chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/yolo_box_kernel.cc

@@ -0,0 +1,145 @@
+// 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/yolo_box_kernel.h"
+#include <array>
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/full_kernel.h"
+#include "paddle/phi/kernels/funcs/yolo_box_util.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void YoloBoxKernel(const Context& dev_ctx,
+                   const DenseTensor& x,
+                   const DenseTensor& img_size,
+                   const std::vector<int>& anchors,
+                   int class_num,
+                   float conf_thresh,
+                   int downsample_ratio,
+                   bool clip_bbox,
+                   float scale_x_y,
+                   bool iou_aware,
+                   float iou_aware_factor,
+                   DenseTensor* boxes,
+                   DenseTensor* scores) {
+  if (x.numel() == 0 || img_size.numel() == 0) {
+    Full<T, Context>(dev_ctx, boxes->dims(), 0, boxes);
+    Full<T, Context>(dev_ctx, scores->dims(), 0, scores);
+    return;
+  }
+
+  auto* input = &x;
+  auto* imgsize = &img_size;
+  float scale = scale_x_y;
+  float bias = -0.5f * (scale - 1.f);
+
+  const int n = static_cast<int>(input->dims()[0]);
+  const int h = static_cast<int>(input->dims()[2]);
+  const int w = static_cast<int>(input->dims()[3]);
+  const int box_num = static_cast<int>(boxes->dims()[1]);
+  const int an_num = static_cast<int>(anchors.size() / 2);
+  int input_size_h = downsample_ratio * h;
+  int input_size_w = downsample_ratio * w;
+
+  const int64_t stride = static_cast<int64_t>(h) * w;
+  const int64_t an_stride = static_cast<int64_t>(class_num + 5) * stride;
+
+  DenseTensor anchors_;
+  anchors_.Resize({an_num * 2});
+  auto anchors_data = dev_ctx.template Alloc<int>(&anchors_);
+  std::copy(anchors.begin(), anchors.end(), anchors_data);
+
+  const T* input_data = input->data<T>();
+  const int* imgsize_data = imgsize->data<int>();
+  boxes->Resize({n, box_num, 4});
+  T* boxes_data = dev_ctx.template Alloc<T>(boxes);
+  memset(boxes_data, 0, boxes->numel() * sizeof(T));
+
+  scores->Resize({n, box_num, class_num});
+  T* scores_data = dev_ctx.template Alloc<T>(scores);
+
+  memset(scores_data, 0, scores->numel() * sizeof(T));
+
+  std::array<T, 4> box = {};
+  for (int i = 0; i < n; i++) {
+    int img_height = imgsize_data[2 * i];
+    int img_width = imgsize_data[2 * i + 1];
+
+    for (int j = 0; j < an_num; j++) {
+      for (int k = 0; k < h; k++) {
+        for (int l = 0; l < w; l++) {
+          int64_t obj_idx = funcs::GetEntryIndex(
+              i, j, k * w + l, an_num, an_stride, stride, 4, iou_aware);
+          T conf = funcs::sigmoid<T>(input_data[obj_idx]);
+          if (iou_aware) {
+            int64_t iou_idx =
+                funcs::GetIoUIndex(i, j, k * w + l, an_num, an_stride, stride);
+            T iou = funcs::sigmoid<T>(input_data[iou_idx]);
+            conf = pow(conf, static_cast<T>(1. - iou_aware_factor)) *
+                   pow(iou, static_cast<T>(iou_aware_factor));
+          }
+          if (conf < conf_thresh) {
+            continue;
+          }
+
+          int64_t box_idx = funcs::GetEntryIndex(
+              i, j, k * w + l, an_num, an_stride, stride, 0, iou_aware);
+          funcs::GetYoloBox<T>(box.data(),
+                               input_data,
+                               anchors_data,
+                               l,
+                               k,
+                               j,
+                               h,
+                               w,
+                               input_size_h,
+                               input_size_w,
+                               box_idx,
+                               stride,
+                               img_height,
+                               img_width,
+                               scale,
+                               bias);
+          box_idx = (i * box_num + j * stride + k * w + l) * 4;
+          funcs::CalcDetectionBox<T>(boxes_data,
+                                     box.data(),
+                                     box_idx,
+                                     img_height,
+                                     img_width,
+                                     clip_bbox);
+
+          int64_t label_idx = funcs::GetEntryIndex(
+              i, j, k * w + l, an_num, an_stride, stride, 5, iou_aware);
+          int64_t score_idx =
+              (i * box_num + j * stride + k * w + l) * class_num;
+          funcs::CalcLabelScore<T>(scores_data,
+                                   input_data,
+                                   label_idx,
+                                   score_idx,
+                                   class_num,
+                                   conf,
+                                   stride);
+        }
+      }
+    }
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(
+    yolo_box, CPU, ALL_LAYOUT, phi::YoloBoxKernel, float, double) {}