提交 eea0097d 编写于 作者: G gaoyuan

NormalizeLayer for SSD

上级 515543ab
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#include "Layer.h"
#include "paddle/math/BaseMatrix.h"
#include "paddle/math/Matrix.h"
namespace paddle {
/**
* This layer applys normalize across the channels of each sample to a
* conv layer's output and scale the output by a group of trainable factors
* which dimensions equal to the channel's number.
* - Input: One and only one input layer are accepted. The input layer must be
* be a data output layer.
* - Output: The normalized data of the input data.
* Reference:
* Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed,
* Cheng-Yang Fu, Alexander C. Berg. SSD: Single Shot MultiBox Detector
*/
class NormalizeLayer : public Layer {
public:
explicit NormalizeLayer(const LayerConfig& config) : Layer(config) {}
bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
void forward(PassType passType);
void backward(const UpdateCallback& callback);
protected:
size_t channels_;
std::unique_ptr<Weight> scale_;
MatrixPtr scaleDiff_;
MatrixPtr normBuffer_;
MatrixPtr dataBuffer_;
MatrixPtr channelBuffer_;
MatrixPtr spatialBuffer_;
MatrixPtr sampleBuffer_;
};
REGISTER_LAYER(normalize, NormalizeLayer);
bool NormalizeLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
Layer::init(layerMap, parameterMap);
CHECK(parameters_[0]);
channels_ = config_.num_filters();
scale_.reset(new Weight(channels_, 1, parameters_[0]));
return true;
}
void NormalizeLayer::forward(PassType passType) {
Layer::forward(passType);
auto in = getInput(0);
MatrixPtr inV = getInputValue(0);
size_t batchSize = inV->getHeight();
size_t dataDim = inV->getWidth();
CHECK_EQ(getSize(), dataDim);
reserveOutput(batchSize, dataDim);
MatrixPtr outV = getOutputValue();
size_t spatialDim = dataDim / channels_;
Matrix::resizeOrCreate(dataBuffer_, batchSize, dataDim, false, useGpu_);
Matrix::resizeOrCreate(spatialBuffer_, 1, spatialDim, false, useGpu_);
Matrix::resizeOrCreate(channelBuffer_, channels_, 1, false, useGpu_);
Matrix::resizeOrCreate(sampleBuffer_, channels_, spatialDim, false, useGpu_);
Matrix::resizeOrCreate(normBuffer_, batchSize, spatialDim, false, useGpu_);
normBuffer_->zeroMem();
spatialBuffer_->zeroMem();
sampleBuffer_->zeroMem();
dataBuffer_->zeroMem();
// add eps to avoid overflow
normBuffer_->addScalar(*normBuffer_, 1e-6);
channelBuffer_->resetOne();
inV->square2(*dataBuffer_);
for (size_t i = 0; i < batchSize; i++) {
spatialBuffer_->zeroMem();
MatrixPtr inTmp = Matrix::create(
inV->getData() + i * dataDim, channels_, spatialDim, false, useGpu_);
MatrixPtr dataTmp = Matrix::create(dataBuffer_->getData() + i * dataDim,
channels_,
spatialDim,
false,
useGpu_);
MatrixPtr outTmp = Matrix::create(
outV->getData() + i * dataDim, channels_, spatialDim, false, useGpu_);
MatrixPtr normTmp = Matrix::create(
normBuffer_->getData() + i * spatialDim, 1, spatialDim, false, useGpu_);
// compute norm.
spatialBuffer_->sumCols(*dataTmp, 1, 1);
spatialBuffer_->sqrt2(*spatialBuffer_);
normTmp->copyFrom(*spatialBuffer_);
sampleBuffer_->mul(*channelBuffer_, *spatialBuffer_, 1., 0.);
sampleBuffer_->dotDiv(*inTmp, *sampleBuffer_);
outTmp->copyFrom(*sampleBuffer_);
// scale the layer.
spatialBuffer_->resetOne();
sampleBuffer_->mul(*scale_->getW(), *spatialBuffer_, 1., 0.);
outTmp->dotMul(*outTmp, *sampleBuffer_);
}
}
void NormalizeLayer::backward(const UpdateCallback& callback) {
MatrixPtr inG = getInputGrad(0);
MatrixPtr inV = getInputValue(0);
MatrixPtr outG = getOutputGrad();
MatrixPtr outV = getOutputValue();
auto in = getInput(0);
size_t batchSize = inG->getHeight();
size_t dataDim = inG->getWidth();
size_t spatialDim = dataDim / channels_;
bool syncFlag = hl_get_sync_flag();
dataBuffer_->dotMul(*outG, *outV);
Matrix::resizeOrCreate(scaleDiff_, channels_, 1, false, useGpu_);
scaleDiff_->zeroMem();
for (size_t i = 0; i < batchSize; i++) {
spatialBuffer_->zeroMem();
channelBuffer_->zeroMem();
// propagate to param.
