refine distillation model (#565)

configs/Distillation/R50_vd_distill_MV3_large_x1_0.yaml

@@ -2,7 +2,7 @@ mode: 'train'
 ARCHITECTURE:
     name: 'ResNet50_vd_distill_MobileNetV3_large_x1_0'
 
-pretrained_model: "./pretrained/ResNet50_vd_ssld_pretrained/"
+pretrained_model: "./pretrained/ResNet50_vd_ssld_pretrained"
 model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167

configs/Distillation/ResX101_32x16d_wsl_distill_R50_vd.yaml

@@ -2,7 +2,7 @@ mode: 'train'
 ARCHITECTURE:
     name: 'ResNeXt101_32x16d_wsl_distill_ResNet50_vd'
 
-pretrained_model: "./pretrained/ResNeXt101_32x16d_wsl_pretrained/"
+pretrained_model: "./pretrained/ResNeXt101_32x16d_wsl_pretrained"
 model_save_dir: "./output/"
 classes_num: 1000
 total_images: 1281167

ppcls/modeling/architectures/distillation_models.py

@@ -32,34 +32,34 @@ __all__ = [
 
 
 class ResNet50_vd_distill_MobileNetV3_large_x1_0(nn.Layer):
-    def __init__(self, class_dim=1000, **args):
+    def __init__(self, class_dim=1000, freeze_teacher=True, **args):
         super(ResNet50_vd_distill_MobileNetV3_large_x1_0, self).__init__()
 
         self.teacher = ResNet50_vd(class_dim=class_dim, **args)
-
         self.student = MobileNetV3_large_x1_0(class_dim=class_dim, **args)
 
-    def forward(self, input):
-        teacher_label = self.teacher(input)
-        teacher_label.stop_gradient = True
-
-        student_label = self.student(input)
+        if freeze_teacher:
+            for param in self.teacher.parameters():
+                param.trainable = False
 
+    def forward(self, x):
+        teacher_label = self.teacher(x)
+        student_label = self.student(x)
         return teacher_label, student_label
 
 
 class ResNeXt101_32x16d_wsl_distill_ResNet50_vd(nn.Layer):
-    def __init__(self, class_dim=1000, **args):
+    def __init__(self, class_dim=1000, freeze_teacher=True, **args):
         super(ResNeXt101_32x16d_wsl_distill_ResNet50_vd, self).__init__()
 
         self.teacher = ResNeXt101_32x16d_wsl(class_dim=class_dim, **args)
-
         self.student = ResNet50_vd(class_dim=class_dim, **args)
 
-    def forward(self, input):
-        teacher_label = self.teacher(input)
-        teacher_label.stop_gradient = True
-
-        student_label = self.student(input)
+        if freeze_teacher:
+            for param in self.teacher.parameters():
+                param.trainable = False
 
+    def forward(self, x):
+        teacher_label = self.teacher(x)
+        student_label = self.student(x)
         return teacher_label, student_label