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72 lines
2.6 KiB
72 lines
2.6 KiB
6 years ago
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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 "paddle/fluid/operators/detection/yolo_box_op.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename T>
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static __global__ void GenDensityPriorBox(
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const int height, const int width, const int im_height, const int im_width,
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const T offset, const T step_width, const T step_height,
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const int num_priors, const T* ratios_shift, bool is_clip, const T var_xmin,
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const T var_ymin, const T var_xmax, const T var_ymax, T* out, T* var) {
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int gidx = blockIdx.x * blockDim.x + threadIdx.x;
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int gidy = blockIdx.y * blockDim.y + threadIdx.y;
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int step_x = blockDim.x * gridDim.x;
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int step_y = blockDim.y * gridDim.y;
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}
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template <typename T>
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class YoloBoxOpCUDAKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* input = ctx.Input<Tensor>("Input");
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auto* boxes = ctx.Output<Tensor>("Boxes");
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auto* scores = ctx.Output<Tensor>("Scores");
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auto anchors = ctx.Attr<std::vector<int>>("anchors");
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int class_num = ctx.Attr<int>("class_num");
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float conf_thresh = ctx.Attr<float>("conf_thresh");
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int downsample_ratio = ctx.Attr<int>("downsample_ratio");
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const int n = input->dims()[0];
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const int h = input->dims()[2];
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const int w = input->dims()[3];
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const int box_num = boxes->dims()[1];
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const int an_num = anchors.size() / 2;
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int input_size = downsample_ratio * h;
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const int stride = h * w;
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const int an_stride = (class_num + 5) * stride;
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const T* input_data = input->data<T>();
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T* boxes_data = boxes->mutable_data<T>({n}, ctx.GetPlace());
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memset(loss_data, 0, boxes->numel() * sizeof(T));
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T* scores_data = scores->mutable_data<T>({n}, ctx.GetPlace());
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memset(scores_data, 0, scores->numel() * sizeof(T));
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}
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}; // namespace operators
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_CUDA_KERNEL(density_prior_box,
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ops::DensityPriorBoxOpCUDAKernel<float>,
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ops::DensityPriorBoxOpCUDAKernel<double>);
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