这种情况下非常简单,只需要注意torch.load时,可以使用map_location指定加载位置即可。 如果保存的是state_dict,那么加载后可以直接通过torch.load_state_dict进行加载。参考代码如下。 device = torch.device("cuda") model = Model().to(device) ckpt = torch.load("model.pth", map_location=device) model.load...
>>> torch.load('', map_location=torch.device('cpu')) # Load all tensors onto the CPU, using a function >>> torch.load('', map_location=lambda storage, loc: storage) # Load all tensors onto GPU 1 >>> torch.load('', map_location=lambda storage, loc: storage.cuda(1)) # Map...
model = MyModel() # 加载检查点 checkpoint_path = 'model.pth' if os.path.exists(checkpoint_path): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") checkpoint = torch.load(checkpoint_path, map_location=device) model.load_state_dict(checkpoint['model_state_...
这是推荐的做法,因为它会限制能够被加载的对象,从而提高安全性。 ckpt=torch.load(file,map_location="cpu",weights_only=True) 信任文件来源: 如果你完全信任要加载的模型文件,那么可以继续使用当前的方式,但请注意潜在的风险。最好避免从不可信或未知来源下载模型。 更新PyTorch 版本: 随着 PyTorch 的更新,该功能...
eval() torch_ckpt = torch.load('./pretrained/rdn-liif.pth', map_location=torch.device('cpu')) m = torch_ckpt['model'] sd = m['sd'] paddle_sd = {} for k, v in sd.items(): if torch.is_tensor(v): if 'imnet.layers' in k and 'weight' in k: print(k) print(v) ...
format(pretrained_path)) pretrained_dict = torch.load(pretrained_path, map_location=device) if "state_dict" in pretrained_dict.keys(): pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.') else: pretrained_dict = remove_prefix(pretrained_dict, 'module.') check_keys(...
cuda() ckpt = torch.load(args.ckpt, map_location='cpu') # if use more than one GPU ckpt_single = OrderedDict() for k, v in ckpt.items(): k = k.replace('module.', '') ckpt_single[k] = v modnet.load_state_dict(ckpt_single) modnet.eval() 本文参与 腾讯云自媒体同步曝光计划,...
join(tmp_path, 'node_map.pt') assert osp.exists(node_map_path) node_map = torch.load(node_map_path) node_map = fs.torch_load(node_map_path) assert node_map.numel() == data.num_nodes edge_map_path = osp.join(tmp_path, 'edge_map.pt') assert osp.exists(edge_map_path) edge...
device('cpu')) sd = torch.load(self.mp_rank_files[0], map_location=torch.device('cpu'), weights_only=False) self.global_state[key] = sd.get(key, None)return self.global_state[key] @@ -169,7 +172,7 @@ def get_2d_parallel_state(self, tp_index: int, pp_index: int) -> ...
1.2. 尝试直接将 ckpt 文件转成 onnx(在Bert模型转换时失败,针对一些简单模型可以使用此方法): ### 方法一 import tensorflow as tf from tensorflow.python.saved_model.signature_def_utils_impl import predict_signature_def tf.enable_resource_variables() config...