Add DetectionOutputLayer and MultiBoxLossLayer.

doc/api/v2/config/layer.rst

@@ -445,6 +445,11 @@ smooth_l1_cost
 ..  autoclass:: paddle.v2.layer.smooth_l1_cost
     :noindex:
 
+multibox_loss
+--------------
+..  autoclass:: paddle.v2.layer.multibox_loss
+    :noindex:
+
 Check Layer
 ============
 
@@ -468,3 +473,11 @@ prelu
 --------
 ..  autoclass:: paddle.v2.layer.prelu
     :noindex:
+
+Detection output Layer
+======================
+
+detection_output
+----------------
+..  autoclass:: paddle.v2.layer.detection_output
+    :noindex:

paddle/gserver/layers/DetectionOutputLayer.cpp

@@ -0,0 +1,154 @@
+/* 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. */
+
+#include "DetectionOutputLayer.h"
+
+namespace paddle {
+
+REGISTER_LAYER(detection_output, DetectionOutputLayer);
+
+bool DetectionOutputLayer::init(const LayerMap& layerMap,
+                                const ParameterMap& parameterMap) {
+  Layer::init(layerMap, parameterMap);
+  auto& layerConf = config_.inputs(0).detection_output_conf();
+  numClasses_ = layerConf.num_classes();
+  inputNum_ = layerConf.input_num();
+  nmsThreshold_ = layerConf.nms_threshold();
+  confidenceThreshold_ = layerConf.confidence_threshold();
+  nmsTopK_ = layerConf.nms_top_k();
+  keepTopK_ = layerConf.keep_top_k();
+  backgroundId_ = layerConf.background_id();
+  return true;
+}
+
+void DetectionOutputLayer::forward(PassType passType) {
+  Layer::forward(passType);
+  size_t batchSize = getInputValue(*getLocInputLayer(0))->getHeight();
+
+  locSizeSum_ = 0;
+  confSizeSum_ = 0;
+  for (size_t n = 0; n < inputNum_; ++n) {
+    const MatrixPtr inLoc = getInputValue(*getLocInputLayer(n));
+    const MatrixPtr inConf = getInputValue(*getConfInputLayer(n));
+    locSizeSum_ += inLoc->getElementCnt();
+    confSizeSum_ += inConf->getElementCnt();
+  }
+
+  Matrix::resizeOrCreate(locTmpBuffer_, 1, locSizeSum_, false, useGpu_);
+  Matrix::resizeOrCreate(
+      confTmpBuffer_, confSizeSum_ / numClasses_, numClasses_, false, useGpu_);
+
+  size_t locOffset = 0;
+  size_t confOffset = 0;
+  auto& layerConf = config_.inputs(0).detection_output_conf();
+  for (size_t n = 0; n < inputNum_; ++n) {
+    const MatrixPtr inLoc = getInputValue(*getLocInputLayer(n));
+    const MatrixPtr inConf = getInputValue(*getConfInputLayer(n));
+
+    size_t height = getInput(*getLocInputLayer(n)).getFrameHeight();
+    if (!height) height = layerConf.height();
+    size_t width = getInput(*getLocInputLayer(n)).getFrameWidth();
+    if (!width) width = layerConf.width();
+    locOffset += appendWithPermute(*inLoc,
+                                   height,
+                                   width,
+                                   locSizeSum_,
+                                   locOffset,
+                                   batchSize,
+                                   *locTmpBuffer_,
+                                   kNCHWToNHWC);
+    confOffset += appendWithPermute(*inConf,
+                                    height,
+                                    width,
+                                    confSizeSum_,
+                                    confOffset,
+                                    batchSize,
+                                    *confTmpBuffer_,
+                                    kNCHWToNHWC);
+  }
+  CHECK_EQ(locOffset, locSizeSum_ / batchSize);
+  CHECK_EQ(confOffset, confSizeSum_ / batchSize);
+
+  MatrixPtr priorValue;
+  if (useGpu_) {
+    Matrix::resizeOrCreate(locCpuBuffer_, 1, locSizeSum_, false, false);
+    Matrix::resizeOrCreate(
+        confCpuBuffer_, confSizeSum_ / numClasses_, numClasses_, false, false);
+    MatrixPtr priorTmpValue = getInputValue(*getPriorBoxLayer());
+    Matrix::resizeOrCreate(
+        priorCpuValue_, 1, priorTmpValue->getElementCnt(), false, false);
+
+    locCpuBuffer_->copyFrom(*locTmpBuffer_);
+    confCpuBuffer_->copyFrom(*confTmpBuffer_);
+    priorCpuValue_->copyFrom(*priorTmpValue);
+
+    locBuffer_ = locCpuBuffer_;
+    confBuffer_ = confCpuBuffer_;
+    priorValue = priorCpuValue_;
+  } else {
+    priorValue = getInputValue(*getPriorBoxLayer());
+    locBuffer_ = locTmpBuffer_;
+    confBuffer_ = confTmpBuffer_;
+  }
+  confBuffer_->softmax(*confBuffer_);
+
+  size_t numPriors = priorValue->getElementCnt() / 8;
+  std::vector<std::vector<NormalizedBBox>> allDecodedBBoxes;
+  for (size_t n = 0; n < batchSize; ++n) {
+    std::vector<NormalizedBBox> decodedBBoxes;
+    for (size_t i = 0; i < numPriors; ++i) {
+      size_t priorOffset = i * 8;
+      size_t locPredOffset = n * numPriors * 4 + i * 4;
+      std::vector<NormalizedBBox> priorBBoxVec;
+      getBBoxFromPriorData(
+          priorValue->getData() + priorOffset, 1, priorBBoxVec);
+      std::vector<std::vector<real>> priorBBoxVar;
+      getBBoxVarFromPriorData(
+          priorValue->getData() + priorOffset, 1, priorBBoxVar);
+      std::vector<real> locPredData;
+      for (size_t j = 0; j < 4; ++j)
+        locPredData.push_back(*(locBuffer_->getData() + locPredOffset + j));
+      NormalizedBBox bbox =
+          decodeBBoxWithVar(priorBBoxVec[0], priorBBoxVar[0], locPredData);
+      decodedBBoxes.push_back(bbox);
+    }
+    allDecodedBBoxes.push_back(decodedBBoxes);
+  }
+
+  std::vector<std::map<size_t, std::vector<size_t>>> allIndices;
+  size_t numKept = getDetectionIndices(confBuffer_->getData(),
+                                       numPriors,
+                                       numClasses_,
+                                       backgroundId_,
+                                       batchSize,
+                                       confidenceThreshold_,
+                                       nmsTopK_,
+                                       nmsThreshold_,
+                                       keepTopK_,
+                                       allDecodedBBoxes,
+                                       &allIndices);
+
+  resetOutput(numKept, 7);
+  MatrixPtr outV = getOutputValue();
+  getDetectionOutput(confBuffer_->getData(),
+                     numKept,
+                     numPriors,
+                     numClasses_,
+                     batchSize,
+                     allIndices,
+                     allDecodedBBoxes,
+                     *outV);
+}
+
+}  // namespace paddle

paddle/gserver/layers/DetectionOutputLayer.h

@@ -0,0 +1,77 @@
+/* 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 applies the
+ * Non-maximum suppression to the all predicted bounding box and keeps the
+ * Top-K bounding boxes.
+ * - Input: This layer needs three input layers: The first input layer
+ *          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 locations.
+ */
+
+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