wehub-resource-sync
80 lines
2.8 KiB
Plaintext
80 lines
2.8 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 <algorithm>
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#include "paddle/common/hostdevice.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/backends/gpu/gpu_primitives.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/lod_utils.h"
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#include "paddle/phi/core/mixed_vector.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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#include "paddle/phi/kernels/gpu/box_clip_kernel.h"
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#include "paddle/phi/kernels/impl/box_clip_kernel_impl.h"
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namespace phi {
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static constexpr int ImInfoSize = 3;
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template <typename T, int BlockSize>
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static __global__ void GPUBoxClip(const T *input,
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const size_t *lod,
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const size_t width,
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const T *im_info,
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T *output) {
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T im_w = round(im_info[blockIdx.x * ImInfoSize + 1] /
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im_info[blockIdx.x * ImInfoSize + 2]);
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T im_h = round(im_info[blockIdx.x * ImInfoSize] /
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im_info[blockIdx.x * ImInfoSize + 2]);
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for (size_t i = threadIdx.x;
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i < (lod[blockIdx.x + 1] - lod[blockIdx.x]) * width;
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i += BlockSize) {
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size_t idx = lod[blockIdx.x] * width + i;
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T im_size = (idx % 2 == 0) ? im_w : im_h;
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output[idx] = max(min(input[idx], im_size - 1), T(0.));
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}
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}
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template <typename T, typename Context>
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void GPUBoxClipKernel(const Context &dev_ctx,
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const DenseTensor &input,
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const DenseTensor &im_info,
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DenseTensor *output) {
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auto *input_p = &input;
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auto *im_info_p = &im_info;
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const int64_t num = input_p->dims()[0];
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const int64_t bbox_width = input_p->numel() / num;
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auto lod = input_p->lod();
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LegacyLoD abs_offset_lod = ToAbsOffset(lod);
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auto stream = dev_ctx.stream();
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const size_t batch_size = lod.back().size() - 1;
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T *output_data = dev_ctx.template Alloc<T>(output);
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MixVector<size_t> mix_vector(&abs_offset_lod[0]);
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GPUBoxClip<T, 512><<<batch_size, 512, 0, stream>>>(
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input_p->data<T>(),
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mix_vector.CUDAMutableData(dev_ctx.GetPlace()),
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bbox_width,
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im_info_p->data<T>(),
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output_data);
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mix_vector.CopyToCPU();
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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box_clip, GPU, ALL_LAYOUT, phi::GPUBoxClipKernel, float, double) {}
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