MatrixPtr dataBufferTmp =
Matrix::create(dataBuffer_->getData() + i * dataDim,
channels_,
spatialDim,
false,
useGpu_);
const MatrixPtr inValueTmp = Matrix::create(
inV->getData() + i * dataDim, channels_, spatialDim, false, useGpu_);
const MatrixPtr outGradTmp = Matrix::create(
outG->getData() + i * dataDim, channels_, spatialDim, false, useGpu_);
MatrixPtr inGradTmp = Matrix::create(
inG->getData() + i * dataDim, channels_, spatialDim, false, useGpu_);
const MatrixPtr normTmp = Matrix::create(
normBuffer_->getData() + i * spatialDim, 1, spatialDim, false, useGpu_);
channelBuffer_->sumRows(*dataBufferTmp, 1, 1);
channelBuffer_->dotDiv(*channelBuffer_, *(scale_->getW()));
// store a / scale[i] in scaleDiff_ temporary
scaleDiff_->add(*channelBuffer_, 1.);
sampleBuffer_->dotMul(*inValueTmp, *outGradTmp);
spatialBuffer_->sumCols(*sampleBuffer_, 1., 1.);
// scale the grad
channelBuffer_->resetOne();
sampleBuffer_->mul(*channelBuffer_, *spatialBuffer_, 1., 0.);
inGradTmp->dotMul(*inValueTmp, *sampleBuffer_);
// divide by square of norm
spatialBuffer_->dotMul(*normTmp, *normTmp);
sampleBuffer_->mul(*channelBuffer_, *spatialBuffer_, 1., 0.);
inGradTmp->dotDiv(*inGradTmp, *sampleBuffer_);
// subtract
inGradTmp->add(*outGradTmp, -1, 1);
// divide by norm
sampleBuffer_->mul(*channelBuffer_, *normTmp, 1., 0.);
inGradTmp->dotDiv(*inGradTmp, *sampleBuffer_);
// scale the diff
spatialBuffer_->resetOne();
sampleBuffer_->mul(*scale_->getW(), *spatialBuffer_, 1., 0.);
inGradTmp->dotMul(*inGradTmp, *sampleBuffer_);
}
// updata scale
if (scale_->getWGrad()) scale_->getWGrad()->copyFrom(*scaleDiff_);
hl_set_sync_flag(false);
hl_set_sync_flag(syncFlag);
scale_->getParameterPtr()->incUpdate(callback);
}
} // namespace paddle
......@@ -45,27 +45,32 @@ protected:
MatrixPtr buffer_;
};
REGISTER_LAYER(priorbox, PriorBoxLayer);
bool PriorBoxLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
Layer::init(layerMap, parameterMap);
auto pbConf = config_.inputs(0).priorbox_conf();
std::vector<real> tmp;
aspectRatio_.push_back(1.);
std::copy(pbConf.min_size().begin(),
pbConf.min_size().end(),
std::back_inserter(minSize_));
std::copy(pbConf.max_size().begin(),
pbConf.max_size().end(),
std::back_inserter(maxSize_));
std::copy(pbConf.aspect_ratio().begin(),
pbConf.aspect_ratio().end(),
std::back_inserter(aspectRatio_));
std::copy(pbConf.variance().begin(),
pbConf.variance().end(),
std::back_inserter(variance_));
std::copy(pbConf.aspect_ratio().begin(),
pbConf.aspect_ratio().end(),
std::back_inserter(tmp));
// flip
int inputRatioLength = aspectRatio_.size();
for (int index = 0; index < inputRatioLength; index++)
aspectRatio_.push_back(1 / aspectRatio_[index]);
aspectRatio_.push_back(1.);
int inputRatioLength = tmp.size();
for (int index = 0; index < inputRatioLength; index++) {
aspectRatio_.push_back(tmp[index]);
aspectRatio_.push_back(1 / tmp[index]);
}
numPriors_ = aspectRatio_.size();
if (maxSize_.size() > 0) numPriors_++;
return true;
......@@ -94,12 +99,12 @@ void PriorBoxLayer::forward(PassType passType) {
for (int w = 0; w < layerWidth; ++w) {
real centerX = (w + 0.5) * stepW;
real centerY = (h + 0.5) * stepH;
int minSize = 0;
real minSize = 0;
for (size_t s = 0; s < minSize_.size(); s++) {
// first prior.
