DetectionOutputLayer.h 2.2 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

#pragma once

#include <map>
#include <vector>
#include "DetectionUtil.h"
#include "Layer.h"

namespace paddle {

/**
 * The detection output layer for a SSD detection task. This layer apply the
 * Non-maximum suppression to the all predicted bounding box and keep the
 * Top-K bounding boxes.
28
 * - Input: This layer needs three input layers: This first input layer
29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77
 *          is the priorbox layer. The rest two input layers are convolution
 *          layers for generating bbox location offset and the classification
 *          confidence.
 * - Output: The predict bounding box location.
 */

class DetectionOutputLayer : public Layer {
public:
  explicit DetectionOutputLayer(const LayerConfig& config) : Layer(config) {}

  bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);

  void forward(PassType passType);

  void backward(const UpdateCallback& callback = nullptr) {}

protected:
  inline LayerPtr getPriorBoxLayer() { return inputLayers_[0]; }

  inline LayerPtr getLocInputLayer(size_t index) {
    return inputLayers_[1 + index];
  }

  inline LayerPtr getConfInputLayer(size_t index) {
    return inputLayers_[1 + inputNum_ + index];
  }

private:
  size_t numClasses_;  // number of classes
  size_t inputNum_;    // number of input layers
  real nmsThreshold_;
  real confidenceThreshold_;
  size_t nmsTopK_;
  size_t keepTopK_;
  size_t backgroundId_;

  size_t locSizeSum_;
  size_t confSizeSum_;

  MatrixPtr locBuffer_;
  MatrixPtr confBuffer_;
  MatrixPtr locTmpBuffer_;
  MatrixPtr confTmpBuffer_;
  MatrixPtr priorCpuValue_;
  MatrixPtr locCpuBuffer_;
  MatrixPtr confCpuBuffer_;
};

}  // namespace paddle