liwenyun
/
CLIP
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Separate Bottleneck ReLU layers

This commit is contained in:
ProGamerGov 2022-02-05 13:21:01 -07:00 committed by GitHub
parent 40f5484c1c
commit eb42b86d36
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 6 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
clip/model.py
View File

@ -25,7 +25,9 @@ class Bottleneck(nn.Module):
self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.relu1 = nn.ReLU(inplace=True)
self.relu2 = nn.ReLU(inplace=True)
self.relu3 = nn.ReLU(inplace=True)
self.downsample = None
self.stride = stride
@ -40,8 +42,8 @@ class Bottleneck(nn.Module):
def forward(self, x: torch.Tensor):
identity = x
out = self.relu(self.bn1(self.conv1(x)))
out = self.relu(self.bn2(self.conv2(out)))
out = self.relu1(self.bn1(self.conv1(x)))
out = self.relu2(self.bn2(self.conv2(out)))
out = self.avgpool(out)
out = self.bn3(self.conv3(out))
@ -49,7 +51,7 @@ class Bottleneck(nn.Module):
identity = self.downsample(x)
out += identity
out = self.relu(out)
out = self.relu3(out)
return out