# Load all tensors onto the CPU, using a function >>> torch.load('tensors.pt', map_location=lambda storage, loc: storage) # Load all tensors onto GPU 1 >>> torch.load('tensors.pt', map_location=lambda storage, loc: storage.cuda(1)) # Map tensors from GPU 1 to GPU 0 >>>...
torch.load(f, map_location=None, pickle_module=<module'pickle'from'...'>) 示例 torch.load('tensors.pt') # Load all tensors onto the CPU torch.load('tensors.pt', map_location=torch.device('cpu')) # Load all tensors onto the CPU, using a function torch.load('tensors.pt', map...
torch.load(f, map_location=None, pickle_module=<module 'pickle' from '...'>) 示例: torch.load('tensors.pt') # Load all tensors onto the CPU torch.load('tensors.pt', map_location=torch.device('cpu')) # Load all tensors onto the CPU, using a function torch.load('tensors.pt'...
1.2.1.2.2 模型加载 # torch.load 需要注意 map_location 的使用# 例子一:defload_model(model_file):model=MobileNetV2()state_dict=torch.load(model_file)model.load_state_dict(state_dict)model.to('cpu')returnmodel# 例子二:net=Net()net.load_state_dict(torch.load(PATH))# 例子三:device=torch....
torch.load(f, map_location=None, pickle_module=<module 'pickle' from '/opt/conda/lib/python3.6/pickle.py'>, **pickle_load_args)[source] 从文件中加载一个用torch.save()保存的对象。 load()使用Python的unpickling工具,但是专门处理存储,它是张量的基础。他们首先在CPU上并行化,然后移动到保存它们的...
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device.replace('gpu:','') net = models.make({'name': 'liif', 'args': { 'encoder_spec': { 'name': 'rdn', 'args': {'no_upsampling': True} }, 'imnet_spec': { 'name': 'mlp', 'args': { 'out_dim': 3, 'hidden_list': [256, 256, 256,256] } } } }, load_sd=...
torch.load()的作用:从文件加载用torch.save()保存的对象。 api: torch.load(f, map_location=None, pickle_module=<module'pickle'from'/opt/conda/lib/python3.6/pickle.py'>, **pickle_load_args) 参数: f:类似文件的对象(必须实现read(),:meth ' readline ',:meth ' tell '和:meth ' seek '),...
load('tensors.pt', map_location=lambda storage, loc: storage.cuda(1)) # Map tensors from GPU 1 to GPU 0 >>> torch.load('tensors.pt', map_location={'cuda:1':'cuda:0'}) # Load tensor from io.BytesIO object >>> with open('tensor.pt', 'rb') as f: buffer = io.BytesIO...
from torch.quantization.quantize_fximportprepare_fx,convert_fx float_model.eval()# 因为是PTQ,所以就推理模式就够了 qconfig=get_default_qconfig("fbgemm")# 指定量化细节配置 qconfig_dict={"":qconfig}# 指定量化选项 defcalibrate(model,data_loader):# 校准功能函数 ...