fix small bugs

ppcls/optimizer/learning_rate.py

@@ -116,9 +116,9 @@ class Constant(LRBase):
                  last_epoch=-1,
                  by_epoch=False,
                  **kwargs):
-        super(Linear, self).__init__(epochs, step_each_epoch, learning_rate,
-                                     warmup_epoch, warmup_start_lr, last_epoch,
-                                     by_epoch)
+        super(Constant, self).__init__(epochs, step_each_epoch, learning_rate,
+                                       warmup_epoch, warmup_start_lr,
+                                       last_epoch, by_epoch)
 
     def __call__(self):
         learning_rate = lr.LRScheduler(
@@ -220,7 +220,7 @@ class Cosine(LRBase):
                  last_epoch=-1,
                  by_epoch=False,
                  **kwargs):
-        super(Linear, self).__init__(epochs, step_each_epoch, learning_rate,
+        super(Cosine, self).__init__(epochs, step_each_epoch, learning_rate,
                                      warmup_epoch, warmup_start_lr, last_epoch,
                                      by_epoch)
         self.T_max = (self.epochs - self.warmup_epoch) * self.step_each_epoch
@@ -269,7 +269,7 @@ class Step(LRBase):
                  last_epoch=-1,
                  by_epoch=False,
                  **kwargs):
-        super(Linear, self).__init__(epochs, step_each_epoch, learning_rate,
+        super(Step, self).__init__(epochs, step_each_epoch, learning_rate,
                                    warmup_epoch, warmup_start_lr, last_epoch,
                                    by_epoch)
         self.step_size = step_size * step_each_epoch
@@ -315,7 +315,7 @@ class Piecewise(LRBase):
                  last_epoch=-1,
                  by_epoch=False,
                  **kwargs):
-        super(Linear,
+        super(Piecewise,
               self).__init__(epochs, step_each_epoch, values[0], warmup_epoch,
                              warmup_start_lr, last_epoch, by_epoch)
         self.values = values
@@ -362,9 +362,9 @@ class MultiStepDecay(LRBase):
                  last_epoch=-1,
                  by_epoch=False,
                  **kwargs):
-        super(Linear, self).__init__(epochs, step_each_epoch, learning_rate,
-                                     warmup_epoch, warmup_start_lr, last_epoch,
-                                     by_epoch)
+        super(MultiStepDecay, self).__init__(
+            epochs, step_each_epoch, learning_rate, warmup_epoch,
+            warmup_start_lr, last_epoch, by_epoch)
         self.milestones = [x * step_each_epoch for x in milestones]
         self.gamma = gamma
         if self.by_epoch: