For example, if you are using TensorFlow, you might save your model as a .h5 file using the Keras API. If you are using PyTorch, you might save your model as a .pt file using the torch.save() function. By saving your model as a file, you can easily load it into a deployment environment (such as FastAPI) and use it to make predictions on new images

def compare_state_dict(dict1, dict2): # compare keys for key in dict1: if key not in dict2: return False for key in dict2: if key not in dict1: return False for (k,v) in dict1.items(): if not torch.all(torch.isclose(v, dict2[k])) return False return True

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("forward", &lltm_forward, "LLTM forward"); m.def("backward", &lltm_backward, "LLTM backward"); }

可能在返回Tensor底层数据中使用了新的内存

需要先使用 t2 的 stride (1, 4) 转换到 t2 的结构，再基于 t2 的结构使用 stride (1,) 转换为形状为 (12,)的 b 。但这不是view工作的方式，view 仅在底层数组上使用指定的形状进行变形，即使 view 不报错，它返回的数据是

Tensor底层一维数组元素的存储顺序与Tensor按行优先一维展开的元素顺序是否一致。

torch.view等方法操作需要连续的Tensor

peterjc123 / packages / pytorch

Each tensor has a .grad_fn attribute that references a Function that has created the Tensor (except for Tensors created by the user - their grad_fn is None).

A replacement for numpy to use the power of GPUs