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 tensors from GPU...
1.pth 文件基本信息的查看 import torchpthfile = r'F:/GNN/graph-rcnn/graph-rcnn/datasets/sg_baseline_ckpt.pth' #faster_rcnn_ckpt.pthnet = torch.load(pthfile,map_location=torch.device('cpu')) # 由于模型原本是用GPU保存的,如果此电脑上没有GPU,需要转化到CPU上# print(type(net)) # 类型...
bert_model.load_state_dict(torch.load("best_bert_model.pth", map_location=torch.device('cpu'))) #转onnx input_names = ["word_embeddings","position_embeddings","token_type_embeddings"] output_names = ["logits"] word_embeddings=torch.zeros([1,512], dtype=torch.int) position_embeddings=...
sd = torch.load(path, map_location="cpu")["state_dict"] File "/mnt/parscratch/users/aca20sv/anaconda/.envs/vgen/lib/python3.8/site-packages/torch/serialization.py", line 699, in load with _open_file_like(f, 'rb') as opened_file: ...
trainer.torch.flash_checkpoint.engine import CheckpointEngine torch_native_save = torch.save torch_native_load = torch.load class HfFlashCheckpointer(object): def __init__(self, checkpoint_dir, storage=None): self.checkpoint_dir = checkpoint_dir self.storage = PosixDiskStorage() if not sto...
(ckpt_dir, ckpt_name), map_location='cpu'), strict=True) File "C:\stable-diffusion-webui\venv\lib\site-packages\torch\serialization.py", line 771, in load with _open_file_like(f, 'rb') as opened_file: File "C:\stable-diffusion-webui\venv\lib\site-packages\torch\serialization.py"...
( "--pth_path", type=str, default=None, help="Model name or the path of the model's checkpoint file given by OpenAI", ) args = parser.parse_args(args) return args def pytorch_params(pth_file): par_dict = torch.load(pth_file, map_location="cpu").state_dict() pt_params = []...