test_torch.py 文件源码

def test_from_numpy(self):
        dtypes = [
            np.double,
            np.float,
            np.float16,
            np.int64,
            np.int32,
            np.int16,
            np.uint8
        ]
        for dtype in dtypes:
            array = np.array([1, 2, 3, 4], dtype=dtype)
            tensor_from_array = torch.from_numpy(array)
            # TODO: change to tensor equality check once HalfTensor
            # implements `==`
            for i in range(len(array)):
                self.assertEqual(tensor_from_array[i], array[i])

        # check storage offset
        x = np.linspace(1, 125, 125)
        x.shape = (5, 5, 5)
        x = x[1]
        expected = torch.arange(1, 126).view(5, 5, 5)[1]
        self.assertEqual(torch.from_numpy(x), expected)

        # check noncontiguous
        x = np.linspace(1, 25, 25)
        x.shape = (5, 5)
        expected = torch.arange(1, 26).view(5, 5).t()
        self.assertEqual(torch.from_numpy(x.T), expected)

        # check noncontiguous with holes
        x = np.linspace(1, 125, 125)
        x.shape = (5, 5, 5)
        x = x[:, 1]
        expected = torch.arange(1, 126).view(5, 5, 5)[:, 1]
        self.assertEqual(torch.from_numpy(x), expected)

        # check zero dimensional
        x = np.zeros((0, 2))
        self.assertEqual(torch.from_numpy(x).shape, tuple())
        self.assertEqual(torch.autograd.Variable.from_numpy(x).shape, [0])