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() {
void ConvProjection::initCudnn() {
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(&outputDesc_);
hl_create_convolution_descriptor(&convDesc_,
......@@ -86,7 +87,7 @@ void ConvProjection::initCudnn() {
void ConvProjection::reshapeTensorDesc(int batchSize) {
hl_tensor_reshape(inputDesc_,
batchSize,
channels_,
channels_ / groups_,
imageH_,
imageW_,
channels_ * imageH_ * imageW_,
......@@ -115,7 +116,7 @@ void ConvProjection::reshapeTensorDesc(int batchSize) {
hl_tensor_reshape(outputDesc_,
batchSize,
numFilters_,
numFilters_ / groups_,
outputH_,
outputW_,
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);
// matches the given result
MatrixPtr doOneConvTest(size_t imgSize, size_t output_x, size_t stride,
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) {
TestConfig config;
config.biasSize = numfilters;
......@@ -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_shared_biases(true);
size_t weightSize = channel* filter_size * filter_size *
config.layerConfig.num_filters() / groups;
config.inputDefs.push_back({INPUT_DATA, "layer_0",
imgSize * imgSize * channel,
channel* filter_size * filter_size * config.layerConfig.num_filters()});
weightSize});
LayerInputConfig* input = config.layerConfig.add_inputs();
ConvConfig* conv = input->mutable_conv_conf();
conv->set_filter_size(filter_size);
......@@ -63,8 +65,8 @@ MatrixPtr doOneConvTest(size_t imgSize, size_t output_x, size_t stride,
conv->set_padding_y(padding);
conv->set_stride(stride);
conv->set_stride_y(stride);
conv->set_groups(1);
conv->set_filter_channels(channel);
conv->set_groups(groups);
conv->set_filter_channels(channel/groups);
conv->set_img_size(imgSize);
conv->set_output_x(output_x);
......@@ -87,7 +89,7 @@ MatrixPtr doOneConvTest(size_t imgSize, size_t output_x, size_t stride,
convLayer->getBiasParameter()->zeroMem();
convLayer->getParameters()[0]->zeroMem();
convLayer->getParameters()[0]->getBuf(PARAMETER_VALUE)->copyFrom(param,
channel* filter_size * filter_size * config.layerConfig.num_filters());
weightSize);
convLayer->forward(PASS_GC);
return convLayer->getOutputValue();
......@@ -113,6 +115,7 @@ TEST(Layer, convParaUnified) {
/* filter_size */ 3,
/*channel*/ 1,
/*numfilters*/ 2,
/*groups*/ 1,
input, param, false);
resultGpu = doOneConvTest(/* imgSize */ 4,
......@@ -122,6 +125,7 @@ TEST(Layer, convParaUnified) {
/* filter_size */ 3,
/*channel*/ 1,
/*numfilters*/ 2,
/*groups*/ 1,
input, param, true);
checkMatrixEqual(resultCpu, resultGpu);
......@@ -145,6 +149,7 @@ TEST(Layer, convParaUnified) {
/* filter_size */ 2,
/*channel*/ 2,
/*numfilters*/ 2,
/*groups*/ 1,
input, param2, false);
resultGpu = doOneConvTest(/* imgSize */ 3,
......@@ -154,8 +159,34 @@ TEST(Layer, convParaUnified) {
/* filter_size */ 2,
/*channel*/ 2,
/*numfilters*/ 2,
/*groups*/ 1,
input, param2, true);
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
}
......
......@@ -213,6 +213,53 @@ TEST(Projection, conv) {
NUM_FILTERS,
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
TEST(Layer, BilinearInterpLayer) {
......
......@@ -255,6 +255,15 @@ TEST(Compare, img_conv) {
compareNetwork(config_file_a, config_file_b);
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
P_DEFINE_string(config_file_a, "", "config of one network to compare");
......
......@@ -129,6 +129,9 @@ class LayerType(object):
HSIGMOID = 'hsigmoid'
CONV_LAYER = "conv"
CONVTRANS_LAYER = "convt"
EXCONV_LAYER = "exconv"
EXCONVTRANS_LAYER = "exconvt"
CUDNNCONV_LAYER = "cudnn_conv"
POOL_LAYER = "pool"
BATCH_NORM_LAYER = 'batch_norm'
NORM_LAYER = 'norm'
......@@ -1762,7 +1765,8 @@ def img_conv_layer(input,
filter_size_y=None,
stride_y=None,
padding_y=None,
trans=False):
trans=False,
layer_type=None):
"""
Convolution layer for image. Paddle only support square input currently and
thus input image's width equals height.
......@@ -1829,6 +1833,10 @@ def img_conv_layer(input,
:type layer_attr: ExtraLayerAttribute
:param trans: true if it is a convTransLayer, false if it is a convLayer
: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.
:rtype: LayerOutput
"""
......@@ -1865,6 +1873,13 @@ def img_conv_layer(input,
param_attr.attr["initial_strategy"] = 0
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
l = Layer(
......
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