revert trainer
@@ -38,7 +38,6 @@ from ppcls.utils.config import print_config
from ppcls.data import build_dataloader
from ppcls.arch import build_model
from ppcls.arch import apply_to_static
-from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
from ppcls.loss import build_loss
from ppcls.metric import build_metrics
from ppcls.optimizer import build_optimizer
@@ -76,11 +75,6 @@ class Trainer(object):
self.is_rec = False
self.model = build_model(self.config["Arch"])
- if "return_patterns" in self.config["Arch"] and isinstance(self.model, TheseusLayer):
- self.model.update_res(self.config["Arch"]["return_patterns"])
- self.return_inter = True
- else:
- self.return_inter = False
# set @to_static for benchmark, skip this by default.
apply_to_static(self.config, self.model)
@@ -397,11 +391,11 @@ class Trainer(object):
self.model.train()
return eval_result
- def forward(self, batch, res_dict=None):
+ def forward(self, batch):
if not self.is_rec:
- out = self.model(batch[0], res_dict=res_dict)
+ out = self.model(batch[0])
else:
- out = self.model(batch[0], batch[1], return_dict=res_dict)
+ out = self.model(batch[0], batch[1])
return out
@paddle.no_grad()
@@ -659,11 +653,7 @@ class Trainer(object):
image_file_list.append(image_file)
if len(batch_data) >= batch_size or idx == len(image_list) - 1:
batch_tensor = paddle.to_tensor(batch_data)
- if self.return_inter:
- res_dict = {}
- else:
- res_dict = None
- out = self.forward([batch_tensor], res_dict)
+ out = self.forward([batch_tensor])
if isinstance(out, list):
out = out[0]
result = postprocess_func(out, image_file_list)
