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Added ParseEvaluate Layer.
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‎include/caffe/layers/parse_evaluate_layer.hpp‎

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#ifndef CAFFE_PARSE_EVALUATE_LAYER_HPP_
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#define CAFFE_PARSE_EVALUATE_LAYER_HPP_
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#include <set>
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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 Count the prediction and ground truth statistics for each datum.
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*
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* NOTE: This does not implement Backwards operation.
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*/
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template <typename Dtype>
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class ParseEvaluateLayer : public Layer<Dtype> {
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public:
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/**
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* @param param provides ParseEvaluateParameter parse_evaluate_param,
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* with ParseEvaluateLayer options:
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* - num_labels (\b optional int32.).
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* number of labels. must provide!!
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* - ignore_label (\b repeated int32).
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* If any, ignore evaluating the corresponding label for each
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* image.
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*/
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explicit ParseEvaluateLayer(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 "ParseEvaluate"; }
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virtual inline int ExactNumBottomBlobs() const { return 2; }
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virtual inline int ExactNumTopBlobs() const { return 1; }
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protected:
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/**
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* @param bottom input Blob vector (length 2)
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* -# @f$ (N \times 1 \times H \times W) @f$
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* the prediction label @f$ x @f$
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* -# @f$ (N \times 1 \times H \times W) @f$
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* the ground truth label @f$ x @f$
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* @param top output Blob vector (length 1)
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* -# @f$ (N \times C \times 1 \times 3) @f$
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* the counts for different class @f$
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*/
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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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/// @brief Not implemented (non-differentiable function)
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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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NOT_IMPLEMENTED;
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}
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// number of total labels
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int num_labels_;
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// store ignored labels
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std::set<Dtype> ignore_labels_;
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};
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} // namespace caffe
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#endif // CAFFE_PARSE_EVALUATE_LAYER_HPP_

‎src/caffe/layers/parse_evaluate_layer.cpp‎

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#include <algorithm>
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#include <functional>
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#include <utility>
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#include <vector>
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#include "caffe/layer.hpp"
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#include "caffe/layers/parse_evaluate_layer.hpp"
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#include "caffe/util/io.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 ParseEvaluateLayer<Dtype>::LayerSetUp(
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const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
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const ParseEvaluateParameter& parse_evaluate_param =
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this->layer_param_.parse_evaluate_param();
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CHECK(parse_evaluate_param.has_num_labels()) << "Must have num_labels!!";
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num_labels_ = parse_evaluate_param.num_labels();
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ignore_labels_.clear();
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int num_ignore_label = parse_evaluate_param.ignore_label().size();
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for (int i = 0; i < num_ignore_label; ++i) {
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ignore_labels_.insert(parse_evaluate_param.ignore_label(i));
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}
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}
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template <typename Dtype>
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void ParseEvaluateLayer<Dtype>::Reshape(
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const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
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CHECK_EQ(bottom[0]->num(), bottom[1]->num())
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<< "The data and label should have the same number.";
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CHECK_EQ(bottom[0]->channels(), bottom[1]->channels());
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CHECK_EQ(bottom[0]->channels(), 1);
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CHECK_EQ(bottom[0]->height(), bottom[1]->height());
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CHECK_GE(bottom[0]->width(), bottom[1]->width());
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top[0]->Reshape(1, num_labels_, 1, 3);
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}
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template <typename Dtype>
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void ParseEvaluateLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
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const vector<Blob<Dtype>*>& top) {
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CHECK_EQ(bottom[0]->num(), bottom[1]->num());
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CHECK_EQ(bottom[0]->count(), bottom[1]->count());
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const Dtype* bottom_pred = bottom[0]->cpu_data();
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const Dtype* bottom_gt = bottom[1]->cpu_data();
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Dtype* top_data = top[0]->mutable_cpu_data();
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caffe_set(top[0]->count(), Dtype(0), top_data);
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int num = bottom[0]->num();
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int spatial_dim = bottom[0]->height() * bottom[0]->width();
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for (int i = 0; i < num; ++i) {
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// count the number of ground truth labels, the predicted labels, and
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// predicted labels happens to be ground truth labels
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for (int j = 0; j < spatial_dim; ++j) {
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int gt_label = bottom_gt[j];
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int pred_label = bottom_pred[j];
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CHECK_LT(pred_label, num_labels_);
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if (ignore_labels_.find(gt_label) != ignore_labels_.end()) {
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continue;
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}
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if (gt_label == pred_label) {
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top_data[gt_label * 3]++;
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}
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top_data[gt_label * 3 + 1]++;
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top_data[pred_label * 3 + 2]++;
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}
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bottom_pred += bottom[0]->offset(1);
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bottom_gt += bottom[1]->offset(1);
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}
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// ParseEvaluate layer should not be used as a loss function.
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}
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INSTANTIATE_CLASS(ParseEvaluateLayer);
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REGISTER_LAYER_CLASS(ParseEvaluate);
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} // namespace caffe

‎src/caffe/proto/caffe.proto‎

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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: 146 (last added: unpooling_param)
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// LayerParameter next available layer-specific ID: 147 (last added: parse_evaluate_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 MemoryDataParameter memory_data_param = 119;
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optional MVNParameter mvn_param = 120;
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optional NormalizeParameter norm_param = 144;
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optional ParseEvaluateParameter parse_evaluate_param = 146;
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optional PoolingParameter pooling_param = 121;
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optional PowerParameter power_param = 122;
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optional PReLUParameter prelu_param = 131;
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optional float eps = 4 [default = 1e-10];
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}
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// Message that stores parameters used by ParseEvaluateLayer
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message ParseEvaluateParameter {
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// Number of total labels. Must provide.
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optional int32 num_labels = 1;
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// Ignore evaluating following labels.
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repeated int32 ignore_label = 2;
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}
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message PoolingParameter {
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enum PoolMethod {
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MAX = 0;

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