Merge pull request #1277 from TingquanGao/dev/fix_opt

fix: support static graph

deploy/python/preprocess.py

@@ -79,7 +79,7 @@ class UnifiedResize(object):
             if isinstance(interpolation, str):
                 interpolation = _cv2_interp_from_str[interpolation.lower()]
             # compatible with opencv < version 4.4.0
-            elif not interpolation:
+            elif interpolation is None:
                 interpolation = cv2.INTER_LINEAR
             self.resize_func = partial(cv2.resize, interpolation=interpolation)
         elif backend.lower() == "pil":

ppcls/data/preprocess/ops/operators.py

@@ -60,7 +60,7 @@ class UnifiedResize(object):
             if isinstance(interpolation, str):
                 interpolation = _cv2_interp_from_str[interpolation.lower()]
             # compatible with opencv < version 4.4.0
-            elif not interpolation:
+            elif interpolation is None:
                 interpolation = cv2.INTER_LINEAR
             self.resize_func = partial(cv2.resize, interpolation=interpolation)
         elif backend.lower() == "pil":

ppcls/optimizer/__init__.py

@@ -41,7 +41,8 @@ def build_lr_scheduler(lr_config, epochs, step_each_epoch):
     return lr
 
 
-def build_optimizer(config, epochs, step_each_epoch, model_list):
+# model_list is None in static graph
+def build_optimizer(config, epochs, step_each_epoch, model_list=None):
     config = copy.deepcopy(config)
     # step1 build lr
     lr = build_lr_scheduler(config.pop('lr'), epochs, step_each_epoch)

ppcls/optimizer/optimizer.py

@@ -18,6 +18,8 @@ from __future__ import print_function
 
 from paddle import optimizer as optim
 
+from ppcls.utils import logger
+
 
 class Momentum(object):
     """
@@ -43,7 +45,9 @@ class Momentum(object):
         self.multi_precision = multi_precision
 
     def __call__(self, model_list):
-        parameters = sum([m.parameters() for m in model_list], [])
+        # model_list is None in static graph
+        parameters = sum([m.parameters() for m in model_list],
+                         []) if model_list else None
         opt = optim.Momentum(
             learning_rate=self.learning_rate,
             momentum=self.momentum,
@@ -79,7 +83,9 @@ class Adam(object):
         self.multi_precision = multi_precision
 
     def __call__(self, model_list):
-        parameters = sum([m.parameters() for m in model_list], [])
+        # model_list is None in static graph
+        parameters = sum([m.parameters() for m in model_list],
+                         []) if model_list else None
         opt = optim.Adam(
             learning_rate=self.learning_rate,
             beta1=self.beta1,
@@ -123,7 +129,9 @@ class RMSProp(object):
         self.grad_clip = grad_clip
 
     def __call__(self, model_list):
-        parameters = sum([m.parameters() for m in model_list], [])
+        # model_list is None in static graph
+        parameters = sum([m.parameters() for m in model_list],
+                         []) if model_list else None
         opt = optim.RMSProp(
             learning_rate=self.learning_rate,
             momentum=self.momentum,
@@ -160,18 +168,28 @@ class AdamW(object):
         self.one_dim_param_no_weight_decay = one_dim_param_no_weight_decay
 
     def __call__(self, model_list):
-        parameters = sum([m.parameters() for m in model_list], [])
+        # model_list is None in static graph
+        parameters = sum([m.parameters() for m in model_list],
+                         []) if model_list else None
+
+        # TODO(gaotingquan): model_list is None when in static graph, "no_weight_decay" not work.
+        if model_list is None:
+            if self.one_dim_param_no_weight_decay or len(
+                    self.no_weight_decay_name_list) != 0:
+                msg = "\"AdamW\" does not support setting \"no_weight_decay\" in static graph. Please use dynamic graph."
+                logger.error(Exception(msg))
+                raise Exception(msg)
 
         self.no_weight_decay_param_name_list = [
             p.name for model in model_list for n, p in model.named_parameters()
             if any(nd in n for nd in self.no_weight_decay_name_list)
-        ]
+        ] if model_list else []
 
         if self.one_dim_param_no_weight_decay:
             self.no_weight_decay_param_name_list += [
                 p.name for model in model_list
                 for n, p in model.named_parameters() if len(p.shape) == 1
-            ]
+            ] if model_list else []
 
         opt = optim.AdamW(
             learning_rate=self.learning_rate,