fix det cml + pact + distribute training bug

deploy/slim/quantization/quant.py

@@ -161,12 +161,6 @@ def main(config, device, logger, vdl_writer):
     if config["Global"]["pretrained_model"] is not None:
         pre_best_model_dict = load_model(config, model)
 
-    quanter = QAT(config=quant_config, act_preprocess=PACT)
-    quanter.quantize(model)
-
-    if config['Global']['distributed']:
-        model = paddle.DataParallel(model)
-
     # build loss
     loss_class = build_loss(config['Loss'])
 
@@ -181,6 +175,12 @@ def main(config, device, logger, vdl_writer):
     if config["Global"]["checkpoints"] is not None:
         pre_best_model_dict = load_model(config, model, optimizer)
 
+    quanter = QAT(config=quant_config, act_preprocess=PACT)
+    quanter.quantize(model)
+
+    if config['Global']['distributed']:
+        model = paddle.DataParallel(model)
+
     # build metric
     eval_class = build_metric(config['Metric'])
 

ppocr/optimizer/optimizer.py

@@ -43,12 +43,15 @@ class Momentum(object):
         self.grad_clip = grad_clip
 
     def __call__(self, model):
+        train_params = [
+            param for param in model.parameters() if param.trainable is True
+        ]
         opt = optim.Momentum(
             learning_rate=self.learning_rate,
             momentum=self.momentum,
             weight_decay=self.weight_decay,
             grad_clip=self.grad_clip,
-            parameters=model.parameters())
+            parameters=train_params)
         return opt
 
 
@@ -76,6 +79,9 @@ class Adam(object):
         self.lazy_mode = lazy_mode
 
     def __call__(self, model):
+        train_params = [
+            param for param in model.parameters() if param.trainable is True
+        ]
         opt = optim.Adam(
             learning_rate=self.learning_rate,
             beta1=self.beta1,
@@ -85,7 +91,7 @@ class Adam(object):
             grad_clip=self.grad_clip,
             name=self.name,
             lazy_mode=self.lazy_mode,
-            parameters=model.parameters())
+            parameters=train_params)
         return opt
 
 
@@ -118,6 +124,9 @@ class RMSProp(object):
         self.grad_clip = grad_clip
 
     def __call__(self, model):
+        train_params = [
+            param for param in model.parameters() if param.trainable is True
+        ]
         opt = optim.RMSProp(
             learning_rate=self.learning_rate,
             momentum=self.momentum,
@@ -125,7 +134,7 @@ class RMSProp(object):
             epsilon=self.epsilon,
             weight_decay=self.weight_decay,
             grad_clip=self.grad_clip,
-            parameters=model.parameters())
+            parameters=train_params)
         return opt
 
 
@@ -149,6 +158,9 @@ class Adadelta(object):
         self.name = name
 
     def __call__(self, model):
+        train_params = [
+            param for param in model.parameters() if param.trainable is True
+        ]
         opt = optim.Adadelta(
             learning_rate=self.learning_rate,
             epsilon=self.epsilon,
@@ -156,7 +168,7 @@ class Adadelta(object):
             weight_decay=self.weight_decay,
             grad_clip=self.grad_clip,
             name=self.name,
-            parameters=model.parameters())
+            parameters=train_params)
         return opt
 
 
@@ -190,17 +202,20 @@ class AdamW(object):
         self.one_dim_param_no_weight_decay = one_dim_param_no_weight_decay
 
     def __call__(self, model):
-        parameters = model.parameters()
+        parameters = [
+            param for param in model.parameters() if param.trainable is True
+        ]
 
         self.no_weight_decay_param_name_list = [
-            p.name for n, p in model.named_parameters() if any(nd in n for nd in self.no_weight_decay_name_list)
+            p.name for n, p in model.named_parameters()
+            if any(nd in n for nd in self.no_weight_decay_name_list)
         ]
 
         if self.one_dim_param_no_weight_decay:
             self.no_weight_decay_param_name_list += [
-                p.name  for n, p in model.named_parameters() if len(p.shape) == 1
+                p.name for n, p in model.named_parameters() if len(p.shape) == 1
             ]
-        
+
         opt = optim.AdamW(
             learning_rate=self.learning_rate,
             beta1=self.beta1,
@@ -216,4 +231,4 @@ class AdamW(object):
         return opt
 
     def _apply_decay_param_fun(self, name):
-        return name not in self.no_weight_decay_param_name_list
+        return name not in self.no_weight_decay_param_name_list