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Update train_new.py #9

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d062d61
Update train_new.py
wujunyi627 Feb 27, 2019
a9b882c
Update train_new.py
wujunyi627 Feb 28, 2019
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39 changes: 28 additions & 11 deletions train_new.py
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Original file line number Diff line number Diff line change
Expand Up @@ -343,9 +343,12 @@ def draw_curve(current_epoch):
def save_network(network, epoch_label):
save_filename = 'net_%s.pth'% epoch_label
save_path = os.path.join('./model',name,save_filename)
torch.save(network.cpu().state_dict(), save_path)
if torch.cuda.is_available:
network.cuda(gpu_ids[0])
if len(gpu_ids)>1:
torch.save(network.module.state_dict(), save_path)
else:
torch.save(network.cpu().state_dict(), save_path)
if torch.cuda.is_available:
network.cuda(gpu_ids[0])


######################################################################
Expand All @@ -367,17 +370,31 @@ def save_network(network, epoch_label):

if use_gpu:
model = model.cuda()

##############################use multiple gpu####################################
if len(gpu_ids)>1:
model=torch.nn.DataParallel(model, device_ids=gpu_ids).cuda()
#################################################################################
criterion = nn.CrossEntropyLoss()

if not opt.PCB:
ignored_params = list(map(id, model.model.fc.parameters() )) + list(map(id, model.classifier.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.model.fc.parameters(), 'lr': opt.lr},
{'params': model.classifier.parameters(), 'lr': opt.lr}
], weight_decay=5e-4, momentum=0.9, nesterov=True)
if len(gpu_ids)>1:
ignored_params = list(map(id, model.module.model.fc.parameters() )) + list(map(id, model.module.classifier.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.module.model.fc.parameters(), 'lr': opt.lr},
{'params': model.module.classifier.parameters(), 'lr': opt.lr}
], weight_decay=5e-4, momentum=0.9, nesterov=True)
else:
ignored_params = list(map(id, model.model.fc.parameters() )) + list(map(id, model.classifier.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.model.fc.parameters(), 'lr': 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.classifier0.parameters() ))
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