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Xucong Zhang
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50
caffe-layers/include/caffe/layers/dspp_layer.hpp
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caffe-layers/include/caffe/layers/dspp_layer.hpp
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#ifndef CAFFE_DSPP_LAYER_HPP_
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#define CAFFE_DSPP_LAYER_HPP_
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#include <string>
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#include <utility>
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#include <vector>
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#include "caffe/blob.hpp"
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#include "caffe/common.hpp"
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#include "caffe/layers/data_layer.hpp"
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#include "caffe/layer.hpp"
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#include "caffe/layers/loss_layer.hpp"
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#include "caffe/layers/neuron_layer.hpp"
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#include "caffe/proto/caffe.pb.h"
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namespace caffe {
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template <typename Dtype>
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class DSPPLayer : public Layer<Dtype> {
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public:
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explicit DSPPLayer(const LayerParameter& param)
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: Layer<Dtype>(param) {}
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virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
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const vector<Blob<Dtype>*>& top);
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virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
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const vector<Blob<Dtype>*>& top);
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virtual inline const char* type() const { return "DSPPLayer"; }
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virtual inline int ExactNumBottomBlobs() const { return 2; };
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virtual inline int MinTopBlobs() const { return 1; }
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protected:
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virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
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const vector<Blob<Dtype>*>& top);
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//virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
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// const vector<Blob<Dtype>*>& top);
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virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
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const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
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//virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
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// const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
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int width_;
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int height_;
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int channel_;
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int num_;
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};
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} // namespace caffe
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#endif // CAFFE_DSPP_LAYER_HPP_
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56
caffe-layers/include/caffe/layers/pose_data_layer.hpp
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caffe-layers/include/caffe/layers/pose_data_layer.hpp
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#ifndef CAFFE_POSE_DATA_LAYER_HPP_
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#define CAFFE_POSE_DATA_LAYER_HPP_
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#include <vector>
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#include "caffe/blob.hpp"
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#include "caffe/layer.hpp"
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#include "caffe/proto/caffe.pb.h"
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#include "caffe/layers/base_data_layer.hpp"
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namespace caffe {
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template <typename Dtype>
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class PoseDataLayer : public BaseDataLayer<Dtype> {
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public:
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explicit PoseDataLayer(const LayerParameter& param)
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: BaseDataLayer<Dtype>(param), has_new_data_(false) {}
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virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
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const vector<Blob<Dtype>*>& top);
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virtual inline const char* type() const { return "PoseData"; }
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virtual inline int ExactNumBottomBlobs() const { return 0; }
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virtual inline int ExactNumTopBlobs() const { return 2; }
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virtual void AddDatumVector(const vector<Datum>& datum_vector);
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virtual void AddMatVector(const vector<cv::Mat>& mat_vector,
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const vector<float>& labels);
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// Reset should accept const pointers, but can't, because the memory
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// will be given to Blob, which is mutable
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void Reset(Dtype* data, Dtype* label, int n);
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void set_batch_size(int new_size);
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int batch_size() { return batch_size_; }
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int channels() { return channels_; }
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int height() { return height_; }
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int width() { return width_; }
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protected:
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virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
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const vector<Blob<Dtype>*>& top);
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int batch_size_, channels_, height_, width_, size_;
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Dtype* data_;
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Dtype* labels_;
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int n_;
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size_t pos_;
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Blob<Dtype> added_data_;
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Blob<Dtype> added_label_;
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bool has_new_data_;
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};
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} // namespace caffe
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#endif
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92
caffe-layers/src/caffe/layers/dspp_layer.cpp
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caffe-layers/src/caffe/layers/dspp_layer.cpp
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#include <cmath>
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#include <algorithm>
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#include <vector>
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#include "caffe/layer.hpp"
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#include "caffe/layers/dspp_layer.hpp"
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#include <boost/spirit/include/phoenix_core.hpp>
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#include <boost/spirit/include/phoenix_operator.hpp>
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namespace caffe {
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template <typename Dtype>
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void DSPPLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
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const vector<Blob<Dtype>*>& top) {
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}
