add the broadcast_to layer

Author: vera121

FEATURES.md

@@ -8,6 +8,7 @@ evconvert (TensorFlow/ONNX/... to Caffe Converter) related
 ----------------------------------------------------------
 atan_layer  
 batch_to_space_nd_layer  
+broadcast_to_layer  
 crop_and_resize  
 depth_to_space_layer  
 div_layer  

include/caffe/layers/broadcast_to_layer.hpp

@@ -0,0 +1,47 @@
+#ifndef CAFFE_BROADCAST_TO_LAYER_HPP_
+#define CAFFE_BROADCAST_TO_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Broadcast an array for a compatible shape.
+ * Dimensions are right alignment;
+ * Two corresponding dimension must have the same value, or one of them is equal to 1.
+ */
+template <typename Dtype>
+class BroadcastToLayer : public Layer<Dtype> {
+ public:
+  explicit BroadcastToLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "BroadcastTo"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  //virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  //    const vector<Blob<Dtype>*>& top);
+
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  //virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+  //    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  vector<int> output_shape_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_BROADCAST_TO_LAYER_HPP_

src/caffe/layers/broadcast_to_layer.cpp

@@ -0,0 +1,82 @@
+#include <vector>
+
+#include "caffe/layers/broadcast_to_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void BroadcastToLayer<Dtype>::LayerSetUp(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const BroadcastToParameter& broadcast_to_param = this->layer_param_.broadcast_to_param();
+  output_shape_.clear();
+  std::copy(broadcast_to_param.shape().begin(),
+    broadcast_to_param.shape().end(),
+    std::back_inserter(output_shape_));
+
+  CHECK_GE(output_shape_.size(), bottom[0]->num_axes()) << "Output shape should not have less axis than input!";
+  int dim_diff = output_shape_.size() - bottom[0]->num_axes();
+  for(int i=output_shape_.size()-1; i>=dim_diff; i--)
+  {
+    CHECK_GT(output_shape_[i], 0) << "Values in output shape must be positive!";
+    CHECK(output_shape_[i]==bottom[0]->shape(i-dim_diff) || bottom[0]->shape(i-dim_diff)==1)
+        << "The broadcasting shape is incompatible with the input!";
+  }
+}
+
+
+template <typename Dtype>
+void BroadcastToLayer<Dtype>::Reshape(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  top[0]->Reshape(output_shape_);
+}
+
+template <typename Dtype>
+void BroadcastToLayer<Dtype>::Forward_cpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  int count = top[0]->count();
+
+  int dim = top[0]->num_axes();
+  int dim_diff = output_shape_.size() - bottom[0]->num_axes();
+  // Assume top index (x,y,z) with top shape (A, B, C)
+  // top offset d = xBC + yC + z
+  // So to count the bottom index, should first figure out x, y, z
+  // x = d / BC
+  // y = (d % BC) / C
+  // z = d % C
+  // Then consider bottom shape (A', B', C'), where A' = 1 or A
+  // So bottom offset = x'B'C' + y'C' + z
+  for(int d=0; d<count; d++)
+  {
+    int offset = 0;
+
+    for(int i=dim_diff;i<dim-1;i++)
+    {
+      int num = (d % top[0]->count(i)) / top[0]->count(i+1);
+      int n0 = 1 == bottom[0]->shape(i-dim_diff) ? 0 : num;
+      offset += n0 * bottom[0]->count(i-dim_diff+1);
+    }
+    int z = d % top[0]->shape(dim-1);
+    int z0 = 1 == bottom[0]->shape(dim-dim_diff-1) ? 0 : z;
+    offset += z0;
+
+    top_data[d] = bottom_data[offset];
+  }
+}
+
+template <typename Dtype>
+void BroadcastToLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  NOT_IMPLEMENTED;
+}
+
+//#ifdef CPU_ONLY
+//STUB_GPU(TileLayer);
+//#endif
+
+INSTANTIATE_CLASS(BroadcastToLayer);
+REGISTER_LAYER_CLASS(BroadcastTo);
+
+}  // namespace caffe

src/caffe/proto/caffe.proto

@@ -460,7 +460,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 242 (last added: squeeze_param)
+// LayerParameter next available layer-specific ID: 243 (last added: broadcast_to_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -645,6 +645,7 @@ message LayerParameter {
   optional TileNDParameter tile_nd_param = 239;
   optional ExpandDimsNDParameter expand_dims_nd_param = 240;
   optional SqueezeParameter squeeze_param = 241;
+  optional BroadcastToParameter broadcast_to_param = 242;
 }
 
 message AccumParameter {
@@ -3075,3 +3076,7 @@ message SqueezeParameter {
   repeated int32 axis = 1;
 }
 
+message BroadcastToParameter {
+	//The shape of the desired output.
+  repeated uint32 shape = 1;
+}