Support static graph code-gen for yolo_loss

paddle/fluid/operators/detection/CMakeLists.txt

@@ -57,7 +57,6 @@ detection_library(generate_proposal_labels_op SRCS
 detection_library(multiclass_nms_op SRCS multiclass_nms_op.cc DEPS gpc)
 detection_library(locality_aware_nms_op SRCS locality_aware_nms_op.cc DEPS gpc)
 detection_library(box_clip_op SRCS box_clip_op.cc box_clip_op.cu)
-detection_library(yolov3_loss_op SRCS yolov3_loss_op.cc)
 detection_library(box_decoder_and_assign_op SRCS box_decoder_and_assign_op.cc
                   box_decoder_and_assign_op.cu)
 detection_library(sigmoid_focal_loss_op SRCS sigmoid_focal_loss_op.cc

paddle/phi/api/yaml/backward.yaml

@@ -2016,3 +2016,13 @@
   kernel :
     func : where_grad
   no_need_buffer : x, y
+
+- backward_op : yolo_loss_grad
+  forward : yolo_loss (Tensor x, Tensor gt_box, Tensor gt_label, Tensor gt_score, int[] anchors={}, int[] anchor_mask={}, int class_num =1 , float ignore_thresh=0.7, int downsample_ratio=32, bool use_label_smooth=true, float scale_x_y=1.0) -> Tensor(loss), Tensor(objectness_mask), Tensor(gt_match_mask)
+  args : (Tensor x, Tensor gt_box, Tensor gt_label, Tensor gt_score, Tensor objectness_mask, Tensor gt_match_mask, Tensor loss_grad, int[] anchors, int[] anchor_mask, int class_num, float ignore_thresh, int downsample_ratio, bool use_label_smooth, float scale_x_y)
+  output : Tensor(x_grad), Tensor(gt_box_grad), Tensor(gt_label_grad), Tensor(gt_score_grad)
+  infer_meta :
+    func : YoloLossGradInferMeta
+  kernel :
+    func : yolo_loss_grad
+  optional : gt_score

paddle/phi/api/yaml/legacy_backward.yaml

@@ -1107,16 +1107,6 @@
     func : uniform_inplace_grad
   inplace : (out_grad -> x_grad)
 
-- backward_op : yolo_loss_grad
-  forward : yolo_loss(Tensor x, Tensor gt_box, Tensor gt_label, Tensor gt_score, int[] anchors, int[] anchor_mask, int class_num, float ignore_thresh, int downsample_ratio, bool use_label_smooth=true, float scale_x_y=1.0) -> Tensor(loss), Tensor(objectness_mask), Tensor(gt_match_mask)
-  args : (Tensor x, Tensor gt_box, Tensor gt_label, Tensor gt_score, Tensor objectness_mask, Tensor gt_match_mask, Tensor loss_grad, int[] anchors, int[] anchor_mask, int class_num, float ignore_thresh, int downsample_ratio, bool use_label_smooth=true, float scale_x_y=1.0)
-  output : Tensor(x_grad), Tensor(gt_box_grad), Tensor(gt_label_grad), Tensor(gt_score_grad)
-  infer_meta :
-    func : YoloLossGradInferMeta
-  kernel :
-    func : yolo_loss_grad
-  optional : gt_score
-
 - backward_op: unpool3d_grad
   forward: unpool3d (Tensor x, Tensor indices, int[] ksize, int[] strides, int[] padding, int[] output_size, str data_format) -> Tensor(out)
   args: (Tensor x, Tensor indices, Tensor out, Tensor out_grad, int[] ksize, int[] strides, int[] padding, int[] output_size, str data_format)

paddle/phi/api/yaml/legacy_ops.yaml

@@ -1353,17 +1353,6 @@
     data_type: x
   backward: unpool3d_grad
 
-- op : yolo_loss
-  args : (Tensor x, Tensor gt_box, Tensor gt_label, Tensor gt_score, int[] anchors, int[] anchor_mask, int class_num, float ignore_thresh, int downsample_ratio, bool use_label_smooth=true, float scale_x_y=1.0)
-  output : Tensor(loss), Tensor(objectness_mask), Tensor(gt_match_mask)
-  infer_meta :
-    func : YoloLossInferMeta
-  kernel :
-    func : yolo_loss
-    data_type : x
-  optional : gt_score
-  backward : yolo_loss_grad
-
 - op : zeros
   args : (IntArray shape, DataType dtype=DataType::FLOAT32, Place place=CPUPlace())
   output : Tensor(out)

paddle/phi/api/yaml/op_compat.yaml

@@ -2340,6 +2340,13 @@
   outputs :
     {boxes : Boxes, scores : Scores}
 
+- op : yolo_loss (yolov3_loss)
+  backward: yolo_loss_grad (yolov3_loss_grad)
+  inputs :
+    {x : X,  gt_box : GTBox, gt_label : GTLabel ,gt_score : GTScore}
+  outputs :
+    {loss : Loss , objectness_mask : ObjectnessMask, gt_match_mask : GTMatchMask}
+
 - op: sigmoid_cross_entropy_with_logits
   backward: sigmoid_cross_entropy_with_logits_grad
   inputs :

paddle/phi/api/yaml/ops.yaml

@@ -2074,3 +2074,14 @@
   kernel :
     func : yolo_box
     data_type : x
+
+- op : yolo_loss
+  args : (Tensor x, Tensor gt_box, Tensor gt_label, Tensor gt_score, int[] anchors={}, int[] anchor_mask={}, int class_num =1 , float ignore_thresh=0.7, int downsample_ratio=32, bool use_label_smooth=true, float scale_x_y=1.0)
+  output : Tensor(loss), Tensor(objectness_mask), Tensor(gt_match_mask)
+  infer_meta :
+    func : YoloLossInferMeta
+  kernel :
+    func : yolo_loss
+    data_type : x
+  optional : gt_score
+  backward : yolo_loss_grad