RuntimeError: fractional_max_pool2d_backward_out_cuda failed with error code 0

class DeepWise_MaxPool(nn.MaxPool1d):
    def __init__(self, channels):
        super(DeepWise_MaxPool, self).__init__(channels)

    def forward(self, input):
        n, c, h, w = input.size()
        input = input.view(n, c, h * w).permute(0, 2, 1)
        pooled = torch.nn.functional.max_pool1d(input, self.kernel_size, self.stride, 
             self.padding, self.dilation, self.ceil_mode, self.return_indices)
        _, _, c = pooled.size()
        pooled = pooled.permute(0, 2, 1)
        return pooled.view(n, c, h, w)

在模型中使用该类作为基本结构的一部分时，网络反向传播报错，使用contiguous()将permute()操作之后的tensor变为连续。

Returns a contiguous in memory tensor containing the same data as self tensor. If self tensor is already in the specified memory format, this function returns the self tensor

class DeepWise_MaxPool(nn.MaxPool1d):
    def __init__(self, channels):
        super(DeepWise_MaxPool, self).__init__(channels)

    def forward(self, input):
        n, c, h, w = input.size()
        input = input.view(n, c, h * w).permute(0, 2, 1).contiguous()
        pooled = torch.nn.functional.max_pool1d(input, self.kernel_size, self.stride,                     
             self.padding,self.dilation, self.ceil_mode, self.return_indices)
        _, _, c = pooled.size()
        pooled = pooled.permute(0, 2, 1).contiguous()
        return pooled.view(n, c, h, w)