set use_fine_grained_loss=True as default, and remove use_fine_grained_loss=True from config

configs/dcn/yolov3_enhance_reader.yml

@@ -2,7 +2,6 @@ TrainReader:
   inputs_def:
     fields: ['image', 'gt_bbox', 'gt_class', 'gt_score']
     num_max_boxes: 50
-  use_fine_grained_loss: true
   dataset:
     !COCODataSet
     image_dir: train2017

configs/dcn/yolov3_r50vd_dcn_db_iouaware_obj365_pretrained_coco.yml

@@ -8,12 +8,10 @@ metric: COCO
 pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar
 weights: output/yolov3_r50vd_dcn_db_iouaware_obj365_pretrained_coco/model_final
 num_classes: 80
-use_fine_grained_loss: true
 
 YOLOv3:
   backbone: ResNet
   yolo_head: YOLOv3Head
-  use_fine_grained_loss: true
 
 ResNet:
   norm_type: sync_bn
@@ -47,7 +45,6 @@ YOLOv3Loss:
   batch_size: 8
   ignore_thresh: 0.7
   label_smooth: false
-  use_fine_grained_loss: true
   iou_loss: IouLoss
   iou_aware_loss: IouAwareLoss
 

configs/dcn/yolov3_r50vd_dcn_db_iouloss_obj365_pretrained_coco.yml

@@ -8,12 +8,10 @@ metric: COCO
 pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar
 weights: output/yolov3_r50vd_dcn_db_iouloss_obj365_pretrained_coco/model_final
 num_classes: 80
-use_fine_grained_loss: true
 
 YOLOv3:
   backbone: ResNet
   yolo_head: YOLOv3Head
-  use_fine_grained_loss: true
 
 ResNet:
   norm_type: sync_bn
@@ -49,7 +47,6 @@ YOLOv3Loss:
   batch_size: 8
   ignore_thresh: 0.7
   label_smooth: false
-  use_fine_grained_loss: true
   iou_loss: IouLoss
 
 IouLoss:

configs/dcn/yolov3_r50vd_dcn_db_obj365_pretrained_coco.yml

@@ -8,12 +8,10 @@ metric: COCO
 pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar
 weights: output/yolov3_r50vd_dcn_db_obj365_pretrained_coco/model_final
 num_classes: 80
-use_fine_grained_loss: true
 
 YOLOv3:
   backbone: ResNet
   yolo_head: YOLOv3Head
-  use_fine_grained_loss: true
 
 ResNet:
   norm_type: sync_bn
@@ -50,7 +48,6 @@ YOLOv3Loss:
   batch_size: 8
   ignore_thresh: 0.7
   label_smooth: false
-  use_fine_grained_loss: true
 
 LearningRate:
   base_lr: 0.001

configs/dcn/yolov3_r50vd_dcn_obj365_pretrained_coco.yml

@@ -8,12 +8,10 @@ metric: COCO
 pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar
 weights: output/yolov3_r50vd_dcn_db_obj365_pretrained_coco/model_final
 num_classes: 80
-use_fine_grained_loss: true
 
 YOLOv3:
   backbone: ResNet
   yolo_head: YOLOv3Head
-  use_fine_grained_loss: true
 
 ResNet:
   norm_type: sync_bn
@@ -48,7 +46,6 @@ YOLOv3Loss:
   batch_size: 8
   ignore_thresh: 0.7
   label_smooth: false
-  use_fine_grained_loss: true
 
 LearningRate:
   base_lr: 0.001

configs/ppyolo/ppyolo.yml

@@ -9,14 +9,12 @@ metric: COCO
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
 weights: output/ppyolo/model_final
 num_classes: 80
-use_fine_grained_loss: true
 use_ema: true
 ema_decay: 0.9998
 
 YOLOv3:
   backbone: ResNet
   yolo_head: YOLOv3Head
-  use_fine_grained_loss: true
 
 ResNet:
   norm_type: sync_bn
@@ -48,7 +46,6 @@ YOLOv3Loss:
   ignore_thresh: 0.7
   scale_x_y: 1.05
   label_smooth: false
-  use_fine_grained_loss: true
   iou_loss: IouLoss
   iou_aware_loss: IouAwareLoss
 

configs/ppyolo/ppyolo_2x.yml

@@ -9,14 +9,12 @@ metric: COCO
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
 weights: output/ppyolo/model_final
 num_classes: 80
-use_fine_grained_loss: true
 use_ema: true
 ema_decay: 0.9998
 
