[phi] move yolov3_loss to phi (#40944)

* mv yolov3_loss op to phi

* fix as review

* update operator.h

paddle/fluid/framework/operator.h

@@ -571,7 +571,11 @@ class OperatorWithKernel : public OperatorBase {
     if (has_phi_kernel) {
       return true;
     } else {
-      auto& op_kernels = OperatorWithKernel::AllOpKernels().at(type_);
+      auto kernel_iter = OperatorWithKernel::AllOpKernels().find(type_);
+      if (kernel_iter == OperatorWithKernel::AllOpKernels().end()) {
+        return false;
+      } else {
+        auto& op_kernels = kernel_iter->second;
         return std::any_of(
             op_kernels.begin(), op_kernels.end(),
             [](OpKernelMap::const_reference kern_pair) {
@@ -579,6 +583,7 @@ class OperatorWithKernel : public OperatorBase {
             });
       }
     }
+  }
 
   bool SupportNPU() const override {
     // TODO(zhiqiu): support phi if needed?

paddle/fluid/operators/detection/yolov3_loss_op.cc

@@ -9,10 +9,12 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#include "paddle/fluid/operators/detection/yolov3_loss_op.h"
 #include <memory>
+#include "paddle/fluid/framework/infershape_utils.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/imperative/type_defs.h"
+#include "paddle/phi/core/infermeta_utils.h"
+#include "paddle/phi/infermeta/multiary.h"
 
 namespace paddle {
 namespace operators {
@@ -22,127 +24,6 @@ using framework::Tensor;
 class Yolov3LossOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "Yolov3LossOp");
-    OP_INOUT_CHECK(ctx->HasInput("GTBox"), "Input", "GTBox", "Yolov3LossOp");
-    OP_INOUT_CHECK(ctx->HasInput("GTLabel"), "Input", "GTLabel",
-                   "Yolov3LossOp");
-    OP_INOUT_CHECK(ctx->HasOutput("Loss"), "Output", "Loss", "Yolov3LossOp");
-    OP_INOUT_CHECK(ctx->HasOutput("ObjectnessMask"), "Output", "ObjectnessMask",
-                   "Yolov3LossOp");
-    OP_INOUT_CHECK(ctx->HasOutput("GTMatchMask"), "Output", "GTMatchMask",
-                   "Yolov3LossOp");
-
-    auto dim_x = ctx->GetInputDim("X");
-    auto dim_gtbox = ctx->GetInputDim("GTBox");
-    auto dim_gtlabel = ctx->GetInputDim("GTLabel");
-    auto anchors = ctx->Attrs().Get<std::vector<int>>("anchors");
-    int anchor_num = anchors.size() / 2;
-    auto anchor_mask = ctx->Attrs().Get<std::vector<int>>("anchor_mask");
-    int mask_num = anchor_mask.size();
-    auto class_num = ctx->Attrs().Get<int>("class_num");
-
-    PADDLE_ENFORCE_EQ(dim_x.size(), 4,
-                      platform::errors::InvalidArgument(
-                          "Input(X) should be a 4-D tensor. But received "
-                          "X dimension size(%s)",
-                          dim_x.size()));
-    PADDLE_ENFORCE_EQ(dim_x[2], dim_x[3],
-                      platform::errors::InvalidArgument(
-                          "Input(X) dim[3] and dim[4] should be euqal."
-                          "But received dim[3](%s) != dim[4](%s)",
-                          dim_x[2], dim_x[3]));
-    PADDLE_ENFORCE_EQ(
-        dim_x[1], mask_num * (5 + class_num),
-        platform::errors::InvalidArgument(
-            "Input(X) dim[1] should be equal to (anchor_mask_number * (5 "
-            "+ class_num))."
-            "But received dim[1](%s) != (anchor_mask_number * "
-            "(5+class_num)(%s).",
-            dim_x[1], mask_num * (5 + class_num)));
-    PADDLE_ENFORCE_EQ(dim_gtbox.size(), 3,
-                      platform::errors::InvalidArgument(
-                          "Input(GTBox) should be a 3-D tensor, but "
-                          "received gtbox dimension size(%s)",
-                          dim_gtbox.size()));
-    PADDLE_ENFORCE_EQ(dim_gtbox[2], 4,
-                      platform::errors::InvalidArgument(
-                          "Input(GTBox) dim[2] should be 4",
-                          "But receive dim[2](%s) != 5. ", dim_gtbox[2]));
-    PADDLE_ENFORCE_EQ(
-        dim_gtlabel.size(), 2,
-        platform::errors::InvalidArgument(
-            "Input(GTLabel) should be a 2-D tensor,"
-            "But received Input(GTLabel) dimension size(%s) != 2.",
-            dim_gtlabel.size()));
-    PADDLE_ENFORCE_EQ(
-        dim_gtlabel[0], dim_gtbox[0],
-        platform::errors::InvalidArgument(
-            "Input(GTBox) dim[0] and Input(GTLabel) dim[0] should be same,"
-            "But received Input(GTLabel) dim[0](%s) != "
-            "Input(GTBox) dim[0](%s)",
-            dim_gtlabel[0], dim_gtbox[0]));
-    PADDLE_ENFORCE_EQ(
-        dim_gtlabel[1], dim_gtbox[1],
-        platform::errors::InvalidArgument(
-            "Input(GTBox) and Input(GTLabel) dim[1] should be same,"
-            "But received Input(GTBox) dim[1](%s) != Input(GTLabel) "
-            "dim[1](%s)",
-            dim_gtbox[1], dim_gtlabel[1]));
-    PADDLE_ENFORCE_GT(anchors.size(), 0,
-                      platform::errors::InvalidArgument(
-                          "Attr(anchors) length should be greater then 0."
-                          "But received anchors length(%s)",
-                          anchors.size()));
-    PADDLE_ENFORCE_EQ(anchors.size() % 2, 0,
-                      platform::errors::InvalidArgument(
-                          "Attr(anchors) length should be even integer."
-                          "But received anchors length(%s)",
-                          anchors.size()));
-    for (size_t i = 0; i < anchor_mask.size(); i++) {
-      PADDLE_ENFORCE_LT(
-          anchor_mask[i], anchor_num,
-          platform::errors::InvalidArgument(
-              "Attr(anchor_mask) should not crossover Attr(anchors)."
-              "But received anchor_mask[i](%s) > anchor_num(%s)",
-              anchor_mask[i], anchor_num));
-    }
-    PADDLE_ENFORCE_GT(class_num, 0,
-                      platform::errors::InvalidArgument(
-                          "Attr(class_num) should be an integer greater then 0."
-                          "But received class_num(%s) < 0",
-                          class_num));
-
-    if (ctx->HasInput("GTScore")) {
-      auto dim_gtscore = ctx->GetInputDim("GTScore");
-      PADDLE_ENFORCE_EQ(dim_gtscore.size(), 2,
-                        platform::errors::InvalidArgument(
-                            "Input(GTScore) should be a 2-D tensor"
-                            "But received GTScore dimension(%s)",
-                            dim_gtbox.size()));
-      PADDLE_ENFORCE_EQ(
-          dim_gtscore[0], dim_gtbox[0],
-          platform::errors::InvalidArgument(
-              "Input(GTBox) and Input(GTScore) dim[0] should be same"
-              "But received GTBox dim[0](%s) != GTScore dim[0](%s)",
-              dim_gtbox[0], dim_gtscore[0]));
-      PADDLE_ENFORCE_EQ(
-          dim_gtscore[1], dim_gtbox[1],
-          platform::errors::InvalidArgument(
-              "Input(GTBox) and Input(GTScore) dim[1] should be same"
-              "But received GTBox dim[1](%s) != GTScore dim[1](%s)",
-              dim_gtscore[1], dim_gtbox[1]));
-    }
-
-    std::vector<int64_t> dim_out({dim_x[0]});
-    ctx->SetOutputDim("Loss", phi::make_ddim(dim_out));
-
-    std::vector<int64_t> dim_obj_mask({dim_x[0], mask_num, dim_x[2], dim_x[3]});
-    ctx->SetOutputDim("ObjectnessMask", phi::make_ddim(dim_obj_mask));
-
-    std::vector<int64_t> dim_gt_match_mask({dim_gtbox[0], dim_gtbox[1]});
-    ctx->SetOutputDim("GTMatchMask", phi::make_ddim(dim_gt_match_mask));
-  }
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
@@ -347,11 +228,10 @@ class Yolov3LossGradMaker : public framework::SingleGradOpMaker<T> {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
+DECLARE_INFER_SHAPE_FUNCTOR(yolov3_loss, Yolov3LossInferShapeFunctor,
+                            PD_INFER_META(phi::Yolov3LossInferMeta));
 REGISTER_OPERATOR(yolov3_loss, ops::Yolov3LossOp, ops::Yolov3LossOpMaker,
                   ops::Yolov3LossGradMaker<paddle::framework::OpDesc>,
-                  ops::Yolov3LossGradMaker<paddle::imperative::OpBase>);
+                  ops::Yolov3LossGradMaker<paddle::imperative::OpBase>,
+                  Yolov3LossInferShapeFunctor);
 REGISTER_OPERATOR(yolov3_loss_grad, ops::Yolov3LossOpGrad);
-REGISTER_OP_CPU_KERNEL(yolov3_loss, ops::Yolov3LossKernel<float>,
-                       ops::Yolov3LossKernel<double>);
-REGISTER_OP_CPU_KERNEL(yolov3_loss_grad, ops::Yolov3LossGradKernel<float>,
-                       ops::Yolov3LossGradKernel<double>);

