提交 4641285c 编写于 作者: W wangyang59

Added a test in test_NetworkCompare to verify cudnn and exconv, fixed a bug in...

Added a test in test_NetworkCompare to verify cudnn and exconv, fixed a bug in cudnn_conv in dealing with groups
上级 ba786b2e
...@@ -59,7 +59,8 @@ void ConvProjection::getConvParams() { ...@@ -59,7 +59,8 @@ void ConvProjection::getConvParams() {
void ConvProjection::initCudnn() { void ConvProjection::initCudnn() {
hl_create_filter_descriptor( hl_create_filter_descriptor(
&filterDesc_, channels_, numFilters_, filterH_, filterW_); &filterDesc_, channels_ / groups_, numFilters_ / groups_,
filterH_, filterW_);
hl_create_tensor_descriptor(&inputDesc_); hl_create_tensor_descriptor(&inputDesc_);
hl_create_tensor_descriptor(&outputDesc_); hl_create_tensor_descriptor(&outputDesc_);
hl_create_convolution_descriptor(&convDesc_, hl_create_convolution_descriptor(&convDesc_,
...@@ -86,7 +87,7 @@ void ConvProjection::initCudnn() { ...@@ -86,7 +87,7 @@ void ConvProjection::initCudnn() {
void ConvProjection::reshapeTensorDesc(int batchSize) { void ConvProjection::reshapeTensorDesc(int batchSize) {
hl_tensor_reshape(inputDesc_, hl_tensor_reshape(inputDesc_,
batchSize, batchSize,
channels_, channels_ / groups_,
imageH_, imageH_,
imageW_, imageW_,
channels_ * imageH_ * imageW_, channels_ * imageH_ * imageW_,
...@@ -115,7 +116,7 @@ void ConvProjection::reshapeTensorDesc(int batchSize) { ...@@ -115,7 +116,7 @@ void ConvProjection::reshapeTensorDesc(int batchSize) {
hl_tensor_reshape(outputDesc_, hl_tensor_reshape(outputDesc_,
batchSize, batchSize,
numFilters_, numFilters_ / groups_,
outputH_, outputH_,
outputW_, outputW_,
nStride, nStride,
......
#edit-mode: -*- python -*-
# Copyright (c) 2016 Baidu, 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.
from paddle.trainer_config_helpers import *
settings(batch_size=10)
data = data_layer(name ="input", size=8*16*16)
conv = img_conv_layer(input=data, filter_size=2, filter_size_y=2,
num_channels=8,
num_filters=16, stride=1,
bias_attr=True,
act=LinearActivation(),
layer_type="exconv")
conv2 = img_conv_layer(input=data, filter_size=3, filter_size_y=3,
num_channels=8,
num_filters=16, stride=2,
bias_attr=True,
act=LinearActivation(),
groups=1,
layer_type="exconv")
outputs(conv, conv2)
