Ah, I see, thecopy_call. Still, if we replace the weight Tensor instead (ensuring the proper shape upon creation), it also works, e.g: kernel = torch.from_numpy(np.random.random_sample((1, 1, 1, 3))).double() ... model[0].weight.data = kernel ...
(dtype.name, repr(mismatch), type(mismatch).name)) TypeError: Expected int32, got list containing Tensors of type '_Message' instead. johndpopementioned this issueAug 5, 2017 TypeError: Expected int32, got list containing Tensors of type '_Message' instead.2014mchidamb/AdversarialChess#4 ...
/pytorch/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead. RuntimeError: zero-dimensional tensor (at position 0) cannot be concatenated object of type <class 'numpy.float64'> cannot be safely interp...
model_new_dict = model_new.state_dict() model_common_dict = {k:v for k, v in model_saved.items() if k in model_new_dict.keys()} model_new_dict.update(model_common_dict) model_new.load_state_dict(model_new_dict) 4. 数据处理 从本地文件读取数据制作数据集 在主路径./train下按照...