pytorch模型载入之gpu和cpu互转_torch.load gpu模型转为cpu模型

Pytorch训练模型fine-tunning、模型推理等环节常常涉及到模型加载，其中会涉及到将不同平台、版本的模型相互转化：

Case-1.载入多GPU模型

pretained_model = torch.load(’muti_gpus_model.pth‘)    # 网络+权重
# 载入为单gpu模型
gpu_model       = pretrained_model.module  # GPU-version
# 载入为cpu模型
model           = ModelArch()
pretained_dict  = pretained_model.module.state_dict()
model.load_state_dict(pretained_dict)  # CPU-version

Case-2.载入多GPU权重

model = ModelArch(para).cuda(0)   # 网络结构
model = torch.nn.DataParallel(model, device_ids=[0])  # 将model转为muit-gpus模式
checkpoint = torch.load(model_path, map_location=lambda storage, loc: storage) # 载入weights
model.load_state_dict(checkpoint)  # 用weights初始化网络
# 载入为单gpu模型
gpu_model = model.module           # GPU-version
# 载入为cpu模型
model = ModelArch(para)
model.load_state_dict(gpu_model.state_dict())
torch.save(cpu_model.state_dict(), 'cpu_mode.pth') # cpu模型存储， 注意这里的state_dict后的()必须加上，否则报'function' object has no attribute 'copy'错误

Case-3.载入CPU权重 | [inference]

# 载入为cpu版本
model = ModelArch(para)
checkpoint = torch.load(model_path, map_location=lambda storage, loc: storage) # 载入weights
 
# 载入为gpu版本
model = ModelArch(para).cuda()   # 网络结构
checkpoint = torch.load(model_path, map_location=lambda storage, loc: storage.cuda(0)) # 载入weights
model.load_state_dict(checkpoint)  # 用weights初始化网络
 
# 载入为muti-gpus版本
model = ModelArch(para).cuda()   # 网络结构
model = torch.nn.DataParallel(model, device_ids=[0, 1]) # device_ids根据自己需求改！
checkpoint = torch.load(model_path, map_location=lambda storage, loc: storage.cuda(0)) # 载入weights
model.module.load_state_dict(checkpoint)  # 用weights初始化网络