if opt.PCB and (opt.LSTM or opt.GGNN):
model_name = 'PCB-128_dim_cls'
model = load_network(model, model_name)
model = PCB_Effi_LSTM(model) if opt.LSTM else PCB_Effi_GGNN(model)
# model_name = 'LSTM' or 'GGNN'
# model = load_network(model, model_name)
print(model)
if not opt.multi_loss:
ignored_params = list(map(id, model.model._fc.parameters()))
ignored_params += (list(map(id, model.classifier.parameters())) +
list(map(id, model.model.parameters())))
base_params = filter(lambda p: id(p) not in ignored_params,
model.parameters())
optimizer_ft = optim.SGD([{
'params': base_params,
'lr': 0.1 * opt.lr
}, {
'params': model.classifier.parameters(),
'lr': opt.lr
}],
weight_decay=5e-4,
momentum=0.9,
nesterov=True)
else:
ignored_params = list(map(id, model.model._fc.parameters()))
ignored_params += (
list(map(id, model.classifierA0.parameters())) +
if opt.GGNN:
model_name = 'PCB-128_dim_cls'
model = load_network(model, model_name)
model = PCB_Effi_GGNN(model, opt.train_backbone)
# model_name = 'LSTM'
# model = load_network(model, model_name)
print(model)
if not opt.multi_loss:
ignored_params = list(map(id, model.model._fc.parameters()))
ignored_params += (list(map(id, model.classifier.parameters())))
if not opt.train_backbone:
ignored_params += (list(map(id, model.model.parameters())))
base_params = filter(
lambda p: id(p) not in ignored_params, model.parameters()
)
optimizer = optim.SGD(
[{'params': base_params, 'lr': 0.1*opt.lr},
{'params': model.classifier.parameters(), 'lr': opt.lr}],
weight_decay=5e-4, momentum=0.9, nesterov=True)
else:
ignored_params = list(map(id, model.model._fc.parameters()))
ignored_params += (
list(map(id, model.classifierA0.parameters()))
+ list(map(id, model.classifierA1.parameters()))
+ list(map(id, model.classifierA2.parameters()))
+ list(map(id, model.classifierA3.parameters()))
+list(map(id, model.classifierB0.parameters() ))
+list(map(id, model.classifierB1.parameters() ))
