wehub-resource-sync
101 lines
3.2 KiB
C++
101 lines
3.2 KiB
C++
// Copyright (c) 2022 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/nms_kernel.h"
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#include <array>
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/tensor_utils.h"
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#include "paddle/phi/kernels/funcs/diagonal.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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namespace phi {
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template <typename T>
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static int64_t NMS(const T* boxes_data,
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int64_t* output_data,
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float threshold,
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int64_t num_boxes) {
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auto num_masks = CeilDivide(num_boxes, 64);
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std::vector<uint64_t> masks(num_masks, 0);
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for (int64_t i = 0; i < num_boxes; ++i) {
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if (masks[i / 64] & 1ULL << (i % 64)) continue;
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std::array<T, 4> box_1;
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for (int k = 0; k < 4; ++k) {
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box_1[k] = boxes_data[i * 4 + k];
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}
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for (int64_t j = i + 1; j < num_boxes; ++j) {
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if (masks[j / 64] & 1ULL << (j % 64)) continue;
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std::array<T, 4> box_2;
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for (int k = 0; k < 4; ++k) {
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box_2[k] = boxes_data[j * 4 + k];
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}
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bool is_overlap = CalculateIoU<T>(box_1.data(), box_2.data(), threshold);
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if (is_overlap) {
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masks[j / 64] |= 1ULL << (j % 64);
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}
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}
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}
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int64_t output_data_idx = 0;
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for (int64_t i = 0; i < num_boxes; ++i) {
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if (masks[i / 64] & 1ULL << (i % 64)) continue;
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output_data[output_data_idx++] = i;
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}
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int64_t num_keep_boxes = output_data_idx;
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for (; output_data_idx < num_boxes; ++output_data_idx) {
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output_data[output_data_idx] = 0;
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}
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return num_keep_boxes;
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}
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template <typename T, typename Context>
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void NMSKernel(const Context& dev_ctx,
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const DenseTensor& boxes,
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float threshold,
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DenseTensor* output) {
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PADDLE_ENFORCE_EQ(
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boxes.dims().size(),
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2,
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common::errors::InvalidArgument("The shape [%s] of boxes must be (N, 4).",
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boxes.dims()));
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PADDLE_ENFORCE_EQ(
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boxes.dims()[1],
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4,
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common::errors::InvalidArgument("The shape [%s] of boxes must be (N, 4).",
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boxes.dims()));
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int64_t num_boxes = boxes.dims()[0];
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DenseTensor output_tmp;
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output_tmp.Resize({num_boxes});
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auto output_tmp_data = dev_ctx.template Alloc<int64_t>(&output_tmp);
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int64_t num_keep_boxes =
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NMS<T>(boxes.data<T>(), output_tmp_data, threshold, num_boxes);
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auto slice_out = output_tmp.Slice(0, num_keep_boxes);
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Copy(dev_ctx, slice_out, dev_ctx.GetPlace(), false, output);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(nms, CPU, ALL_LAYOUT, phi::NMSKernel, float, double) {
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kernel->OutputAt(0).SetDataType(phi::DataType::INT64);
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}
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