#edit-mode: -*- python -*-
# Copyright (c) 2016 Baidu, 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.
from paddle.trainer_config_helpers import *
settings(batch_size=10)
data = data_layer(name ="input", size=8*16*16)
conv = img_conv_layer(input=data, filter_size=2, filter_size_y=2,
num_channels=8,
num_filters=16, stride=1,
bias_attr=True,
act=LinearActivation(),
layer_type="cudnn_conv")
conv2 = img_conv_layer(input=data, filter_size=3, filter_size_y=3,
num_channels=8,
num_filters=16, stride=2,
bias_attr=True,
act=LinearActivation(),
groups=1,
layer_type="cudnn_conv")
outputs(conv, conv2)
...@@ -38,7 +38,7 @@ P_DECLARE_bool(prev_batch_state); ...@@ -38,7 +38,7 @@ P_DECLARE_bool(prev_batch_state);
// matches the given result // matches the given result
MatrixPtr doOneConvTest(size_t imgSize, size_t output_x, size_t stride, MatrixPtr doOneConvTest(size_t imgSize, size_t output_x, size_t stride,
size_t padding, size_t filter_size, size_t channel, size_t padding, size_t filter_size, size_t channel,
size_t numfilters, MatrixPtr& inputData, size_t numfilters, size_t groups, MatrixPtr& inputData,
real* param, bool useGpu) { real* param, bool useGpu) {
TestConfig config; TestConfig config;
config.biasSize = numfilters; config.biasSize = numfilters;
...@@ -51,9 +51,11 @@ MatrixPtr doOneConvTest(size_t imgSize, size_t output_x, size_t stride, ...@@ -51,9 +51,11 @@ MatrixPtr doOneConvTest(size_t imgSize, size_t output_x, size_t stride,
config.layerConfig.set_partial_sum(1); config.layerConfig.set_partial_sum(1);
config.layerConfig.set_shared_biases(true); config.layerConfig.set_shared_biases(true);
size_t weightSize = channel* filter_size * filter_size *
config.layerConfig.num_filters() / groups;
config.inputDefs.push_back({INPUT_DATA, "layer_0", config.inputDefs.push_back({INPUT_DATA, "layer_0",
imgSize * imgSize * channel, imgSize * imgSize * channel,
channel* filter_size * filter_size * config.layerConfig.num_filters()}); weightSize});
LayerInputConfig* input = config.layerConfig.add_inputs(); LayerInputConfig* input = config.layerConfig.add_inputs();
ConvConfig* conv = input->mutable_conv_conf(); ConvConfig* conv = input->mutable_conv_conf();
conv->set_filter_size(filter_size); conv->set_filter_size(filter_size);
...@@ -63,8 +65,8 @@ MatrixPtr doOneConvTest(size_t imgSize, size_t output_x, size_t stride, ...@@ -63,8 +65,8 @@ MatrixPtr doOneConvTest(size_t imgSize, size_t output_x, size_t stride,
conv->set_padding_y(padding); conv->set_padding_y(padding);
conv->set_stride(stride); conv->set_stride(stride);
conv->set_stride_y(stride); conv->set_stride_y(stride);
conv->set_groups(1); conv->set_groups(groups);
conv->set_filter_channels(channel); conv->set_filter_channels(channel/groups);
conv->set_img_size(imgSize); conv->set_img_size(imgSize);
conv->set_output_x(output_x); conv->set_output_x(output_x);
...@@ -87,7 +89,7 @@ MatrixPtr doOneConvTest(size_t imgSize, size_t output_x, size_t stride, ...@@ -87,7 +89,7 @@ MatrixPtr doOneConvTest(size_t imgSize, size_t output_x, size_t stride,
convLayer->getBiasParameter()->zeroMem(); convLayer->getBiasParameter()->zeroMem();
convLayer->getParameters()[0]->zeroMem(); convLayer->getParameters()[0]->zeroMem();
convLayer->getParameters()[0]->getBuf(PARAMETER_VALUE)->copyFrom(param, convLayer->getParameters()[0]->getBuf(PARAMETER_VALUE)->copyFrom(param,
channel* filter_size * filter_size * config.layerConfig.num_filters()); weightSize);
