模型保存和加载

保存模型parameter、buffer

path = '/kaggle/working/state_dict_model.pt'

# 没保存优化器的参数
# 保存所有参数和buffer量
# parameter和buffer https://blog.csdn.net/m0_61899108/article/details/124481684
torch.save(model.state_dict(), path)

n1_model = Model() # 实例化模型类
n1_model.load_state_dict(torch.load(path)) # 参数赋给新模型
n1_model.eval() # 将内部training设为False,不再记录参数梯度值,运行效率高

'''
Model(
  (fc): Linear(in_features=768, out_features=2, bias=True)
)
'''

保存整个模型

path = '/kaggle/working/entire_model.pt'

# 保存整个模型
torch.save(model, path)

n2_model = torch.load(path)
n2_model.eval()
'''
Model(
  (fc): Linear(in_features=768, out_features=2, bias=True)
)
'''

checkpoint 保存和加载

epoch = 5
loss = 0.4
path = '/kaggle/working/5_0.4_checkpoint.pt'

torch.save({
    'epoch': epoch
    ,'loss': loss
    ,'model_state_dict': model.state_dict()
    ,'optimizer_state_dict': optimizer.state_dict()
    ,
}, path)

# 加载
n3_model = Model()
n3_optimizer = torch.optim.AdamW(model.parameters(), lr=5e-4)

checkpoint = torch.load(path)
epoch = checkpoint['epoch']
loss = checkpoint['loss']
n3_model.load_state_dict(checkpoint['model_state_dict'])
n3_optimizer.load_state_dict(checkpoint['optimizer_state_dict'])

n3_model.eval()
# - or -
n3_model.train()

import os
for dirname, _, filenames in os.walk('/kaggle/'):
    for filename in filenames:
        print(os.path.join(dirname, filename))
'''
/kaggle/lib/kaggle/gcp.py
/kaggle/input/chnsenticorp/ChnSentiCorp/dataset_info.json
/kaggle/input/chnsenticorp/ChnSentiCorp/ChnSentiCorp.py
/kaggle/input/chnsenticorp/ChnSentiCorp/chn_senti_corp-train.arrow
/kaggle/input/chnsenticorp/ChnSentiCorp/chn_senti_corp-test.arrow
/kaggle/input/chnsenticorp/ChnSentiCorp/chn_senti_corp-validation.arrow
/kaggle/working/state_dict_model.pt
/kaggle/working/__notebook_source__.ipynb
/kaggle/working/5_0.4_checkpoint.pt
/kaggle/working/entire_model.pt
'''