def main():
batch_size = 64
test_batch_size = 64
lr = 0.1
momentum = 0.9
epochs = 100
epoch_step = 30
weight_decay = 1e-4
teacher_pretrained_path = "{}/dan_resnet50_amazon_2_webcam.pth".format(save_dir)
student_pretrained = False
device = torch.device("cuda")
webcam = os.path.expanduser("~/datasets/webcam/images")
amazon = os.path.expanduser("~/datasets/amazon/images")
dslr = os.path.expanduser("~/datasets/dslr/images")
train_loader_source = DA_datasets.office_loader(amazon, batch_size, 0)
train_loader_target = DA_datasets.office_loader(webcam, batch_size, 0)
testloader_target = DA_datasets.office_test_loader(webcam, test_batch_size, 0)
logger = VisdomLogger(port=10999)
logger = LoggerForSacred(logger)
teacher_model = DAN_model.DANNet_ResNet(ResNet.resnet50, True)
student_model = DAN_model.DANNet_ResNet(ResNet.resnet34, student_pretrained)
if teacher_pretrained_path != "":
teacher_model.load_state_dict(torch.load(teacher_pretrained_path))
if torch.cuda.device_count() > 1:
teacher_model = torch.nn.DataParallel(teacher_model).to(device)
student_model = torch.nn.DataParallel(student_model).to(device)
distiller_model = od_distiller.Distiller_DAN(teacher_model, student_model)
if torch.cuda.device_count() > 1:
distiller_model = torch.nn.DataParallel(distiller_model).to(device)
if torch.cuda.device_count() > 1:
optimizer = torch.optim.SGD(list(student_model.parameters()) + list(distiller_model.module.Connectors.parameters()),
lr, momentum=momentum, weight_decay=weight_decay, nesterov=True)
else:
optimizer = torch.optim.SGD(list(student_model.parameters()) + list(distiller_model.Connectors.parameters()),
lr, momentum=momentum, weight_decay=weight_decay, nesterov=True)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, epoch_step)
od_kd_without_label(epochs, teacher_model, student_model, distiller_model, optimizer, train_loader_target,
testloader_target, device, logger=logger, scheduler=scheduler)
return model_dan, optimizer, best_acc
if __name__ == "__main__":
batch_size = 32
test_batch_size = 32
#train_path = "/home/ens/AN88740/dataset/webcam/images"
#test_path = "/home/ens/AN88740/dataset/amazon/images"
webcam = os.path.expanduser("~/datasets/webcam/images")
amazon = os.path.expanduser("~/datasets/amazon/images")
dslr = os.path.expanduser("~/datasets/dslr/images")
epochs = 200
lr = 0.01
device = torch.device("cuda")
train_loader_source = DA_datasets.office_loader(webcam, batch_size, 0)
train_loader_target = DA_datasets.office_loader(amazon, batch_size, 0)
testloader_1_target = DA_datasets.office_test_loader(amazon, test_batch_size, 0)
logger = VisdomLogger(port=9000)
logger = LoggerForSacred(logger)
#model_dan = DAN_model.DANNet_ResNet(ResNet.resnet50, True).to(device)
model_dan = DAN_model.DANNetVGG16(models.vgg16, True).to(device)
optimizer = torch.optim.SGD(model_dan.parameters(), momentum=0.9, lr=lr, weight_decay=5e-4)
dann_grl_train(epochs, lr, model_dan, train_loader_source, device, train_loader_target, testloader_1_target, optimizer, logger=logger,
logger_id="", scheduler=None, is_debug=False)
def main():
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 = DA_datasets.office_loader(amazon, batch_size, 1)
target_dataloader = DA_datasets.office_loader(webcam, batch_size, 1)
target_testloader = DA_datasets.office_test_loader(webcam, test_batch_size,
1)
logger = LoggerForSacred(None, None, True)
grl_multi_target_hinton_alt(init_lr_da,
device,
epochs,
T,
alpha,
growth_rate,
init_beta,
source_dataloader,
target_dataloader,
target_testloader,
optimizer_da,
optimizer_kd,
teacher_model,
student_model,
logger=logger,
scheduler=None,
is_debug=False)