def train_model(config, dataroot, augment, cv_ratio_test, cv_fold, save_path=None, skip_exist=False):
C.get()
C.get().conf = config
C.get()['aug'] = augment
result = train_and_eval(None, dataroot, cv_ratio_test, cv_fold, save_path=save_path, only_eval=skip_exist)
return C.get()['model']['type'], cv_fold, result
def train_model(config,
dataloaders,
dataroot,
augment,
cv_ratio_test,
cv_id,
save_path=None,
skip_exist=False,
evaluation_interval=5,
gr_assign=None,
gr_dist=None):
C.get()
C.get().conf = config
C.get()['aug'] = augment
result = train_and_eval(None,
dataloaders,
dataroot,
cv_ratio_test,
cv_id,
save_path=save_path,
only_eval=skip_exist,
evaluation_interval=evaluation_interval,
gr_assign=gr_assign,
gr_dist=gr_dist)
return C.get()['model']['type'], cv_id, result
def train_model(
config, dataroot, augment, cv_ratio_test, cv_fold, save_path=None, skip_exist=False
):
C.get()
C.get().conf = config
C.get()["aug"] = augment
# pass the get_dataloaders and num_class instead being imported in the train.py. In this way, these two functions
# can be monkey-patched.
result = train_and_eval(
"search_stage",
None,
dataroot,
cv_ratio_test,
cv_fold,
save_path=save_path,
only_eval=skip_exist,
get_dataloaders=get_dataloaders,
num_class=num_class,
get_model=get_model,
)
return C.get()["model"]["type"], cv_fold, result