def main_train():
"""
训练模型
:return:
"""
print('[INFO] 解析配置...')
config = None
try:
config = process_config('configs/segmention_config.json')
except Exception as e:
print('[Exception] 配置无效, %s' % e)
exit(0)
# np.random.seed(47) # 固定随机数
print('[INFO] 加载数据...')
dataloader = DataLoader(config=config)
dataloader.prepare_dataset()
train_imgs,train_gt=dataloader.get_train_data()
val_imgs,val_gt=dataloader.get_val_data()
print('[INFO] 构造网络...')
model = SegmentionModel(config=config)
#
print('[INFO] 训练网络...')
trainer = SegmentionTrainer(
model=model.model,
data=[train_imgs,train_gt,val_imgs,val_gt],
config=config)
trainer.train()
print('[INFO] 训练完成...')
def main_train():
"""
Training model
:return:
"""
print('[INFO] Reading Configs...')
config = None
try:
config = process_config('configs/segmention_config.json')
except Exception as e:
print('[Exception] Config Error, %s' % e)
exit(0)
# np.random.seed(47) # 固定随机数
print('[INFO] Preparing Data...')
dataloader = DataLoader(config=config)
dataloader.prepare_dataset()
train_imgs, train_gt = dataloader.get_train_data()
val_imgs, val_gt = dataloader.get_val_data()
print('[INFO] Building Model...')
model = SegmentionModel(config=config)
#
print('[INFO] Training...')
trainer = SegmentionTrainer(
model=model.model,
data=[train_imgs, train_gt, val_imgs, val_gt],
config=config)
trainer.train()
print('[INFO] Finishing...')
def main_train():
print('[INFO] Reading configuration files')
config = None
try:
config = process_config(
'/home/dgxuser102/data/team34/experiments/configs/segmention_config.json'
)
except Exception as e:
print('[Exception] Configuration Error, %s' % e)
exit(0)
# np.random.seed(47)
print('[INFO] Preparing Data...')
dataloader = DataLoader(config=config)
dataloader.prepare_dataset()
train_imgs, train_gt = dataloader.get_train_data()
val_imgs, val_gt = dataloader.get_val_data()
print('[INFO] Using our model to train...')
model = SegmentionModel(config=config)
#
print('[INFO] Now Training...')
trainer = SegmentionTrainer(model=model.model,
data=[train_imgs, train_gt, val_imgs, val_gt],
config=config)
trainer.train()
print('[INFO] Finishing the training...')