def main():
batch_size = 16
test_batch_size = 32
p = os.path.expanduser("./image-clef/p")
c = os.path.expanduser("./image-clef/c")
i = os.path.expanduser("./image-clef/i")
is_debug = False
epochs = 400
init_lr_da = 0.0001
init_lr_kd = 0.0005
momentum = 0.9
weight_decay = 5e-4
device = torch.device("cuda")
alpha = 0.5
gamma = 0.5
init_beta = 0.1
end_beta = 0.9
student_pretrained = True
source_dataloader, target_dataloader, target_testloader = DA_datasets.get_source_target_loader("ImageClef",
p,
c,
batch_size, 0)
if torch.cuda.device_count() > 1:
teacher_model = nn.DataParallel(DANN_GRL.DANN_GRL_Resnet(ResNet.resnet101, True, source_dataloader.dataset.num_classes)).to(device)
student_model = nn.DataParallel(DANN_GRL.DANN_GRL_Resnet(ResNet.resnet34, student_pretrained, source_dataloader.dataset.num_classes)).to(device)
else:
teacher_model = DANN_GRL.DANN_GRL_Resnet(ResNet.resnet101, True, source_dataloader.dataset.num_classes).to(device)
student_model = DANN_GRL.DANN_GRL_Resnet(ResNet.resnet34, student_pretrained, source_dataloader.dataset.num_classes).to(device)
distil = distiller.Distiller(teacher_model, student_model)
distil = distil.to(device)
criterion = nn.CrossEntropyLoss()
growth_rate = torch.log(torch.FloatTensor([end_beta / init_beta])) / torch.FloatTensor([epochs])
optimizer_da = torch.optim.SGD(distil.t_net.parameters(), init_lr_da,
momentum=momentum, weight_decay=weight_decay)
optimizer_kd = torch.optim.SGD(list(distil.s_net.parameters()) + list(distil.Connectors.parameters()), init_lr_kd,
momentum=momentum, weight_decay=weight_decay)
mmd_hinton_alt(init_lr_da, init_lr_kd, device, epochs, alpha, gamma, growth_rate, init_beta, source_dataloader,
target_dataloader, target_testloader, optimizer_da, optimizer_kd, distil, criterion, is_scheduler_kd=False, is_scheduler_da=True, is_debug=False, best_teacher_acc = 0)
def main():
a = os.path.expanduser('./datasets/amazon/images')
w = os.path.expanduser('./datasets/webcam/images')
d = os.path.expanduser('./datasets/dslr/images')
dataset_name = 'Office31'
source_dataset_path = a
target_dataset_path_1 = w
init_beta = 0.1
end_beta = 0.6
init_lr_da = 0.001
init_lr_kd = 0.01
momentum = 0.9
T = 20
batch_size = 32
alpha = 0.5
gamma = 0.5
epochs = 400
scheduler_kd_fn = None
batch_norm = True
device = 'cuda'
weight_decay = 5e-4
is_scheduler_da = True
is_scheduler_kd = True
source_dataloader, targets_dataloader, targets_testloader = DA_datasets.get_source_target_loader(
dataset_name,
source_dataset_path,
target_dataset_path_1,
batch_size,
0,
drop_last=True)
teacher_model_1 = DANN_GRL.DANN_GRL_Resnet(
ResNet.resnet101, True,
source_dataloader.dataset.num_classes).to(device)
student_model = DANN_GRL.DANN_GRL_Resnet(
ResNet.resnet34, True,
source_dataloader.dataset.num_classes).to(device)
targets_dataloader = [targets_dataloader]
targets_testloader = [targets_testloader]
if torch.cuda.device_count() > 1:
teacher_model_1 = nn.DataParallel(teacher_model_1).to(device)
student_model = nn.DataParallel(student_model).to(device)
teacher_models = [teacher_model_1]
growth_rate = torch.zeros(1)
if init_beta != 0.0:
growth_rate = torch.log(torch.FloatTensor(
[end_beta / init_beta])) / torch.FloatTensor([epochs])
optimizer_da_1 = torch.optim.SGD(teacher_model_1.parameters(),
init_lr_da,
momentum=momentum,
weight_decay=weight_decay)
optimizer_das = [optimizer_da_1]
optimizer_kd = torch.optim.SGD(student_model.parameters(),
init_lr_kd,
momentum=momentum,
weight_decay=weight_decay)
scheduler_kd = None
if scheduler_kd_fn is not None:
scheduler_kd = scheduler_kd_fn(optimizer_kd, scheduler_kd_steps,
scheduler_kd_gamma)
best_student_acc = grl_multi_target_hinton_alt(
init_lr_da,
init_lr_kd,
device,
epochs,
T,
alpha,
gamma,
growth_rate,
init_beta,
source_dataloader,
targets_dataloader,
targets_testloader,
optimizer_das,
optimizer_kd,
teacher_models,
student_model,
logger=None,
is_scheduler_da=is_scheduler_da,
is_scheduler_kd=is_scheduler_kd,
scheduler_kd=None,
scheduler_da=None,
is_debug=False,
batch_norm=batch_norm)
def main():
useVisdomLogger = True
batch_size = 32
test_batch_size = 32
webcam = os.path.expanduser("~/datasets/webcam/images")
amazon = os.path.expanduser("~/datasets/amazon/images")
dslr = os.path.expanduser("~/datasets/dslr/images")
is_debug = False
epochs = 400
init_lr_da = 0.001
init_lr_kd = 0.001
momentum = 0.9
weight_decay = 5e-4
device = torch.device("cuda")
T = 20
alpha = 0.3
init_beta = 0.1
end_beta = 0.9
student_pretrained = True
if torch.cuda.device_count() > 1:
teacher_model = nn.DataParallel(
DAN_model.DANNet_ResNet(ResNet.resnet50, True)).to(device)
student_model = nn.DataParallel(
DAN_model.DANNet_ResNet(ResNet.resnet34,
student_pretrained)).to(device)
else:
teacher_model = DAN_model.DANNet_ResNet(ResNet.resnet50,
True).to(device)
student_model = DAN_model.DANNet_ResNet(ResNet.resnet34,
student_pretrained).to(device)
growth_rate = torch.log(torch.FloatTensor(
[end_beta / init_beta])) / torch.FloatTensor([epochs])
optimizer_da = torch.optim.SGD(list(teacher_model.parameters()) +
list(student_model.parameters()),
init_lr_da,
momentum=momentum,
weight_decay=weight_decay)
optimizer_kd = torch.optim.SGD(list(teacher_model.parameters()) +
list(student_model.parameters()),
init_lr_kd,
momentum=momentum,
weight_decay=weight_decay)
source_dataloader, target_dataloader, target_testloader = DA_datasets.get_source_target_loader(
"Office31", amazon, webcam, batch_size, 0)
logger = None
if useVisdomLogger:
logger = VisdomLogger(port=9000)
logger = LoggerForSacred(logger, always_print=True)
mmd_hinton_alt(init_lr_da,
init_lr_kd,
device,
epochs,
T,
alpha,
growth_rate,
init_beta,
source_dataloader,
target_dataloader,
target_testloader,
optimizer_da,
optimizer_kd,
teacher_model,
student_model,
logger=logger,
is_scheduler_kd=False,
is_scheduler_da=True,
is_debug=False)