def early_stop(self, patience):
logger = logging.getLogger(__name__)
#logger.info(dir(self.storage))
# if 'history' not in dir(self.storage) or 'historys' not in dir(self.storage):
# return False
val_data_name = self.cfg.DATASETS.TEST[0]
if len(self.cfg.DATASETS.TEST) == 1:
val_name = 'bbox/AP50'
else:
val_name = f'{val_data_name}/bbox/AP50'
#logger.info(f'==========={val_name}')
his_dict = self.storage.histories()
tmp = his_dict.get(val_name).values()
logger.info(f'Score List@{self.iter}===:{tmp}')
# Keep only score remove inter sn
tmp = [item[0] for item in tmp]
self.best_score = np.max(tmp)
if tmp[-1] >= np.max(tmp):
self.checkpointer.save(self.cfg.best_model)
if np.max(tmp[-patience:]) < np.max(tmp):
#最大值在不在最后几次评估
logger.info(f'Early stop with===:{patience}, {tmp}')
return True
else:
return False
def train(self, patience=3):
"""
Args:
start_iter, max_iter (int): See docs above
"""
logger = logging.getLogger(__name__)
logger.info("Starting training from iteration {}".format(
self.start_iter))
self.iter = start_iter = self.start_iter
max_iter = self.max_iter = self.cfg.SOLVER.MAX_ITER
from detectron2.utils.events import EventStorage
with EventStorage(start_iter) as self.storage:
try:
self.before_train()
print('start_iter, max_iter', start_iter, max_iter)
for self.iter in range(start_iter, max_iter):
self.before_step()
self.run_step()
self.after_step()
if (self.iter + 1
) % self.cfg.TEST.EVAL_PERIOD == 0 and self.early_stop(
patience):
break
finally:
self.after_train()
def save_stack_feature(train: pd.DataFrame, test: pd.DataFrame, file_path):
train.to_hdf(file_path, 'train', mode='a')
test.to_hdf(file_path, 'test', mode='a')
logger.info(f'OOF file save to :{file_path}')
return train, test