paddle/fluid/operators/detection/yolov3_loss_op.h

-/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-   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. */
-
-#pragma once
-#include <algorithm>
-#include <vector>
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/phi/kernels/funcs/math_function.h"
-
-namespace paddle {
-namespace operators {
-
-using Tensor = framework::Tensor;
-template <typename T, size_t D, int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
-template <typename T, int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
-
-template <typename T>
-static inline bool LessEqualZero(T x) {
-  return x < 1e-6;
-}
-
-template <typename T>
-static T SigmoidCrossEntropy(T x, T label) {
-  return (x > 0 ? x : 0.0) - x * label + std::log(1.0 + std::exp(-std::abs(x)));
-}
-
-template <typename T>
-static T L1Loss(T x, T y) {
-  return std::abs(y - x);
-}
-
-template <typename T>
-static T SigmoidCrossEntropyGrad(T x, T label) {
-  return 1.0 / (1.0 + std::exp(-x)) - label;
-}
-
-template <typename T>
-static T L1LossGrad(T x, T y) {
-  return x > y ? 1.0 : -1.0;
-}
-
-static int GetMaskIndex(std::vector<int> mask, int val) {
-  for (size_t i = 0; i < mask.size(); i++) {
-    if (mask[i] == val) {
-      return i;
-    }
-  }
-  return -1;
-}
-
-template <typename T>
-struct Box {
-  T x, y, w, h;
-};
-
-template <typename T>
-static inline T sigmoid(T x) {
-  return 1.0 / (1.0 + std::exp(-x));
-}
-
-template <typename T>
-static inline Box<T> GetYoloBox(const T* x, std::vector<int> anchors, int i,
-                                int j, int an_idx, int grid_size,
-                                int input_size, int index, int stride,
-                                float scale, float bias) {
-  Box<T> b;
-  b.x = (i + sigmoid<T>(x[index]) * scale + bias) / grid_size;
-  b.y = (j + sigmoid<T>(x[index + stride]) * scale + bias) / grid_size;
-  b.w = std::exp(x[index + 2 * stride]) * anchors[2 * an_idx] / input_size;
-  b.h = std::exp(x[index + 3 * stride]) * anchors[2 * an_idx + 1] / input_size;
-  return b;
-}
-
-template <typename T>
-static inline Box<T> GetGtBox(const T* gt, int batch, int max_boxes, int idx) {
-  Box<T> b;
-  b.x = gt[(batch * max_boxes + idx) * 4];
-  b.y = gt[(batch * max_boxes + idx) * 4 + 1];
-  b.w = gt[(batch * max_boxes + idx) * 4 + 2];
-  b.h = gt[(batch * max_boxes + idx) * 4 + 3];
-  return b;
-}
-
-template <typename T>
-static inline T BoxOverlap(T c1, T w1, T c2, T w2) {
-  T l1 = c1 - w1 / 2.0;
-  T l2 = c2 - w2 / 2.0;
-  T left = l1 > l2 ? l1 : l2;
-  T r1 = c1 + w1 / 2.0;
-  T r2 = c2 + w2 / 2.0;
-  T right = r1 < r2 ? r1 : r2;
-  return right - left;
-}
-
-template <typename T>
-static inline T CalcBoxIoU(Box<T> b1, Box<T> b2) {
-  T w = BoxOverlap(b1.x, b1.w, b2.x, b2.w);
-  T h = BoxOverlap(b1.y, b1.h, b2.y, b2.h);
-  T inter_area = (w < 0 || h < 0) ? 0.0 : w * h;
-  T union_area = b1.w * b1.h + b2.w * b2.h - inter_area;
-  return inter_area / union_area;
-}
-
-static inline int GetEntryIndex(int batch, int an_idx, int hw_idx, int an_num,
-                                int an_stride, int stride, int entry) {
-  return (batch * an_num + an_idx) * an_stride + entry * stride + hw_idx;
-}
-
-template <typename T>
-static void CalcBoxLocationLoss(T* loss, const T* input, Box<T> gt,
-                                std::vector<int> anchors, int an_idx,
-                                int box_idx, int gi, int gj, int grid_size,
-                                int input_size, int stride, T score) {
-  T tx = gt.x * grid_size - gi;
-  T ty = gt.y * grid_size - gj;
-  T tw = std::log(gt.w * input_size / anchors[2 * an_idx]);
-  T th = std::log(gt.h * input_size / anchors[2 * an_idx + 1]);
-
-  T scale = (2.0 - gt.w * gt.h) * score;
-  loss[0] += SigmoidCrossEntropy<T>(input[box_idx], tx) * scale;
-  loss[0] += SigmoidCrossEntropy<T>(input[box_idx + stride], ty) * scale;
-  loss[0] += L1Loss<T>(input[box_idx + 2 * stride], tw) * scale;
-  loss[0] += L1Loss<T>(input[box_idx + 3 * stride], th) * scale;
-}
-
-template <typename T>
-static void CalcBoxLocationLossGrad(T* input_grad, const T loss, const T* input,
-                                    Box<T> gt, std::vector<int> anchors,
-                                    int an_idx, int box_idx, int gi, int gj,
-                                    int grid_size, int input_size, int stride,
-                                    T score) {
-  T tx = gt.x * grid_size - gi;
-  T ty = gt.y * grid_size - gj;
-  T tw = std::log(gt.w * input_size / anchors[2 * an_idx]);
-  T th = std::log(gt.h * input_size / anchors[2 * an_idx + 1]);
-
-  T scale = (2.0 - gt.w * gt.h) * score;
-  input_grad[box_idx] =
-      SigmoidCrossEntropyGrad<T>(input[box_idx], tx) * scale * loss;
-  input_grad[box_idx + stride] =
-      SigmoidCrossEntropyGrad<T>(input[box_idx + stride], ty) * scale * loss;
-  input_grad[box_idx + 2 * stride] =
-      L1LossGrad<T>(input[box_idx + 2 * stride], tw) * scale * loss;
-  input_grad[box_idx + 3 * stride] =
-      L1LossGrad<T>(input[box_idx + 3 * stride], th) * scale * loss;
-}
-
-template <typename T>
-static inline void CalcLabelLoss(T* loss, const T* input, const int index,
-                                 const int label, const int class_num,
-                                 const int stride, const T pos, const T neg,
-                                 T score) {
-  for (int i = 0; i < class_num; i++) {
-    T pred = input[index + i * stride];
-    loss[0] += SigmoidCrossEntropy<T>(pred, (i == label) ? pos : neg) * score;
-  }
-}
-
-template <typename T>
-static inline void CalcLabelLossGrad(T* input_grad, const T loss,
-                                     const T* input, const int index,
-                                     const int label, const int class_num,
-                                     const int stride, const T pos, const T neg,
-                                     T score) {
-  for (int i = 0; i < class_num; i++) {
-    T pred = input[index + i * stride];
-    input_grad[index + i * stride] =
-        SigmoidCrossEntropyGrad<T>(pred, (i == label) ? pos : neg) * score *
-        loss;
-  }
-}
-
-template <typename T>
-static inline void CalcObjnessLoss(T* loss, const T* input, const T* objness,
-                                   const int n, const int an_num, const int h,
-                                   const int w, const int stride,
-                                   const int an_stride) {
-  for (int i = 0; i < n; i++) {
-    for (int j = 0; j < an_num; j++) {
-      for (int k = 0; k < h; k++) {
-        for (int l = 0; l < w; l++) {
-          T obj = objness[k * w + l];
-          if (obj > 1e-5) {
-            // positive sample: obj = mixup score
-            loss[i] += SigmoidCrossEntropy<T>(input[k * w + l], 1.0) * obj;
-          } else if (obj > -0.5) {
-            // negetive sample: obj = 0
-            loss[i] += SigmoidCrossEntropy<T>(input[k * w + l], 0.0);
-          }
-        }
-      }
-      objness += stride;
-      input += an_stride;
-    }
-  }
-}
-
-template <typename T>
-static inline void CalcObjnessLossGrad(T* input_grad, const T* loss,
-                                       const T* input, const T* objness,
-                                       const int n, const int an_num,
-                                       const int h, const int w,
-                                       const int stride, const int an_stride) {
-  for (int i = 0; i < n; i++) {
-    for (int j = 0; j < an_num; j++) {
-      for (int k = 0; k < h; k++) {
-        for (int l = 0; l < w; l++) {
-          T obj = objness[k * w + l];
-          if (obj > 1e-5) {
-            input_grad[k * w + l] =
-                SigmoidCrossEntropyGrad<T>(input[k * w + l], 1.0) * obj *
-                loss[i];
-          } else if (obj > -0.5) {
-            input_grad[k * w + l] =
-                SigmoidCrossEntropyGrad<T>(input[k * w + l], 0.0) * loss[i];
-          }
-        }
-      }
-      objness += stride;
-      input += an_stride;
-      input_grad += an_stride;
-    }
-  }
-}
-
-template <typename T>
-static void inline GtValid(bool* valid, const T* gtbox, const int n,
-                           const int b) {
-  for (int i = 0; i < n; i++) {
-    for (int j = 0; j < b; j++) {
-      if (LessEqualZero(gtbox[j * 4 + 2]) || LessEqualZero(gtbox[j * 4 + 3])) {
-        valid[j] = false;
-      } else {
-        valid[j] = true;
-      }
-    }
-    valid += b;
-    gtbox += b * 4;
-  }
-}
-
-template <typename T>
-class Yolov3LossKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* input = ctx.Input<Tensor>("X");
-    auto* gt_box = ctx.Input<Tensor>("GTBox");
-    auto* gt_label = ctx.Input<Tensor>("GTLabel");
-    auto* gt_score = ctx.Input<Tensor>("GTScore");
-    auto* loss = ctx.Output<Tensor>("Loss");
-    auto* objness_mask = ctx.Output<Tensor>("ObjectnessMask");
-    auto* gt_match_mask = ctx.Output<Tensor>("GTMatchMask");
-    auto anchors = ctx.Attr<std::vector<int>>("anchors");
-    auto anchor_mask = ctx.Attr<std::vector<int>>("anchor_mask");
-    int class_num = ctx.Attr<int>("class_num");
-    float ignore_thresh = ctx.Attr<float>("ignore_thresh");
-    int downsample_ratio = ctx.Attr<int>("downsample_ratio");
-    bool use_label_smooth = ctx.Attr<bool>("use_label_smooth");
-    float scale = ctx.Attr<float>("scale_x_y");
-    float bias = -0.5 * (scale - 1.);
-