minSize = minSize_[s];
int boxWidth = minSize;
int boxHeight = minSize;
real boxWidth = minSize;
real boxHeight = minSize;
// xmin, ymin, xmax, ymax.
tmpPtr[idx++] = (centerX - boxWidth / 2.) / imageWidth;
tmpPtr[idx++] = (centerY - boxHeight / 2.) / imageHeight;
......@@ -112,7 +117,7 @@ void PriorBoxLayer::forward(PassType passType) {
CHECK_EQ(minSize_.size(), maxSize_.size());
// second prior.
for (size_t s = 0; s < maxSize_.size(); s++) {
int maxSize = maxSize_[s];
real maxSize = maxSize_[s];
boxWidth = boxHeight = sqrt(minSize * maxSize);
tmpPtr[idx++] = (centerX - boxWidth / 2.) / imageWidth;
tmpPtr[idx++] = (centerY - boxHeight / 2.) / imageHeight;
......@@ -145,6 +150,5 @@ void PriorBoxLayer::forward(PassType passType) {
MatrixPtr outV = getOutputValue();
outV->copyFrom(buffer_->data_, dim * 2);
}
REGISTER_LAYER(priorbox, PriorBoxLayer);
} // namespace paddle
......@@ -1623,6 +1623,20 @@ TEST(Layer, PadLayer) {
}
}
TEST(Layer, NormalizeLayer) {
TestConfig config;
config.layerConfig.set_type("normalize");
config.layerConfig.set_size(100);
config.layerConfig.set_num_filters(10);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 100, 10});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "normalize", 10, false, useGpu, false, 5);
}
}
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
initMain(argc, argv);
......
......@@ -1619,6 +1619,16 @@ class PriorBoxLayer(LayerBase):
self.config.size = size
@config_layer('normalize')
class NormalizeLayer(LayerBase):
def __init__(self, name, inputs, size, num_filters, **xargs):
super(NormalizeLayer, self).__init__(name, 'normalize', 0, inputs,
**xargs)
self.config.size = size
self.config.num_filters = num_filters
self.create_input_parameter(0, num_filters, [num_filters, 1])
@config_layer('data')
class DataLayer(LayerBase):
def __init__(self, name, size, height=None, width=None, device=None):
......
......@@ -111,6 +111,7 @@ __all__ = [
'out_prod_layer',
'print_layer',
'priorbox_layer',
'normalize_layer',
'spp_layer',
'pad_layer',
'eos_layer',
......@@ -184,6 +185,7 @@ class LayerType(object):
PRINT_LAYER = "print"
PRIORBOX_LAYER = "priorbox"
NORMALIZE_LAYER = "normalize"
CTC_LAYER = "ctc"
WARP_CTC_LAYER = "warp_ctc"
......@@ -998,6 +1000,35 @@ def priorbox_layer(input,
size=size)
@wrap_name_default("normalize")
def normalize_layer(input, name=None, param_attr=None):
"""
Normalize a layer's output. This layer is necessary for ssd.
This layer applys normalize across the channels of each sample to
a conv layer's output and scale the output by a group of trainable
factors which dimensions equal to the channel's number.
:param name: The Layer Name.
:type name: basestring
:param input: The input layer.
:type input: LayerOutput
:param param_attr: The Parameter Attribute|list.
:type param_attr: ParameterAttribute
:return: LayerOutput
"""
Layer(
name=name,
type=LayerType.NORMALIZE_LAYER,
inputs=[Input(input.name, **param_attr.attr)],
size=input.size,
num_filters=input.num_filters)
return LayerOutput(
name,
LayerType.NORMALIZE_LAYER,
parents=input,
num_filters=input.num_filters,
size=input.size)
@wrap_name_default("seq_pooling")
@wrap_bias_attr_default(has_bias=False)
@wrap_param_default(['pooling_type'], default_factory=lambda _: MaxPooling())
......
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