无法将列表转换为数组:ValueError:只有一个元素张量可以转换为 Python 标量 [英] Cannot convert list to array: ValueError: only one element tensors can be converted to Python scalars

问题描述

我目前正在使用 PyTorch 框架并试图理解外国代码.我遇到了索引问题，想打印列表的形状.
这样做的唯一方法(据 Google 告诉我)是将列表转换为 numpy 数组，然后使用 numpy.ndarray.shape() 获取形状.

I'm currently working with the PyTorch framework and trying to understand foreign code. I got an indices issue and wanted to print the shape of a list.
The only way of doing so (as far as Google tells me) is to convert the list into a numpy array and then getting the shape with numpy.ndarray.shape().

但是在尝试将我的列表转换为数组时，我得到了一个 ValueError: only one element tensors can be convert to Python scalars.

But trying to convert my list into an array, I got a ValueError: only one element tensors can be converted to Python scalars.

My List 是一个转换后的 PyTorch Tensor (list(pytorchTensor))，看起来有点像这样:

My List is a converted PyTorch Tensor (list(pytorchTensor)) and looks somewhat like this:

[张量([[-0.2781, -0.2567, -0.2353, ..., -0.9640, -0.9855, -1.0069],
[-0.2781, -0.2567, -0.2353, ..., -1.0069, -1.0283, -1.0927],
[-0.2567, -0.2567, -0.2138, ..., -1.0712, -1.1141, -1.1784],
...,
[-0.6640, -0.6425, -0.6211, ..., -1.0712, -1.1141, -1.0927],
[-0.6640, -0.6425, -0.5997, ..., -0.9426, -0.9640, -0.9640],
[-0.6640, -0.6425, -0.5997, ..., -0.9640, -0.9426, -0.9426]]), 张量([[-0.0769, -0.0980, -0.076 9, ..., -0.93959, -0.959, -0.9426]]-0.9808],
[-0.0559, -0.0769, -0.0980, ..., -0.9598, -1.0018, -1.0228],
[-0.0559, -0.0769, -0.0769, ..., -1.0228, -1.0439, -1.0859],
...,
[-0.4973, -0.4973, -0.4973, ..., -1.0018, -1.0439, -1.0228],
[-0.4973, -0.4973, -0.4973, ..., -0.8757, -0.9177, -0.9177],
[-0.4973, -0.4973, -0.4973, ..., -0.9177, -0.8967, -0.8967]]), 张量([[-0.1313, -0.1313, -0.110 0, ..., -0.81132, -0.8967]]-0.8753],
[-0.1313, -0.1525, -0.1313, ..., -0.8541, -0.8966, -0.9391],
[-0.1100, -0.1313, -0.1100, ..., -0.9391, -0.9816, -1.0666],
...,
[-0.4502, -0.4714, -0.4502, ..., -0.8966, -0.8966, -0.8966],
[-0.4502, -0.4714, -0.4502, ..., -0.8115, -0.8115, -0.7903],
[-0.4502, -0.4714, -0.4502, ..., -0.8115, -0.7690, -0.7690]])]

[tensor([[-0.2781, -0.2567, -0.2353, ..., -0.9640, -0.9855, -1.0069],
[-0.2781, -0.2567, -0.2353, ..., -1.0069, -1.0283, -1.0927],
[-0.2567, -0.2567, -0.2138, ..., -1.0712, -1.1141, -1.1784],
...,
[-0.6640, -0.6425, -0.6211, ..., -1.0712, -1.1141, -1.0927],
[-0.6640, -0.6425, -0.5997, ..., -0.9426, -0.9640, -0.9640],
[-0.6640, -0.6425, -0.5997, ..., -0.9640, -0.9426, -0.9426]]), tensor([[-0.0769, -0.0980, -0.076 9, ..., -0.9388, -0.9598, -0.9808],
[-0.0559, -0.0769, -0.0980, ..., -0.9598, -1.0018, -1.0228],
[-0.0559, -0.0769, -0.0769, ..., -1.0228, -1.0439, -1.0859],
...,
[-0.4973, -0.4973, -0.4973, ..., -1.0018, -1.0439, -1.0228],
[-0.4973, -0.4973, -0.4973, ..., -0.8757, -0.9177, -0.9177],
[-0.4973, -0.4973, -0.4973, ..., -0.9177, -0.8967, -0.8967]]), tensor([[-0.1313, -0.1313, -0.110 0, ..., -0.8115, -0.8328, -0.8753],
[-0.1313, -0.1525, -0.1313, ..., -0.8541, -0.8966, -0.9391],
[-0.1100, -0.1313, -0.1100, ..., -0.9391, -0.9816, -1.0666],
...,
[-0.4502, -0.4714, -0.4502, ..., -0.8966, -0.8966, -0.8966],
[-0.4502, -0.4714, -0.4502, ..., -0.8115, -0.8115, -0.7903],
[-0.4502, -0.4714, -0.4502, ..., -0.8115, -0.7690, -0.7690]])]

有没有办法在不将其转换为 numpy 数组的情况下获取该列表的形状?

Is there a way of getting the shape of that list without converting it into a numpy array?

推荐答案

您似乎有一个张量列表.对于每个张量，您可以看到它的 size()(无需转换为列表/numpy).如果您坚持，您可以使用 numpy 将张量转换为 numpy 数组():

It seems like you have a list of tensors. For each tensor you can see its size() (no need to convert to list/numpy). If you insist, you can convert a tensor to numpy array using numpy():

返回张量形状列表:

>> [t.size() for t in my_list_of_tensors]

返回一个 numpy 数组列表:

Returns a list of numpy arrays:

>> [t.numpy() for t in my_list_of_tensors]

在性能方面，最好避免将张量转换为 numpy 数组，因为这可能会导致设备/主机内存同步.如果您只需要检查张量的 shape，请使用 size() 函数.

In terms of performance, it is always best to avoid casting of tensors into numpy arrays, as it may incur sync of device/host memory. If you only need to check the shape of a tensor, use size() function.

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