def __init__(self, loss, resnet_type="32", num_classes=10):
super(SimpleLearner, self).__init__(loss,
resnet_type=resnet_type,
num_classes=num_classes)
self.features_extractor = get_resnet(resnet_type)
self.features_extractor.fc = nn.Sequential()
def __init__(self, loss, resnet_type="32", num_classes=10):
super(LwF, self).__init__(loss,
resnet_type=resnet_type,
num_classes=num_classes)
self.features_extractor = get_resnet(resnet_type)
self.features_extractor.fc = nn.Sequential()
self.previous_model = None
def init_erm_phase(self):
if self.args.ctr_model_name == 'lenet':
from models.lenet import LeNet5
ctr_phi = LeNet5().to(self.cuda)
if self.args.ctr_model_name == 'alexnet':
from models.alexnet import alexnet
ctr_phi = alexnet(self.args.out_classes, self.args.pre_trained,
'matchdg_ctr').to(self.cuda)
if self.args.ctr_model_name == 'fc':
from models.fc import FC
fc_layer = 0
ctr_phi = FC(self.args.out_classes, fc_layer).to(self.cuda)
if 'resnet' in self.args.ctr_model_name:
from models.resnet import get_resnet
fc_layer = 0
ctr_phi = get_resnet(self.args.ctr_model_name,
self.args.out_classes, fc_layer,
self.args.img_c, self.args.pre_trained,
self.args.os_env).to(self.cuda)
if 'densenet' in self.args.ctr_model_name:
from models.densenet import get_densenet
fc_layer = 0
ctr_phi = get_densenet(self.args.ctr_model_name,
self.args.out_classes, fc_layer,
self.args.img_c, self.args.pre_trained,
self.args.os_env).to(self.cuda)
# Load MatchDG CTR phase model from the saved weights
if self.args.os_env:
base_res_dir = os.getenv(
'PT_DATA_DIR'
) + '/' + self.args.dataset_name + '/' + 'matchdg_ctr' + '/' + self.args.ctr_match_layer + '/' + 'train_' + str(
self.args.train_domains)
else:
base_res_dir = "results/" + self.args.dataset_name + '/' + 'matchdg_ctr' + '/' + self.args.ctr_match_layer + '/' + 'train_' + str(
self.args.train_domains)
save_path = base_res_dir + '/Model_' + self.ctr_load_post_string + '.pth'
ctr_phi.load_state_dict(torch.load(save_path))
ctr_phi.eval()
#Inferred Match Case
if self.args.match_case == -1:
inferred_match = 1
data_match_tensor, label_match_tensor, indices_matched, perfect_match_rank = get_matched_pairs(
self.args, self.cuda, self.train_dataset, self.domain_size,
self.total_domains, self.training_list_size, ctr_phi,
self.args.match_case, self.args.perfect_match, inferred_match)
# x% percentage match initial strategy
else:
inferred_match = 0
data_match_tensor, label_match_tensor, indices_matched, perfect_match_rank = get_matched_pairs(
self.args, self.cuda, self.train_dataset, self.domain_size,
self.total_domains, self.training_list_size, ctr_phi,
self.args.match_case, self.args.perfect_match, inferred_match)
return data_match_tensor, label_match_tensor
def get_model(self, run_matchdg_erm=0):
if self.args.model_name == 'lenet':
from models.lenet import LeNet5
phi = LeNet5()
if self.args.model_name == 'fc':
from models.fc import FC
if self.args.method_name in ['csd', 'matchdg_ctr']:
fc_layer = 0
else:
fc_layer = self.args.fc_layer
phi = FC(self.args.out_classes, fc_layer)
if self.args.model_name == 'domain_bed_mnist':
from models.domain_bed_mnist import DomainBed
phi = DomainBed(self.args.img_c)
if self.args.model_name == 'alexnet':
from models.alexnet import alexnet
if self.args.method_name in ['csd', 'matchdg_ctr']:
fc_layer = 0
else:
fc_layer = self.args.fc_layer
phi = alexnet(self.args.model_name, self.args.out_classes,
fc_layer, self.args.img_c, self.args.pre_trained,
self.args.os_env)
if 'resnet' in self.args.model_name:
from models.resnet import get_resnet
if self.args.method_name in ['csd', 'matchdg_ctr']:
fc_layer = 0
else:
fc_layer = self.args.fc_layer
phi = get_resnet(self.args.model_name, self.args.out_classes,
fc_layer, self.args.img_c, self.args.pre_trained,
self.args.os_env)
if 'densenet' in self.args.model_name:
from models.densenet import get_densenet
if self.args.method_name in ['csd', 'matchdg_ctr']:
fc_layer = 0
else:
fc_layer = self.args.fc_layer
phi = get_densenet(self.args.model_name, self.args.out_classes,
fc_layer, self.args.img_c,
self.args.pre_trained, self.args.os_env)
print('Model Architecture: ', self.args.model_name)
self.phi = phi.to(self.cuda)
self.load_model(run_matchdg_erm)
return
def __init__(self, loss, resnet_type="32", num_classes=10):
super(MultiTaskLearner, self).__init__()
self.n_classes = num_classes
self.n_known = 0
self.loss = loss
self.features_extractor = get_resnet(resnet_type)
self.features_extractor.fc = nn.Sequential()
self.classifier = nn.Linear(self.features_extractor.out_dim,
num_classes)
def get_model(self):
if self.args.model_name == 'lenet':
from models.lenet import LeNet5
phi= LeNet5()
if self.args.model_name == 'alexnet':
from models.alexnet import alexnet
phi= alexnet(self.args.out_classes, self.args.pre_trained, self.args.method_name)
if self.args.model_name == 'resnet18':
from models.resnet import get_resnet
phi= get_resnet('resnet18', self.args.out_classes, self.args.method_name,
self.args.img_c, self.args.pre_trained)
print('Model Architecture: ', self.args.model_name)
phi= phi.to(self.cuda)
return phi
def get_model(self, run_matchdg_erm=0):
if self.args.model_name == 'lenet':
from models.lenet import LeNet5
phi = LeNet5()
if self.args.model_name == 'alexnet':
from models.alexnet import alexnet
phi = alexnet(self.args.out_classes, self.args.pre_trained,
self.args.method_name)
if self.args.model_name == 'domain_bed_mnist':
from models.domain_bed_mnist import DomainBed
phi = DomainBed(self.args.img_c)
if 'resnet' in self.args.model_name:
from models.resnet import get_resnet
phi = get_resnet(self.args.model_name, self.args.out_classes,
self.args.method_name, self.args.img_c,
self.args.pre_trained)
print('Model Architecture: ', self.args.model_name)
self.phi = phi.to(self.cuda)
self.load_model(run_matchdg_erm)
return
def get_model(self):
if self.args.model_name == 'lenet':
from models.lenet import LeNet5
phi = LeNet5()
if self.args.model_name == 'alexnet':
from models.alexnet import alexnet
phi = alexnet(self.args.out_classes, self.args.pre_trained,
self.args.method_name)
if self.args.model_name == 'domain_bed_mnist':
from models.domain_bed_mnist import DomainBed
phi = DomainBed(self.args.img_c)
if 'resnet' in self.args.model_name:
from models.resnet import get_resnet
if self.args.method_name in ['csd', 'matchdg_ctr']:
fc_layer = 0
else:
fc_layer = self.args.fc_layer
phi = get_resnet(self.args.model_name, self.args.out_classes,
fc_layer, self.args.img_c, self.args.pre_trained)
print('Model Architecture: ', self.args.model_name)
phi = phi.to(self.cuda)
return phi