10 examples of 'numpy array to torch tensor' in Python

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def to_torch(array):
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    if len(array.shape) == 4:
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        array = np.transpose(array, (0, 3, 1, 2))
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    return torch.from_numpy(array).to(device).float()

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def torch2numpy(X):
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    """Convert a torch float32 tensor to a numpy array, sharing the same memory.
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    """
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    return X.detach().numpy()

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def tensor_to_ndarray(tensor):
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    """
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    Convert float tensor into numpy image
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    :param tensor: input tensor
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    :type tensor: torch.Tensor
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    :return: numpy image
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    :rtype: np.ndarray
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    """
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    tensor_np = tensor.permute(1, 2, 0).cpu().numpy()
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    tensor_np = tensor_np.astype(np.float32)
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    tensor_np = (tensor_np * 255).astype(np.uint8)
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    return tensor_np

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@staticmethod
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def videos_to_numpy(tensor):
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    generated = tensor.transpose(0, 1, 2, 3, 4)
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    generated[generated < -1] = -1
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    generated[generated > 1] = 1
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    generated = (generated + 1) / 2 * 255
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    return generated.astype('uint8')

20
def to_torch(ndarray):
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    if type(ndarray).__module__ == 'numpy':
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        return torch.from_numpy(ndarray)
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    elif not torch.is_tensor(ndarray):
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        raise ValueError("Cannot convert {} to torch tensor".format(type(ndarray)))
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    return ndarray

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def to_tensor(x, cuda=False):
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  if x.ndim == 4:
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    x = x.transpose([0, 3, 1, 2]) / 255.0
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  elif x.ndim == 5:
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    x = x.transpose([0, 1, 4, 2, 3]) / 255.0
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  else:
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    raise ValueError(f"Tensor dimension error: {x.dim()} != 4 or 5")
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  x = torch.as_tensor(x, dtype=torch.float32)
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  if cuda and torch.cuda.is_available():
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    x = x.cuda()
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  return x

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def to_torch(nparray):
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    tensor = torch.from_numpy(nparray).float().cuda()
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    return torch.autograd.Variable(tensor, requires_grad=False)

29
@staticmethod
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def to_numpy(tensor):
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    if isinstance(tensor, cp.ndarray):
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        return cp.asnumpy(tensor)
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    return tensor

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def array(self, arr, dtype=None):
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    """ make an array """
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    if dtype is None:
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        dtype = torch.get_default_dtype()
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    return torch.tensor(arr, device="cpu", dtype=dtype)

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def _tensor_to_cuda(x):
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    if x.is_cuda:
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        return x
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    else:
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        return x.cuda()