From 80b8ca3f23089dd88dd6b35b8309e3a459c72f1a Mon Sep 17 00:00:00 2001
From: HydrogenSulfate <490868991@qq.com>
Date: Wed, 20 Apr 2022 12:16:40 +0800
Subject: [PATCH] fix optimizer/init.py
---
ppcls/optimizer/__init__.py | 24 +++++++++++++++---------
1 file changed, 15 insertions(+), 9 deletions(-)
diff --git a/ppcls/optimizer/__init__.py b/ppcls/optimizer/__init__.py
index cc0041137..b7b4d4210 100644
--- a/ppcls/optimizer/__init__.py
+++ b/ppcls/optimizer/__init__.py
@@ -51,7 +51,7 @@ def build_optimizer(config, epochs, step_each_epoch, model_list=None):
optim_name = optim_config.pop("name")
optim_config: List[Dict[str, Dict]] = [{
optim_name: {
- 'scope': config["Arch"].get("name"),
+ 'scope': "all",
**
optim_config
}
@@ -59,10 +59,10 @@ def build_optimizer(config, epochs, step_each_epoch, model_list=None):
optim_list = []
lr_list = []
for optim_item in optim_config:
- # optim_cfg = {optim_name1: {scope: xxx, **optim_cfg}}
+ # optim_cfg = {optim_name: {scope: xxx, **optim_cfg}}
# step1 build lr
- optim_name = list(optim_item.keys())[0] # get optim_name1
- optim_scope = optim_item[optim_name].pop('scope') # get scope
+ 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)
@@ -78,7 +78,8 @@ 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 ({}) success..".format(reg))
+ logger.debug("build regularizer ({}) for scope ({}) success..".
+ format(reg, optim_scope))
# step3 build optimizer
if 'clip_norm' in optim_cfg:
clip_norm = optim_cfg.pop('clip_norm')
@@ -87,11 +88,16 @@ def build_optimizer(config, epochs, step_each_epoch, model_list=None):
grad_clip = None
optim_model = []
for i in range(len(model_list)):
- class_name = model_list[i].__class__.__name__
- if class_name == optim_scope:
+ if len(model_list[i].parameters()) == 0:
+ continue
+ if optim_scope == "all":
optim_model.append(model_list[i])
- assert len(optim_model) == 1 and len(optim_model[0].parameters()) > 0, \
- f"Invalid optim model for optim scope({optim_scope}), number of optim_model={len(optim_model)}, and number of optim_model's params={len(optim_model[0].parameters())}"
+ else:
+ for m in model_list[i].sublayers(True):
+ if m.__class__.__name__ == optim_scope:
+ optim_model.append(model_list[i])
+ assert len(optim_model) == 1, \
+ "Invalid optim model for optim scope({}), number of optim_model={}".format(optim_scope, len(optim_model))
optim = getattr(optimizer, optim_name)(
learning_rate=lr, grad_clip=grad_clip,
**optim_cfg)(model_list=optim_model)