How to have two optimizers such that one optimizer trains the whole parameter and the other trains partial of the parameter?
I have a model:

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = nn.Conv2d(128, 128, (3,3))
        self.conv2 = nn.Conv2d(128, 256, (3,3))
        self.conv3 = nn.Conv2d(256, 256, (3,3))

    def forward(self,):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        return x

model = MyModel()


I want to train model in such a way that in every training step DATA_X1 should train
 ['conv1', 'conv2', 'conv3'] layers and DATA_X2 should train only ['conv3'] layers.

I tried making two optimizer:

# Full parameters train
all_params = model.parameters()
all_optimizer = optim.Adam(all_params, lr=0.01)

# Partial parameters train
partial_params = model.parameters()
for p, (name, param) in zip(list(partial_params), model.named_parameters()):
    if name in ['conv3']:
        p.requires_grad = True
    else:
        p.requires_grad = False
partial_optimizer = optim.Adam(partial_params, lr=0.01)


But this affects both the optimizer with required_grad = False

Is there any way I can do this? 

Why not build this functionality into the model?

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = nn.Conv2d(128, 128, (3,3))
        self.conv2 = nn.Conv2d(128, 256, (3,3))
        self.conv3 = nn.Conv2d(256, 256, (3,3))
        self.partial_grad = False  # a flag

    def forward(self, x):
        if self.partial_grad:
            with torch.no_grad():
                x = F.relu(self.conv1(x))
                x = F.relu(self.conv2(x))
        else:
            x = F.relu(self.conv1(x))
            x = F.relu(self.conv2(x))     
        x = F.relu(self.conv3(x))
        return x


Now you can have a single optimizer with all the parameters, and you can switch model.partial_grad on and off according to your training data:

optimizer.zero_grad()
model.partial_grad = False  # prep for DATA_X1 training
x1, y1 = DATA_X1.item()  # this is not really a code, but you get the point
out = model(x1)
loss = criterion(out, y1)
loss.backward()
optimizer.step()  

# do a partial opt for DATA_X2
optimizer.zero_grad()
model.partial_grad = True  # prep for DATA_X2 training
x2, y2 = DATA_X2.item()  # this is not really a code, but you get the point
out = model(x2)
loss = criterion(out, y2)
loss.backward()
optimizer.step()  


Having a single optimizer should be more beneficial since you can track the momentum and the change of parameters across both datasets.
