fix yolo eval (#613)

ppdet/modeling/anchor_heads/yolo_head.py

@@ -68,7 +68,8 @@ class YOLOv3Head(object):
                      background_label=-1).__dict__,
                  weight_prefix_name='',
                  downsample=[32, 16, 8],
-                 scale_x_y=1.0):
+                 scale_x_y=1.0,
+                 clip_bbox=True):
         self.norm_decay = norm_decay
         self.num_classes = num_classes
         self.anchor_masks = anchor_masks
@@ -86,6 +87,7 @@ class YOLOv3Head(object):
         self.downsample = downsample
         # TODO(guanzhong) activate scale_x_y in Paddle 2.0
         #self.scale_x_y = scale_x_y
+        self.clip_bbox = clip_bbox
 
     def _conv_bn(self,
                  input,
@@ -325,7 +327,7 @@ class YOLOv3Head(object):
                 conf_thresh=self.nms.score_threshold,
                 downsample_ratio=self.downsample[i],
                 name=self.prefix_name + "yolo_box" + str(i),
-                clip_bbox=False)
+                clip_bbox=self.clip_bbox)
             boxes.append(box)
             scores.append(fluid.layers.transpose(score, perm=[0, 2, 1]))
 
@@ -352,25 +354,25 @@ class YOLOv4Head(YOLOv3Head):
     __inject__ = ['nms', 'yolo_loss']
     __shared__ = ['num_classes', 'weight_prefix_name']
 
-    def __init__(
-            self,
-            anchors=[[12, 16], [19, 36], [40, 28], [36, 75], [76, 55],
-                     [72, 146], [142, 110], [192, 243], [459, 401]],
-            anchor_masks=[[0, 1, 2], [3, 4, 5], [6, 7, 8]],
-            nms=MultiClassNMS(
-                score_threshold=0.01,
-                nms_top_k=-1,
-                keep_top_k=-1,
-                nms_threshold=0.45,
-                background_label=-1).__dict__,
-            spp_stage=5,
-            num_classes=80,
-            weight_prefix_name='',
-            downsample=[8, 16, 32],
-            scale_x_y=[1.2, 1.1, 1.05],
-            yolo_loss="YOLOv3Loss",
-            iou_aware=False,
-            iou_aware_factor=0.4, ):
+    def __init__(self,
+                 anchors=[[12, 16], [19, 36], [40, 28], [36, 75], [76, 55],
+                          [72, 146], [142, 110], [192, 243], [459, 401]],
+                 anchor_masks=[[0, 1, 2], [3, 4, 5], [6, 7, 8]],
+                 nms=MultiClassNMS(
+                     score_threshold=0.01,
+                     nms_top_k=-1,
+                     keep_top_k=-1,
+                     nms_threshold=0.45,
+                     background_label=-1).__dict__,
+                 spp_stage=5,
+                 num_classes=80,
+                 weight_prefix_name='',
+                 downsample=[8, 16, 32],
+                 scale_x_y=[1.2, 1.1, 1.05],
+                 yolo_loss="YOLOv3Loss",
+                 iou_aware=False,
+                 iou_aware_factor=0.4,
+                 clip_bbox=False):
         super(YOLOv4Head, self).__init__(
             anchors=anchors,
             anchor_masks=anchor_masks,
@@ -381,7 +383,8 @@ class YOLOv4Head(YOLOv3Head):
             scale_x_y=scale_x_y,
             yolo_loss=yolo_loss,
             iou_aware=iou_aware,
-            iou_aware_factor=iou_aware_factor)
+            iou_aware_factor=iou_aware_factor,
+            clip_box=clip_bbox)
         self.spp_stage = spp_stage
 
     def _upsample(self, input, scale=2, name=None):

ppdet/modeling/architectures/yolo.py

@@ -42,7 +42,7 @@ class YOLOv3(object):
 
     def __init__(self,
                  backbone,
-                 yolo_head='YOLOv4Head',
+                 yolo_head='YOLOv3Head',
                  use_fine_grained_loss=False):
         super(YOLOv3, self).__init__()
         self.backbone = backbone

ppdet/utils/coco_eval.py

@@ -275,11 +275,11 @@ def bbox2out(results, clsid2catid, is_bbox_normalized=False):
                     w *= im_width
                     h *= im_height
                 else:
-                    im_size = t['im_size'][0][i].tolist()
-                    xmin, ymin, xmax, ymax = \
-                            clip_bbox([xmin, ymin, xmax, ymax], im_size)
-                    w = xmax - xmin
-                    h = ymax - ymin
+                    # for yolov4
+                    # w = xmax - xmin
+                    # h = ymax - ymin
+                    w = xmax - xmin + 1
+                    h = ymax - ymin + 1
 
                 bbox = [xmin, ymin, w, h]
                 coco_res = {

tools/eval.py

@@ -111,7 +111,7 @@ def main():
     extra_keys = []
 
     if cfg.metric == 'COCO':
-        extra_keys = ['im_info', 'im_id', 'im_shape', 'im_size']
+        extra_keys = ['im_info', 'im_id', 'im_shape']
     if cfg.metric == 'VOC':
         extra_keys = ['gt_bbox', 'gt_class', 'is_difficult']