perf: add parameter validation (#1249)

When using warm up, the total epoch num must be greater than warm up epoch num. Otherwise,
there will be raising warning and warm up epoch num will be set to total epoch num.

ppcls/optimizer/learning_rate.py

@@ -11,12 +11,15 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
 from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
 
 from paddle.optimizer import lr
 from paddle.optimizer.lr import LRScheduler
 
+from ppcls.utils import logger
+
 
 class Linear(object):
     """
@@ -41,7 +44,11 @@ class Linear(object):
                  warmup_start_lr=0.0,
                  last_epoch=-1,
                  **kwargs):
-        super(Linear, self).__init__()
+        super().__init__()
+        if warmup_epoch >= epochs:
+            msg = f"When using warm up, the value of \"Global.epochs\" must be greater than value of \"Optimizer.lr.warmup_epoch\". The value of \"Optimizer.lr.warmup_epoch\" has been set to {epochs}."
+            logger.warning(msg)
+            warmup_epoch = epochs
         self.learning_rate = learning_rate
         self.steps = (epochs - warmup_epoch) * step_each_epoch
         self.end_lr = end_lr
@@ -56,7 +63,8 @@ class Linear(object):
             decay_steps=self.steps,
             end_lr=self.end_lr,
             power=self.power,
-            last_epoch=self.last_epoch)
+            last_epoch=self.
+            last_epoch) if self.steps > 0 else self.learning_rate
         if self.warmup_steps > 0:
             learning_rate = lr.LinearWarmup(
                 learning_rate=learning_rate,
@@ -90,7 +98,11 @@ class Cosine(object):
                  warmup_start_lr=0.0,
                  last_epoch=-1,
                  **kwargs):
-        super(Cosine, self).__init__()
+        super().__init__()
+        if warmup_epoch >= epochs:
+            msg = f"When using warm up, the value of \"Global.epochs\" must be greater than value of \"Optimizer.lr.warmup_epoch\". The value of \"Optimizer.lr.warmup_epoch\" has been set to {epochs}."
+            logger.warning(msg)
+            warmup_epoch = epochs
         self.learning_rate = learning_rate
         self.T_max = (epochs - warmup_epoch) * step_each_epoch
         self.eta_min = eta_min
@@ -103,7 +115,8 @@ class Cosine(object):
             learning_rate=self.learning_rate,
             T_max=self.T_max,
             eta_min=self.eta_min,
-            last_epoch=self.last_epoch)
+            last_epoch=self.
+            last_epoch) if self.T_max > 0 else self.learning_rate
         if self.warmup_steps > 0:
             learning_rate = lr.LinearWarmup(
                 learning_rate=learning_rate,
@@ -132,12 +145,17 @@ class Step(object):
                  learning_rate,
                  step_size,
                  step_each_epoch,
+                 epochs,
                  gamma,
                  warmup_epoch=0,
                  warmup_start_lr=0.0,
                  last_epoch=-1,
                  **kwargs):
-        super(Step, self).__init__()
+        super().__init__()
+        if warmup_epoch >= epochs:
+            msg = f"When using warm up, the value of \"Global.epochs\" must be greater than value of \"Optimizer.lr.warmup_epoch\". The value of \"Optimizer.lr.warmup_epoch\" has been set to {epochs}."
+            logger.warning(msg)
+            warmup_epoch = epochs
         self.step_size = step_each_epoch * step_size
         self.learning_rate = learning_rate
         self.gamma = gamma
@@ -177,11 +195,16 @@ class Piecewise(object):
                  step_each_epoch,
                  decay_epochs,
                  values,
+                 epochs,
                  warmup_epoch=0,
                  warmup_start_lr=0.0,
                  last_epoch=-1,
                  **kwargs):
-        super(Piecewise, self).__init__()
+        super().__init__()
+        if warmup_epoch >= epochs:
+            msg = f"When using warm up, the value of \"Global.epochs\" must be greater than value of \"Optimizer.lr.warmup_epoch\". The value of \"Optimizer.lr.warmup_epoch\" has been set to {epochs}."
+            logger.warning(msg)
+            warmup_epoch = epochs
         self.boundaries = [step_each_epoch * e for e in decay_epochs]
         self.values = values
         self.last_epoch = last_epoch
@@ -294,8 +317,7 @@ class MultiStepDecay(LRScheduler):
             raise ValueError('gamma should be < 1.0.')
         self.milestones = [x * step_each_epoch for x in milestones]
         self.gamma = gamma
-        super(MultiStepDecay, self).__init__(learning_rate, last_epoch,
-                                             verbose)
+        super().__init__(learning_rate, last_epoch, verbose)
 
     def get_lr(self):
         for i in range(len(self.milestones)):