change hrhrnet eval heat_thresh=0.1; fix crowdpose eval (#2945)

ppdet/data/source/keypoint_coco.py

@@ -60,7 +60,6 @@ class KeypointBottomUpBaseDataset(DetDataset):
         self.test_mode = test_mode
 
         self.ann_info['num_joints'] = num_joints
-
         self.img_ids = []
 
     def __len__(self):

ppdet/engine/trainer.py

@@ -147,6 +147,7 @@ class Trainer(object):
                 eval_dataset.check_or_download_dataset()
                 anno_file = eval_dataset.get_anno()
 
+            IouType = self.cfg['IouType'] if 'IouType' in self.cfg else 'bbox'
             self._metrics = [
                 COCOMetric(
                     anno_file=anno_file,
@@ -154,6 +155,7 @@ class Trainer(object):
                     classwise=classwise,
                     output_eval=output_eval,
                     bias=bias,
+                    IouType=IouType,
                     save_prediction_only=save_prediction_only)
             ]
         elif self.cfg.metric == 'VOC':

ppdet/metrics/coco_utils.py

@@ -107,7 +107,6 @@ def cocoapi_eval(jsonfile,
         coco_eval.params.maxDets = list(max_dets)
     elif style == 'keypoints_crowd':
         coco_eval = COCOeval(coco_gt, coco_dt, style, sigmas, use_area)
-        coco_gt.anno_file.append("")
     else:
         coco_eval = COCOeval(coco_gt, coco_dt, style)
     coco_eval.evaluate()

ppdet/modeling/architectures/keypoint_hrhrnet.py

@@ -52,7 +52,7 @@ class HigherHRNet(BaseArch):
         super(HigherHRNet, self).__init__()
         self.backbone = backbone
         self.hrhrnet_head = hrhrnet_head
-        self.post_process = HrHRNetPostProcess()
+        self.post_process = post_process
         self.flip = eval_flip
         self.flip_perm = paddle.to_tensor(flip_perm)
         self.deploy = False
@@ -85,6 +85,7 @@ class HigherHRNet(BaseArch):
             return self.hrhrnet_head(body_feats, self.inputs)
         else:
             outputs = self.hrhrnet_head(body_feats)
+
             if self.flip and not self.deploy:
                 outputs = [paddle.split(o, 2) for o in outputs]
                 output_rflip = [
@@ -105,7 +106,6 @@ class HigherHRNet(BaseArch):
             w = self.inputs['im_shape'][0, 1].numpy().item()
             kpts, scores = self.post_process(*outputs, h, w)
             res_lst.append([kpts, scores])
-
             return res_lst
 
     def get_loss(self):
@@ -157,7 +157,7 @@ class HrHRNetPostProcess(object):
         original_height, original_width (float): the original image size
     '''
 
-    def __init__(self, max_num_people=30, heat_thresh=0.2, tag_thresh=1.):
+    def __init__(self, max_num_people=30, heat_thresh=0.1, tag_thresh=1.):
         self.max_num_people = max_num_people
         self.heat_thresh = heat_thresh
         self.tag_thresh = tag_thresh