Add some other lr scheduler (#609)

* test=master add some other lr scheduler

* test=master fix something and validate them

ppdet/optimizer.py

@@ -39,12 +39,18 @@ class PiecewiseDecay(object):
     Multi step learning rate decay
 
     Args:
-        gamma (float): decay factor
+        gamma (float | list): decay factor
         milestones (list): steps at which to decay learning rate
     """
 
-    def __init__(self, gamma=0.1, milestones=[60000, 80000], values=None):
+    def __init__(self, gamma=[0.1, 0.1], milestones=[60000, 80000],
+                 values=None):
         super(PiecewiseDecay, self).__init__()
+        if type(gamma) is not list:
+            self.gamma = []
+            for i in range(len(milestones)):
+                self.gamma.append(gamma / 10**i)
+        else:
             self.gamma = gamma
         self.milestones = milestones
         self.values = values
@@ -54,13 +60,56 @@ class PiecewiseDecay(object):
             return fluid.layers.piecewise_decay(self.milestones, self.values)
         assert base_lr is not None, "either base LR or values should be provided"
         values = [base_lr]
-        lr = base_lr
-        for _ in self.milestones:
-            lr *= self.gamma
-            values.append(lr)
+        for g in self.gamma:
+            new_lr = base_lr * g
+            values.append(new_lr)
         return fluid.layers.piecewise_decay(self.milestones, values)
 
 
+@serializable
+class PolynomialDecay(object):
+    """
+    Applies polynomial decay to the initial learning rate.
+    Args:
+        max_iter (int) – The learning rate decay steps. 
+        end_lr(float) – End learning rate.
+        power (float) – Polynomial attenuation coefficient
+    """
+
+    def __init__(self, max_iter=180000, end_lr=0.0001, power=1.0):
+        super(PolynomialDecay).__init__()
+        self.max_iter = max_iter
+        self.end_lr = end_lr
+        self.power = power
+
+    def __call__(self, base_lr=None, learning_rate=None):
+        assert base_lr is not None, "either base LR or values should be provided"
+        lr = fluid.layers.polynomial_decay(base_lr, self.max_iter, self.end_lr,
+                                           self.power)
+        return lr
+
+
+@serializable
+class ExponentialDecay(object):
+    """
+    Applies exponential decay to the learning rate.
+    Args:
+        max_iter (int) – The learning rate decay steps. 
+        decay_rate (float) – The learning rate decay rate. 
+    """
+
+    def __init__(self, max_iter, decay_rate):
+        super(ExponentialDecay).__init__()
+        self.max_iter = max_iter
+        self.decay_rate = decay_rate
+
+    def __call__(self, base_lr=None, learning_rate=None):
+        assert base_lr is not None, "either base LR or values should be provided"
+        lr = fluid.layers.exponential_decay(base_lr, self.max_iter,
+                                            self.decay_rate)
+        return lr
+
+
 @serializable
 class CosineDecay(object):
     """
@@ -78,6 +127,7 @@ class CosineDecay(object):
     def __call__(self, base_lr=None, learning_rate=None):
         assert base_lr is not None, "either base LR or values should be provided"
         lr = fluid.layers.cosine_decay(base_lr, 1, self.max_iters)
+        return lr
 
 
 @serializable