fix voc eval on release/2.0 (#2732)

configs/ppyolo/ppyolo_r50vd_dcn_voc.yml

@@ -21,6 +21,10 @@ TrainReader:
   mixup_epoch: 350
   batch_size: 12
 
+EvalReader:
+  batch_transforms:
+  - PadBatch: {pad_gt: True}
+
 epoch: 583
 
 LearningRate:

configs/ssd/ssd_mobilenet_v1_300_120e_voc.yml

@@ -6,3 +6,7 @@ _BASE_: [
   '_base_/ssd_mobilenet_reader.yml',
 ]
 weights: output/ssd_mobilenet_v1_300_120e_voc/model_final
+
+EvalReader:
+  batch_transforms:
+  - PadBatch: {pad_gt: True}

configs/ssd/ssd_vgg16_300_240e_voc.yml

@@ -6,3 +6,7 @@ _BASE_: [
   '_base_/ssd_reader.yml',
 ]
 weights: output/ssd_vgg16_300_240e_voc/model_final
+
+EvalReader:
+  batch_transforms:
+  - PadBatch: {pad_gt: True}

configs/yolov3/yolov3_darknet53_270e_voc.yml

@@ -8,3 +8,7 @@ _BASE_: [
 
 snapshot_epoch: 5
 weights: output/yolov3_darknet53_270e_voc/model_final
+
+EvalReader:
+  batch_transforms:
+  - PadBatch: {pad_gt: True}

configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml

@@ -40,6 +40,10 @@ TrainReader:
   drop_last: true
   mixup_epoch: 250
 
+EvalReader:
+  batch_transforms:
+  - PadBatch: {pad_gt: True}
+
 LearningRate:
   base_lr: 0.001
   schedulers:

configs/yolov3/yolov3_mobilenet_v1_ssld_270e_voc.yml

@@ -41,6 +41,10 @@ TrainReader:
   drop_last: true
   mixup_epoch: 250
 
+EvalReader:
+  batch_transforms:
+  - PadBatch: {pad_gt: True}
+
 LearningRate:
   base_lr: 0.001
   schedulers:

configs/yolov3/yolov3_mobilenet_v3_large_270e_voc.yml

@@ -40,6 +40,10 @@ TrainReader:
   drop_last: true
   mixup_epoch: 250
 
+EvalReader:
+  batch_transforms:
+  - PadBatch: {pad_gt: True}
+
 LearningRate:
   base_lr: 0.001
   schedulers:

configs/yolov3/yolov3_mobilenet_v3_large_ssld_270e_voc.yml

@@ -41,6 +41,10 @@ TrainReader:
   drop_last: true
   mixup_epoch: 250
 
+EvalReader:
+  batch_transforms:
+  - PadBatch: {pad_gt: True}
+
 LearningRate:
   base_lr: 0.001
   schedulers:

ppdet/data/transform/batch_operators.py

@@ -115,6 +115,7 @@ class PadBatch(BaseOperator):
                 gt_box_data = -np.ones([gt_num_max, 4], dtype=np.float32)
                 gt_class_data = -np.ones([gt_num_max], dtype=np.int32)
                 is_crowd_data = np.ones([gt_num_max], dtype=np.int32)
+                difficult_data = np.ones([gt_num_max], dtype=np.int32)
 
                 if pad_mask:
                     poly_num_max = max(poly_num)
@@ -127,7 +128,12 @@ class PadBatch(BaseOperator):
                 gt_num = data['gt_bbox'].shape[0]
                 gt_box_data[0:gt_num, :] = data['gt_bbox']
                 gt_class_data[0:gt_num] = np.squeeze(data['gt_class'])
+                if 'is_crowd' in data:
                     is_crowd_data[0:gt_num] = np.squeeze(data['is_crowd'])
+                    data['is_crowd'] = is_crowd_data
+                if 'difficult' in data:
+                    difficult_data[0:gt_num] = np.squeeze(data['difficult'])
+                    data['difficult'] = difficult_data
                 if pad_mask:
                     for j, poly in enumerate(data['gt_poly']):
                         for k, p_p in enumerate(poly):
@@ -136,7 +142,6 @@ class PadBatch(BaseOperator):
                     data['gt_poly'] = gt_masks_data
                 data['gt_bbox'] = gt_box_data
                 data['gt_class'] = gt_class_data
-                data['is_crowd'] = is_crowd_data
 
         return samples