Open shiyongde opened 5 years ago
There are 4 same optimizer,why?
raw_parameters = list(net.pretrained_model.parameters()) part_parameters = list(net.proposal_net.parameters()) concat_parameters = list(net.concat_net.parameters()) partcls_parameters = list(net.partcls_net.parameters())
raw_optimizer = torch.optim.SGD(raw_parameters, lr=LR, momentum=0.9, weight_decay=WD) concat_optimizer = torch.optim.SGD(concat_parameters, lr=LR, momentum=0.9, weight_decay=WD) part_optimizer = torch.optim.SGD(part_parameters, lr=LR, momentum=0.9, weight_decay=WD) partcls_optimizer = torch.optim.SGD(partcls_parameters, lr=LR, momentum=0.9, weight_decay=WD) schedulers = [MultiStepLR(raw_optimizer, milestones=[60, 100], gamma=0.1), MultiStepLR(concat_optimizer, milestones=[60, 100], gamma=0.1), MultiStepLR(part_optimizer, milestones=[60, 100], gamma=0.1), MultiStepLR(partcls_optimizer, milestones=[60, 100], gamma=0.1)]
I think jusk one optimizer can make it?
or some part of nets need different update?
Maybe the author want to train different parameters with different setting at the begining.
There are 4 same optimizer,why?
raw_parameters = list(net.pretrained_model.parameters()) part_parameters = list(net.proposal_net.parameters()) concat_parameters = list(net.concat_net.parameters()) partcls_parameters = list(net.partcls_net.parameters())
raw_optimizer = torch.optim.SGD(raw_parameters, lr=LR, momentum=0.9, weight_decay=WD) concat_optimizer = torch.optim.SGD(concat_parameters, lr=LR, momentum=0.9, weight_decay=WD) part_optimizer = torch.optim.SGD(part_parameters, lr=LR, momentum=0.9, weight_decay=WD) partcls_optimizer = torch.optim.SGD(partcls_parameters, lr=LR, momentum=0.9, weight_decay=WD) schedulers = [MultiStepLR(raw_optimizer, milestones=[60, 100], gamma=0.1), MultiStepLR(concat_optimizer, milestones=[60, 100], gamma=0.1), MultiStepLR(part_optimizer, milestones=[60, 100], gamma=0.1), MultiStepLR(partcls_optimizer, milestones=[60, 100], gamma=0.1)]
I think jusk one optimizer can make it?
or some part of nets need different update?