fix eval bug

ppocr/metrics/det_metric.py

@@ -55,9 +55,9 @@ class DetMetric(object):
             result = self.evaluator.evaluate_image(gt_info_list, det_info_list)
             self.results.append(result)
 
-        metircs = self.evaluator.combine_results(self.results)
-        self.reset()
-        return metircs
+        # metircs = self.evaluator.combine_results(self.results)
+        # self.reset()
+        # return metircs
 
     def get_metric(self):
         """

ppocr/metrics/distillation_metric.py

@@ -24,8 +24,8 @@ from .cls_metric import ClsMetric
 class DistillationMetric(object):
     def __init__(self,
                  key=None,
-                 base_metric_name="RecMetric",
-                 main_indicator='acc',
+                 base_metric_name=None,
+                 main_indicator=None,
                  **kwargs):
         self.main_indicator = main_indicator
         self.key = key
@@ -42,16 +42,13 @@ class DistillationMetric(object):
                 main_indicator=self.main_indicator, **self.kwargs)
             self.metrics[key].reset()
 
-    def __call__(self, preds, *args, **kwargs):
+    def __call__(self, preds, batch, **kwargs):
         assert isinstance(preds, dict)
         if self.metrics is None:
             self._init_metrcis(preds)
         output = dict()
         for key in preds:
-            metric = self.metrics[key].__call__(preds[key], *args, **kwargs)
-            for sub_key in metric:
-                output["{}_{}".format(key, sub_key)] = metric[sub_key]
-        return output
+            self.metrics[key].__call__(preds[key], batch, **kwargs)
 
     def get_metric(self):
         """

ppocr/modeling/architectures/distillation_model.py

@@ -46,7 +46,7 @@ class DistillationModel(nn.Layer):
                 pretrained = model_config.pop("pretrained")
             model = BaseModel(model_config)
             if pretrained is not None:
-                load_pretrained_params(model, pretrained)
+                model = load_pretrained_params(model, pretrained)
             if freeze_params:
                 for param in model.parameters():
                     param.trainable = False

ppocr/postprocess/db_postprocess.py

@@ -189,29 +189,27 @@ class DBPostProcess(object):
         return boxes_batch
 
 
-class DistillationDBPostProcess(DBPostProcess):
-    def __init__(self,
-                 model_name=["student"],
+class DistillationDBPostProcess(object):
+    def __init__(self, model_name=["student"],
                  key=None,
                  thresh=0.3,
-                 box_thresh=0.7,
+                 box_thresh=0.6,
                  max_candidates=1000,
-                 unclip_ratio=2.0,
+                 unclip_ratio=1.5,
                  use_dilation=False,
                  score_mode="fast",
                  **kwargs):
-        super().__init__()
-        if not isinstance(model_name, list):
-            model_name = [model_name]
         self.model_name = model_name
         self.key = key
+        self.post_process = DBPostProcess(thresh=thresh,
+                 box_thresh=box_thresh,
+                 max_candidates=max_candidates,
+                 unclip_ratio=unclip_ratio,
+                 use_dilation=use_dilation,
+                 score_mode=score_mode)
 
     def __call__(self, predicts, shape_list):
         results = {}
-        for name in self.model_name:
-            pred = predicts[name]
-            if self.key is not None:
-                pred = pred[self.key]
-            results[name] = super().__call__(pred, shape_list=shape_list)
-
+        for k in self.model_name:
+            results[k] = self.post_process(predicts[k], shape_list=shape_list)
         return results

ppocr/utils/save_load.py

@@ -136,7 +136,7 @@ def load_pretrained_params(model, path):
             )
     model.set_state_dict(new_state_dict)
     print(f"load pretrain successful from {path}")
-    return True
+    return model
 
 def save_model(model,
                optimizer,

tools/eval.py

@@ -27,7 +27,7 @@ from ppocr.data import build_dataloader
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
 from ppocr.metrics import build_metric
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import init_model, load_pretrained_params
 from ppocr.utils.utility import print_dict
 import tools.program as program
 
@@ -59,7 +59,8 @@ def main():
         model_type = config['Architecture']['model_type']
     else:
         model_type = None
-    best_model_dict = init_model(config, model)
+
+    best_model_dict = init_model(config, model, model_type)
     if len(best_model_dict):
         logger.info('metric in ckpt ***************')
         for k, v in best_model_dict.items():

tools/program.py

@@ -374,6 +374,7 @@ def eval(model,
                 eval_class(preds, batch)
             else:
                 post_result = post_process_class(preds, batch[1])
+                # post_result = post_result_["Student"]
                 eval_class(post_result, batch)
             pbar.update(1)
             total_frame += len(images)

tools/train.py

@@ -97,8 +97,8 @@ def main(config, device, logger, vdl_writer):
     # build metric
     eval_class = build_metric(config['Metric'])
     # load pretrain model
-    pre_best_model_dict = load_dygraph_params(config, model, logger, optimizer)
-
+    #pre_best_model_dict = load_dygraph_params(config, model, logger, optimizer)
+    pre_best_model_dict = {}
     logger.info('train dataloader has {} iters'.format(len(train_dataloader)))
     if valid_dataloader is not None:
         logger.info('valid dataloader has {} iters'.format(