-    const int n = input->dims()[0];
-    const int h = input->dims()[2];
-    const int w = input->dims()[3];
-    const int an_num = anchors.size() / 2;
-    const int mask_num = anchor_mask.size();
-    const int b = gt_box->dims()[1];
-    int input_size = downsample_ratio * h;
-
-    const int stride = h * w;
-    const int an_stride = (class_num + 5) * stride;
-
-    T label_pos = 1.0;
-    T label_neg = 0.0;
-    if (use_label_smooth) {
-      T smooth_weight = std::min(1.0 / static_cast<T>(class_num), 1.0 / 40);
-      label_pos = 1.0 - smooth_weight;
-      label_neg = smooth_weight;
-    }
-
-    const T* input_data = input->data<T>();
-    const T* gt_box_data = gt_box->data<T>();
-    const int* gt_label_data = gt_label->data<int>();
-    T* loss_data = loss->mutable_data<T>({n}, ctx.GetPlace());
-    memset(loss_data, 0, loss->numel() * sizeof(T));
-    T* obj_mask_data =
-        objness_mask->mutable_data<T>({n, mask_num, h, w}, ctx.GetPlace());
-    memset(obj_mask_data, 0, objness_mask->numel() * sizeof(T));
-    int* gt_match_mask_data =
-        gt_match_mask->mutable_data<int>({n, b}, ctx.GetPlace());
-
-    const T* gt_score_data;
-    Tensor gtscore;
-    if (!gt_score) {
-      gtscore.mutable_data<T>({n, b}, ctx.GetPlace());
-      phi::funcs::SetConstant<platform::CPUDeviceContext, T>()(
-          ctx.template device_context<platform::CPUDeviceContext>(), &gtscore,
-          static_cast<T>(1.0));
-      gt_score = &gtscore;
-      gt_score_data = gtscore.data<T>();
-    } else {
-      gt_score_data = gt_score->data<T>();
-    }
-
-    // calc valid gt box mask, avoid calc duplicately in following code
-    Tensor gt_valid_mask;
-    bool* gt_valid_mask_data =
-        gt_valid_mask.mutable_data<bool>({n, b}, ctx.GetPlace());
-    GtValid<T>(gt_valid_mask_data, gt_box_data, n, b);
-
-    for (int i = 0; i < n; i++) {
-      for (int j = 0; j < mask_num; j++) {
-        for (int k = 0; k < h; k++) {
-          for (int l = 0; l < w; l++) {
-            // each predict box find a best match gt box, if overlap is bigger
-            // then ignore_thresh, ignore the objectness loss.
-            int box_idx =
-                GetEntryIndex(i, j, k * w + l, mask_num, an_stride, stride, 0);
-            Box<T> pred =
-                GetYoloBox(input_data, anchors, l, k, anchor_mask[j], h,
-                           input_size, box_idx, stride, scale, bias);
-            T best_iou = 0;
-            for (int t = 0; t < b; t++) {
-              if (!gt_valid_mask_data[i * b + t]) {
-                continue;
-              }
-              Box<T> gt = GetGtBox(gt_box_data, i, b, t);
-              T iou = CalcBoxIoU(pred, gt);
-              if (iou > best_iou) {
-                best_iou = iou;
-              }
-            }
-
-            // If best IoU is bigger then ignore_thresh,
-            // ignore the objectness loss.
-            if (best_iou > ignore_thresh) {
-              int obj_idx = (i * mask_num + j) * stride + k * w + l;
-              obj_mask_data[obj_idx] = static_cast<T>(-1);
-            }
-            // all losses should be calculated if best IoU
-            // is bigger then truth thresh, but currently,
-            // truth thresh is an unreachable value as 1.0.
-          }
-        }
-      }
-      for (int t = 0; t < b; t++) {
-        if (!gt_valid_mask_data[i * b + t]) {
-          gt_match_mask_data[i * b + t] = -1;
-          continue;
-        }
-        Box<T> gt = GetGtBox(gt_box_data, i, b, t);
-        int gi = static_cast<int>(gt.x * w);
-        int gj = static_cast<int>(gt.y * h);
-        Box<T> gt_shift = gt;
-        gt_shift.x = 0.0;
-        gt_shift.y = 0.0;
-        T best_iou = 0.0;
-        int best_n = 0;
-        // each gt box find a best match anchor box as positive sample,
-        // for positive sample, all losses should be calculated, and for
-        // other samples, only objectness loss is required.
-        for (int an_idx = 0; an_idx < an_num; an_idx++) {
-          Box<T> an_box;
-          an_box.x = 0.0;
-          an_box.y = 0.0;
-          an_box.w = anchors[2 * an_idx] / static_cast<T>(input_size);
-          an_box.h = anchors[2 * an_idx + 1] / static_cast<T>(input_size);
-          float iou = CalcBoxIoU<T>(an_box, gt_shift);
-          if (iou > best_iou) {
-            best_iou = iou;
-            best_n = an_idx;
-          }
-        }
-
-        int mask_idx = GetMaskIndex(anchor_mask, best_n);
-        gt_match_mask_data[i * b + t] = mask_idx;
-        if (mask_idx >= 0) {
-          T score = gt_score_data[i * b + t];
-          int box_idx = GetEntryIndex(i, mask_idx, gj * w + gi, mask_num,
-                                      an_stride, stride, 0);
-          CalcBoxLocationLoss<T>(loss_data + i, input_data, gt, anchors, best_n,
-                                 box_idx, gi, gj, h, input_size, stride, score);
-
-          int obj_idx = (i * mask_num + mask_idx) * stride + gj * w + gi;
-          obj_mask_data[obj_idx] = score;
-
-          int label = gt_label_data[i * b + t];
-          int label_idx = GetEntryIndex(i, mask_idx, gj * w + gi, mask_num,
-                                        an_stride, stride, 5);
-          CalcLabelLoss<T>(loss_data + i, input_data, label_idx, label,
-                           class_num, stride, label_pos, label_neg, score);
-        }
-      }
-    }
-
-    CalcObjnessLoss<T>(loss_data, input_data + 4 * stride, obj_mask_data, n,
-                       mask_num, h, w, stride, an_stride);
-  }
-};
-
-template <typename T>
-class Yolov3LossGradKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* input = ctx.Input<Tensor>("X");
-    auto* gt_box = ctx.Input<Tensor>("GTBox");
-    auto* gt_label = ctx.Input<Tensor>("GTLabel");
-    auto* gt_score = ctx.Input<Tensor>("GTScore");
-    auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
-    auto* loss_grad = ctx.Input<Tensor>(framework::GradVarName("Loss"));
-    auto* objness_mask = ctx.Input<Tensor>("ObjectnessMask");
-    auto* gt_match_mask = ctx.Input<Tensor>("GTMatchMask");
-    auto anchors = ctx.Attr<std::vector<int>>("anchors");
-    auto anchor_mask = ctx.Attr<std::vector<int>>("anchor_mask");
-    int class_num = ctx.Attr<int>("class_num");
-    int downsample_ratio = ctx.Attr<int>("downsample_ratio");
-    bool use_label_smooth = ctx.Attr<bool>("use_label_smooth");
-
-    const int n = input_grad->dims()[0];
-    const int c = input_grad->dims()[1];
-    const int h = input_grad->dims()[2];
-    const int w = input_grad->dims()[3];
-    const int mask_num = anchor_mask.size();
-    const int b = gt_match_mask->dims()[1];
-    int input_size = downsample_ratio * h;
-
-    const int stride = h * w;
-    const int an_stride = (class_num + 5) * stride;
-
-    T label_pos = 1.0;
-    T label_neg = 0.0;
-    if (use_label_smooth) {
-      T smooth_weight = std::min(1.0 / static_cast<T>(class_num), 1.0 / 40);
-      label_pos = 1.0 - smooth_weight;
-      label_neg = smooth_weight;
-    }
-
-    const T* input_data = input->data<T>();
-    const T* gt_box_data = gt_box->data<T>();
-    const int* gt_label_data = gt_label->data<int>();
-    const T* loss_grad_data = loss_grad->data<T>();
-    const T* obj_mask_data = objness_mask->data<T>();
-    const int* gt_match_mask_data = gt_match_mask->data<int>();
-    T* input_grad_data =
-        input_grad->mutable_data<T>({n, c, h, w}, ctx.GetPlace());
-    memset(input_grad_data, 0, input_grad->numel() * sizeof(T));
-
-    const T* gt_score_data;
-    Tensor gtscore;
-    if (!gt_score) {
-      gtscore.mutable_data<T>({n, b}, ctx.GetPlace());
-      phi::funcs::SetConstant<platform::CPUDeviceContext, T>()(
-          ctx.template device_context<platform::CPUDeviceContext>(), &gtscore,
-          static_cast<T>(1.0));
-      gt_score = &gtscore;
-      gt_score_data = gtscore.data<T>();
-    } else {
-      gt_score_data = gt_score->data<T>();
-    }
-
-    for (int i = 0; i < n; i++) {
-      for (int t = 0; t < b; t++) {
-        int mask_idx = gt_match_mask_data[i * b + t];
-        if (mask_idx >= 0) {
-          T score = gt_score_data[i * b + t];
-          Box<T> gt = GetGtBox(gt_box_data, i, b, t);
-          int gi = static_cast<int>(gt.x * w);
-          int gj = static_cast<int>(gt.y * h);
-
-          int box_idx = GetEntryIndex(i, mask_idx, gj * w + gi, mask_num,
-                                      an_stride, stride, 0);
-          CalcBoxLocationLossGrad<T>(input_grad_data, loss_grad_data[i],
-                                     input_data, gt, anchors,
-                                     anchor_mask[mask_idx], box_idx, gi, gj, h,
-                                     input_size, stride, score);
-
-          int label = gt_label_data[i * b + t];
-          int label_idx = GetEntryIndex(i, mask_idx, gj * w + gi, mask_num,
-                                        an_stride, stride, 5);
-          CalcLabelLossGrad<T>(input_grad_data, loss_grad_data[i], input_data,
-                               label_idx, label, class_num, stride, label_pos,
-                               label_neg, score);
-        }
-      }
-    }
-
-    CalcObjnessLossGrad<T>(input_grad_data + 4 * stride, loss_grad_data,
-                           input_data + 4 * stride, obj_mask_data, n, mask_num,
-                           h, w, stride, an_stride);
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle

paddle/phi/infermeta/multiary.cc

@@ -1229,6 +1229,153 @@ void WhereInferMeta(const MetaTensor& condition,
   out->share_meta(x);
 }
 
+void Yolov3LossInferMeta(const MetaTensor& x,
+                         const MetaTensor& gt_box,
+                         const MetaTensor& gt_label,
+                         const paddle::optional<const MetaTensor&> gt_score,
+                         const std::vector<int>& anchors,
+                         const std::vector<int>& anchor_mask,
+                         int class_num,
+                         float ignore_thresh,
+                         int downsample_ratio,
+                         bool use_label_smooth,
+                         float scale_x_y,
+                         MetaTensor* loss,
+                         MetaTensor* objectness_mask,
+                         MetaTensor* gt_match_mask) {
+  auto dim_x = x.dims();
+  auto dim_gtbox = gt_box.dims();
+  auto dim_gtlabel = gt_label.dims();
+  int anchor_num = anchors.size() / 2;
+  int mask_num = anchor_mask.size();
+
+  PADDLE_ENFORCE_EQ(dim_x.size(),
+                    4,
+                    phi::errors::InvalidArgument(
+                        "Input(X) should be a 4-D tensor. But received "
+                        "X dimension size(%s)",
+                        dim_x.size()));
+  PADDLE_ENFORCE_EQ(
+      dim_x[2],
+      dim_x[3],
+      phi::errors::InvalidArgument("Input(X) dim[3] and dim[4] should be euqal."
+                                   "But received dim[3](%s) != dim[4](%s)",
+                                   dim_x[2],
+                                   dim_x[3]));
+  PADDLE_ENFORCE_EQ(
+      dim_x[1],
+      mask_num * (5 + class_num),
+      phi::errors::InvalidArgument(
+          "Input(X) dim[1] should be equal to (anchor_mask_number * (5 "
+          "+ class_num))."
+          "But received dim[1](%s) != (anchor_mask_number * "
+          "(5+class_num)(%s).",
+          dim_x[1],
+          mask_num * (5 + class_num)));
+  PADDLE_ENFORCE_EQ(
+      dim_gtbox.size(),
+      3,
+      phi::errors::InvalidArgument("Input(GTBox) should be a 3-D tensor, but "
+                                   "received gtbox dimension size(%s)",
+                                   dim_gtbox.size()));
+  PADDLE_ENFORCE_EQ(
+      dim_gtbox[2],
+      4,
+      phi::errors::InvalidArgument("Input(GTBox) dim[2] should be 4",
+                                   "But receive dim[2](%s) != 5. ",
+                                   dim_gtbox[2]));
+  PADDLE_ENFORCE_EQ(dim_gtlabel.size(),
+                    2,
+                    phi::errors::InvalidArgument(
+                        "Input(GTLabel) should be a 2-D tensor,"
+                        "But received Input(GTLabel) dimension size(%s) != 2.",
+                        dim_gtlabel.size()));
+  PADDLE_ENFORCE_EQ(
+      dim_gtlabel[0],
+      dim_gtbox[0],
+      phi::errors::InvalidArgument(
+          "Input(GTBox) dim[0] and Input(GTLabel) dim[0] should be same,"
+          "But received Input(GTLabel) dim[0](%s) != "
+          "Input(GTBox) dim[0](%s)",
+          dim_gtlabel[0],
+          dim_gtbox[0]));
+  PADDLE_ENFORCE_EQ(
+      dim_gtlabel[1],
+      dim_gtbox[1],
+      phi::errors::InvalidArgument(
+          "Input(GTBox) and Input(GTLabel) dim[1] should be same,"
+          "But received Input(GTBox) dim[1](%s) != Input(GTLabel) "
+          "dim[1](%s)",
+          dim_gtbox[1],
+          dim_gtlabel[1]));
+  PADDLE_ENFORCE_GT(anchors.size(),
+                    0,
+                    phi::errors::InvalidArgument(
+                        "Attr(anchors) length should be greater then 0."
+                        "But received anchors length(%s)",
+                        anchors.size()));
+  PADDLE_ENFORCE_EQ(anchors.size() % 2,
+                    0,
+                    phi::errors::InvalidArgument(
+                        "Attr(anchors) length should be even integer."
+                        "But received anchors length(%s)",
+                        anchors.size()));
+  for (size_t i = 0; i < anchor_mask.size(); i++) {
+    PADDLE_ENFORCE_LT(
+        anchor_mask[i],
+        anchor_num,
+        phi::errors::InvalidArgument(
+            "Attr(anchor_mask) should not crossover Attr(anchors)."
+            "But received anchor_mask[i](%s) > anchor_num(%s)",
+            anchor_mask[i],
+            anchor_num));
+  }
+  PADDLE_ENFORCE_GT(class_num,
+                    0,
+                    phi::errors::InvalidArgument(
+                        "Attr(class_num) should be an integer greater then 0."
+                        "But received class_num(%s) < 0",
+                        class_num));
+
+  if (gt_score.get_ptr()) {
+    auto dim_gtscore = gt_score->dims();
+    PADDLE_ENFORCE_EQ(
+        dim_gtscore.size(),
+        2,
+        phi::errors::InvalidArgument("Input(GTScore) should be a 2-D tensor"
+                                     "But received GTScore dimension(%s)",
+                                     dim_gtbox.size()));
+    PADDLE_ENFORCE_EQ(
+        dim_gtscore[0],
+        dim_gtbox[0],
+        phi::errors::InvalidArgument(
+            "Input(GTBox) and Input(GTScore) dim[0] should be same"
+            "But received GTBox dim[0](%s) != GTScore dim[0](%s)",
+            dim_gtbox[0],
+            dim_gtscore[0]));
+    PADDLE_ENFORCE_EQ(
+        dim_gtscore[1],
+        dim_gtbox[1],
+        phi::errors::InvalidArgument(
+            "Input(GTBox) and Input(GTScore) dim[1] should be same"
+            "But received GTBox dim[1](%s) != GTScore dim[1](%s)",
+            dim_gtscore[1],
+            dim_gtbox[1]));
+  }
+
+  std::vector<int64_t> dim_out({dim_x[0]});
+  loss->set_dims(phi::make_ddim(dim_out));
+  loss->set_dtype(x.dtype());
+
+  std::vector<int64_t> dim_obj_mask({dim_x[0], mask_num, dim_x[2], dim_x[3]});
+  objectness_mask->set_dims(phi::make_ddim(dim_obj_mask));
+  objectness_mask->set_dtype(x.dtype());
+
+  std::vector<int64_t> dim_gt_match_mask({dim_gtbox[0], dim_gtbox[1]});
+  gt_match_mask->set_dims(phi::make_ddim(dim_gt_match_mask));
+  gt_match_mask->set_dtype(x.dtype());
+}
+
 }  // namespace phi
 
 PD_REGISTER_INFER_META_FN(batch_norm, phi::BatchNormInferMeta);

paddle/phi/infermeta/multiary.h

@@ -245,4 +245,19 @@ void WhereInferMeta(const MetaTensor& condition,
                     const MetaTensor& y,
                     MetaTensor* out);
 
+void Yolov3LossInferMeta(const MetaTensor& x,
+                         const MetaTensor& gt_box,
+                         const MetaTensor& gt_label,
+                         const paddle::optional<const MetaTensor&> gt_score,
+                         const std::vector<int>& anchors,
+                         const std::vector<int>& anchor_mask,
+                         int class_num,
+                         float ignore_thresh,
+                         int downsample_ratio,
+                         bool use_label_smooth,
+                         float scale_x_y,
+                         MetaTensor* loss,
+                         MetaTensor* objectness_mask,
+                         MetaTensor* gt_match_mask);
+
 }  // namespace phi

paddle/phi/kernels/cpu/yolov3_loss_functor.h

+// 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.
+
+#pragma once
+
+namespace phi {
+
+template <typename T>
+struct Box {
+  T x, y, w, h;
+};
+
+template <typename T>
+static inline T sigmoid(T x) {
+  return 1.0 / (1.0 + std::exp(-x));
+}
+
+template <typename T>
+static inline Box<T> GetGtBox(const T* gt, int batch, int max_boxes, int idx) {
+  Box<T> b;
+  b.x = gt[(batch * max_boxes + idx) * 4];
+  b.y = gt[(batch * max_boxes + idx) * 4 + 1];
+  b.w = gt[(batch * max_boxes + idx) * 4 + 2];
+  b.h = gt[(batch * max_boxes + idx) * 4 + 3];
+  return b;
+}
+
+static inline int GetEntryIndex(int batch,
+                                int an_idx,
+                                int hw_idx,
+                                int an_num,
+                                int an_stride,
+                                int stride,
+                                int entry) {
+  return (batch * an_num + an_idx) * an_stride + entry * stride + hw_idx;
+}
+
+}  // namespace phi

paddle/phi/kernels/cpu/yolov3_loss_grad_kernel.cc

+// 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 <algorithm>
+#include <vector>
+
+#include "paddle/phi/kernels/yolov3_loss_grad_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/cpu/yolov3_loss_functor.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+
+namespace phi {
+
+template <typename T>
+static T SigmoidCrossEntropyGrad(T x, T label) {
+  return 1.0 / (1.0 + std::exp(-x)) - label;
+}
+
+template <typename T>
+static T L1LossGrad(T x, T y) {
+  return x > y ? 1.0 : -1.0;
+}
+
+template <typename T>
+static void CalcBoxLocationLossGrad(T* input_grad,
+                                    const T loss,
+                                    const T* input,
+                                    Box<T> gt,
+                                    std::vector<int> anchors,
+                                    int an_idx,
+                                    int box_idx,
+                                    int gi,
+                                    int gj,
+                                    int grid_size,
+                                    int input_size,
+                                    int stride,
+                                    T score) {
+  T tx = gt.x * grid_size - gi;
+  T ty = gt.y * grid_size - gj;
+  T tw = std::log(gt.w * input_size / anchors[2 * an_idx]);
+  T th = std::log(gt.h * input_size / anchors[2 * an_idx + 1]);
+
+  T scale = (2.0 - gt.w * gt.h) * score;
+  input_grad[box_idx] =
+      SigmoidCrossEntropyGrad<T>(input[box_idx], tx) * scale * loss;
+  input_grad[box_idx + stride] =
+      SigmoidCrossEntropyGrad<T>(input[box_idx + stride], ty) * scale * loss;
+  input_grad[box_idx + 2 * stride] =
+      L1LossGrad<T>(input[box_idx + 2 * stride], tw) * scale * loss;
+  input_grad[box_idx + 3 * stride] =
+      L1LossGrad<T>(input[box_idx + 3 * stride], th) * scale * loss;
+}
+
+template <typename T>
+static inline void CalcLabelLossGrad(T* input_grad,
+                                     const T loss,
+                                     const T* input,
+                                     const int index,
+                                     const int label,
+                                     const int class_num,
+                                     const int stride,
+                                     const T pos,
+                                     const T neg,
+                                     T score) {
+  for (int i = 0; i < class_num; i++) {
+    T pred = input[index + i * stride];
+    input_grad[index + i * stride] =
+        SigmoidCrossEntropyGrad<T>(pred, (i == label) ? pos : neg) * score *
+        loss;
+  }
+}
+
+template <typename T>
+static inline void CalcObjnessLossGrad(T* input_grad,
+                                       const T* loss,
+                                       const T* input,
+                                       const T* objness,
+                                       const int n,
+                                       const int an_num,
+                                       const int h,
+                                       const int w,
+                                       const int stride,
+                                       const int an_stride) {
+  for (int i = 0; i < n; i++) {
+    for (int j = 0; j < an_num; j++) {
+      for (int k = 0; k < h; k++) {
+        for (int l = 0; l < w; l++) {
+          T obj = objness[k * w + l];
+          if (obj > 1e-5) {
+            input_grad[k * w + l] =
+                SigmoidCrossEntropyGrad<T>(input[k * w + l], 1.0) * obj *
+                loss[i];
+          } else if (obj > -0.5) {
+            input_grad[k * w + l] =
+                SigmoidCrossEntropyGrad<T>(input[k * w + l], 0.0) * loss[i];
+          }
+        }
+      }
+      objness += stride;
+      input += an_stride;
+      input_grad += an_stride;
+    }
+  }
+}
+
+template <typename T, typename Context>
+void Yolov3LossGradKernel(const Context& dev_ctx,
+                          const DenseTensor& x,
+                          const DenseTensor& gt_box,
+                          const DenseTensor& gt_label,
+                          paddle::optional<const DenseTensor&> gt_score,
+                          const DenseTensor& loss_grad,
+                          const DenseTensor& objectness_mask,
+                          const DenseTensor& gt_match_mask,
+                          const std::vector<int>& anchors,
+                          const std::vector<int>& anchor_mask,
+                          int class_num,
+                          float ignore_thresh,
+                          int downsample_ratio,
+                          bool use_label_smooth,
+                          float scale_x_y,
+                          DenseTensor* x_grad,
+                          DenseTensor* gt_box_grad,
+                          DenseTensor* gt_label_grad,
+                          DenseTensor* gt_score_grad) {
+  auto* input = &x;
+  auto input_grad = x_grad;
+  auto* objness_mask = &objectness_mask;
+
+  const int n = input_grad->dims()[0];
+  const int c = input_grad->dims()[1];
+  const int h = input_grad->dims()[2];
+  const int w = input_grad->dims()[3];
+  const int mask_num = anchor_mask.size();
+  const int b = gt_match_mask.dims()[1];
+  int input_size = downsample_ratio * h;
+
+  const int stride = h * w;
+  const int an_stride = (class_num + 5) * stride;
+
+  T label_pos = 1.0;
+  T label_neg = 0.0;
+  if (use_label_smooth) {
+    T smooth_weight = std::min(1.0 / static_cast<T>(class_num), 1.0 / 40);
+    label_pos = 1.0 - smooth_weight;
+    label_neg = smooth_weight;
+  }
+
+  const T* input_data = input->data<T>();
+  const T* gt_box_data = gt_box.data<T>();
+  const int* gt_label_data = gt_label.data<int>();
+  const T* loss_grad_data = loss_grad.data<T>();
+  const T* obj_mask_data = objness_mask->data<T>();
+  const int* gt_match_mask_data = gt_match_mask.data<int>();
+  input_grad->Resize({n, c, h, w});
+  T* input_grad_data = dev_ctx.template Alloc<T>(input_grad);
+  memset(input_grad_data, 0, input_grad->numel() * sizeof(T));
+
+  const T* gt_score_data;
+  DenseTensor gtscore;
+  if (!(gt_score.is_initialized())) {
+    gtscore.Resize({n, b});
+    dev_ctx.template Alloc<T>(&gtscore);
+    phi::funcs::SetConstant<Context, T>()(
+        dev_ctx, &gtscore, static_cast<T>(1.0));
+    gt_score_data = gtscore.data<T>();
+  } else {
+    gt_score_data = gt_score.get_ptr()->data<T>();
+  }
+
+  for (int i = 0; i < n; i++) {
+    for (int t = 0; t < b; t++) {
+      int mask_idx = gt_match_mask_data[i * b + t];
+      if (mask_idx >= 0) {
+        T score = gt_score_data[i * b + t];
+        Box<T> gt = GetGtBox(gt_box_data, i, b, t);
+        int gi = static_cast<int>(gt.x * w);
+        int gj = static_cast<int>(gt.y * h);
+
+        int box_idx = GetEntryIndex(
+            i, mask_idx, gj * w + gi, mask_num, an_stride, stride, 0);
+        CalcBoxLocationLossGrad<T>(input_grad_data,
+                                   loss_grad_data[i],
+                                   input_data,
+                                   gt,
+                                   anchors,
+                                   anchor_mask[mask_idx],
+                                   box_idx,
+                                   gi,
+                                   gj,
+                                   h,
+                                   input_size,
+                                   stride,
+                                   score);
+
+        int label = gt_label_data[i * b + t];
+        int label_idx = GetEntryIndex(
+            i, mask_idx, gj * w + gi, mask_num, an_stride, stride, 5);
+        CalcLabelLossGrad<T>(input_grad_data,
+                             loss_grad_data[i],
+                             input_data,
+                             label_idx,
+                             label,
+                             class_num,
+                             stride,
+                             label_pos,
+                             label_neg,
+                             score);
+      }
+    }
+  }
+
+  CalcObjnessLossGrad<T>(input_grad_data + 4 * stride,
+                         loss_grad_data,
+                         input_data + 4 * stride,
+                         obj_mask_data,
+                         n,
+                         mask_num,
+                         h,
+                         w,
+                         stride,
+                         an_stride);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(yolov3_loss_grad,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::Yolov3LossGradKernel,
+                   float,
+                   double) {}