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template <typename Dtype>
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void DSPPLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
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num_ = bottom[1]->shape()[0];
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channel_ = bottom[1]->shape()[1]; // the input data size
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height_ = bottom[1]->shape()[2];
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width_ = bottom[1]->shape()[3];
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// init output size
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vector<int> output_shape;
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output_shape.push_back(num_);
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output_shape.push_back(channel_);
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output_shape.push_back(height_);
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output_shape.push_back(width_);
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top[0]->Reshape(output_shape);
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}
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template <typename Dtype>
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void DSPPLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
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const vector<Blob<Dtype>*>& top) {
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Dtype* top_data = top[0]->mutable_cpu_data();
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caffe_set<Dtype>(top[0]->count(), 0, top_data); // initilize to be 0
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for (int n=0; n<num_; ++n) {
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for (int h = 0; h < height_; ++h) { // for the input data size
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for (int w = 0; w < width_; ++w) {
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for (int c = 0; c < channel_; ++c) {
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top_data[top[0]->offset(n, c, h, w)] = bottom[1]->data_at(n, c, h, w) * bottom[0]->data_at(n, 0, h, w);
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}
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}
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}
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}
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top_data = NULL;
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}
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template <typename Dtype>
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void DSPPLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
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const vector<bool>& propagate_down,
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const vector<Blob<Dtype>*>& bottom) {
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if (propagate_down[0]) {
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const Dtype* top_diff = top[0]->cpu_diff();
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Dtype* data_diff = bottom[1]->mutable_cpu_diff();
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Dtype* heat_map_diff = bottom[0]->mutable_cpu_diff();
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caffe_set<Dtype>(bottom[1]->count(), 0, data_diff);
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caffe_set<Dtype>(bottom[0]->count(), 0, heat_map_diff);
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// Dtype activation_h, activation_w;
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for (int n = 0; n < num_; ++n) {
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for (int h = 0; h < height_; ++h) {
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for (int w = 0; w < width_; ++w) {
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for (int c = 0; c < channel_; ++c) {
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Dtype buffer = top_diff[top[0]->offset(n, c, h, w)];
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data_diff[bottom[1]->offset(n, c, h, w)] = buffer * (bottom[0]->data_at(n, 0, h, w));
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buffer *= bottom[1]->data_at(n,c,h,w) / channel_;
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heat_map_diff[bottom[0]->offset(n,0,h,w)] += buffer;
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}
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}
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}
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}
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top_diff = NULL;
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data_diff = NULL;
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heat_map_diff = NULL;
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}
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}
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INSTANTIATE_CLASS(DSPPLayer);
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REGISTER_LAYER_CLASS(DSPP);
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} // namespace caffe
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128
caffe-layers/src/caffe/layers/pose_data_layer.cpp
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caffe-layers/src/caffe/layers/pose_data_layer.cpp
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#include <opencv2/core/core.hpp>
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#include <vector>
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#include "caffe/layers/pose_data_layer.hpp"
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namespace caffe {
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template <typename Dtype>
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void PoseDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
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const vector<Blob<Dtype>*>& top) {
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batch_size_ = this->layer_param_.memory_data_param().batch_size();
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channels_ = this->layer_param_.memory_data_param().channels();
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height_ = this->layer_param_.memory_data_param().height();
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width_ = this->layer_param_.memory_data_param().width();
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size_ = channels_ * height_ * width_;
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CHECK_GT(batch_size_ * size_, 0) <<
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"batch_size, channels, height, and width must be specified and"
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" positive in memory_data_param";
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int label_shape_[] = {batch_size_, 4};
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vector<int> label_shape(label_shape_, label_shape_+2);
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top[0]->Reshape(batch_size_, channels_, height_, width_);
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top[1]->Reshape(label_shape);
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added_data_.Reshape(batch_size_, channels_, height_, width_);
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added_label_.Reshape(label_shape);
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data_ = NULL;
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labels_ = NULL;
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added_data_.cpu_data();
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added_label_.cpu_data();
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}
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template <typename Dtype>
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void PoseDataLayer<Dtype>::AddDatumVector(const vector<Datum>& datum_vector) {
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CHECK(!has_new_data_) <<
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"Can't add data until current data has been consumed.";
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size_t num = datum_vector.size();
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CHECK_GT(num, 0) << "There is no datum to add.";
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CHECK_EQ(num % batch_size_, 0) <<
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"The added data must be a multiple of the batch size.";
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added_data_.Reshape(num, channels_, height_, width_);
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int label_shape_[] = {(int)num, 4};
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vector<int> label_shape(label_shape_, label_shape_+2);
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added_label_.Reshape(label_shape);
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// Apply data transformations (mirror, scale, crop...)
