From 707a9c9bbd67e936efeea134cc6eaf2f5fffe464 Mon Sep 17 00:00:00 2001 From: gaoyuan Date: Thu, 15 Dec 2016 13:33:36 +0800 Subject: [PATCH] Fix variable name and add the annotation --- paddle/gserver/layers/PriorBox.cpp | 130 ++++++++---------- python/paddle/trainer/config_parser.py | 2 - .../paddle/trainer_config_helpers/layers.py | 10 +- 3 files changed, 63 insertions(+), 79 deletions(-) diff --git a/paddle/gserver/layers/PriorBox.cpp b/paddle/gserver/layers/PriorBox.cpp index 4b8573f0581..c9194235fd1 100644 --- a/paddle/gserver/layers/PriorBox.cpp +++ b/paddle/gserver/layers/PriorBox.cpp @@ -17,6 +17,15 @@ limitations under the License. */ #include "paddle/math/BaseMatrix.h" namespace paddle { +/** + * @brief A layer for generate prior box locations and variances. + * - Input: Two and only two input layer are accepted. The input layer must be + * be a data output layer and a convolution output layer. + * - Output: The prior box locations and variances of the input data. + * Reference: + * Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, + * Cheng-Yang Fu, Alexander C. Berg. SSD: Single Shot MultiBox Detector + */ class PriorBoxLayer : public Layer { public: @@ -24,106 +33,84 @@ public: bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); void forward(PassType passType); void backward(const UpdateCallback& callback) {} - void forwardImp(const Argument& featureMap, const Argument& imageShape); int numPriors_; std::vector minSize_; std::vector maxSize_; std::vector aspectRatio_; std::vector variance_; - std::vector tmpCpuInput_; MatrixPtr buffer_; }; bool PriorBoxLayer::init(const LayerMap& layerMap, const ParameterMap& parameterMap) { Layer::init(layerMap, parameterMap); - auto pb_conf = config_.inputs(0).priorbox_conf(); - std::copy(pb_conf.min_size().begin(), - pb_conf.min_size().end(), + auto pbConf = config_.inputs(0).priorbox_conf(); + std::copy(pbConf.min_size().begin(), + pbConf.min_size().end(), std::back_inserter(minSize_)); - std::copy(pb_conf.max_size().begin(), - pb_conf.max_size().end(), + std::copy(pbConf.max_size().begin(), + pbConf.max_size().end(), std::back_inserter(maxSize_)); - std::copy(pb_conf.aspect_ratio().begin(), - pb_conf.aspect_ratio().end(), + std::copy(pbConf.aspect_ratio().begin(), + pbConf.aspect_ratio().end(), std::back_inserter(aspectRatio_)); - std::copy(pb_conf.variance().begin(), - pb_conf.variance().end(), + std::copy(pbConf.variance().begin(), + pbConf.variance().end(), std::back_inserter(variance_)); // flip - int input_ratio_length = aspectRatio_.size(); - for (int index = 0; index < input_ratio_length; index++) + int inputRatioLength = aspectRatio_.size(); + for (int index = 0; index < inputRatioLength; index++) aspectRatio_.push_back(1 / aspectRatio_[index]); aspectRatio_.push_back(1.); numPriors_ = aspectRatio_.size(); if (maxSize_.size() > 0) numPriors_++; - buffer_ = Matrix::create(1, 1, false, false); - if (useGpu_) { - tmpCpuInput_.reserve(inputLayers_.size()); - for (size_t i = 0; i < inputLayers_.size(); i++) { - tmpCpuInput_.push_back(Argument()); - } - } return true; } void PriorBoxLayer::forward(PassType passType) { Layer::forward(passType); - if (useGpu_) { - for (size_t i = 0; i < inputLayers_.size(); i++) { - tmpCpuInput_[i].resizeAndCopyFrom( - getInput(i), false, HPPL_STREAM_DEFAULT); - hl_stream_synchronize(HPPL_STREAM_DEFAULT); - forwardImp(tmpCpuInput_[0], tmpCpuInput_[1]); - } - } else { - forwardImp(getInput(0), getInput(1)); - } -} - -void PriorBoxLayer::forwardImp(const Argument& featureMap, - const Argument& imageShape) { - int layer_width = featureMap.getFrameWidth(); - int layer_height = featureMap.getFrameHeight(); + auto input = getInput(0); + int layerWidth = input.getFrameWidth(); + int layerHeight = input.getFrameHeight(); - MatrixPtr inV1 = imageShape.value; - int image_width = inV1->getElement(0, 0); - int image_height = inV1->getElement(0, 1); - float step_w = static_cast(image_width) / layer_width; - float step_h = static_cast(image_height) / layer_height; - int dim = layer_height * layer_width * numPriors_ * 4; + auto image = getInput(1); + int imageWidth = image.getFrameWidth(); + int imageHeight = image.getFrameHeight(); + float stepW = static_cast(imageWidth) / layerWidth; + float stepH = static_cast(imageHeight) / layerHeight; + int dim = layerHeight * layerWidth * numPriors_ * 4; reserveOutput(1, dim * 2); // use a cpu buffer to compute Matrix::resizeOrCreate(buffer_, 1, dim * 2, false, false); - auto* tmp_ptr = buffer_->getData(); + auto* tmpPtr = buffer_->getData(); int idx = 0; - for (int h = 0; h < layer_height; ++h) { - for (int w = 0; w < layer_width; ++w) { - float center_x = (w + 0.5) * step_w; - float center_y = (h + 0.5) * step_h; - int min_size = 0; + for (int h = 0; h < layerHeight; ++h) { + for (int w = 0; w < layerWidth; ++w) { + float centerX = (w + 0.5) * stepW; + float centerY = (h + 0.5) * stepH; + int minSize = 0; for (size_t s = 0; s < minSize_.size(); s++) { // first prior. - min_size = minSize_[s]; - int box_width = min_size; - int box_height = min_size; + minSize = minSize_[s]; + int boxWidth = minSize; + int boxHeight = minSize; // xmin, ymin, xmax, ymax. - tmp_ptr[idx++] = (center_x - box_width / 2.) / image_width; - tmp_ptr[idx++] = (center_y - box_height / 2.) / image_height; - tmp_ptr[idx++] = (center_x + box_width / 2.) / image_width; - tmp_ptr[idx++] = (center_y + box_height / 2.) / image_height; + tmpPtr[idx++] = (centerX - boxWidth / 2.) / imageWidth; + tmpPtr[idx++] = (centerY - boxHeight / 2.) / imageHeight; + tmpPtr[idx++] = (centerX + boxWidth / 2.) / imageWidth; + tmpPtr[idx++] = (centerY + boxHeight / 2.) / imageHeight; if (maxSize_.size() > 0) { CHECK_EQ(minSize_.size(), maxSize_.size()); // second prior. for (size_t s = 0; s < maxSize_.size(); s++) { - int max_size = maxSize_[s]; - box_width = box_height = sqrt(min_size * max_size); - tmp_ptr[idx++] = (center_x - box_width / 2.) / image_width; - tmp_ptr[idx++] = (center_y - box_height / 2.) / image_height; - tmp_ptr[idx++] = (center_x + box_width / 2.) / image_width; - tmp_ptr[idx++] = (center_y + box_height / 2.) / image_height; + int maxSize = maxSize_[s]; + boxWidth = boxHeight = sqrt(minSize * maxSize); + tmpPtr[idx++] = (centerX - boxWidth / 2.) / imageWidth; + tmpPtr[idx++] = (centerY - boxHeight / 2.) / imageHeight; + tmpPtr[idx++] = (centerX + boxWidth / 2.) / imageWidth; + tmpPtr[idx++] = (centerY + boxHeight / 2.) / imageHeight; } } } @@ -131,27 +118,26 @@ void PriorBoxLayer::forwardImp(const Argument& featureMap, for (size_t r = 0; r < aspectRatio_.size(); r++) { float ar = aspectRatio_[r]; if (fabs(ar - 1.) < 1e-6) continue; - float box_width = min_size * sqrt(ar); - float box_height = min_size / sqrt(ar); - tmp_ptr[idx++] = (center_x - box_width / 2.) / image_width; - tmp_ptr[idx++] = (center_y - box_height / 2.) / image_height; - tmp_ptr[idx++] = (center_x + box_width / 2.) / image_width; - tmp_ptr[idx++] = (center_y + box_height / 2.) / image_height; + float boxWidth = minSize * sqrt(ar); + float boxHeight = minSize / sqrt(ar); + tmpPtr[idx++] = (centerX - boxWidth / 2.) / imageWidth; + tmpPtr[idx++] = (centerY - boxHeight / 2.) / imageHeight; + tmpPtr[idx++] = (centerX + boxWidth / 2.) / imageWidth; + tmpPtr[idx++] = (centerY + boxHeight / 2.) / imageHeight; } } } // clip the prior's coordidate such that it is within [0, 1] for (int d = 0; d < dim; ++d) - tmp_ptr[d] = std::min(std::max(tmp_ptr[d], (float)0.), (float)1.); + tmpPtr[d] = std::min(std::max(tmpPtr[d], (float)0.), (float)1.); // set the variance. - for (int h = 0; h < layer_height; h++) - for (int w = 0; w < layer_width; w++) + for (int h = 0; h < layerHeight; h++) + for (int w = 0; w < layerWidth; w++) for (int i = 0; i < numPriors_; i++) - for (int j = 0; j < 4; j++) tmp_ptr[idx++] = variance_[j]; + for (int j = 0; j < 4; j++) tmpPtr[idx++] = variance_[j]; MatrixPtr outV = getOutputValue(); outV->copyFrom(buffer_->data_, dim * 2); } - REGISTER_LAYER(priorbox, PriorBoxLayer); } // namespace paddle diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 8a82e5d667a..0f7c601fe0d 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -1589,8 +1589,6 @@ class PriorBoxLayer(LayerBase): self.config.inputs[0].priorbox_conf.aspect_ratio.extend(aspect_ratio) self.config.inputs[0].priorbox_conf.variance.extend(variance) self.config.size = size - input_layer0 = self.get_input_layer(0) - input_layer1 = self.get_input_layer(1) @config_layer('data') diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 80c421aa2ec..4bcdb9f35e2 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -938,7 +938,7 @@ def print_layer(input, name=None): @wrap_name_default("priorbox") def priorbox_layer(input, - img_shape, + image, aspect_ratio, variance, min_size, @@ -951,8 +951,8 @@ def priorbox_layer(input, :type name: basestring :param input: The input layer. :type input: LayerOutput - :param img_shape: The width and height of the network input image. - :type img_shape: LayerOutput + :param image: The network input image. + :type image: LayerOutput :param aspect_ratio: The aspect ratio. :type aspect_ratio: list :param variance: The bounding box variance. @@ -968,7 +968,7 @@ def priorbox_layer(input, Layer( name=name, type=LayerType.PRIORBOX_LAYER, - inputs=[input.name, img_shape.name], + inputs=[input.name, image.name], size=size, min_size=min_size, max_size=max_size, @@ -977,7 +977,7 @@ def priorbox_layer(input, return LayerOutput( name, LayerType.PRIORBOX_LAYER, - parents=[input, img_shape], + parents=[input, image], num_filters=num_filters, size=size) -- GitLab