fix metric etc.al

configs/det/ch_ppocr_v2.1/ch_det_lite_train_cml_v2.1.yml

@@ -90,14 +90,14 @@ Loss:
       - ["Student", "Student2"]
       maps_name: "thrink_maps"
       weight: 1.0
-      act: "softmax"
+      # act: None
       model_name_pairs: ["Student", "Student2"]
       key: maps
   - DistillationDBLoss:
       weight: 1.0
       model_name_list: ["Student", "Student2"]
       # key: maps
-      name: DBLoss
+      # name: DBLoss
       balance_loss: true
       main_loss_type: DiceLoss
       alpha: 5
@@ -119,8 +119,8 @@ Optimizer:
 
 PostProcess:
   name: DistillationDBPostProcess
-  model_name: ["Student", "Student2"]
-  key: head_out
+  model_name: ["Student", "Student2", "Teacher"]
+  # key: maps
   thresh: 0.3
   box_thresh: 0.6
   max_candidates: 1000

ppocr/losses/basic_loss.py

@@ -54,6 +54,27 @@ class CELoss(nn.Layer):
         return loss
 
 
+class KLJSLoss(object):
+    def __init__(self, mode='kl'):
+        assert mode in ['kl', 'js', 'KL', 'JS'], "mode can only be one of ['kl', 'js', 'KL', 'JS']"
+        self.mode = mode
+
+    def __call__(self, p1, p2, reduction="mean"):
+
+        loss = paddle.multiply(p2, paddle.log( (p2+1e-5)/(p1+1e-5) + 1e-5))
+
+        if self.mode.lower() == "js":
+            loss += paddle.multiply(p1, paddle.log((p1+1e-5)/(p2+1e-5) + 1e-5))
+            loss *= 0.5
+        if reduction == "mean":
+            loss = paddle.mean(loss, axis=[1,2])
+        elif reduction=="none" or reduction is None:
+            return loss 
+        else:
+            loss = paddle.sum(loss, axis=[1,2])
+
+        return loss 
+
 class DMLLoss(nn.Layer):
     """
     DMLLoss
@@ -70,16 +91,20 @@ class DMLLoss(nn.Layer):
         else:
             self.act = None
         
+        self.jskl_loss = KLJSLoss(mode="js")
+
     def forward(self, out1, out2):
         if self.act is not None:
             out1 = self.act(out1)
             out2 = self.act(out2)
-
+        if len(out1.shape) < 2:
             log_out1 = paddle.log(out1)
             log_out2 = paddle.log(out2)
             loss = (F.kl_div(
                 log_out1, out2, reduction='batchmean') + F.kl_div(
                     log_out2, out1, reduction='batchmean')) / 2.0
+        else:
+            loss = self.jskl_loss(out1, out2)
         return loss
 
 

ppocr/losses/combined_loss.py

@@ -55,7 +55,5 @@ class CombinedLoss(nn.Layer):
                     loss_all += loss[key] * weight
                 else:
                     loss_dict["{}_{}".format(key, idx)] = loss[key]
-                    # loss[f"{key}_{idx}"] = loss[key]
-            loss_dict.update(loss)
         loss_dict["loss"] = loss_all
         return loss_dict

ppocr/losses/distillation_loss.py

@@ -46,13 +46,13 @@ class DistillationDMLLoss(DMLLoss):
                  act=None,
                  key=None,
                  maps_name=None,
-                 name="loss_dml"):
+                 name="dml"):
         super().__init__(act=act)
         assert isinstance(model_name_pairs, list)
         self.key = key
         self.model_name_pairs = self._check_model_name_pairs(model_name_pairs)
         self.name = name
-        self.maps_name = maps_name
+        self.maps_name = self._check_maps_name(maps_name)
     
     def _check_model_name_pairs(self, model_name_pairs):
         if not isinstance(model_name_pairs, list):
@@ -76,11 +76,11 @@ class DistillationDMLLoss(DMLLoss):
         new_outs = {}
         for k in self.maps_name:
             if k == "thrink_maps":
-                new_outs[k] = paddle.slice(outs, axes=[1], starts=[0], ends=[1])
+                new_outs[k] = outs[:, 0, :, :]
             elif k == "threshold_maps":
-                new_outs[k] = paddle.slice(outs, axes=[1], starts=[1], ends=[2])
+                new_outs[k] = outs[:, 1, :, :]
             elif k == "binary_maps":
-                new_outs[k] = paddle.slice(outs, axes=[1], starts=[2], ends=[3])
+                new_outs[k] = outs[:, 2, :, :]
             else:
                 continue
         return new_outs
@@ -105,14 +105,14 @@ class DistillationDMLLoss(DMLLoss):
             else:
                 outs1 = self._slice_out(out1)
                 outs2 = self._slice_out(out2)
-                for k in outs1.keys():
+                for _c, k in enumerate(outs1.keys()):
                     loss = super().forward(outs1[k], outs2[k])
                     if isinstance(loss, dict):
                         for key in loss:
                             loss_dict["{}_{}_{}_{}_{}".format(key, pair[
                                 0], pair[1], map_name, idx)] = loss[key]
                     else:
-                        loss_dict["{}_{}_{}".format(self.name, self.maps_name,
+                        loss_dict["{}_{}_{}".format(self.name, self.maps_name[_c],
                                                     idx)] = loss
         
         loss_dict = _sum_loss(loss_dict)
@@ -152,7 +152,7 @@ class DistillationDBLoss(DBLoss):
                  beta=10,
                  ohem_ratio=3,
                  eps=1e-6,
-                 name="db_loss",
+                 name="db",
                  **kwargs):
         super().__init__()
         self.model_name_list = model_name_list

ppocr/metrics/det_metric.py

@@ -55,6 +55,10 @@ 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
+
     def get_metric(self):
         """
         return metrics {

ppocr/postprocess/db_postprocess.py

@@ -200,21 +200,18 @@ class DistillationDBPostProcess(DBPostProcess):
                  use_dilation=False,
                  score_mode="fast",
                  **kwargs):
-        super(DistillationDBPostProcess, self).__init__(
-            thresh, box_thresh, max_candidates, unclip_ratio, use_dilation,
-            score_mode)
+        super().__init__()
         if not isinstance(model_name, list):
             model_name = [model_name]
         self.model_name = model_name
-
         self.key = key
 
-    def forward(self, predicts, shape_list):
+    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=label)
+            results[name] = super().__call__(pred, shape_list=shape_list)
 
         return results

ppocr/utils/save_load.py

@@ -135,6 +135,7 @@ def load_pretrained_params(model, path):
                 f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !"
             )
     model.set_state_dict(new_state_dict)
+    print(f"load pretrain successful from {path}")
     return True
 
 def save_model(model,

tools/eval.py

@@ -55,8 +55,10 @@ def main():
 
     model = build_model(config['Architecture'])
     use_srn = config['Architecture']['algorithm'] == "SRN"
+    if "model_type" in config['Architecture'].keys():
         model_type = config['Architecture']['model_type']
-
+    else:
+        model_type = None
     best_model_dict = init_model(config, model)
     if len(best_model_dict):
         logger.info('metric in ckpt ***************')