10 examples of 'pytorch tensor to numpy' in Python

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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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@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 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')

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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 as_numpy(tensor):
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    if isinstance(tensor, list):
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        return [as_numpy(t) for t in tensor]
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    assert isinstance(tensor, core.LoDTensor)
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    lod = tensor.lod()
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    tensor_data = np.array(tensor)
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    if len(lod) == 0:
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        ans = tensor_data
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    else:
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        #raise RuntimeError("LoD Calculate lacks unit tests and buggy")
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        ans = tensor_data
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    # elif len(lod) == 1:
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    #     ans = []
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    #     idx = 0
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    #     while idx < len(lod) - 1:
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    #         ans.append(tensor_data[lod[idx]:lod[idx + 1]])
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    #         idx += 1
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    # else:
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    #     for l in reversed(lod):
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    #         ans = []
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    #         idx = 0
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    #         while idx < len(l) - 1:
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    #             ans.append(tensor_data[l[idx]:l[idx + 1]])
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    #             idx += 1
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    #         tensor_data = ans
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    #     ans = tensor_data
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    return ans

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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 transform_to_numpy_image(tensor_image):
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    image = tensor_image.cpu().detach().numpy()
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    image = np.transpose(image, (0, 2, 3, 1))
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    if image.shape[3] != 3:
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        image = np.repeat(image, 3, axis=3)
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    else:
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        image = (image / 2 + 0.5)
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    return image

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def from_numpy(np_data):
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    return th.from_numpy(np_data)

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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)