Cannot convert list to array: ValueError: only one element tensors can be converted to Python scalars

Cannot convert list to array: ValueError: only one element tensors can be converted to Python scalars

我目前正在使用 PyTorch 框架并尝试理解外国代码。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

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

我的列表是转换后的 PyTorch 张量 (list(pytorchTensor)),看起来有点像这样:

[
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, -0.0769,  ..., -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, -0.1100,  ..., -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?

您似乎有一个张量列表。对于每个张量,您可以看到它的 size() (no need to convert to list/numpy). If you insist, you can convert a tensor to numpy array using numpy():

Return张量形状列表:

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

Returns 一个 numpy 数组列表:

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

就性能而言,最好避免将张量转换为 numpy 数组,因为它可能会导致 device/host 内存同步。如果你只需要检查张量的shape,使用size()函数。

将pytorch张量转换为numpy数组的最简单方法是:

nparray = tensor.numpy()

此外,对于大小和形状:

tensor_size = tensor.size()
tensor_shape = tensor.shape()
tensor_size
>>> (1080)
tensor_shape
>>> (32, 3, 128, 128)

一个真实世界的例子,需要处理 :

with torch.no_grad():
    probs = [t.numpy() for t in my_tensors]

probs = [t.detach().numpy() for t in my_tensors]