test_graph_convolution.py 文件源码

def check_backward(self, x_data, W_data, b_data, y_grad, use_gpu=False):
        xp = cuda.get_array_module(x_data)
        if not self.c_contiguous:
            x_data = xp.asfortranarray(x_data)
            W_data = xp.asfortranarray(W_data)
            y_grad = xp.asfortranarray(y_grad)
            self.assertFalse(x_data.flags.c_contiguous)
            self.assertFalse(W_data.flags.c_contiguous)
            self.assertFalse(y_grad.flags.c_contiguous)
            if b_data is not None:
                b = xp.empty((len(b_data) * 2,), dtype=self.b.dtype)
                b[::2] = b_data
                b_data = b[::2]
                self.assertFalse(b_data.flags.c_contiguous)

        func = graph_convolution.GraphConvolutionFunction(self.L, self.K)
        if use_gpu:
            func.to_gpu()

        args = (x_data, W_data)
        if b_data is not None:
            args = args + (b_data,)
        gradient_check.check_backward(
            func, args, y_grad,
            **self.check_backward_options
        )