refine code

ppcls/engine/train/utils.py

@@ -39,7 +39,7 @@ def update_loss(trainer, loss_dict, batch_size):
 
 def log_info(trainer, batch_size, epoch_id, iter_id):
     lr_msg = ", ".join([
-        "lr_{}: {:.8f}".format(i + 1, lr.get_lr())
+        "lr({}): {:.8f}".format(lr.__class__.__name__, lr.get_lr())
         for i, lr in enumerate(trainer.lr_sch)
     ])
     metric_msg = ", ".join([

ppcls/optimizer/__init__.py

@@ -44,10 +44,9 @@ def build_lr_scheduler(lr_config, epochs, step_each_epoch):
 
 # model_list is None in static graph
 def build_optimizer(config, epochs, step_each_epoch, model_list=None):
-    config = copy.deepcopy(config)
-    optim_config = config["Optimizer"]
+    optim_config = copy.deepcopy(config)
     if isinstance(optim_config, dict):
-        # convert {'name': xxx, **optim_cfg} to [{name: {scope: xxx, **optim_cfg}}]
+        # convert {'name': xxx, **optim_cfg} to [{'name': {'scope': xxx, **optim_cfg}}]
         optim_name = optim_config.pop("name")
         optim_config: List[Dict[str, Dict]] = [{
             optim_name: {
@@ -61,19 +60,19 @@ def build_optimizer(config, epochs, step_each_epoch, model_list=None):
     """NOTE:
     Currently only support optim objets below.
     1. single optimizer config.
-    2. next level uner Arch, such as Arch.backbone, Arch.neck, Arch.head.
-    3. loss which has parameters, such as CenterLoss.
+    2. model(entire Arch), backbone, neck, head.
+    3. loss(entire Loss), specific loss listed in ppcls/loss/__init__.py.
     """
     for optim_item in optim_config:
-        # optim_cfg = {optim_name: {scope: xxx, **optim_cfg}}
+        # optim_cfg = {optim_name: {'scope': xxx, **optim_cfg}}
         # step1 build lr
         optim_name = list(optim_item.keys())[0]  # get optim_name
         optim_scope = optim_item[optim_name].pop('scope')  # get optim_scope
         optim_cfg = optim_item[optim_name]  # get optim_cfg
 
         lr = build_lr_scheduler(optim_cfg.pop('lr'), epochs, step_each_epoch)
-        logger.debug("build lr ({}) for scope ({}) success..".format(
-            lr, optim_scope))
+        logger.info("build lr ({}) for scope ({}) success..".format(
+            lr.__class__.__name__, optim_scope))
         # step2 build regularization
         if 'regularizer' in optim_cfg and optim_cfg['regularizer'] is not None:
             if 'weight_decay' in optim_cfg:
@@ -84,8 +83,8 @@ def build_optimizer(config, epochs, step_each_epoch, model_list=None):
             reg_name = reg_config.pop('name') + 'Decay'
             reg = getattr(paddle.regularizer, reg_name)(**reg_config)
             optim_cfg["weight_decay"] = reg
-            logger.debug("build regularizer ({}) for scope ({}) success..".
-                         format(reg, optim_scope))
+            logger.info("build regularizer ({}) for scope ({}) success..".
+                        format(reg.__class__.__name__, optim_scope))
         # step3 build optimizer
         if 'clip_norm' in optim_cfg:
             clip_norm = optim_cfg.pop('clip_norm')
@@ -93,30 +92,42 @@ def build_optimizer(config, epochs, step_each_epoch, model_list=None):
         else:
             grad_clip = None
         optim_model = []
-        for i in range(len(model_list)):
-            if len(model_list[i].parameters()) == 0:
-                continue
-            if optim_scope == "all":
-                # optimizer for all
-                optim_model.append(model_list[i])
-            else:
-                if optim_scope.endswith("Loss"):
-                    # optimizer for loss
-                    for m in model_list[i].sublayers(True):
-                        if m.__class_name == optim_scope:
-                            optim_model.append(m)
-                else:
-                    # opmizer for module in model, such as backbone, neck, head...
-                    if hasattr(model_list[i], optim_scope):
-                        optim_model.append(getattr(model_list[i], optim_scope))
 
-        assert len(optim_model) == 1, \
-            "Invalid optim model for optim scope({}), number of optim_model={}".format(optim_scope, len(optim_model))
+        # for static graph
+        if model_list is None:
+            optim = getattr(optimizer, optim_name)(
+                learning_rate=lr, grad_clip=grad_clip,
+                **optim_cfg)(model_list=optim_model)
+            return optim, lr
+
+        # for dynamic graph
+        if optim_scope == "all":
+            optim_model = model_list
+        elif optim_scope == "model":
+            optim_model = [model_list[0], ]
+        elif optim_scope in ["backbone", "neck", "head"]:
+            optim_model = [getattr(model_list[0], optim_scope, None), ]
+        elif optim_scope == "loss":
+            optim_model = [model_list[1], ]
+        else:
+            optim_model = [
+                model_list[1].loss_func[i]
+                for i in range(len(model_list[1].loss_func))
+                if model_list[1].loss_func[i].__class__.__name__ == optim_scope
+            ]
+        optim_model = [
+            optim_model[i] for i in range(len(optim_model))
+            if (optim_model[i] is not None
+                ) and (len(optim_model[i].parameters()) > 0)
+        ]
+        assert len(optim_model) > 0, \
+            f"optim_model is empty for optim_scope({optim_scope})"
+
         optim = getattr(optimizer, optim_name)(
             learning_rate=lr, grad_clip=grad_clip,
             **optim_cfg)(model_list=optim_model)
-        logger.debug("build optimizer ({}) for scope ({}) success..".format(
-            optim, optim_scope))
+        logger.info("build optimizer ({}) for scope ({}) success..".format(
+            optim.__class__.__name__, optim_scope))
         optim_list.append(optim)
         lr_list.append(lr)
     return optim_list, lr_list