add Const lr

configs/vqa/re/layoutlmv2.yml

@@ -34,10 +34,12 @@ Optimizer:
   beta2: 0.999
   clip_norm: 10
   lr:
-    name: Piecewise
-    values: [0.000005, 0.00005]
-    decay_epochs: [10]
-    warmup_epoch: 0
+    # name: Piecewise
+    # values: [0.000005, 0.00005]
+    # decay_epochs: [10]
+    # warmup_epoch: 0
+    learning_rate: 0.00005
+    warmup_epoch: 10
   regularizer:
     name: L2
     factor: 0.00000

configs/vqa/re/layoutxlm.yml

@@ -34,10 +34,8 @@ Optimizer:
   beta2: 0.999
   clip_norm: 10
   lr:
-    name: Piecewise
-    values: [0.000005, 0.00005]
-    decay_epochs: [10]
-    warmup_epoch: 0
+    learning_rate: 0.00005
+    warmup_epoch: 10
   regularizer:
     name: L2
     factor: 0.00000

ppocr/optimizer/__init__.py

@@ -25,11 +25,8 @@ __all__ = ['build_optimizer']
 def build_lr_scheduler(lr_config, epochs, step_each_epoch):
     from . import learning_rate
     lr_config.update({'epochs': epochs, 'step_each_epoch': step_each_epoch})
-    if 'name' in lr_config:
-        lr_name = lr_config.pop('name')
+    lr_name = lr_config.pop('name', 'Const')
     lr = getattr(learning_rate, lr_name)(**lr_config)()
-    else:
-        lr = lr_config['learning_rate']
     return lr
 
 

ppocr/optimizer/learning_rate.py

@@ -276,3 +276,35 @@ class OneCycle(object):
                 end_lr=self.max_lr,
                 last_epoch=self.last_epoch)
         return learning_rate
+
+
+class Const(object):
+    """
+    Const learning rate decay
+    Args:
+        learning_rate(float): initial learning rate
+        step_each_epoch(int): steps each epoch
+        last_epoch (int, optional):  The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
+    """
+
+    def __init__(self,
+                 learning_rate,
+                 step_each_epoch,
+                 warmup_epoch=0,
+                 last_epoch=-1,
+                 **kwargs):
+        super(Const, self).__init__()
+        self.learning_rate = learning_rate
+        self.last_epoch = last_epoch
+        self.warmup_epoch = round(warmup_epoch * step_each_epoch)
+
+    def __call__(self):
+        learning_rate = self.learning_rate
+        if self.warmup_epoch > 0:
+            learning_rate = lr.LinearWarmup(
+                learning_rate=learning_rate,
+                warmup_steps=self.warmup_epoch,
+                start_lr=0.0,
+                end_lr=self.learning_rate,
+                last_epoch=self.last_epoch)
+        return learning_rate