I am Jin L, graduated in June 2022, now an algorithm engineer
- 🧐 Interested in ML/DL & RecoSys & Graph & NLP.
Take a look at my repositories and let's get in touch!
Implementation of CCNet: Criss-Cross Attention for Semantic Segmentation
`class CC_module(nn.Module):
def init(self,in_dim):
super(CC_module, self).init()
self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim//8, kernel_size=1)
self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim//8, kernel_size=1)
self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
self.softmax = Softmax(dim=3)
self.INF = INF
self.gamma = nn.Parameter(torch.zeros(1))
def forward(self, x):
m_batchsize, _, height, width = x.size()
proj_query = self.query_conv(x)
proj_query_H = proj_query.permute(0,3,1,2).contiguous().view(m_batchsizewidth,-1,height).permute(0, 2, 1)
proj_query_W = proj_query.permute(0,2,1,3).contiguous().view(m_batchsizeheight,-1,width).permute(0, 2, 1)
proj_key = self.key_conv(x)
proj_key_H = proj_key.permute(0,3,1,2).contiguous().view(m_batchsizewidth,-1,height)
proj_key_W = proj_key.permute(0,2,1,3).contiguous().view(m_batchsizeheight,-1,width)
proj_value = self.value_conv(x)
proj_value_H = proj_value.permute(0,3,1,2).contiguous().view(m_batchsizewidth,-1,height)
proj_value_W = proj_value.permute(0,2,1,3).contiguous().view(m_batchsizeheight,-1,width)
energy_H = (torch.bmm(proj_query_H, proj_key_H)+self.INF(m_batchsize, height, width)).view(m_batchsize,width,height,height).permute(0,2,1,3)
energy_W = torch.bmm(proj_query_W, proj_key_W).view(m_batchsize,height,width,width)
concate = self.softmax(torch.cat([energy_H, energy_W], 3))
att_H = concate[:,:,:,0:height].permute(0,2,1,3).contiguous().view(m_batchsize*width,height,height)
#print(concate)
#print(att_H)
att_W = concate[:,:,:,height:height+width].contiguous().view(m_batchsize*height,width,width)
out_H = torch.bmm(proj_value_H, att_H.permute(0, 2, 1)).view(m_batchsize,width,-1,height).permute(0,2,3,1)
out_W = torch.bmm(proj_value_W, att_W.permute(0, 2, 1)).view(m_batchsize,height,-1,width).permute(0,2,1,3)
#print(out_H.size(),out_W.size())
return self.gamma*(out_H + out_W) + x`
I am very confused with self.gamma = torch.zero(1)
作者您好!
我查看了一些其他人的CC-Attention的实现,大部分似乎都需要通过什么cuda算子,不清楚为什么。 另外,我看了您的CC-attention的实现,觉得深受启发,有两个小问题咨询您:
1. energy_H = (energy_H + self.INF(m_batchsize, height, width)) , 这里为什么要加入 INF?
2. 您每个q, k, v都分成两个h, w部分,想问问这样的实现方法,是否与理论的CC-attention逻辑一致?
你好,请问你训练、验证和测试数据分别大概使用了多少张多大的图片?
不好意思,因为是个初学者,想请教一下,如果直接改多分类为二分类,image和label给的是[0,1]的值,经过model的输出,output的tensor值应该是什么呢 是每个点所对应的label置信度吗 但是我这样训练的网络 tensor是负值
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