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139 lines
4.5 KiB
139 lines
4.5 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "CropLayer.h"
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#include "paddle/utils/Stat.h"
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namespace paddle {
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REGISTER_LAYER(crop, CropLayer);
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bool CropLayer::init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) {
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/* Initialize the basic parent class */
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Layer::init(layerMap, parameterMap);
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auto& crop_conf = config_.inputs(0).crop_conf();
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crop_axis_ = crop_conf.axis();
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for (int i = 0; i < crop_conf.offset_size(); i++) {
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crop_offsets_[i] = crop_conf.offset(i);
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}
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// 1. get input_0 shape
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auto& input0_img_conf = config_.inputs(0).image_conf();
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inDims_ = TensorShape({0,
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input0_img_conf.channels(),
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input0_img_conf.has_img_size_y()
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? input0_img_conf.img_size_y()
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: input0_img_conf.img_size(),
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input0_img_conf.img_size()});
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// 2. get output shape from input_1 or crop shap conf
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if (config_.inputs_size() == 2) {
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auto& input1_img_conf = config_.inputs(1).image_conf();
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targetDims_ = TensorShape({0,
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input1_img_conf.channels(),
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input1_img_conf.has_img_size_y()
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? input1_img_conf.img_size_y()
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: input1_img_conf.img_size(),
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input1_img_conf.img_size()});
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} else {
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targetDims_ = TensorShape({crop_conf.shape(0),
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crop_conf.shape(1),
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crop_conf.shape(2),
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crop_conf.shape(3)});
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}
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// 3. get final crop shape
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int dimSize = 4;
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for (int i = 0; i < dimSize; i++) {
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if (i >= crop_axis_) {
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crop_shape_[i] = targetDims_[i];
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} else {
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crop_shape_[i] = inDims_[i];
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}
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}
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// 4. get final crop corner
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crop_corner_ = {0, 0, 0, 0};
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for (int i = 0; i < dimSize; i++) {
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if (i >= crop_axis_) {
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if (crop_offsets_.size() > 1) {
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crop_corner_[i] = crop_offsets_[i - crop_axis_];
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} else {
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crop_corner_[i] = crop_offsets_[0];
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}
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}
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}
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outDims_ = TensorShape(4);
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setOutDims(0);
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createFunction(forward_,
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"Crop",
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FuncConfig()
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.set("crop_corner", crop_corner_)
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.set("crop_shape", crop_shape_));
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createFunction(backward_,
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"CropGrad",
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FuncConfig()
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.set("crop_corner", crop_corner_)
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.set("crop_shape", crop_shape_));
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return true;
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}
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void CropLayer::setOutDims(const size_t batchSize) {
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outDims_.reshape({batchSize, crop_shape_[1], crop_shape_[2], crop_shape_[3]});
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}
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void CropLayer::setTensorDim(const size_t batchSize) {
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CHECK_EQ(static_cast<int>(inputLayers_.size()), 1);
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inDims_.setDim(0, batchSize);
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int h = inputLayers_[0]->getOutput().getFrameHeight();
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if (h != 0) inDims_.setDim(2, h);
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int w = inputLayers_[0]->getOutput().getFrameWidth();
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if (w != 0) inDims_.setDim(3, w);
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setOutDims(batchSize);
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}
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void CropLayer::forward(PassType passType) {
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Layer::forward(passType);
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MatrixPtr input = inputLayers_[0]->getOutputValue();
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size_t batchSize = input->getHeight();
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setTensorDim(batchSize);
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int size = outDims_[1] * outDims_[2] * outDims_[3];
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resetOutput(batchSize, size);
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MatrixPtr outV = getOutputValue();
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REGISTER_TIMER_INFO("CropForward", getName().c_str());
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BufferArgs inputs;
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BufferArgs outputs;
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inputs.addArg(*getInputValue(0), inDims_);
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outputs.addArg(*getOutputValue(), outDims_, ASSIGN_TO);
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forward_[0]->calc(inputs, outputs);
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}
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void CropLayer::backward(const UpdateCallback& callback) {
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(void)callback;
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REGISTER_TIMER_INFO("CropBackward", getName().c_str());
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BufferArgs inputs;
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BufferArgs outputs;
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inputs.addArg(*getOutputGrad(), outDims_);
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outputs.addArg(*getInputGrad(0), inDims_, ADD_TO);
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backward_[0]->calc(inputs, outputs);
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}
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} // namespace paddle
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