropFilter(input_sizes=[1, 3, 6, 1],
filter_sizes=[2, 2, 1, 1],
output_sizes=[1, 2, 3, 1],
strides=[1, 2],
padding="VALID",
expected=expected,
data_format=data_format,
use_gpu=use_gpu)
def testConv2DStrideTwoFilterOneSameBackpropFilter(self):
expected_output = [78.]
for (data_format, use_gpu) in GetTestConfigs():
self._RunAndVerifyBackpropFilter(input_sizes=[1, 4, 4, 1],
filter_sizes=[1, 1, 1, 1],
output_sizes=[1, 2, 2, 1],
strides=[2, 2],
padding="SAME",
expected=expected_output,
data_format=data_format,
use_gpu=use_gpu)
# Gradient checkers
def ConstructAndTestGradient(self, batch, input_rows, input_cols, filter_rows,
filter_cols, in_depth, out_depth, stride_rows,
stride_cols, padding, test_input, data_format,
use_gpu):
input_shape = [batch, input_rows, input_cols, in_depth]
filter_shape = [filter_rows, filter_cols, in_depth, out_depth]
# TODO(yangke): re-factor the computation of output shape.
if padding == "VALID":
output_rows = (input_rows - filter_rows + stride_rows) // stride_rows
output_cols = (input_cols - filter_cols + stride_cols) // stride_cols
else:
output_rows = (input_rows + stride_rows - 1) // stride_rows
output_cols = (input_cols + stride_cols - 1) // stride_cols
output_shape = [batch, output_rows, output_cols, out_depth]
input_size = 1
for x in input_shape:
input_size *= x
filter_size = 1
for x in filter_shape:
filter_size *= x
input_data = [x * 1.0 / input_size for x in range(0, input_size)]
filter_data = [x * 1.0 / filter_size for x in range(0, filter_size)]
# Conv2DGrad functions are not compiled for double due to
# a problem in the way Eigen's Conv2DGrad works for double.
# So we disable the DOUBLE path. We should re-enable this
# when double support returns for CPU and/or GPU.
for dtype in self._DtypesToTest(use_gpu=use_gpu):
with self.test_session(use_gpu=use_gpu):
input_tensor = tf.constant(input_data, shape=input_shape,
dtype=dtype, name="input")
filter_tensor = tf.constant(filter_data, shape=filter_shape,
dtype=dtype, name="filter")
strides = [1, stride_rows, stride_cols, 1]
if data_format == "NCHW":
new_input_tensor = NHWCToNCHW(input_tensor)
strides = NHWCToNCHW(strides)
else:
new_input_tensor = input_tensor
conv = tf.nn.conv2d(new_input_tensor,
filter_tensor,
strides,
padding,
data_format=data_format,
name="conv")
if data_format == "NCHW":
conv = NCHWToNHWC(conv)
self.assertEqual(output_shape, conv.get_shape())
if test_input:
jacob_t, jacob_n = tf.test.compute_gradient(input_tensor, input_shape,
conv, output_shape)
else:
jacob_t, jacob_n = tf.test.compute_gradient(
filter_tensor, filter_shape, conv, output_shape)
if dtype == tf.float32:
reference_jacob_t = jacob_t
err = np.fabs(jacob_t - jacob_n).max()
else:
