[英]How to extract tensors to numpy arrays or lists from a larger pytorch tensor
I have a list of pytorch tensors as shown below:我有一个 pytorch 张量列表,如下所示:
data = [[tensor([0, 0, 0]), tensor([1, 2, 3])],
[tensor([0, 0, 0]), tensor([4, 5, 6])]]
Now this is just a sample data, the actual one is quite large but the structure is similar.现在这只是一个示例数据,实际数据很大但结构相似。
Question: I want to extract the tensor([1, 2, 3])
, tensor([4, 5, 6])
ie, the index 1 tensors from data
to either a numpy array or a list in flattened form.问题:我想提取tensor([1, 2, 3])
, tensor([4, 5, 6])
即索引 1 张量从data
到 numpy 数组或扁平形式的列表。
Expected Output:预期 Output:
out = array([1, 2, 3, 4, 5, 6])
OR或者
out = [1, 2, 3, 4, 5, 6]
I have tried several ways one including map
function like:我尝试了几种方法,其中一种包括map
function,例如:
map(lambda x: x[1].numpy(), data)
This gives:这给出了:
[array([1, 2, 3]),
array([4, 5, 6])]
And I'm unable to get the desired result with any other method I'm using.而且我无法使用我正在使用的任何其他方法获得所需的结果。 Any help would be appreciated.任何帮助,将不胜感激。
Thanks in advance:)提前致谢:)
OK, you can just do this.好的,你可以这样做。
out = np.concatenate(list(map(lambda x: x[1].numpy(), data)))
You can convert a nested list of tensors to a tensor/numpy array with a nested stack:您可以将张量的嵌套列表转换为具有嵌套堆栈的张量/numpy 数组:
data = np.stack([np.stack([d for d in d_]) for d_ in data])
You can then easily index this, and concatenate the output:然后,您可以轻松地对其进行索引,并连接 output:
>>> np.concatenate(data[:,1])
array([[1, 2, 3],
[4, 5, 6]])
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