convLayer->forward(PASS_GC); convLayer->forward(PASS_GC);
return convLayer->getOutputValue(); return convLayer->getOutputValue();
...@@ -113,6 +115,7 @@ TEST(Layer, convParaUnified) { ...@@ -113,6 +115,7 @@ TEST(Layer, convParaUnified) {
/* filter_size */ 3, /* filter_size */ 3,
/*channel*/ 1, /*channel*/ 1,
/*numfilters*/ 2, /*numfilters*/ 2,
/*groups*/ 1,
input, param, false); input, param, false);
resultGpu = doOneConvTest(/* imgSize */ 4, resultGpu = doOneConvTest(/* imgSize */ 4,
...@@ -122,6 +125,7 @@ TEST(Layer, convParaUnified) { ...@@ -122,6 +125,7 @@ TEST(Layer, convParaUnified) {
/* filter_size */ 3, /* filter_size */ 3,
/*channel*/ 1, /*channel*/ 1,
/*numfilters*/ 2, /*numfilters*/ 2,
/*groups*/ 1,
input, param, true); input, param, true);
checkMatrixEqual(resultCpu, resultGpu); checkMatrixEqual(resultCpu, resultGpu);
...@@ -145,6 +149,7 @@ TEST(Layer, convParaUnified) { ...@@ -145,6 +149,7 @@ TEST(Layer, convParaUnified) {
/* filter_size */ 2, /* filter_size */ 2,
/*channel*/ 2, /*channel*/ 2,
/*numfilters*/ 2, /*numfilters*/ 2,
/*groups*/ 1,
input, param2, false); input, param2, false);
resultGpu = doOneConvTest(/* imgSize */ 3, resultGpu = doOneConvTest(/* imgSize */ 3,
...@@ -154,8 +159,34 @@ TEST(Layer, convParaUnified) { ...@@ -154,8 +159,34 @@ TEST(Layer, convParaUnified) {
/* filter_size */ 2, /* filter_size */ 2,
/*channel*/ 2, /*channel*/ 2,
/*numfilters*/ 2, /*numfilters*/ 2,
/*groups*/ 1,
input, param2, true); input, param2, true);
checkMatrixEqual(resultCpu, resultGpu); checkMatrixEqual(resultCpu, resultGpu);
float param3[] = {1, 2, 3, 4,
4, 3, 2, 1};
resultCpu = doOneConvTest(/* imgSize */ 3,
/* output_x */ 2,
/* stride */ 1,
/* padding */ 0,
/* filter_size */ 2,
/*channel*/ 2,
/*numfilters*/ 2,
/*groups*/ 2,
input, param3, false);
resultGpu = doOneConvTest(/* imgSize */ 3,
/* output_x */ 2,
/* stride */ 1,
/* padding */ 0,
/* filter_size */ 2,
/*channel*/ 2,
/*numfilters*/ 2,
/*groups*/ 2,
input, param3, true);
checkMatrixEqual(resultCpu, resultGpu);
#endif #endif
} }
......
...@@ -213,6 +213,53 @@ TEST(Projection, conv) { ...@@ -213,6 +213,53 @@ TEST(Projection, conv) {
NUM_FILTERS, NUM_FILTERS,
true); true);
} }
TEST(Projection, conv2) {
const int NUM_FILTERS = 18;
const int FILTER_SIZE = 2;
const int FILTER_SIZE_Y = 3;
const int CHANNELS = 3;
const int IMAGE_SIZE = 16;
ProjectionConfig conf;
conf.set_type("conv");
conf.set_num_filters(NUM_FILTERS);
ConvConfig* conv = conf.mutable_conv_conf();
conv->set_filter_size(FILTER_SIZE);
conv->set_filter_size_y(FILTER_SIZE_Y);
conv->set_channels(CHANNELS);
conv->set_padding(0);
conv->set_padding_y(1);
conv->set_stride(2);
conv->set_stride_y(2);
conv->set_groups(3);
conv->set_filter_channels(conv->channels() / conv->groups());
conv->set_img_size(IMAGE_SIZE);
int output_x = outputSize(conv->img_size(),
conv->filter_size(),
conv->padding(),
conv->stride(),
/* caffeMode */ true);
int output_y = outputSize(conv->img_size(),
conv->filter_size_y(),
conv->padding_y(),
conv->stride_y(),
/* caffeMode */ true);
conv->set_output_x(output_x);
conf.set_input_size(IMAGE_SIZE * IMAGE_SIZE * CHANNELS);
conf.set_output_size(output_x * output_y * NUM_FILTERS);
testProjectionGrad(
conf,
INPUT_DATA,
/* parameterSize */ NUM_FILTERS * FILTER_SIZE * FILTER_SIZE_Y,
/* batchSize */ 100,
true,
false,
NUM_FILTERS,
true);
}
#endif #endif
TEST(Layer, BilinearInterpLayer) { TEST(Layer, BilinearInterpLayer) {
......
