keep the option 'num_classes' and 'with_background' consistent (#727)

configs/anchor_free/fcos_dcn_r50_fpn_1x.yml

@@ -8,7 +8,7 @@ save_dir: output
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
 metric: COCO
 weights: output/fcos_dcn_r50_fpn_1x/model_final
-num_classes: 81
+num_classes: 80
 
 FCOS:
   backbone: ResNet
@@ -32,7 +32,7 @@ FPN:
   has_extra_convs: true
 
 FCOSHead:
-  num_classes: 81
+  num_classes: 80
   fpn_stride: [8, 16, 32, 64, 128]
   num_convs: 4
   norm_type: "gn"
@@ -81,7 +81,7 @@ TrainReader:
     image_dir: train2017
     anno_path: annotations/instances_train2017.json
     dataset_dir: dataset/coco
-    with_background: true
+    with_background: false
   sample_transforms:
   - !DecodeImage
     to_rgb: true
@@ -111,7 +111,7 @@ TrainReader:
     norm_reg_targets: True
   batch_size: 2
   shuffle: true
-  worker_num: 16
+  worker_num: 4
   use_process: false
 
 EvalReader:
@@ -144,9 +144,9 @@ EvalReader:
   - !PadBatch
     pad_to_stride: 128
     use_padded_im_info: true
-  batch_size: 8
+  batch_size: 1
   shuffle: false
-  worker_num: 2
+  worker_num: 1
   use_process: false
 
 TestReader:

configs/anchor_free/fcos_r50_fpn_1x.yml

@@ -8,7 +8,7 @@ save_dir: output
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
 metric: COCO
 weights: output/fcos_r50_fpn_1x/model_final
-num_classes: 81
+num_classes: 80
 
 FCOS:
   backbone: ResNet
@@ -31,7 +31,7 @@ FPN:
   has_extra_convs: true
 
 FCOSHead:
-  num_classes: 81
+  num_classes: 80
   fpn_stride: [8, 16, 32, 64, 128]
   num_convs: 4
   norm_type: "gn"
@@ -80,7 +80,7 @@ TrainReader:
     image_dir: train2017
     anno_path: annotations/instances_train2017.json
     dataset_dir: dataset/coco
-    with_background: true
+    with_background: false
   sample_transforms:
   - !DecodeImage
     to_rgb: true
@@ -110,7 +110,7 @@ TrainReader:
     norm_reg_targets: True
   batch_size: 2
   shuffle: true
-  worker_num: 16
+  worker_num: 4
   use_process: false
 
 EvalReader:
@@ -143,7 +143,7 @@ EvalReader:
   - !PadBatch
     pad_to_stride: 128
     use_padded_im_info: true
-  batch_size: 8
+  batch_size: 1
   shuffle: false
   worker_num: 2
   use_process: false

configs/anchor_free/fcos_r50_fpn_multiscale_2x.yml

@@ -8,7 +8,7 @@ save_dir: output
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
 metric: COCO
 weights: output/fcos_r50_fpn_multiscale_2x/model_final
-num_classes: 81
+num_classes: 80
 
 FCOS:
   backbone: ResNet
@@ -31,7 +31,7 @@ FPN:
   has_extra_convs: true
 
 FCOSHead:
-  num_classes: 81
+  num_classes: 80
   fpn_stride: [8, 16, 32, 64, 128]
   num_convs: 4
   norm_type: "gn"
@@ -80,7 +80,7 @@ TrainReader:
     image_dir: train2017
     anno_path: annotations/instances_train2017.json
     dataset_dir: dataset/coco
-    with_background: true
+    with_background: false
   sample_transforms:
   - !DecodeImage
     to_rgb: true
@@ -110,7 +110,7 @@ TrainReader:
     norm_reg_targets: True
   batch_size: 2
   shuffle: true
-  worker_num: 16
+  worker_num: 4
   use_process: false
 
 EvalReader:
@@ -143,7 +143,7 @@ EvalReader:
   - !PadBatch
     pad_to_stride: 128
     use_padded_im_info: true
-  batch_size: 8
+  batch_size: 1
   shuffle: false
   worker_num: 2
   use_process: false

ppdet/data/transform/batch_operators.py

@@ -439,7 +439,7 @@ class Gt2FCOSTarget(BaseOperator):
             points2gtarea[is_match_current_level == 0] = self.INF
             points2min_area = points2gtarea.min(axis=1)
             points2min_area_ind = points2gtarea.argmin(axis=1)
-            labels = gt_class[points2min_area_ind]
+            labels = gt_class[points2min_area_ind] + 1
             labels[points2min_area == self.INF] = 0
             reg_targets = reg_targets[range(xs.shape[0]), points2min_area_ind]
             ctn_targets = np.sqrt((reg_targets[:, [0, 2]].min(axis=1) / \

ppdet/modeling/anchor_heads/fcos_head.py

@@ -50,7 +50,7 @@ class FCOSHead(object):
     __shared__ = ['num_classes']
 
     def __init__(self,
-                 num_classes=81,
+                 num_classes=80,
                  fpn_stride=[8, 16, 32, 64, 128],
                  prior_prob=0.01,
                  num_convs=4,
@@ -65,7 +65,7 @@ class FCOSHead(object):
                      keep_top_k=100,
                      nms_threshold=0.45,
                      background_label=-1).__dict__):
-        self.num_classes = num_classes - 1
+        self.num_classes = num_classes
         self.fpn_stride = fpn_stride[::-1]
         self.prior_prob = prior_prob
         self.num_convs = num_convs