layers.py 文件源码

def forward(self, input, n_out, dilation, ks = (3,3), groups=1):
        # print(ks, self.kernel_size, dilation, (self.kernel_size[0] - ks[0]) //2, self.kernel_size[0] + (self.kernel_size[0] - ks[0]) //2, (ks[0] + ((ks[0] - 1 ) * (dilation[0] - 1 ))) // 2)
        # print(dilation,ks, tuple(int(item) for item in ( (ks[0] + ((ks[0] - 1 ) * (dilation[0] - 1 ))) // 2, (ks[1] + ((ks[1] - 1 ) * (dilation[1] - 1 ))) // 2)))
        return F.conv2d(input,
                        weight=self.weight[:n_out, 
                             :input.size(1) // groups, 
                             (self.kernel_size[0] - ks[0]) //2 : ks[0] + (self.kernel_size[0] - ks[0]) //2,
                             (self.kernel_size[1] - ks[1]) //2 : ks[1] + (self.kernel_size[1] - ks[1]) //2].contiguous(),
                        dilation=tuple(int(d) for d  in dilation),
                        padding=tuple(int(item) for item in ( (ks[0] + ((ks[0] - 1 ) * (dilation[0] - 1 ))) // 2, (ks[1] + ((ks[1] - 1 ) * (dilation[1] - 1 ))) // 2)),
                        groups=int(groups),
                        bias=None)    
# Simple class that dynamically inserts a nonlinearity between a batchnorm and a conv