def main():
set_randomness(GLOBAL_SEED, deterministic=True, benchmark=False)
# Prepare data
datasets = data[args.dataset]
K = num_class[args.dataset]
print(args)
target_dataset = datasets[args.tgt]
train_target_file_path = target_dataset
batch_size = 16
train_target_loader = load_images(train_target_file_path,
batch_size=batch_size,
resize_size=256,
is_train=True,
crop_size=224,
worker_init=gen_init_fn(GLOBAL_SEED),
prefix=args.prefix)
writer = SummaryWriter('%s/%s' % (args.logdir, args.key))
# Init model
target_model = torch.load(args.saved_model)
# Evaluate model
result = evaluate_classification_perclass(target_model, K,
train_target_loader)
file_path = 'distance_test/classaccu_%s.txt' % args.key
with open(file_path, 'w') as result_file:
result_file.writelines(result)
print('write result into %s.' % file_path)
def main():
set_randomness(GLOBAL_SEED, deterministic=True, benchmark=False)
# Prepare data
datasets = data[args.dataset]
print(args)
train_target_file_path = datasets[args.tgt]
train_source_file_path = datasets[args.src]
batch_size = 16
train_source_loader = load_images(train_source_file_path,
batch_size=batch_size,
resize_size=256,
is_train=True,
crop_size=224,
worker_init=gen_init_fn(GLOBAL_SEED),
prefix=args.prefix)
train_target_loader = load_images(train_target_file_path,
batch_size=batch_size,
resize_size=256,
is_train=True,
crop_size=224,
worker_init=gen_init_fn(GLOBAL_SEED),
prefix=args.prefix)
writer = SummaryWriter('%s/%s' % (args.logdir, args.key))
# Init model
target_model = torch.load(args.saved_model)
# Evaluate model
vis_tsne(target_model, [train_source_loader, train_target_loader], writer)
print('Finish visualization.')
def main():
set_randomness(GLOBAL_SEED, deterministic=True, benchmark=False)
# Prepare data
datasets = data[args.dataset]
K = num_class[args.dataset]
print(args)
train_target_file_path = datasets[args.tgt]
train_source_file_path = datasets[args.src]
batch_size = 16
train_source_loader = load_images(train_source_file_path, batch_size=batch_size, resize_size=256, is_train=True, crop_size=224, worker_init=gen_init_fn(GLOBAL_SEED), prefix=args.prefix)
train_target_loader = load_images(train_target_file_path, batch_size=batch_size, resize_size=256, is_train=True, crop_size=224, worker_init=gen_init_fn(GLOBAL_SEED), prefix=args.prefix)
writer = SummaryWriter('%s/%s' % (args.logdir, args.key))
# Init model
target_model = torch.load(args.saved_model)
model_instance = Distance(g_net=target_model.g_net, class_num=K, alpha=args.alpha, beta=args.beta, gamma=args.gamma)
# Evaluate model
result = evaluate_distance(model_instance, train_source_loader, train_target_loader)
file_path = 'distance_test/%s.txt' % args.key
with open(file_path, 'w') as result_file:
result_file.writelines(result)
print('write result into %s.' % file_path)
def main():
set_randomness(GLOBAL_SEED, deterministic=True, benchmark=False)
# Prepare data
datasets = data[args.dataset]
K = num_class[args.dataset]
print(args)
train_target_file_path = datasets[args.tgt]
train_source_file_path = datasets[args.src]
batch_size = 16
train_source_loader = load_images(train_source_file_path, batch_size=batch_size, resize_size=256, is_train=True, crop_size=224, worker_init=gen_init_fn(GLOBAL_SEED), prefix=args.prefix)
train_target_loader = load_images(train_target_file_path, batch_size=batch_size, resize_size=256, is_train=True, crop_size=224, worker_init=gen_init_fn(GLOBAL_SEED), prefix=args.prefix)
writer = SummaryWriter('%s/%s' % (args.logdir, args.key))
# Init model
model_instance = ADis(class_num=K, alpha=args.alpha, beta=args.beta, gamma=args.gamma)
parameter_list = model_instance.get_parameter_list()
optimizer_list = []
for parameter in parameter_list:
optimizer = optim.SGD(parameter, lr=args.lr, momentum=0.9, weight_decay=0.0005, nesterov=True)
optimizer_list.append(optimizer)
scheduler = INVScheduler(gamma=0.001, power=0.75, decay_rate=0.0002, init_lr=args.lr)
train_da_evaluator(model_instance, train_source_loader, train_target_loader, num_iterations=100000, optimizer_list=optimizer_list, lr_scheduler=scheduler, writer=writer)
def main():
set_randomness(GLOBAL_SEED, deterministic=True, benchmark=False)
# Prepare data
datasets = data[args.dataset]
K = num_class[args.dataset]
print(args)
train_target_file_path = datasets[args.tgt]
