Hi, I have a question about training with multiple models in single GPU.
I want to train only the first model and just feed output from the first model to other next models.
To be more specific, I have three models.

Pretrained Resnet50 : model1
Pretrained MLP : model2
Pretrained MLP2 : model3

in here, I want to hold model 2) and 3) but train model 1).
First, I load pretrained model and weights
model1 = models.resnet50(pretrained=True)
criterion1 = nn.L1loss()
optimizer1 = torch.optim.Adam(model1.parameters(), lr=lr)
model2 = model2()
optimizer2 = torch.optim.Adam(model2.parameters(), lr=lr)
model2.load_state_dict(pretrained_model2[‘state_dict’]
optimizer2 .load_state_dict(pretrained_model2[‘optimizer’]
model3 = model3()
optimizer3 = torch.optim.Adam(model3.parameters(), lr=lr)
model3.load_state_dict(pretrained_model3[‘state_dict’]
optimizer3 .load_state_dict(pretrained_model3[‘optimizer’]
and then for training, I try to like below:
model1.train()
model2.eval()
model3.eval()
outputs = model1(img)
outputs = model2(outputs)
outputs = model3(outputs)
loss = criterion1(outputs, labels)
optimizer1.zero_grad()
loss.backward()
optimizer1.step()
I make only one loss and optimizer for the first model1 to training, is this right to training my model?
Thank you for your time.