"config": config,
"updates": updates,
"optim": optim_state_dict,
}
torch.save(checkpoints, path)
if __name__ == "__main__":
# Combine command-line arguments and yaml file arguments
opt = opts.model_opts()
config = yaml.load(open(opt.config, "r"))
config = Namespace(**config, **vars(opt))
writer = misc_utils.set_tensorboard(config)
device, devices_id = misc_utils.set_cuda(config)
misc_utils.set_seed(config.seed)
if config.label_dict_file:
with open(config.label_dict_file, "r") as f:
label_dict = json.load(f)
if config.restore:
print("loading checkpoint...\n")
checkpoints = torch.load(config.restore,
map_location=lambda storage, loc: storage)
else:
checkpoints = None
data = load_data(config)
print('data: ', data.keys())
# exit()
"config": config,
"updates": updates,
"optim": optim_state_dict,
}
torch.save(checkpoints, path)
if __name__ == "__main__":
# Combine command-line arguments and yaml file arguments
opt = opts.model_opts() # opt=options
config = yaml.load(open(opt.config, "r")) # yaml对象
config = Namespace(**config, **vars(opt)) # 转换成Namespace对象
writer = misc_utils.set_tensorboard(config)
device, devices_id = misc_utils.set_cuda(config)
misc_utils.set_seed(config.seed) # 可重复的随机性
if config.label_dict_file:
with open(config.label_dict_file, "r") as f:
label_dict = json.load(f)
# 还不确定label_dict_file是什么文件，是一个json文件
if config.restore: # 模型的位置
print("loading checkpoint...\n")
checkpoints = torch.load(config.restore,
map_location=lambda storage, loc: storage)
else:
checkpoints = None
data = load_data(config)
model, optim = build_model(checkpoints, config, device)