paddle/phi/kernels/cpu/yolov3_loss_kernel.cc

+// 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 <algorithm>
+#include <vector>
+
+#include "paddle/phi/kernels/yolov3_loss_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/cpu/yolov3_loss_functor.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+
+namespace phi {
+
+template <typename T>
+static inline bool LessEqualZero(T x) {
+  return x < 1e-6;
+}
+
+template <typename T>
+static T SigmoidCrossEntropy(T x, T label) {
+  return (x > 0 ? x : 0.0) - x * label + std::log(1.0 + std::exp(-std::abs(x)));
+}
+
+template <typename T>
+static T L1Loss(T x, T y) {
+  return std::abs(y - x);
+}
+
+static int GetMaskIndex(std::vector<int> mask, int val) {
+  for (size_t i = 0; i < mask.size(); i++) {
+    if (mask[i] == val) {
+      return i;
+    }
+  }
+  return -1;
+}
+
+template <typename T>
+static inline Box<T> GetYoloBox(const T* x,
+                                std::vector<int> anchors,
+                                int i,
+                                int j,
+                                int an_idx,
+                                int grid_size,
+                                int input_size,
+                                int index,
+                                int stride,
+                                float scale,
+                                float bias) {
+  Box<T> b;
+  b.x = (i + sigmoid<T>(x[index]) * scale + bias) / grid_size;
+  b.y = (j + sigmoid<T>(x[index + stride]) * scale + bias) / grid_size;
+  b.w = std::exp(x[index + 2 * stride]) * anchors[2 * an_idx] / input_size;
+  b.h = std::exp(x[index + 3 * stride]) * anchors[2 * an_idx + 1] / input_size;
+  return b;
+}
+
+template <typename T>
+static inline T BoxOverlap(T c1, T w1, T c2, T w2) {
+  T l1 = c1 - w1 / 2.0;
+  T l2 = c2 - w2 / 2.0;
+  T left = l1 > l2 ? l1 : l2;
+  T r1 = c1 + w1 / 2.0;
+  T r2 = c2 + w2 / 2.0;
+  T right = r1 < r2 ? r1 : r2;
+  return right - left;
+}
+
+template <typename T>
+static inline T CalcBoxIoU(Box<T> b1, Box<T> b2) {
+  T w = BoxOverlap(b1.x, b1.w, b2.x, b2.w);
+  T h = BoxOverlap(b1.y, b1.h, b2.y, b2.h);
+  T inter_area = (w < 0 || h < 0) ? 0.0 : w * h;
+  T union_area = b1.w * b1.h + b2.w * b2.h - inter_area;
+  return inter_area / union_area;
+}
+
+template <typename T>
+static void CalcBoxLocationLoss(T* loss,
+                                const T* input,
+                                Box<T> gt,
+                                std::vector<int> anchors,
+                                int an_idx,
+                                int box_idx,
+                                int gi,
+                                int gj,
+                                int grid_size,
+                                int input_size,
+                                int stride,
+                                T score) {
+  T tx = gt.x * grid_size - gi;
+  T ty = gt.y * grid_size - gj;
+  T tw = std::log(gt.w * input_size / anchors[2 * an_idx]);
+  T th = std::log(gt.h * input_size / anchors[2 * an_idx + 1]);
+
+  T scale = (2.0 - gt.w * gt.h) * score;
+  loss[0] += SigmoidCrossEntropy<T>(input[box_idx], tx) * scale;
+  loss[0] += SigmoidCrossEntropy<T>(input[box_idx + stride], ty) * scale;
+  loss[0] += L1Loss<T>(input[box_idx + 2 * stride], tw) * scale;
+  loss[0] += L1Loss<T>(input[box_idx + 3 * stride], th) * scale;
+}
+
+template <typename T>
+static inline void CalcLabelLoss(T* loss,
+                                 const T* input,
+                                 const int index,
+                                 const int label,
+                                 const int class_num,
+                                 const int stride,
+                                 const T pos,
+                                 const T neg,
+                                 T score) {
+  for (int i = 0; i < class_num; i++) {
+    T pred = input[index + i * stride];
+    loss[0] += SigmoidCrossEntropy<T>(pred, (i == label) ? pos : neg) * score;
+  }
+}
+
+template <typename T>
+static inline void CalcObjnessLoss(T* loss,
+                                   const T* input,
+                                   const T* objness,
+                                   const int n,
+                                   const int an_num,
+                                   const int h,
+                                   const int w,
+                                   const int stride,
+                                   const int an_stride) {
+  for (int i = 0; i < n; i++) {
+    for (int j = 0; j < an_num; j++) {
+      for (int k = 0; k < h; k++) {
+        for (int l = 0; l < w; l++) {
+          T obj = objness[k * w + l];
+          if (obj > 1e-5) {
+            // positive sample: obj = mixup score
+            loss[i] += SigmoidCrossEntropy<T>(input[k * w + l], 1.0) * obj;
+          } else if (obj > -0.5) {
+            // negetive sample: obj = 0
+            loss[i] += SigmoidCrossEntropy<T>(input[k * w + l], 0.0);
+          }
+        }
+      }
+      objness += stride;
+      input += an_stride;
+    }
+  }
+}
+
+template <typename T>
+static void inline GtValid(bool* valid,
+                           const T* gtbox,
+                           const int n,
+                           const int b) {
+  for (int i = 0; i < n; i++) {
+    for (int j = 0; j < b; j++) {
+      if (LessEqualZero(gtbox[j * 4 + 2]) || LessEqualZero(gtbox[j * 4 + 3])) {
+        valid[j] = false;
+      } else {
+        valid[j] = true;
+      }
+    }
+    valid += b;
+    gtbox += b * 4;
+  }
+}
+
+template <typename T, typename Context>
+void Yolov3LossKernel(const Context& dev_ctx,
+                      const DenseTensor& x,
+                      const DenseTensor& gt_box,
+                      const DenseTensor& gt_label,
+                      paddle::optional<const DenseTensor&> gt_score,
+                      const std::vector<int>& anchors,
+                      const std::vector<int>& anchor_mask,
+                      int class_num,
+                      float ignore_thresh,
+                      int downsample_ratio,
+                      bool use_label_smooth,
+                      float scale_x_y,
+                      DenseTensor* loss,
+                      DenseTensor* objectness_mask,
+                      DenseTensor* gt_match_mask) {
+  auto* input = &x;
+  auto objness_mask = objectness_mask;
