Arithmetics between Pytorch tensor and Numpy array

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Arithmetics between Pytorch tensor and Numpy array (without explicit casting) are not allowed.

For the following experiments:

PyTorch version: 1.2.0.dev20190611

Numpy version: 1.16.4

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a = np.array([1, 6, 5])
b = torch.tensor([2, 8, 9])
print(a + b)

Out:
TypeError: add(): argument 'other' (position 1) must be Tensor, not numpy.ndarray

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a = np.array([1, 6, 5])
b = torch.tensor([2, 8, 9])
print(b + a)

Out:
TypeError: add(): argument 'other' (position 1) must be Tensor, not numpy.ndarray

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a = np.array([1, 6, 5])
b = torch.tensor([2, 8, 9])
print(a * b)

Out:
TypeError: mul(): argument 'other' (position 1) must be Tensor, not numpy.ndarray

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a = np.array([1, 6, 5])
b = torch.tensor([2, 8, 9])
print(b * a)

Out:
TypeError: mul(): argument 'other' (position 1) must be Tensor, not numpy.ndarray

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a = [1, 6, 5]
b = torch.tensor([2, 8, 9])
print(a * b)

Out:
TypeError: mul(): argument 'other' (position 1) must be Tensor, not list

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a = 3
b = torch.tensor([2, 8, 9])
print(a * b)

Out:
tensor([ 6, 24, 27])

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a = np.array([1, 6, 5])
b = torch.tensor([2, 8, 9])
print(a * b.numpy())

Out:
[ 2 48 45]

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a = np.array([1, 6, 5])
b = torch.tensor([2, 8, 9])
print(torch.from_numpy(a) * b)

Out:
tensor([ 2, 48, 45])

According to Can’t call numpy() on Variable that requires grad:

Moving to numpy will break the computation graph and so no gradient will be computed. If you don’t actually need gradients, then you can explicitly .detach() the Tensor that requires grad to get a tensor with the same content that does not require grad. This other Tensor can then be converted to a numpy array.