# Compare fp16 theoretical gradients to fp32 theoretical gradients,
# since fp16 numerical gradients are too imprecise.
err = np.fabs(jacob_t - reference_jacob_t).max()
print("conv_2d gradient error = ", err)
self.assertLess(err, 0.002)
def testInputGradientValidPaddingStrideOne(self):
for (data_format, use_gpu) in GetTestConfigs():
self.ConstructAndTestGradient(batch=2,
input_rows=5,
input_cols=4,
filter_rows=3,
filter_cols=3,
in_depth=2,
out_depth=3,
stride_rows=1,
stride_cols=1,
padding="VALID",
test_input=True,
data_format=data_format,
use_gpu=use_gpu)
def testFilterGradientValidPaddingStrideOne(self):
for (data_format, use_gpu) in GetTestConfigs():
self.ConstructAndTestGradient(batch=4,
input_rows=6,
input_cols=5,
filter_rows=2,
filter_cols=2,
in_depth=2,
out_depth=3,
stride_rows=1,
stride_cols=1,
padding="VALID",
test_input=False,
data_format=data_format,
use_gpu=use_gpu)
def testInputGradientValidPaddingStrideTwo(self):
for (data_format, use_gpu) in GetTestConfigs():
self.ConstructAndTestGradient(batch=2,
input_rows=4,
input_cols=5,
filter_rows=3,
filter_cols=3,
in_depth=2,
out_depth=3,
stride_rows=2,
stride_cols=2,
padding="VALID",
test_input=True,
data_format=data_format,
use_gpu=use_gpu)
def testFilterGradientValidPaddingStrideTwo(self):
for (data_format, use_gpu) in GetTestConfigs():
self.ConstructAndTestGradient(batch=4,
input_rows=6,
input_cols=5,
filter_rows=2,
filter_cols=2,
in_depth=2,
out_depth=3,
stride_rows=2,
stride_cols=2,
padding="VALID",
test_input=False,
data_format=data_format,
use_gpu=use_gpu)
def testInputGradientValidPaddingStrideThree(self):
for (data_format, use_gpu) in GetTestConfigs():
self.ConstructAndTestGradient(batch=2,
input_rows=7,
input_cols=6,
filter_rows=3,
filter_cols=3,
in_depth=4,
out_depth=5,
stride_rows=3,
stride_cols=3,
padding="VALID",
test_input=True,
data_format=data_format,
use_gpu=use_gpu)
def testFilterGradientValidPaddingStrideThree(self):
for (data_format, use_gpu) in GetTestConfigs():
self.ConstructAndTestGradient(batch=2,
input_rows=8,
input_cols=7,
filter_rows=4,
filter_cols=4,
in_depth=2,
out_depth=3,
stride_rows=3,
stride_cols=3,
padding="VALID",
test_input=False,
data_format=data_format,
use_gpu=use_gpu)
def testInputGradientSamePaddingStrideOne(self):
for (data_format, use_gpu) in GetTestConfigs():
self.ConstructAndTestGradient(batch=2,
input_rows=7,
input_cols=6,
filter_rows=3,
filter_cols=3,
in_depth=2,
out_depth=3,
stride_rows=1,
stride_cols=1,
padding="SAME",
test_input=True,
data_format=data_format,
use_gpu=use_gpu)
def testFilterGradientSamePaddingStrideOne(self):
for (data_format, use_gpu) in GetTestConfigs():
self.ConstructAndTestGradient(batch=4,
input_rows=6,
input_cols=5,
filter_rows=2,
filter_cols=2,
in_depth=2,
out_depth=3,
stride_rows=1,
stride_cols=1,
padding="SAME",
test_input=False,
data_format=data_format,
use_gpu=use_gpu)
def testInputGradientSamePaddingStrideTwo(self):
for (data_format, use_gpu) in GetTestConfigs():
self.ConstructAndTestGradient(batch=2,
input_rows=5,
input_cols=4,
filter_rows=3,
filter_cols=3,
in_depth=3,
out_depth=3,
stride_rows=2,
stride_cols=2,
padding="SAME",
test_input=True,
data_format=data_format,
use_gpu=use_gpu)
def testFilterGradientSamePaddingStrideTwo(self):
for (data_format, use_gpu) in GetTestConfigs():
self.ConstructAndTestGradient(batch=4,
input_rows=6,
input_cols=5,
filter_rows=2,
filter_cols=2,
in_depth=2,
out_depth=3,
stride_rows=2,
stride_cols=2,
padding="SAME",
test_input=False,
data_format=data_format,
use_gpu=use_gpu)
def testInputGradientSamePaddingStrideThree(self):
for (data_format, use_gpu) in GetTestConfigs():
self.ConstructAndTestGradient(batch=2,
input_rows=7,
input_cols=6,
filter_rows=3,
filter_cols=3,
in_depth=4,
out_depth=5,
stride_rows=3,
stride_cols=3,
padding="SAME",
test_input=True,
data_format=data_format,
use_gpu=use_gpu)
def testFilterGradientSamePaddingStrideThree(self):
for (data_format, use_gpu) in GetTestConfigs():
self.ConstructAndTestGradient(batch=2,
input_rows=8,
input_cols=7,
filter_rows=4,
filter_cols=4,
in_depth=2,
out_depth=3,
stride_rows=3,
stride_cols=3,
padding="SAME",
test_input=False,
data_format=data_format,
use_gpu=use_gpu)
def testFilterGradientSamePaddingStride2x1(self):
for (data_format, use_gpu) in GetTestConfigs():
self.ConstructAndTestGradient(batch=2,
input_rows=8,
input_cols=7,
filter_rows=4,
filter_cols=4,
in_depth=2,
out_depth=3,
stride_rows=2,
stride_cols=1,
padding="SAME",
test_input=False,
data_format=data_format,
use_gpu=use_gpu)
def testShapeFunctionEdgeCases(self):