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this->data_transformer_->Transform(datum_vector, &added_data_);
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// Copy Labels
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Dtype* top_label = added_label_.mutable_cpu_data();
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for (int item_id = 0; item_id < num; ++item_id) {
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top_label[item_id] = datum_vector[item_id].label();
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}
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// num_images == batch_size_
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Dtype* top_data = added_data_.mutable_cpu_data();
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Reset(top_data, top_label, num);
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has_new_data_ = true;
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}
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template <typename Dtype>
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void PoseDataLayer<Dtype>::AddMatVector(const vector<cv::Mat>& mat_vector,
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const vector<float>& labels) {
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size_t num = mat_vector.size();
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CHECK(!has_new_data_) <<
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"Can't add mat until current data has been consumed.";
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CHECK_GT(num, 0) << "There is no mat to add";
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CHECK_EQ(num % batch_size_, 0) <<
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"The added data must be a multiple of the batch size.";
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added_data_.Reshape(num, channels_, height_, width_);
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int label_shape_[] = {(int)num, 4};
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vector<int> label_shape(label_shape_, label_shape_+2);
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added_label_.Reshape(label_shape);
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// Apply data transformations (mirror, scale, crop...)
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this->data_transformer_->Transform(mat_vector, &added_data_);
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// Copy Labels
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Dtype* top_label = added_label_.mutable_cpu_data();
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for (int item_id = 0; item_id < num; ++item_id) {
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top_label[item_id] = labels[item_id];
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}
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// num_images == batch_size_
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Dtype* top_data = added_data_.mutable_cpu_data();
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Reset(top_data, top_label, num);
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has_new_data_ = true;
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}
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template <typename Dtype>
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void PoseDataLayer<Dtype>::Reset(Dtype* data, Dtype* labels, int n) {
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CHECK(data);
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CHECK(labels);
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CHECK_EQ(n % batch_size_, 0) << "n must be a multiple of batch size";
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// Warn with transformation parameters since a memory array is meant to
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// be generic and no transformations are done with Reset().
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//if (this->layer_param_.has_transform_param()) {
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// LOG(WARNING) << this->type() << " does not transform array data on Reset()";
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//}
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data_ = data;
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labels_ = labels;
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n_ = n;
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pos_ = 0;
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}
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template <typename Dtype>
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void PoseDataLayer<Dtype>::set_batch_size(int new_size) {
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CHECK(!has_new_data_) <<
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"Can't change batch_size until current data has been consumed.";
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batch_size_ = new_size;
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added_data_.Reshape(batch_size_, channels_, height_, width_);
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int label_shape_[] = {(int)batch_size_, 4};
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vector<int> label_shape(label_shape_, label_shape_+2);
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added_label_.Reshape(label_shape);
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}
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template <typename Dtype>
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void PoseDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
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const vector<Blob<Dtype>*>& top) {
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CHECK(data_) << "PoseDataLayer needs to be initalized by calling Reset";
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top[0]->Reshape(batch_size_, channels_, height_, width_);
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int label_shape_[] = {(int)batch_size_, 4};
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vector<int> label_shape(label_shape_, label_shape_+2);
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added_label_.Reshape(label_shape);
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top[0]->set_cpu_data(data_ + pos_ * size_);
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top[1]->set_cpu_data(labels_ + pos_);
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pos_ = (pos_ + batch_size_) % n_;
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if (pos_ == 0)
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has_new_data_ = false;
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}
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INSTANTIATE_CLASS(PoseDataLayer);
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REGISTER_LAYER_CLASS(PoseData);
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} // namespace caffe
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