wehub-resource-sync
115 lines
4.7 KiB
Plaintext
115 lines
4.7 KiB
Plaintext
// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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/phi/kernels/gpu/yolo_box_head_kernel.h"
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#include "paddle/common/enforce.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/backends/gpu/gpu_launch_config.h"
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#include "paddle/phi/common/memory_utils.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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#include "paddle/phi/kernels/funcs/yolo_box_util.h"
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namespace phi {
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template <typename T>
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inline __device__ T SigmoidGPU(const T& x) {
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return 1.0f / (1.0f + __expf(-x));
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}
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template <typename T>
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__global__ void YoloBoxHeadCudaKernel(const T* input,
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T* output,
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const int grid_size_x,
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const int grid_size_y,
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const int class_num,
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const int anchors_num) {
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int64_t x_id =
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static_cast<int64_t>(blockIdx.x) * static_cast<int64_t>(blockDim.x) +
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static_cast<int64_t>(threadIdx.x);
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int64_t y_id =
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static_cast<int64_t>(blockIdx.y) * static_cast<int64_t>(blockDim.y) +
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static_cast<int64_t>(threadIdx.y);
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int64_t z_id =
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static_cast<int64_t>(blockIdx.z) * static_cast<int64_t>(blockDim.z) +
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static_cast<int64_t>(threadIdx.z);
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if ((x_id >= grid_size_x) || (y_id >= grid_size_y) || (z_id >= anchors_num)) {
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return;
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}
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const int grids_num = grid_size_x * grid_size_y;
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const int bbindex = y_id * grid_size_x + x_id;
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// objectness
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output[bbindex + grids_num * (z_id * (5 + class_num) + 4)] =
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SigmoidGPU(input[bbindex + grids_num * (z_id * (5 + class_num) + 4)]);
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// x
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output[bbindex + grids_num * (z_id * (5 + class_num) + 0)] =
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SigmoidGPU(input[bbindex + grids_num * (z_id * (5 + class_num) + 0)]);
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// y
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output[bbindex + grids_num * (z_id * (5 + class_num) + 1)] =
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SigmoidGPU(input[bbindex + grids_num * (z_id * (5 + class_num) + 1)]);
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// w
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output[bbindex + grids_num * (z_id * (5 + class_num) + 2)] =
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__expf(input[bbindex + grids_num * (z_id * (5 + class_num) + 2)]);
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// h
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output[bbindex + grids_num * (z_id * (5 + class_num) + 3)] =
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__expf(input[bbindex + grids_num * (z_id * (5 + class_num) + 3)]);
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// Probabilities of classes
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for (int i = 0; i < class_num; ++i) {
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output[bbindex + grids_num * (z_id * (5 + class_num) + (5 + i))] =
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SigmoidGPU(
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input[bbindex + grids_num * (z_id * (5 + class_num) + (5 + i))]);
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}
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}
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template <typename T, typename Context>
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void YoloBoxHeadKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const std::vector<int>& anchors,
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int class_num,
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DenseTensor* out) {
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auto x_dims = x.dims();
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PADDLE_ENFORCE_LE_INT_MAX(x_dims[0], "batch_size");
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PADDLE_ENFORCE_LE_INT_MAX(x_dims[2], "grid_size_y");
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PADDLE_ENFORCE_LE_INT_MAX(x_dims[3], "grid_size_x");
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const int batch_size = static_cast<int>(x_dims[0]);
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const int h = static_cast<int>(x_dims[2]);
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const int w = static_cast<int>(x_dims[3]);
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const int grid_size_x = w;
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const int grid_size_y = h;
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const int anchors_num = anchors.size() / 2;
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const T* input_data = x.data<T>();
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T* output_data = dev_ctx.template Alloc<T>(out, out->numel() * sizeof(T));
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auto stream = dev_ctx.stream();
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const int64_t volume_64 = x_dims[1] * h * w;
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PADDLE_ENFORCE_LE_INT_MAX(volume_64, "volume");
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const int volume = static_cast<int>(volume_64);
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dim3 block(16, 16, 4);
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dim3 grid((grid_size_x / block.x) + 1,
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(grid_size_y / block.y) + 1,
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(anchors_num / block.z) + 1);
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for (int n = 0; n < batch_size; n++) {
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YoloBoxHeadCudaKernel<<<grid, block, 0, stream>>>(input_data + n * volume,
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output_data + n * volume,
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grid_size_x,
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grid_size_y,
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class_num,
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anchors_num);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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yolo_box_head, GPU, ALL_LAYOUT, phi::YoloBoxHeadKernel, float) {}
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