fix metric

configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_distillation_v2.1.yml

@@ -95,17 +95,17 @@ Loss:
       model_name_pairs:
       - ["Student", "Teacher"]
       key: backbone_out
-  
-      
 
 PostProcess:
   name: DistillationCTCLabelDecode
-  model_name: "Student"
+  model_name: ["Student", "Teacher"]
   key: head_out
 
 Metric:
-  name: RecMetric
+  name: DistillationMetric
+  base_metric_name: RecMetric
   main_indicator: acc
+  key: "Student"
 
 Train:
   dataset:

ppocr/losses/basic_loss.py

@@ -22,9 +22,8 @@ from paddle.nn import SmoothL1Loss
 
 
 class CELoss(nn.Layer):
-    def __init__(self, name="loss_ce", epsilon=None):
+    def __init__(self, epsilon=None):
         super().__init__()
-        self.name = name
         if epsilon is not None and (epsilon <= 0 or epsilon >= 1):
             epsilon = None
         self.epsilon = epsilon
@@ -52,9 +51,7 @@ class CELoss(nn.Layer):
             else:
                 soft_label = False
             loss = F.cross_entropy(x, label=label, soft_label=soft_label)
-
-        loss_dict[self.name] = paddle.mean(loss)
-        return loss_dict
+        return loss
 
 
 class DMLLoss(nn.Layer):
@@ -62,11 +59,10 @@ class DMLLoss(nn.Layer):
     DMLLoss
     """
 
-    def __init__(self, act=None, name="loss_dml"):
+    def __init__(self, act=None):
         super().__init__()
         if act is not None:
             assert act in ["softmax", "sigmoid"]
-        self.name = name
         if act == "softmax":
             self.act = nn.Softmax(axis=-1)
         elif act == "sigmoid":
@@ -75,7 +71,6 @@ class DMLLoss(nn.Layer):
             self.act = None
 
     def forward(self, out1, out2):
-        loss_dict = {}
         if self.act is not None:
             out1 = self.act(out1)
             out2 = self.act(out2)
@@ -85,18 +80,16 @@ class DMLLoss(nn.Layer):
         loss = (F.kl_div(
             log_out1, out2, reduction='batchmean') + F.kl_div(
                 log_out2, log_out1, reduction='batchmean')) / 2.0
-        loss_dict[self.name] = loss
-        return loss_dict
+        return loss
 
 
 class DistanceLoss(nn.Layer):
     """
     DistanceLoss:
         mode: loss mode
-        name: loss key in the output dict
     """
 
-    def __init__(self, mode="l2", name="loss_dist", **kargs):
+    def __init__(self, mode="l2", **kargs):
         super().__init__()
         assert mode in ["l1", "l2", "smooth_l1"]
         if mode == "l1":
@@ -106,7 +99,5 @@ class DistanceLoss(nn.Layer):
         elif mode == "smooth_l1":
             self.loss_func = nn.SmoothL1Loss(**kargs)
 
-        self.name = "{}_{}".format(name, mode)
-
     def forward(self, x, y):
-        return {self.name: self.loss_func(x, y)}
+        return self.loss_func(x, y)

ppocr/losses/distillation_loss.py

@@ -26,10 +26,11 @@ class DistillationDMLLoss(DMLLoss):
 
     def __init__(self, model_name_pairs=[], act=None, key=None,
                  name="loss_dml"):
-        super().__init__(act=act, name=name)
+        super().__init__(act=act)
         assert isinstance(model_name_pairs, list)
         self.key = key
         self.model_name_pairs = model_name_pairs
+        self.name = name
 
     def forward(self, predicts, batch):
         loss_dict = dict()
@@ -42,8 +43,8 @@ class DistillationDMLLoss(DMLLoss):
             loss = super().forward(out1, out2)
             if isinstance(loss, dict):
                 for key in loss:
-                    loss_dict["{}_{}_{}".format(self.name, key, idx)] = loss[
-                        key]
+                    loss_dict["{}_{}_{}_{}".format(key, pair[0], pair[1],
+                                                   idx)] = loss[key]
             else:
                 loss_dict["{}_{}".format(self.name, idx)] = loss
         return loss_dict
@@ -82,10 +83,11 @@ class DistillationDistanceLoss(DistanceLoss):
                  key=None,
                  name="loss_distance",
                  **kargs):
-        super().__init__(mode=mode, name=name, **kargs)
+        super().__init__(mode=mode, **kargs)
         assert isinstance(model_name_pairs, list)
         self.key = key
         self.model_name_pairs = model_name_pairs
+        self.name = name + "_l2"
 
     def forward(self, predicts, batch):
         loss_dict = dict()
@@ -101,5 +103,6 @@ class DistillationDistanceLoss(DistanceLoss):
                     loss_dict["{}_{}_{}".format(self.name, key, idx)] = loss[
                         key]
             else:
-                loss_dict["{}_{}".format(self.name, idx)] = loss
+                loss_dict["{}_{}_{}_{}".format(self.name, pair[0], pair[1],
+                                               idx)] = loss
         return loss_dict

ppocr/metrics/__init__.py

@@ -19,20 +19,23 @@ from __future__ import unicode_literals
 
 import copy
 
-__all__ = ['build_metric']
+__all__ = ["build_metric"]
 
+from .det_metric import DetMetric
+from .rec_metric import RecMetric
+from .cls_metric import ClsMetric
+from .e2e_metric import E2EMetric
+from .distillation_metric import DistillationMetric
 
-def build_metric(config):
-    from .det_metric import DetMetric
-    from .rec_metric import RecMetric
-    from .cls_metric import ClsMetric
-    from .e2e_metric import E2EMetric
 
-    support_dict = ['DetMetric', 'RecMetric', 'ClsMetric', 'E2EMetric']
+def build_metric(config):
+    support_dict = [
+        "DetMetric", "RecMetric", "ClsMetric", "E2EMetric", "DistillationMetric"
+    ]
 
     config = copy.deepcopy(config)
-    module_name = config.pop('name')
+    module_name = config.pop("name")
     assert module_name in support_dict, Exception(
-        'metric only support {}'.format(support_dict))
+        "metric only support {}".format(support_dict))
     module_class = eval(module_name)(**config)
     return module_class

ppocr/postprocess/rec_postprocess.py

@@ -135,19 +135,25 @@ class DistillationCTCLabelDecode(CTCLabelDecode):
                  character_dict_path=None,
                  character_type='ch',
                  use_space_char=False,
-                 model_name="student",
+                 model_name=["student"],
                  key=None,
                  **kwargs):
         super(DistillationCTCLabelDecode, self).__init__(
             character_dict_path, character_type, use_space_char)
+        if not isinstance(model_name, list):
+            model_name = [model_name]
         self.model_name = model_name
+
         self.key = key
 
     def __call__(self, preds, label=None, *args, **kwargs):
-        pred = preds[self.model_name]
-        if self.key is not None:
-            pred = pred[self.key]
-        return super().__call__(pred, label=label, *args, **kwargs)
+        output = dict()
+        for name in self.model_name:
+            pred = preds[name]
+            if self.key is not None:
+                pred = pred[self.key]
+            output[name] = super().__call__(pred, label=label, *args, **kwargs)
+        return output
 
 
 class AttnLabelDecode(BaseRecLabelDecode):