# All shapes unknown.
c1 = tf.nn.conv2d(tf.placeholder(tf.float32),
tf.placeholder(tf.float32),
strides=[1, 1, 1, 1], padding="SAME")
self.assertEqual([None, None, None, None], c1.get_shape().as_list())
# Incorrect input shape.
with self.assertRaises(ValueError):
tf.nn.conv2d(tf.placeholder(tf.float32, shape=[1, 3]),
tf.placeholder(tf.float32),
strides=[1, 1, 1, 1], padding="SAME")
# Incorrect filter shape.
with self.assertRaises(ValueError):
tf.nn.conv2d(tf.placeholder(tf.float32),
tf.placeholder(tf.float32, shape=[1, 3]),
strides=[1, 1, 1, 1], padding="SAME")
# Depth mismatch.
with self.assertRaises(ValueError):
tf.nn.conv2d(tf.placeholder(tf.float32,
shape=[32, 20, 20, 3]),
tf.placeholder(tf.float32,
shape=[4, 4, 2, 2]),
strides=[1, 1, 1, 1], padding="SAME")
def testOpEdgeCases(self):
with self.test_session() as sess:
# Illegal strides.
with self.assertRaisesRegexp(tf.errors.InvalidArgumentError,
"strides in the batch and depth"):
sess.run(
tf.nn.conv2d(
tf.placeholder(tf.float32),
tf.placeholder(tf.float32),
strides=[2, 1, 1, 1],
padding="SAME"))
with self.assertRaisesRegexp(tf.errors.InvalidArgumentError,
"strides in the batch and depth"):
sess.run(
tf.nn.conv2d(
tf.placeholder(tf.float32),
tf.placeholder(tf.float32),
strides=[1, 1, 1, 2],
padding="SAME"))
# Filter larger than input.
with self.assertRaisesRegexp(ValueError, "Negative dimension size"):
sess.run(
tf.nn.conv2d(
tf.placeholder(
tf.float32, shape=[32, 20, 20, 3]),
tf.placeholder(
tf.float32, shape=[20, 21, 3, 2]),
strides=[1, 1, 1, 1],
padding="VALID"))
with self.assertRaisesRegexp(ValueError, "Negative dimension size"):
sess.run(
tf.nn.conv2d(
tf.placeholder(
tf.float32, shape=[32, 20, 20, 3]),
tf.placeholder(
tf.float32, shape=[21, 20, 3, 2]),
strides=[1, 1, 1, 1],
padding="VALID"))
# This is only a very simple test. More comprehensive tests live in
# //learning/dist_belief/experimental/brain_compatibility/conv_nn_test.py
# where we compare the numeric results of the depthwise conv op with the
# depthwise weighted sum transformer in dist_belief.
class DepthwiseConv2DTest(tf.test.TestCase):
def _VerifyValues(self, tensor_in_sizes, filter_in_sizes, stride,
padding, expected):
"""Verifies the output values of the convolution function.
Args:
tensor_in_sizes: Input tensor dimensions in
[batch, input_rows, input_cols, input_depth].
filter_in_sizes: Filter tensor dimensions in
[filter_rows, filter_cols, input_depth, depth_multiplier].
stride: Stride.
padding: Padding type.
expected: An array containing the expected operation outputs.
"""
total_size_1 = 1
total_size_2 = 1
for s in tensor_in_sizes:
total_size_1 *= s
for s in filter_in_sizes:
total_size_2 *= s
# Initializes the input tensor with array containing incrementing
# numbers from 1.
x1 = [f * 1.0 for f in range(1, total_size_1 + 1)]
x2 = [f * 1.0 for f in range(1, total_size_2 + 1)]
with self.test_session() as sess:
t1 = tf.constant(x1, shape=tensor_in_sizes)
t1.set_shape(tensor_in_sizes)
t2 = tf.constant(x2, shape=filter_in_sizes)
conv = tf.nn.depthwise_conv2d(t1, t2, strides=[1, stride, stride, 1],
padding=padding)
value = sess.run(conv)
print("value = ", value)
self.assertArrayNear(expected, np.ravel(value), 1e-5)
self.assertShapeEqual(value, conv)