Merge pull request #7404 from LDOUBLEV/26_doc

fix cml

configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml

@@ -94,14 +94,11 @@ Loss:
       - ["Student", "Student2"]
       maps_name: "thrink_maps"
       weight: 1.0
-      # act: None
       model_name_pairs: ["Student", "Student2"]
       key: maps
   - DistillationDBLoss:
       weight: 1.0
       model_name_list: ["Student", "Student2"]
-      # key: maps
-      # name: DBLoss
       balance_loss: true
       main_loss_type: DiceLoss
       alpha: 5
@@ -191,7 +188,6 @@ Eval:
           channel_first: False
       - DetLabelEncode: # Class handling label
       - DetResizeForTest:
-#           image_shape: [736, 1280]
       - NormalizeImage:
           scale: 1./255.
           mean: [0.485, 0.456, 0.406]

configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml

@@ -24,6 +24,7 @@ Architecture:
   model_type: det
   Models:
     Student:
+      pretrained:
       model_type: det
       algorithm: DB
       Transform: null
@@ -40,6 +41,7 @@ Architecture:
         name: DBHead
         k: 50
     Student2:
+      pretrained: 
       model_type: det
       algorithm: DB
       Transform: null
@@ -56,6 +58,7 @@ Architecture:
         name: DBHead
         k: 50
     Teacher:
+      pretrained: 
       freeze_params: true
       return_all_feats: false
       model_type: det
@@ -91,14 +94,11 @@ Loss:
       - ["Student", "Student2"]
       maps_name: "thrink_maps"
       weight: 1.0
-      # act: None
       model_name_pairs: ["Student", "Student2"]
       key: maps
   - DistillationDBLoss:
       weight: 1.0
       model_name_list: ["Student", "Student2"]
-      # key: maps
-      # name: DBLoss
       balance_loss: true
       main_loss_type: DiceLoss
       alpha: 5
@@ -204,31 +204,21 @@ Eval:
     label_file_list:
       - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
     transforms:
-    - DecodeImage:
+      - DecodeImage: # load image
           img_mode: BGR
-        channel_first: false
-    - DetLabelEncode: null
-    - DetResizeForTest: null
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
       - NormalizeImage:
           scale: 1./255.
-        mean:
-        - 0.485
-        - 0.456
-        - 0.406
-        std:
-        - 0.229
-        - 0.224
-        - 0.225
-        order: hwc
-    - ToCHWImage: null
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - ToCHWImage:
       - KeepKeys:
-        keep_keys:
-        - image
-        - shape
-        - polys
-        - ignore_tags
+          keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
   loader:
-    shuffle: false
-    drop_last: false
-    batch_size_per_card: 1
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1 # must be 1
     num_workers: 2

ppocr/losses/basic_loss.py

@@ -60,19 +60,19 @@ class KLJSLoss(object):
                         ], "mode can only be one of ['kl', 'KL', 'js', 'JS']"
         self.mode = mode
 
-    def __call__(self, p1, p2, reduction="mean"):
+    def __call__(self, p1, p2, reduction="mean", eps=1e-5):
 
         if self.mode.lower() == 'kl':
             loss = paddle.multiply(p2,
-                                   paddle.log((p2 + 1e-5) / (p1 + 1e-5) + 1e-5))
+                                   paddle.log((p2 + eps) / (p1 + eps) + eps))
             loss += paddle.multiply(
-                p1, paddle.log((p1 + 1e-5) / (p2 + 1e-5) + 1e-5))
+                p1, paddle.log((p1 + eps) / (p2 + eps) + eps))
             loss *= 0.5
         elif self.mode.lower() == "js":
             loss = paddle.multiply(
-                p2, paddle.log((2 * p2 + 1e-5) / (p1 + p2 + 1e-5) + 1e-5))
+                p2, paddle.log((2 * p2 + eps) / (p1 + p2 + eps) + eps))
             loss += paddle.multiply(
-                p1, paddle.log((2 * p1 + 1e-5) / (p1 + p2 + 1e-5) + 1e-5))
+                p1, paddle.log((2 * p1 + eps) / (p1 + p2 + eps) + eps))
             loss *= 0.5
         else:
             raise ValueError(
@@ -125,7 +125,7 @@ class DMLLoss(nn.Layer):
             loss = (
                 self._kldiv(log_out1, out2) + self._kldiv(log_out2, out1)) / 2.0
         else:
-            # for detection distillation log is not needed
+            # distillation log is not needed for detection 
             loss = self.jskl_loss(out1, out2)
         return loss