 YOLOv3:
   backbone: ResNet
   yolo_head: YOLOv3Head
-  use_fine_grained_loss: true
 
 ResNet:
   norm_type: sync_bn
@@ -48,7 +46,6 @@ YOLOv3Loss:
   ignore_thresh: 0.7
   scale_x_y: 1.05
   label_smooth: false
-  use_fine_grained_loss: true
   iou_loss: IouLoss
   iou_aware_loss: IouAwareLoss
 

configs/ppyolo/ppyolo_r18vd.yml

@@ -9,14 +9,12 @@ metric: COCO
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_vd_pretrained.tar
 weights: output/ppyolo_tiny/model_final
 num_classes: 80
-use_fine_grained_loss: true
 use_ema: true
 ema_decay: 0.9998
 
 YOLOv3:
   backbone: ResNet
   yolo_head: YOLOv3Head
-  use_fine_grained_loss: true
 
 ResNet:
   norm_type: sync_bn
@@ -43,7 +41,6 @@ YOLOv3Loss:
   ignore_thresh: 0.7
   scale_x_y: 1.05
   label_smooth: false
-  use_fine_grained_loss: true
   iou_loss: IouLoss
 
 IouLoss:

configs/ppyolo/ppyolo_test.yml

@@ -11,7 +11,6 @@ metric: COCO
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
 weights: output/ppyolo/model_final
 num_classes: 80
-use_fine_grained_loss: true
 use_ema: true
 ema_decay: 0.9998
 save_prediction_only: True
@@ -19,7 +18,6 @@ save_prediction_only: True
 YOLOv3:
   backbone: ResNet
   yolo_head: YOLOv3Head
-  use_fine_grained_loss: true
 
 ResNet:
   norm_type: sync_bn

configs/yolov4/yolov4_cspdarknet.yml

@@ -8,7 +8,6 @@ metric: COCO
 pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams
 weights: output/yolov4_cspdarknet/model_final
 num_classes: 80
-use_fine_grained_loss: true
 save_prediction_only: True
 
 YOLOv4:

configs/yolov4/yolov4_cspdarknet_coco.yml

@@ -8,7 +8,6 @@ metric: COCO
 pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/CSPDarkNet53_pretrained.pdparams
 weights: output/yolov4_cspdarknet_coco/model_final
 num_classes: 80
-use_fine_grained_loss: true
 
 YOLOv4:
   backbone: CSPDarkNet

configs/yolov4/yolov4_cspdarknet_voc.yml

@@ -8,7 +8,6 @@ metric: VOC
 pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams
 weights: output/yolov4_cspdarknet_voc/model_final
 num_classes: 20
-use_fine_grained_loss: true
 
 YOLOv4:
   backbone: CSPDarkNet

ppdet/data/reader.py

@@ -199,7 +199,7 @@ class Reader(object):
                  class_aware_sampling=False,
                  worker_num=-1,
                  use_process=False,
-                 use_fine_grained_loss=False,
+                 use_fine_grained_loss=True,
                  num_classes=80,
                  bufsize=-1,
                  memsize='3G',

ppdet/modeling/architectures/yolo.py

@@ -43,7 +43,7 @@ class YOLOv3(object):
     def __init__(self,
                  backbone,
                  yolo_head='YOLOv3Head',
-                 use_fine_grained_loss=False):
+                 use_fine_grained_loss=True):
         super(YOLOv3, self).__init__()
         self.backbone = backbone
         self.yolo_head = yolo_head
@@ -182,7 +182,7 @@ class YOLOv4(YOLOv3):
     def __init__(self,
                  backbone,
                  yolo_head='YOLOv4Head',
-                 use_fine_grained_loss=False):
+                 use_fine_grained_loss=True):
         super(YOLOv4, self).__init__(
             backbone=backbone,
             yolo_head=yolo_head,

ppdet/modeling/losses/yolo_loss.py

@@ -45,7 +45,7 @@ class YOLOv3Loss(object):
                  batch_size=8,
                  ignore_thresh=0.7,
                  label_smooth=True,
-                 use_fine_grained_loss=False,
+                 use_fine_grained_loss=True,
                  iou_loss=None,
                  iou_aware_loss=None,
                  downsample=[32, 16, 8],