语义分割学习笔记（三）——SegNet Upsample层解析

1 参数设置

message UpsampleParameter {  // DEPRECATED. No need to specify upsampling scale factors when  // exact output shape is given by upsample_h, upsample_w parameters.  optional uint32 scale = 1 [default = 2];  // DEPRECATED. No need to specify upsampling scale factors when  // exact output shape is given by upsample_h, upsample_w parameters.  optional uint32 scale_h = 2;  // DEPRECATED. No need to specify upsampling scale factors when  // exact output shape is given by upsample_h, upsample_w parameters.  optional uint32 scale_w = 3;  // DEPRECATED. Specify exact output height using upsample_h. This  // parameter only works when scale is 2  optional bool pad_out_h = 4 [default = false];  // DEPRECATED. Specify exact output width using upsample_w. This  // parameter only works when scale is 2  optional bool pad_out_w = 5 [default = false];  optional uint32 upsample_h = 6;  optional uint32 upsample_w = 7;}

可设置参数为：
    scale 
    scale_h scale_w
    pad_out_h pad_out_w
    upsample_h upsample_w

2  top层特征图大小计算

（1）先判断是否指定 upsample_hupsample_w，如果指定，大小为指定大小，否则（2）

（2）判断是否指定 scale_h， 如果未指定， scale_h_ = scale_w_ = scale，否则（3）

（3）scale_h_  = scale_h   scale_w_=scale_w

   只有scale_h_ = scale_w_ =2时，才可以指定pad_out_h，pad_out_w，否则错误，如果是（2）（3）则top特征图大小为：

       upsample_h_ = bottom[0]->height() *  scale_h_ - int(pad_out_h) 

       upsample_w_ = bottom[0]->width() *  scale_w_ - int(pad_out_w) 

3 源码

#include <algorithm>#include <cfloat>#include <vector>#include <iostream>#include "caffe/layers/upsample_layer.hpp"namespace caffe {template <typename Dtype>void UpsampleLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {  UpsampleParameter upsample_param = this->layer_param_.upsample_param();  CHECK((upsample_param.has_upsample_h() && upsample_param.has_upsample_w())      || (!upsample_param.has_scale() && upsample_param.has_scale_h()      && upsample_param.has_scale_w())      || (!upsample_param.has_scale_h() && !upsample_param.has_scale_w()))      << "upsample_h & upsample_w are required, else (DEPRECATED) "      << "scale OR scale_h & scale_w are required.";  if (upsample_param.has_upsample_h() && upsample_param.has_upsample_w()) {    upsample_h_ = upsample_param.upsample_h(); //根据upsample_h upsample_w参数设定    upsample_w_ = upsample_param.upsample_w();    CHECK_GT(upsample_h_, 1);    CHECK_GT(upsample_w_, 1);  } else {    LOG(INFO) << "Params 'pad_out_{}_' are deprecated. Please declare upsample"        << " height and width useing the upsample_h, upsample_w parameters.";    if (!upsample_param.has_scale_h()) { //根据scale设定  没有scale_h直接根据scale设定      scale_h_ = scale_w_ = upsample_param.scale();      CHECK_GT(scale_h_, 1);    } else {      scale_h_ = upsample_param.scale_h();      scale_w_ = upsample_param.scale_w();      CHECK_GT(scale_h_, 1);      CHECK_GT(scale_w_, 1);    }    pad_out_h_ = upsample_param.pad_out_h();    pad_out_w_ = upsample_param.pad_out_w();    CHECK(!pad_out_h_ || scale_h_ == 2)  //只有scale_h scale_w=2时，才可以指定        << "Output height padding compensation requires scale_h == 2, otherwise "        << "the output size is ill-defined.";    CHECK(!pad_out_w_ || scale_w_ == 2)         << "Output width padding compensation requires scale_w == 2, otherwise "        << "the output size is ill-defined.";    upsample_h_ = upsample_w_ = -1;  // flag to calculate in Reshape  }}template <typename Dtype>void UpsampleLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "      << "corresponding to (num, channels, height, width)";  CHECK_EQ(4, bottom[1]->num_axes()) << "Input mask must have 4 axes, "      << "corresponding to (num, channels, height, width)";  CHECK_EQ(bottom[0]->num(), bottom[1]->num());  CHECK_EQ(bottom[0]->channels(), bottom[1]->channels());  CHECK_EQ(bottom[0]->height(), bottom[1]->height());  CHECK_EQ(bottom[0]->width(), bottom[1]->width());  if (upsample_h_ <= 0 || upsample_w_ <= 0) {    upsample_h_ = bottom[0]->height() * scale_h_ - int(pad_out_h_); // upsample_h_ = height*scale-pad_out     upsample_w_ = bottom[0]->width() * scale_w_ - int(pad_out_w_);  }  top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(), upsample_h_,      upsample_w_);  channels_ = bottom[0]->channels();  height_ = bottom[0]->height();  width_ = bottom[0]->width();}template <typename Dtype>void UpsampleLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {  const Dtype* bottom_data = bottom[0]->cpu_data();  const Dtype* bottom_mask_data = bottom[1]->cpu_data();  Dtype* top_data = top[0]->mutable_cpu_data();  // Initialize  const int top_count = top[0]->count();  caffe_set(top_count, Dtype(0), top_data);  // The main loop  for (int n = 0; n < bottom[0]->num(); ++n) {    for (int c = 0; c < channels_; ++c) {      for (int i = 0; i < height_ * width_; ++i) {        const int idx = static_cast<int>(bottom_mask_data[i]);        if (idx >= upsample_h_ * upsample_w_) {          // this can happen if the pooling layer that created the input mask          // had an input with different size to top[0]          LOG(FATAL) << "upsample top index " << idx << " out of range - "            << "check scale settings match input pooling layer's "            << "downsample setup";        }        top_data[idx] = bottom_data[i];      }      // compute offset      bottom_data += bottom[0]->offset(0, 1);      bottom_mask_data += bottom[1]->offset(0, 1);      top_data += top[0]->offset(0, 1);    }  }}template <typename Dtype>void UpsampleLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {  if (propagate_down[0]) {    const Dtype* top_diff = top[0]->cpu_diff();    const Dtype* bottom_mask_data = bottom[1]->cpu_data();    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();    const int bottom_count = bottom[0]->count();    caffe_set(bottom_count, Dtype(0), bottom_diff);    // The main loop    for (int n = 0; n < bottom[0]->num(); ++n) {      for (int c = 0; c < channels_; ++c) {        for (int i = 0; i < height_ * width_; ++i) {          const int idx = static_cast<int>(bottom_mask_data[i]);          if (idx >= height_ * width_ * scale_h_ * scale_w_) {            // this can happen if the pooling layer that created            // the input mask had an input with different size to top[0]            LOG(FATAL) << "upsample top index " << idx << " out of range - "              << "check scale settings match input pooling layer's downsample setup";          }          bottom_diff[i] = top_diff[idx];        }        // compute offset        bottom_diff += bottom[0]->offset(0, 1);        bottom_mask_data += bottom[1]->offset(0, 1);        top_diff += top[0]->offset(0, 1);      }    }  }}#ifdef CPU_ONLYSTUB_GPU(UpsampleLayer);#endifINSTANTIATE_CLASS(UpsampleLayer);REGISTER_LAYER_CLASS(Upsample);}  // namespace caffe