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().
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 is a converted PyTorch Tensor (list(pytorchTensor)) and looks somewhat like this:
[
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]]),
]
Is there a way of getting the shape of that list without converting it into a numpy array?
3 Answers
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():
Return a list of tensor shapes:
>> [t.size() for t in my_list_of_tensors]
Returns a list of numpy arrays:
>> [t.numpy() for t in my_list_of_tensors]
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.
The simplest way to convert pytorch tensor to numpy array is:
nparray = tensor.numpy()
Also, for size and shape:
tensor_size = tensor.size()
tensor_shape = tensor.shape()
tensor_size
>>> (1080)
tensor_shape
>>> (32, 3, 128, 128)
A real-world example, would require to handle torch no grad issue:
with torch.no_grad():
probs = [t.numpy() for t in my_tensors]
or
probs = [t.detach().numpy() for t in my_tensors]
