set pretrain_weights to None in qat

dygraph/paddlex/cv/models/base.py

@@ -134,12 +134,14 @@ class BaseModel:
 
         if self.status == 'Quantized':
             self.quanter.save_quantized_model(
-                self.net, save_dir, input_spec=self.test_inputs)
+                self.net,
+                osp.join(save_dir, 'model'),
+                input_spec=self.test_inputs)
         else:
             paddle.save(self.net.state_dict(),
-                        os.path.join(save_dir, 'model.pdparams'))
+                        osp.join(save_dir, 'model.pdparams'))
             paddle.save(self.optimizer.state_dict(),
-                        os.path.join(save_dir, 'model.pdopt'))
+                        osp.join(save_dir, 'model.pdopt'))
 
         with open(
                 osp.join(save_dir, 'model.yml'), encoding='utf-8',

dygraph/paddlex/cv/models/classifier.py

@@ -252,7 +252,6 @@ class BaseClassifier(BaseModel):
                           save_interval_epochs=1,
                           log_interval_steps=10,
                           save_dir='output',
-                          pretrain_weights='IMAGENET',
                           learning_rate=.025,
                           warmup_steps=0,
                           warmup_start_lr=0.0,
@@ -276,8 +275,6 @@ class BaseClassifier(BaseModel):
             save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1.
             log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10.
             save_dir(str, optional): Directory to save the model. Defaults to 'output'.
-            pretrain_weights(str or None, optional):
-                None or name/path of pretrained weights. If None, no pretrained weights will be loaded. Defaults to 'IMAGENET'.
             learning_rate(float, optional): Learning rate for training. Defaults to .025.
             warmup_steps(int, optional): The number of steps of warm-up training. Defaults to 0.
             warmup_start_lr(float, optional): Start learning rate of warm-up training. Defaults to 0..
@@ -303,7 +300,7 @@ class BaseClassifier(BaseModel):
             save_interval_epochs=save_interval_epochs,
             log_interval_steps=log_interval_steps,
             save_dir=save_dir,
-            pretrain_weights=pretrain_weights,
+            pretrain_weights=None,
             learning_rate=learning_rate,
             warmup_steps=warmup_steps,
             warmup_start_lr=warmup_start_lr,

dygraph/paddlex/cv/models/detector.py

@@ -255,7 +255,6 @@ class BaseDetector(BaseModel):
                           save_interval_epochs=1,
                           log_interval_steps=10,
                           save_dir='output',
-                          pretrain_weights='IMAGENET',
                           learning_rate=.001,
                           warmup_steps=0,
                           warmup_start_lr=0.0,
@@ -281,8 +280,6 @@ class BaseDetector(BaseModel):
             save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1.
             log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10.
             save_dir(str, optional): Directory to save the model. Defaults to 'output'.
-            pretrain_weights(str or None, optional):
-                None or name/path of pretrained weights. If None, no pretrained weights will be loaded. Defaults to 'IMAGENET'.
             learning_rate(float, optional): Learning rate for training. Defaults to .001.
             warmup_steps(int, optional): The number of steps of warm-up training. Defaults to 0.
             warmup_start_lr(float, optional): Start learning rate of warm-up training. Defaults to 0..
@@ -310,7 +307,7 @@ class BaseDetector(BaseModel):
             save_interval_epochs=save_interval_epochs,
             log_interval_steps=log_interval_steps,
             save_dir=save_dir,
-            pretrain_weights=pretrain_weights,
+            pretrain_weights=None,
             learning_rate=learning_rate,
             warmup_steps=warmup_steps,
             warmup_start_lr=warmup_start_lr,

dygraph/paddlex/cv/models/segmenter.py

@@ -237,7 +237,6 @@ class BaseSegmenter(BaseModel):
                           save_interval_epochs=1,
                           log_interval_steps=2,
                           save_dir='output',
-                          pretrain_weights='CITYSCAPES',
                           learning_rate=0.01,
                           lr_decay_power=0.9,
                           early_stop=False,
@@ -258,8 +257,6 @@ class BaseSegmenter(BaseModel):
             save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1.
             log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10.
             save_dir(str, optional): Directory to save the model. Defaults to 'output'.
-            pretrain_weights(str or None, optional):
-                None or name/path of pretrained weights. If None, no pretrained weights will be loaded. Defaults to 'IMAGENET'.
             learning_rate(float, optional): Learning rate for training. Defaults to .025.
             lr_decay_power(float, optional): Learning decay power. Defaults to .9.
             early_stop(bool, optional): Whether to adopt early stop strategy. Defaults to False.
@@ -281,7 +278,7 @@ class BaseSegmenter(BaseModel):
             save_interval_epochs=save_interval_epochs,
             log_interval_steps=log_interval_steps,
             save_dir=save_dir,
-            pretrain_weights=pretrain_weights,
+            pretrain_weights=None,
             learning_rate=learning_rate,
             lr_decay_power=lr_decay_power,
             early_stop=early_stop,