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add the TileND layer for multiple dimension tile
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‎include/caffe/layers/tile_nd_layer.hpp‎

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#ifndef CAFFE_TILE_ND_LAYER_HPP_
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#define CAFFE_TILE_ND_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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namespace caffe {
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/**
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* @brief Copy a Blob along specified dimensions.
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*/
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template <typename Dtype>
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class TileNDLayer : public Layer<Dtype> {
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public:
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explicit TileNDLayer(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 "TileND"; }
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virtual inline int ExactNumBottomBlobs() const { return 1; }
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virtual inline int ExactNumTopBlobs() 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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vector<int> axis_;
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vector<int> tiles_;
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vector<int> outer_dim_, inner_dim_;
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vector<int> top_inner_dim_;
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Blob<Dtype> top_temp_;
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};
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} // namespace caffe
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#endif // CAFFE_TILE_ND_LAYER_HPP_

‎src/caffe/layers/tile_nd_layer.cpp‎

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#include <vector>
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#include "caffe/layers/tile_nd_layer.hpp"
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#include "caffe/util/math_functions.hpp"
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namespace caffe {
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template <typename Dtype>
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void TileNDLayer<Dtype>::LayerSetUp(
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const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
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const TileNDParameter& tile_nd_param = this->layer_param_.tile_nd_param();
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axis_.clear();
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std::copy(tile_nd_param.axis().begin(),
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tile_nd_param.axis().end(),
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std::back_inserter(axis_));
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tiles_.clear();
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std::copy(tile_nd_param.tiles().begin(),
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tile_nd_param.tiles().end(),
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std::back_inserter(tiles_));
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CHECK_EQ(axis_.size(), tiles_.size()) << "Number of tiles must be equal to axis!";
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CHECK_GT(tiles_.size(), 0) << "Number of tiles must be positive!";
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for(int i=0;i<axis_.size();i++)
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{
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axis_[i] = bottom[0]->CanonicalAxisIndex(axis_[i]);
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CHECK_GT(tiles_[i], 0) << "Value of tiles must be positive!";
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}
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}
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template <typename Dtype>
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void TileNDLayer<Dtype>::Reshape(
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const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
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vector<int> top_shape = bottom[0]->shape();
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for(int i=axis_.size()-1;i>=0;i--)
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top_shape[axis_[i]] = bottom[0]->shape(axis_[i]) * tiles_[i];
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top[0]->Reshape(top_shape);
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top_temp_.Reshape(top_shape);
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outer_dim_.clear();
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inner_dim_.clear();
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int multiple = 1;
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for(int i=axis_.size()-1; i>=0; i--) //notice the order
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{
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outer_dim_.push_back(bottom[0]->count(0, axis_[i]));
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inner_dim_.push_back(bottom[0]->count(axis_[i]));
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top_inner_dim_.push_back(bottom[0]->count(axis_[i])*multiple);
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multiple *= tiles_[i];
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}
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// reverse the order to make it align with the tiles and handling (from right to left dimension)
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vector<int> tmp(outer_dim_);
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outer_dim_.clear();
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std::copy(tmp.rbegin(), tmp.rend(), std::back_inserter(outer_dim_));
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tmp = inner_dim_;
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inner_dim_.clear();
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std::copy(tmp.rbegin(), tmp.rend(), std::back_inserter(inner_dim_));
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tmp = top_inner_dim_;
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top_inner_dim_.clear();
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std::copy(tmp.rbegin(), tmp.rend(), std::back_inserter(top_inner_dim_));
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//for(int i=0;i<axis_.size();i++)
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// LOG(INFO)<<"out: "<<outer_dim_[i]<<", in: "<<inner_dim_[i]<<", top: "<<top_inner_dim_[i]<<"\n";
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}
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template <typename Dtype>
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void TileNDLayer<Dtype>::Forward_cpu(
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const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
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const Dtype* bottom_data = bottom[0]->cpu_data();
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//Dtype* top_data = top[0]->mutable_cpu_data();
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int s = outer_dim_.size()-1; //copy from most right dimension
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Dtype* top_data_temp = top_temp_.mutable_cpu_data();
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for (int i = 0; i < outer_dim_[s]; ++i) {
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for (int t = 0; t < tiles_[s]; ++t) {
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caffe_copy(inner_dim_[s], bottom_data, top_data_temp);
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top_data_temp += inner_dim_[s];
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}
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bottom_data += inner_dim_[s];
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}
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//top_data_temp = top_temp_.mutable_cpu_data();
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//for (int i = 0; i < top_temp_.count(); ++i)
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// LOG(INFO)<<top_data_temp[i]<<"\n";
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//LOG(INFO)<<"stage2\n";
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for(int s=outer_dim_.size()-2;s>0;s--)
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{
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Dtype* input_data = top_temp_.mutable_cpu_data();
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Dtype* output_data = top[0]->mutable_cpu_data();
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for (int i = 0; i < outer_dim_[s]; ++i) {
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for (int t = 0; t < tiles_[s]; ++t) {
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caffe_copy(top_inner_dim_[s], input_data, output_data);
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output_data += top_inner_dim_[s];
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}
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input_data += top_inner_dim_[s];
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}
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//top_data_temp = top[0]->mutable_cpu_data();
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//for (int i = 0; i < top[0]->count(); ++i)
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// LOG(INFO)<<top_data_temp[i]<<"\n";
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//LOG(INFO)<<"stage3\n";
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Dtype* next_input_data = top[0]->mutable_cpu_data();
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Dtype* copy_data = top_temp_.mutable_cpu_data();
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caffe_copy(top[0]->count(axis_[s-1]), next_input_data, copy_data); //reset for the next loop
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//top_data_temp = top_temp_.mutable_cpu_data();
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//for (int i = 0; i < top_temp_.count(); ++i)
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// LOG(INFO)<<top_data_temp[i]<<"\n";
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//LOG(INFO)<<"stage4\n";
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}
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// final(first) dimension to tile
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s = 0;
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Dtype* input_data = top_temp_.mutable_cpu_data();
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Dtype* output_data = top[0]->mutable_cpu_data();
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for (int i = 0; i < outer_dim_[s]; ++i) {
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for (int t = 0; t < tiles_[s]; ++t) {
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caffe_copy(top_inner_dim_[s], input_data, output_data);
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output_data += top_inner_dim_[s];
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}
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input_data += top_inner_dim_[s];
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}
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}
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template <typename Dtype>
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void TileNDLayer<Dtype>::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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if (!propagate_down[0]) { return; }
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const Dtype* top_diff = top[0]->cpu_diff();
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Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
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for(int s=outer_dim_.size()-1;s>=0;s--)
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{
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for (int i = 0; i < outer_dim_[s]; ++i) {
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caffe_copy(inner_dim_[s], top_diff, bottom_diff);
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top_diff += inner_dim_[s];
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for (int t = 1; t < tiles_[s]; ++t) {
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caffe_axpy(inner_dim_[s], Dtype(1), top_diff, bottom_diff);
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top_diff += inner_dim_[s];
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}
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bottom_diff += inner_dim_[s];
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}
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}
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}
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//#ifdef CPU_ONLY
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//STUB_GPU(TileLayer);
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//#endif
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INSTANTIATE_CLASS(TileNDLayer);
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REGISTER_LAYER_CLASS(TileND);
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} // namespace caffe

