Method _transpose_and_arithmetic_op_broadcast_helper

test/python/test_api.py:291–338

(self, dev)

        self._tensor_arithmetic_op_broadcast_helper(gpu_dev)

    def _transpose_and_arithmetic_op_broadcast_helper(self, dev):

        def _test(s1, s2, axis1, axis2, s3, s_op, n_op, dev):
            x_0 = np.random.random(s1).astype(np.float32)
            y_0 = np.random.random(s2).astype(np.float32)

            x0 = tensor.Tensor(device=dev, data=x_0)
            y0 = tensor.Tensor(device=dev, data=y_0)

            x1 = x0.transpose(axis1)
            y1 = y0.transpose(axis2)

            z0 = tensor._call_singa_func(s_op, x1.data, y1.data)
            z0.to_host()

            np.testing.assert_array_almost_equal(
                tensor.to_numpy(z0),
                n_op(x_0.transpose(axis1), y_0.transpose(axis2)))
            np.testing.assert_array_almost_equal(z0.shape, s3)
            return

        for s_op, n_op in zip([
                singa_api.Pow,
                singa_api.__add__,
                singa_api.__div__,
                singa_api.__sub__,
                singa_api.__mul__,
        ], [np.power, np.add, np.divide, np.subtract, np.multiply]):
            s1 = [1, 5, 1, 3]
            s2 = [3, 1, 1, 4]
            axis1 = [3, 2, 1, 0]  # 3121
            axis2 = [1, 0, 2, 3]  # 1314
            s3 = [3, 3, 5, 4]
            _test(s1, s2, axis1, axis2, s3, s_op, n_op, dev)

            s1 = [1, 5, 1]
            s2 = [1, 3, 2]
            axis1 = [2, 1, 0]  # 151
            axis2 = [1, 0, 2]  # 312
            s3 = [3, 5, 2]
            _test(s1, s2, axis1, axis2, s3, s_op, n_op, dev)

            s1 = [5, 1]
            s2 = [1, 3]
            axis1 = [1, 0]  # 15
            axis2 = [1, 0]  # 31
            s3 = [3, 5]
            _test(s1, s2, axis1, axis2, s3, s_op, n_op, dev)

    def test_transpose_and_arithmetic_op_broadcast_cpu(self):
        self._transpose_and_arithmetic_op_broadcast_helper(cpu_dev)