def set_model(args, n_data):
# set the model
if args.model == 'alexnet':
if args.view == 'Lab':
model = alexnet(in_channel=(1, 2), feat_dim=args.feat_dim)
elif args.view == 'Rot':
model = alexnet(in_channel=(3, 3), feat_dim=args.feat_dim)
elif args.view == 'LabRot':
model = alexnet(in_channel=(1, 2), feat_dim=args.feat_dim)
else:
raise NotImplemented('view not implemented {}'.format(args.view))
elif args.model.startswith('resnet'):
model = ResNetV2(args.model)
else:
raise ValueError('model not supported yet {}'.format(args.model))
contrast = NCEAverage(args.feat_dim, n_data, args.nce_k, args.nce_t,
args.nce_m)
criterion_l = NCECriterion(n_data)
criterion_ab = NCECriterion(n_data)
if torch.cuda.is_available():
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model).cuda()
else:
model = model.cuda()
contrast = contrast.cuda()
criterion_ab = criterion_ab.cuda()
criterion_l = criterion_l.cuda()
cudnn.benchmark = True
return model, contrast, criterion_ab, criterion_l
def set_model(n_data):
contrast = NCEAverage(128, n_data, 4096, 0.1, 0.5)
criterion_l = NCECriterion(n_data)
criterion_ab = NCECriterion(n_data)
if torch.cuda.is_available():
contrast = contrast.cuda()
criterion_ab = criterion_ab.cuda()
criterion_l = criterion_l.cuda()
cudnn.benchmark = True
return contrast, criterion_ab, criterion_l
def set_model(args, n_data):
# set the model
if args.model == 'alexnet':
model = alexnet(args.feat_dim)
else:
raise ValueError('model not supported yet {}'.format(args.model))
contrast = NCEAverage(args.feat_dim, n_data, args.nce_k, args.nce_t,
args.nce_m)
criterion_l = NCECriterion(n_data)
criterion_ab = NCECriterion(n_data)
if torch.cuda.is_available():
model = model.cuda()
contrast = contrast.cuda()
criterion_ab = criterion_ab.cuda()
criterion_l = criterion_l.cuda()
cudnn.benchmark = True
return model, contrast, criterion_ab, criterion_l
def set_model(args, n_data):
# set the model
if args.model == 'alexnet':
model = MyAlexNetCMC(args.feat_dim)
elif args.model.startswith('resnet'):
model = MyResNetsCMC(args.model)
else:
raise ValueError('model not supported yet {}'.format(args.model))
contrast = NCEAverage(args.feat_dim, n_data, args.nce_k, args.nce_t, args.nce_m, args.softmax)
criterion_l = NCESoftmaxLoss() if args.softmax else NCECriterion(n_data)
criterion_ab = NCESoftmaxLoss() if args.softmax else NCECriterion(n_data)
if torch.cuda.is_available():
model = model.cuda()
contrast = contrast.cuda()
criterion_ab = criterion_ab.cuda()
criterion_l = criterion_l.cuda()
cudnn.benchmark = True
return model, contrast, criterion_ab, criterion_l