‎src/caffe/proto/caffe.proto‎

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@@ -460,7 +460,7 @@ message ParamSpec {
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// NOTE
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// Update the next available ID when you add a new LayerParameter field.
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//
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// LayerParameter next available layer-specific ID: 238 (last added: mirror_pad_param)
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// LayerParameter next available layer-specific ID: 240 (last added: tile_nd_param)
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message LayerParameter {
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optional string name = 1; // the layer name
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optional string type = 2; // the layer type
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optional BatchToSpaceNDParameter batch_to_space_nd_param = 235;
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optional SpaceToBatchNDParameter space_to_batch_nd_param = 236;
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optional MirrorPadParameter mirror_pad_param = 238;
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optional TileNDParameter tile_nd_param = 239;
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}
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message AccumParameter {
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}
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message UnstackParameter {
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optional uint32 num = 1[default = 0];
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optional int32 axis = 2[default = 0];
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optional uint32 num = 1[default = 0];
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optional int32 axis = 2[default = 0];
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}
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message SpaceToBatchNDParameter{
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repeated uint32 block_shape = 1;
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repeated uint32 crops = 2;
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}
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// Message that stores parameters used by TileNDLayer
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message TileNDParameter {
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// The index of the axis to tile.
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repeated int32 axis = 1;
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// The number of copies (tiles) of the blob to output.
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repeated uint32 tiles = 2;
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}

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