takeaway:
OCNet是与DAnet同期的文章，也是self-attention的应用，不同之处是探索了与psp及aspp等模块的结合效果
https://github.com/openseg-group/OCNet.pytorch

这里的ocp就是self-attention

ocp就是self-attention, Pyramid-OC是类似于psp的方法先划分成不同的区域后单独做 self-attention，后面的结果显示这样没有明显的提升，单也没有下降，asp-oc是用ocp代替了原来的gap，效果还有提升

class _SelfAttentionBlock(nn.Module):
    '''
    The basic implementation for self-attention block/non-local block
    Input:
        N X C X H X W
    Parameters:
        in_channels       : the dimension of the input feature map
        key_channels      : the dimension after the key/query transform
        value_channels    : the dimension after the value transform
        scale             : choose the scale to downsample the input feature maps (save memory cost)
    Return:
        N X C X H X W
        position-aware context features.(w/o concate or add with the input)
    '''
    def __init__(self, in_channels, key_channels, value_channels, out_channels=None, scale=1):
        super(_SelfAttentionBlock, self).__init__()
        self.scale = scale
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.key_channels = key_channels
        self.value_channels = value_channels
        if out_channels == None:
            self.out_channels = in_channels
        self.pool = nn.MaxPool2d(kernel_size=(scale, scale))
        self.f_key = nn.Sequential(
            nn.Conv2d(in_channels=self.in_channels, out_channels=self.key_channels,
                kernel_size=1, stride=1, padding=0),
            InPlaceABNSync(self.key_channels),
        )
        self.f_query = self.f_key
        self.f_value = nn.Conv2d(in_channels=self.in_channels, out_channels=self.value_channels,
            kernel_size=1, stride=1, padding=0)
        self.W = nn.Conv2d(in_channels=self.value_channels, out_channels=self.out_channels,
            kernel_size=1, stride=1, padding=0)
        nn.init.constant(self.W.weight, 0)
        nn.init.constant(self.W.bias, 0)


    def forward(self, x):
        batch_size, h, w = x.size(0), x.size(2), x.size(3)
        if self.scale > 1:
            x = self.pool(x)

        value = self.f_value(x).view(batch_size, self.value_channels, -1)
        value = value.permute(0, 2, 1)
        query = self.f_query(x).view(batch_size, self.key_channels, -1)
        query = query.permute(0, 2, 1)
        key = self.f_key(x).view(batch_size, self.key_channels, -1)

        sim_map = torch.matmul(query, key)
        sim_map = (self.key_channels**-.5) * sim_map
        sim_map = F.softmax(sim_map, dim=-1)

        context = torch.matmul(sim_map, value)
        context = context.permute(0, 2, 1).contiguous()
        context = context.view(batch_size, self.value_channels, *x.size()[2:])
        context = self.W(context)
        if self.scale > 1:
            if torch_ver == '0.4':
                context = F.upsample(input=context, size=(h, w), mode='bilinear', align_corners=True)
            elif torch_ver == '0.3':
                context = F.upsample(input=context, size=(h, w), mode='bilinear')
        return context