train_source_file_path = datasets[args.src]
test_file_path = datasets[args.tgttest]
model_path = 'results/final_model_%s.pkl' % args.key
batch_size = 48
train_source_loader = load_images(train_source_file_path, batch_size=batch_size, resize_size=256, is_train=True, crop_size=224, worker_init=worker_init_fn, prefix=args.prefix)
train_target_loader = load_images(train_target_file_path, batch_size=batch_size, resize_size=256, is_train=True, crop_size=224, worker_init=worker_init_fn, prefix=args.prefix)
test_target_loader = load_images(test_file_path, batch_size=batch_size, resize_size=256, is_train=False, crop_size=224, worker_init=worker_init_fn, prefix=args.prefix)
writer = SummaryWriter('%s/%s' % (args.logdir, args.key))
# Init model
if args.resume == 1:
model_instance = torch.load(args.saved_model)
model_instance.alpha = args.alpha
model_instance.beta = args.beta
model_instance.gmma = args.gamma
else:
model_instance = DANN(class_num=K, alpha=args.alpha, beta=args.beta, gamma=args.gamma)
start_iter = args.start_iter
parameter_list = model_instance.get_parameter_list()
optimizer_list = []
for parameter in parameter_list:
optimizer = optim.SGD(parameter, lr=args.lr, momentum=0.9, weight_decay=0.0005, nesterov=True)
optimizer_list.append(optimizer)
scheduler = INVScheduler(gamma=0.001, power=0.75, decay_rate=0.0005, init_lr=args.lr)
train_da(model_instance, train_source_loader, train_target_loader, test_target_loader, num_iterations=100000, optimizer_list=optimizer_list, lr_scheduler=scheduler, writer=writer, key=args.key, do_eval=True, model_dir='results', start_iter=start_iter)
# Evaluate model
print("All training is finished.")
eval_result = evaluate_classification(model_instance, test_target_loader)
print(eval_result)
# Save model
torch.save(model_instance, model_path)
def main():
set_randomness(GLOBAL_SEED, deterministic=True, benchmark=False)
# Prepare data
datasets = data[args.dataset]
K = num_class[args.dataset]
print(args)
train_target_file_path = datasets[args.tgt]
train_source_file_path = datasets[args.src]
test_file_path = datasets[args.tgttest]
model_path = 'results/final_model_%s.pkl' % args.key
batch_size = 32
train_source_loader = load_images(train_source_file_path,
batch_size=batch_size,
resize_size=256,
is_train=True,
crop_size=224,
worker_init=gen_init_fn(GLOBAL_SEED),
prefix=args.prefix)
train_target_loader = load_images(train_target_file_path,
batch_size=batch_size,
resize_size=256,
is_train=True,
crop_size=224,
worker_init=gen_init_fn(GLOBAL_SEED),
prefix=args.prefix)
test_source_loader = load_images(train_source_file_path,
batch_size=batch_size,
resize_size=256,
is_train=False,
crop_size=224,
worker_init=gen_init_fn(GLOBAL_SEED),
prefix=args.prefix)
test_target_loader = load_images(test_file_path,
batch_size=batch_size,
resize_size=256,
is_train=False,
crop_size=224,
worker_init=gen_init_fn(GLOBAL_SEED),
prefix=args.prefix)
writer = SummaryWriter('%s/%s' % (args.logdir, args.key))
# Init model
target_model = torch.load(args.saved_model)
model_instance = LambdaTest(g_net=target_model.g_net,
class_num=K,
alpha=args.alpha,
beta=args.beta,
gamma=args.gamma)
parameter_list = model_instance.get_parameter_list()
optimizer_list = []
for parameter in parameter_list:
optimizer = optim.SGD(parameter,
lr=args.lr,
momentum=0.9,
weight_decay=0.0005,
nesterov=True)
optimizer_list.append(optimizer)
scheduler = INVScheduler(gamma=0.001,
power=0.75,
decay_rate=0.0005,
init_lr=args.lr)
# Train model
train_da(model_instance,
train_source_loader,
train_target_loader,
test_target_loader,
num_iterations=50000,
optimizer_list=optimizer_list,
lr_scheduler=scheduler,
writer=writer,
key=args.key,
do_eval=False,
model_dir='results')
# Evaluate model
print("All training is finished.")
ori_train_state = model_instance.is_train
model_instance.set_train(False)
src_eval_result = evaluate_classification_by_net(
model_instance.src_predict, test_source_loader)
tgt_eval_result = evaluate_classification_by_net(
model_instance.tgt_predict, test_target_loader)
model_instance.set_train(ori_train_state)
print('src:')
print(src_eval_result)
print('tgt:')
print(tgt_eval_result)
lambda_value = (1 - src_eval_result['accuracy'] + 1 -
tgt_eval_result['accuracy']) / 2
print('lambda value: %s' % lambda_value)
# Save model
torch.save(model_instance, model_path)