Datasets:
jblitzar
/
github-python

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data_transforms = {
source_data: transforms.Compose([
transforms.Scale((256, 256)),
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
target_data: transforms.Compose([
transforms.Scale((256, 256)),
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
evaluation_data: transforms.Compose([
transforms.Scale((256, 256)),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
use_gpu = torch.cuda.is_available()
source_loader, target_loader, \
test_loader, target_folder = get_loader(source_data, target_data,
evaluation_data, data_transforms,
batch_size=batch_size, return_id=True,
balanced=conf.data.dataloader.class_balance)
dataset_test = test_loader
n_share = conf.data.dataset.n_share
n_source_private = conf.data.dataset.n_source_private
num_class = n_share + n_source_private
G, C1 = get_model_mme(conf.model.base_model, num_class=num_class,
temp=conf.model.temp)
device = torch.device("cuda")
if args.cuda:
G.cuda()
C1.cuda()
G.to(device)
C1.to(device)
ndata = target_folder.__len__()
## Memory
lemniscate = LinearAverage(2048, ndata, conf.model.temp, conf.train.momentum).cuda()
params = []
for key, value in dict(G.named_parameters()).items():
if value.requires_grad and "features" in key:
if 'bias' in key:
params += [{'params': [value], 'lr': conf.train.multi,
'weight_decay': conf.train.weight_decay}]
else:
params += [{'params': [value], 'lr': conf.train.multi,
'weight_decay': conf.train.weight_decay}]
else:
if 'bias' in key:
params += [{'params': [value], 'lr': 1.0,
'weight_decay': conf.train.weight_decay}]
else:
params += [{'params': [value], 'lr': 1.0,
'weight_decay': conf.train.weight_decay}]
criterion = torch.nn.CrossEntropyLoss().cuda()
opt_g = optim.SGD(params, momentum=conf.train.sgd_momentum,
weight_decay=0.0005, nesterov=True)
opt_c1 = optim.SGD(list(C1.parameters()), lr=1.0,
momentum=conf.train.sgd_momentum, weight_decay=0.0005,
nesterov=True)
[G, C1], [opt_g, opt_c1] = amp.initialize([G, C1],
[opt_g, opt_c1],
opt_level="O1")
G = nn.DataParallel(G)
C1 = nn.DataParallel(C1)
param_lr_g = []
for param_group in opt_g.param_groups:
param_lr_g.append(param_group["lr"])
param_lr_f = []
for param_group in opt_c1.param_groups:
param_lr_f.append(param_group["lr"])
def train():
criterion = nn.CrossEntropyLoss().cuda()
print('train start!')
data_iter_s = iter(source_loader)
data_iter_t = iter(target_loader)
len_train_source = len(source_loader)
len_train_target = len(target_loader)
for step in range(conf.train.min_step + 1):
G.train()
C1.train()
if step % len_train_target == 0:
data_iter_t = iter(target_loader)
if step % len_train_source == 0:
data_iter_s = iter(source_loader)
data_t = next(data_iter_t)
data_s = next(data_iter_s)
inv_lr_scheduler(param_lr_g, opt_g, step,
init_lr=conf.train.lr,