+  float scale = scale_x_y;
+  float bias = -0.5 * (scale - 1.);
+
+  const int n = input->dims()[0];
+  const int h = input->dims()[2];
+  const int w = input->dims()[3];
+  const int an_num = anchors.size() / 2;
+  const int mask_num = anchor_mask.size();
+  const int b = gt_box.dims()[1];
+  int input_size = downsample_ratio * h;
+
+  const int stride = h * w;
+  const int an_stride = (class_num + 5) * stride;
+
+  T label_pos = 1.0;
+  T label_neg = 0.0;
+  if (use_label_smooth) {
+    T smooth_weight = std::min(1.0 / static_cast<T>(class_num), 1.0 / 40);
+    label_pos = 1.0 - smooth_weight;
+    label_neg = smooth_weight;
+  }
+
+  const T* input_data = input->data<T>();
+  const T* gt_box_data = gt_box.data<T>();
+  const int* gt_label_data = gt_label.data<int>();
+  loss->Resize({n});
+  T* loss_data = dev_ctx.template Alloc<T>(loss);
+  memset(loss_data, 0, loss->numel() * sizeof(T));
+  objness_mask->Resize({n, mask_num, h, w});
+  T* obj_mask_data = dev_ctx.template Alloc<T>(objness_mask);
+  memset(obj_mask_data, 0, objness_mask->numel() * sizeof(T));
+  gt_match_mask->Resize({n, b});
+  int* gt_match_mask_data = dev_ctx.template Alloc<int>(gt_match_mask);
+
+  const T* gt_score_data;
+  DenseTensor gtscore;
+  if (!(gt_score.is_initialized())) {
+    gtscore.Resize({n, b});
+    dev_ctx.template Alloc<T>(&gtscore);
+    phi::funcs::SetConstant<Context, T>()(
+        dev_ctx, &gtscore, static_cast<T>(1.0));
+    gt_score_data = gtscore.data<T>();
+  } else {
+    gt_score_data = gt_score.get_ptr()->data<T>();
+  }
+
+  // calc valid gt box mask, avoid calc duplicately in following code
+  DenseTensor gt_valid_mask;
+  gt_valid_mask.Resize({n, b});
+  bool* gt_valid_mask_data = dev_ctx.template Alloc<bool>(&gt_valid_mask);
+  GtValid<T>(gt_valid_mask_data, gt_box_data, n, b);
+
+  for (int i = 0; i < n; i++) {
+    for (int j = 0; j < mask_num; j++) {
+      for (int k = 0; k < h; k++) {
+        for (int l = 0; l < w; l++) {
+          // each predict box find a best match gt box, if overlap is bigger
+          // then ignore_thresh, ignore the objectness loss.
+          int box_idx =
+              GetEntryIndex(i, j, k * w + l, mask_num, an_stride, stride, 0);
+          Box<T> pred = GetYoloBox(input_data,
+                                   anchors,
+                                   l,
+                                   k,
+                                   anchor_mask[j],
+                                   h,
+                                   input_size,
+                                   box_idx,
+                                   stride,
+                                   scale,
+                                   bias);
+          T best_iou = 0;
+          for (int t = 0; t < b; t++) {
+            if (!gt_valid_mask_data[i * b + t]) {
+              continue;
+            }
+            Box<T> gt = GetGtBox(gt_box_data, i, b, t);
+            T iou = CalcBoxIoU(pred, gt);
+            if (iou > best_iou) {
+              best_iou = iou;
+            }
+          }
+
+          // If best IoU is bigger then ignore_thresh,
+          // ignore the objectness loss.
+          if (best_iou > ignore_thresh) {
+            int obj_idx = (i * mask_num + j) * stride + k * w + l;
+            obj_mask_data[obj_idx] = static_cast<T>(-1);
+          }
+          // all losses should be calculated if best IoU
+          // is bigger then truth thresh, but currently,
+          // truth thresh is an unreachable value as 1.0.
+        }
+      }
+    }
+    for (int t = 0; t < b; t++) {
+      if (!gt_valid_mask_data[i * b + t]) {
+        gt_match_mask_data[i * b + t] = -1;
+        continue;
+      }
+      Box<T> gt = GetGtBox(gt_box_data, i, b, t);
+      int gi = static_cast<int>(gt.x * w);
+      int gj = static_cast<int>(gt.y * h);
+      Box<T> gt_shift = gt;
+      gt_shift.x = 0.0;
+      gt_shift.y = 0.0;
+      T best_iou = 0.0;
+      int best_n = 0;
+      // each gt box find a best match anchor box as positive sample,
+      // for positive sample, all losses should be calculated, and for
+      // other samples, only objectness loss is required.
+      for (int an_idx = 0; an_idx < an_num; an_idx++) {
+        Box<T> an_box;
+        an_box.x = 0.0;
+        an_box.y = 0.0;
+        an_box.w = anchors[2 * an_idx] / static_cast<T>(input_size);
+        an_box.h = anchors[2 * an_idx + 1] / static_cast<T>(input_size);
+        float iou = CalcBoxIoU<T>(an_box, gt_shift);
+        if (iou > best_iou) {
+          best_iou = iou;
+          best_n = an_idx;
+        }
+      }
+
+      int mask_idx = GetMaskIndex(anchor_mask, best_n);
+      gt_match_mask_data[i * b + t] = mask_idx;
+      if (mask_idx >= 0) {
+        T score = gt_score_data[i * b + t];
+        int box_idx = GetEntryIndex(
+            i, mask_idx, gj * w + gi, mask_num, an_stride, stride, 0);
+        CalcBoxLocationLoss<T>(loss_data + i,
+                               input_data,
+                               gt,
+                               anchors,
+                               best_n,
+                               box_idx,
+                               gi,
+                               gj,
+                               h,
+                               input_size,
+                               stride,
+                               score);
+
+        int obj_idx = (i * mask_num + mask_idx) * stride + gj * w + gi;
+        obj_mask_data[obj_idx] = score;
+
+        int label = gt_label_data[i * b + t];
+        int label_idx = GetEntryIndex(
+            i, mask_idx, gj * w + gi, mask_num, an_stride, stride, 5);
+        CalcLabelLoss<T>(loss_data + i,
+                         input_data,
+                         label_idx,
+                         label,
+                         class_num,
+                         stride,
+                         label_pos,
+                         label_neg,
+                         score);
+      }
+    }
+  }
+
+  CalcObjnessLoss<T>(loss_data,
+                     input_data + 4 * stride,
+                     obj_mask_data,
+                     n,
+                     mask_num,
+                     h,
+                     w,
+                     stride,
+                     an_stride);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(
+    yolov3_loss, CPU, ALL_LAYOUT, phi::Yolov3LossKernel, float, double) {}

