chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/nms_kernel.cc

@@ -0,0 +1,100 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/phi/kernels/nms_kernel.h"
+#include <array>
+#include "paddle/phi/backends/cpu/cpu_context.h"
+
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/core/tensor_utils.h"
+#include "paddle/phi/kernels/funcs/diagonal.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+
+namespace phi {
+
+template <typename T>
+static int64_t NMS(const T* boxes_data,
+                   int64_t* output_data,
+                   float threshold,
+                   int64_t num_boxes) {
+  auto num_masks = CeilDivide(num_boxes, 64);
+  std::vector<uint64_t> masks(num_masks, 0);
+
+  for (int64_t i = 0; i < num_boxes; ++i) {
+    if (masks[i / 64] & 1ULL << (i % 64)) continue;
+    std::array<T, 4> box_1;
+    for (int k = 0; k < 4; ++k) {
+      box_1[k] = boxes_data[i * 4 + k];
+    }
+    for (int64_t j = i + 1; j < num_boxes; ++j) {
+      if (masks[j / 64] & 1ULL << (j % 64)) continue;
+      std::array<T, 4> box_2;
+      for (int k = 0; k < 4; ++k) {
+        box_2[k] = boxes_data[j * 4 + k];
+      }
+      bool is_overlap = CalculateIoU<T>(box_1.data(), box_2.data(), threshold);
+      if (is_overlap) {
+        masks[j / 64] |= 1ULL << (j % 64);
+      }
+    }
+  }
+
+  int64_t output_data_idx = 0;
+  for (int64_t i = 0; i < num_boxes; ++i) {
+    if (masks[i / 64] & 1ULL << (i % 64)) continue;
+    output_data[output_data_idx++] = i;
+  }
+
+  int64_t num_keep_boxes = output_data_idx;
+
+  for (; output_data_idx < num_boxes; ++output_data_idx) {
+    output_data[output_data_idx] = 0;
+  }
+
+  return num_keep_boxes;
+}
+
+template <typename T, typename Context>
+void NMSKernel(const Context& dev_ctx,
+               const DenseTensor& boxes,
+               float threshold,
+               DenseTensor* output) {
+  PADDLE_ENFORCE_EQ(
+      boxes.dims().size(),
+      2,
+      common::errors::InvalidArgument("The shape [%s] of boxes must be (N, 4).",
+                                      boxes.dims()));
+
+  PADDLE_ENFORCE_EQ(
+      boxes.dims()[1],
+      4,
+      common::errors::InvalidArgument("The shape [%s] of boxes must be (N, 4).",
+                                      boxes.dims()));
+
+  int64_t num_boxes = boxes.dims()[0];
+  DenseTensor output_tmp;
+  output_tmp.Resize({num_boxes});
+  auto output_tmp_data = dev_ctx.template Alloc<int64_t>(&output_tmp);
+
+  int64_t num_keep_boxes =
+      NMS<T>(boxes.data<T>(), output_tmp_data, threshold, num_boxes);
+  auto slice_out = output_tmp.Slice(0, num_keep_boxes);
+  Copy(dev_ctx, slice_out, dev_ctx.GetPlace(), false, output);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(nms, CPU, ALL_LAYOUT, phi::NMSKernel, float, double) {
+  kernel->OutputAt(0).SetDataType(phi::DataType::INT64);
+}