If you are using dropout as a module, you could manipulate the .p attribute after your specified number of epochs:
drop = nn.Dropout(p=0.5)

x = torch.randn(10)
out = drop(x)
print((out==0.).sum())
> tensor(5)

drop.p = 0.2
out = drop(x)
print((out==0.).sum())
> tensor(1)

In your case, the manipulation would of course look like this:
model.drop1.p = 0.2

Or alternatively you could also use the functional API from the beginning, and provide the drop probability in the forward method of your model:
def forward(self, x, p):
    x = F.dropout(x, p, training=self.training)
    return x