...@@ -255,6 +255,15 @@ TEST(Compare, img_conv) { ...@@ -255,6 +255,15 @@ TEST(Compare, img_conv) {
compareNetwork(config_file_a, config_file_b); compareNetwork(config_file_a, config_file_b);
FLAGS_use_gpu = useGpu; FLAGS_use_gpu = useGpu;
} }
TEST(Compare, img_conv2) {
std::string config_file_a = "./gserver/tests/img_conv2_a.conf";
std::string config_file_b = "./gserver/tests/img_conv2_b.conf";
bool useGpu = FLAGS_use_gpu;
FLAGS_use_gpu = true;
compareNetwork(config_file_a, config_file_b);
FLAGS_use_gpu = useGpu;
}
#endif #endif
P_DEFINE_string(config_file_a, "", "config of one network to compare"); P_DEFINE_string(config_file_a, "", "config of one network to compare");
......
...@@ -129,6 +129,9 @@ class LayerType(object): ...@@ -129,6 +129,9 @@ class LayerType(object):
HSIGMOID = 'hsigmoid' HSIGMOID = 'hsigmoid'
CONV_LAYER = "conv" CONV_LAYER = "conv"
CONVTRANS_LAYER = "convt" CONVTRANS_LAYER = "convt"
EXCONV_LAYER = "exconv"
EXCONVTRANS_LAYER = "exconvt"
CUDNNCONV_LAYER = "cudnn_conv"
POOL_LAYER = "pool" POOL_LAYER = "pool"
BATCH_NORM_LAYER = 'batch_norm' BATCH_NORM_LAYER = 'batch_norm'
NORM_LAYER = 'norm' NORM_LAYER = 'norm'
...@@ -1762,7 +1765,8 @@ def img_conv_layer(input, ...@@ -1762,7 +1765,8 @@ def img_conv_layer(input,
filter_size_y=None, filter_size_y=None,
stride_y=None, stride_y=None,
padding_y=None, padding_y=None,
trans=False): trans=False,
layer_type=None):
""" """
Convolution layer for image. Paddle only support square input currently and Convolution layer for image. Paddle only support square input currently and
thus input image's width equals height. thus input image's width equals height.
...@@ -1829,6 +1833,10 @@ def img_conv_layer(input, ...@@ -1829,6 +1833,10 @@ def img_conv_layer(input,
:type layer_attr: ExtraLayerAttribute :type layer_attr: ExtraLayerAttribute
:param trans: true if it is a convTransLayer, false if it is a convLayer :param trans: true if it is a convTransLayer, false if it is a convLayer
:type trans: bool :type trans: bool
:param layer_type: specify the layer_type, default is None. If trans=True,
layer_type has to be "exconvt", otherwise layer_type
has to be either "exconv" or "cudnn_conv"
:type layer_type: String
:return: LayerOutput object. :return: LayerOutput object.
:rtype: LayerOutput :rtype: LayerOutput
""" """
...@@ -1865,6 +1873,13 @@ def img_conv_layer(input, ...@@ -1865,6 +1873,13 @@ def img_conv_layer(input,
param_attr.attr["initial_strategy"] = 0 param_attr.attr["initial_strategy"] = 0
param_attr.attr["initial_smart"] = False param_attr.attr["initial_smart"] = False
if layer_type:
if trans:
assert layer_type in ["exconvt"]
else:
assert layer_type in ["exconv", "cudnn_conv"]
lt = layer_type
else:
lt = LayerType.CONVTRANS_LAYER if trans else LayerType.CONV_LAYER lt = LayerType.CONVTRANS_LAYER if trans else LayerType.CONV_LAYER
l = Layer( l = Layer(
......
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