paddle/phi/kernels/yolov3_loss_grad_kernel.h

+// 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.
+
+#pragma once
+
+#include "paddle/phi/core/dense_tensor.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void Yolov3LossGradKernel(const Context& dev_ctx,
+                          const DenseTensor& x,
+                          const DenseTensor& gt_box,
+                          const DenseTensor& gt_label,
+                          paddle::optional<const DenseTensor&> gt_score,
+                          const DenseTensor& loss_grad,
+                          const DenseTensor& objectness_mask,
+                          const DenseTensor& gt_match_mask,
+                          const std::vector<int>& anchors,
+                          const std::vector<int>& anchor_mask,
+                          int class_num,
+                          float ignore_thresh,
+                          int downsample_ratio,
+                          bool use_label_smooth,
+                          float scale_x_Y,
+                          DenseTensor* x_grad,
+                          DenseTensor* gt_box_grad,
+                          DenseTensor* gt_label_grad,
+                          DenseTensor* gt_score_grad);
+
+}  // namespace phi

paddle/phi/kernels/yolov3_loss_kernel.h

+// 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.
+
+#pragma once
+
+#include "paddle/phi/core/dense_tensor.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void Yolov3LossKernel(const Context& dev_ctx,
+                      const DenseTensor& x,
+                      const DenseTensor& gt_box,
+                      const DenseTensor& gt_label,
+                      paddle::optional<const DenseTensor&> gt_score,
+                      const std::vector<int>& anchors,
+                      const std::vector<int>& anchor_mask,
+                      int class_num,
+                      float ignore_thresh,
+                      int downsample_ratio,
+                      bool use_label_smooth,
+                      float scale_x_Y,
+                      DenseTensor* loss,
+                      DenseTensor* objectness_mask,
+                      DenseTensor* gt_match_mask);
+
+}  // namespace phi

paddle/phi/ops/compat/yolov3_loss_sig.cc

+// 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/core/compat/op_utils.h"
+
+namespace phi {
+
+KernelSignature Yolov3LossOpArgumentMapping(const ArgumentMappingContext& ctx) {
+  return KernelSignature("yolov3_loss",
+                         {"X", "GTBox", "GTLabel", "GTScore"},
+                         {"anchors",
+                          "anchor_mask",
+                          "class_num",
+                          "ignore_thresh",
+                          "downsample_ratio",
+                          "use_label_smooth",
+                          "scale_x_y"},
+                         {"Loss", "ObjectnessMask", "GTMatchMask"});
+}
+
+KernelSignature Yolov3LossGradOpArgumentMapping(
+    const ArgumentMappingContext& ctx) {
+  return KernelSignature("yolov3_loss_grad",
+                         {"X",
+                          "GTBox",
+                          "GTLabel",
+                          "GTScore",
+                          GradVarName("Loss"),
+                          "ObjectnessMask",
+                          "GTMatchMask"},
+                         {"anchors",
+                          "anchor_mask",
+                          "class_num",
+                          "ignore_thresh",
+                          "downsample_ratio",
+                          "use_label_smooth",
+                          "scale_x_y"},
+                         {GradVarName("X"),
+                          GradVarName("GTBox"),
+                          GradVarName("GTLabel"),
+                          GradVarName("GTScore")});
+}
+}  // namespace phi
+
+PD_REGISTER_ARG_MAPPING_FN(yolov3_loss, phi::Yolov3LossOpArgumentMapping);
+PD_REGISTER_ARG_MAPPING_FN(yolov3_loss_grad,
+                           phi::Yolov3LossGradOpArgumentMapping);