I’m trying to convert CNN model code from Keras to Pytorch.
here is the original keras model: it comes from this article: " Squeeze-and-Excitation Networks"
def se_block(in_block, ch, ratio=16):
x = GlobalAveragePooling2D()(in_block)
x = Dense(ch//ratio, activation='relu')(x)
x = Dense(ch, activation='sigmoid')(x)
return multiply()([in_block, x])
here is what i have done but i am unsure since i have mixed results when adding this module to my network:
def GlobalAveragePooling(x):
x=torch.mean(x.view(x.size(0),x.size(1),-1),dim=2)
return x
class se_block(nn.Module):
def __init__(self,in_block,ch,ratio=16):
super(se_block,self).__init__()
self.GlobalAveragePooling=GlobalAveragePooling()
self.Dense1=nn.Linear(in_features=in_block,out_features=ch//ratio,)
self.Dense2=nn.Linear(in_features=ch//ratio,out_features=ch)
def forward(x):
x=GlobalAveragePooling(x)
x=Dense1(x)
x=F.relu(x)
x=Dense2(x)
x=F.sigmoid(x)
return x
Thanks 