test_torch.py 文件源码

def test_ctor_with_numpy_array(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)

            # Upcast
            tensor = torch.DoubleTensor(array)
            for i in range(len(array)):
                self.assertEqual(tensor[i], array[i])

            if torch.cuda.is_available():
                tensor = torch.cuda.DoubleTensor(array)
                for i in range(len(array)):
                    self.assertEqual(tensor[i], array[i])

            # Downcast (sometimes)
            tensor = torch.FloatTensor(array)
            for i in range(len(array)):
                self.assertEqual(tensor[i], array[i])

            tensor = torch.HalfTensor(array)
            for i in range(len(array)):
                self.assertEqual(tensor[i], array[i])

            if torch.cuda.is_available():
                tensor = torch.cuda.FloatTensor(array)
                for i in range(len(array)):
                    self.assertEqual(tensor[i], array[i])

                # CUDA HalfTensor is not supported on Windows yet
                if not IS_WINDOWS:
                    tensor = torch.cuda.HalfTensor(array)
                    for i in range(len(array)):
                        self.assertEqual(tensor[i], array[i])