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main_web_ignored.py
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import os
import torch
import yaml
import numpy as np
import os.path as osp
from torchvision import datasets
from utils.transforms import *
from utils.dataset import *
from utils import *
from torch.utils.data.dataloader import DataLoader
from models import models
from mcd_trainer import MCDTrainer
from coteaching_trainer import CoTeachingTrainer
from models.build import build_mcd_model,build_dg_model,build_digits_dg_model,build_digits_mcd_model
def set_random_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def build_mcd_optimizer(model, config):
if config['optimizer']['name'] == 'SGD':
opt_F = torch.optim.SGD(list(model["F"].parameters()),
**config['optimizer']['params'])
opt_C1 = torch.optim.SGD(list(model["C1"].parameters()),
**config['optimizer']['params'])
opt_C2 = torch.optim.SGD(list(model["C2"].parameters()),
**config['optimizer']['params'])
else:
opt_F = torch.optim.Adam(list(model["F"].parameters()),
**config['optimizer']['params'])
opt_C1 = torch.optim.Adam(list(model["C1"].parameters()),
**config['optimizer']['params'])
opt_C2 = torch.optim.Adam(list(model["C2"].parameters()),
**config['optimizer']['params'])
return {"F": opt_F, "C1": opt_C1, "C2": opt_C2}
def build_dg_optimizer(model, config):
if config['optimizer']['name'] == 'SGD':
opt_F1 = torch.optim.SGD(list(model["F1"].parameters()),
**config['optimizer']['params'])
opt_F2 = torch.optim.SGD(list(model["F2"].parameters()),
**config['optimizer']['params'])
opt_C1 = torch.optim.SGD(list(model["C1"].parameters()),
**config['optimizer']['params'])
opt_C2 = torch.optim.SGD(list(model["C2"].parameters()),
**config['optimizer']['params'])
else:
opt_F1 = torch.optim.Adam(list(model["F1"].parameters()),
**config['optimizer']['params'])
opt_F2 = torch.optim.Adam(list(model["F2"].parameters()),
**config['optimizer']['params'])
opt_C1 = torch.optim.Adam(list(model["C1"].parameters()),
**config['optimizer']['params'])
opt_C2 = torch.optim.Adam(list(model["C2"].parameters()),
**config['optimizer']['params'])
return {"F1": opt_F1,"F2": opt_F2, "C1": opt_C1, "C2": opt_C2 }
def build_dataloader(data_list, batch_size, config, transform=None, istrain=True):
dataset = base_dataset(data_list, transform=transform)
return DataLoader(dataset, batch_size=batch_size,
num_workers=config['trainer']['num_workers'],
drop_last=istrain, shuffle=istrain)
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--task', default='art2clipart', help='task name')
args = parser.parse_args()
set_random_seed(1)
config = yaml.load(open("./config/" + args.task + ".yaml", "r"), Loader=yaml.FullLoader)
config['trainer']['save_model_addr'] = f"{config['trainer']['save_model_addr']}_ignored"
config['log']['save_name'] = f"{config['log']['save_name']}_ignored"
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# logger info
logdir = osp.join(osp.dirname(__file__), config['log']['save_addr'], config['log']['save_name'] + '.log')
setup_logger(logdir)
print(config)
# network 建立网络模型，即骨干网络
mcd_model_1 = build_mcd_model(config).to(device)
#mcd_model_2 = build_mcd_model(config).to(device) #两个APL模块的网络
dg_model = build_dg_model(config).to(device) #DCG过程的网络
# optim
mcd_opt_1 = build_mcd_optimizer(mcd_model_1, config)
#mcd_opt_2 = build_mcd_optimizer(mcd_model_2, config)
dg_opt = build_dg_optimizer(dg_model, config)
# data_list, transform
input_size = config['data_transforms']['input_size']
batch_size = config['trainer']['batch_size']
#transform设置
mcd_transform_test = simple_transform_test(input_size=input_size, type = config['data']['type'])
mcd_transform_train = simple_transform_train(input_size=input_size, type = config['data']['type'])
#读取数据
impath_label_x = get_image_dirs(root=config['data']['root'],
dname=config['data']['source_domain_x'],
split="train") #源域1
impath_label_u_1 = get_web_image_dirs(root=config['data']['webroot']
) #web数据
impath_label_t = get_image_dirs(root=config['data']['root'],
dname=config['data']['target_domain'],
split="train")
fake_dg_u_1 = []
# trainer
trainer_mcd_1 = MCDTrainer(mcd_model_1, mcd_opt_1, device, 1, **config['trainer'])
for index in range(3):
print(f"Round {index}: Training MCD.".center(100, "#"))
# dataloader
train_data_1 = impath_label_x + fake_dg_u_1
dataloader_x_1 = build_dataloader(train_data_1, batch_size, config, mcd_transform_train)
print('dataset dataloader_x_1: {}'.format(len(dataloader_x_1)))
dataloader_u_1 = build_dataloader(impath_label_u_1, batch_size, config, mcd_transform_train)
print('dataset dataloader_u_1: {}'.format(len(dataloader_u_1)))
# train
trainer_mcd_1.update_lr(index + 1)
trainer_mcd_1.train_mcd(dataloader_x_1, dataloader_u_1, 30)
del dataloader_x_1, dataloader_u_1
# test dataloader.
dataloader_u_1 = build_dataloader(impath_label_u_1, batch_size, config, mcd_transform_test, False)
# test
print("test dataloader_u_1.".center(60, "#"))
trainer_mcd_1.test(dataloader_u_1)
# get pseudo label.
print("get pseudo label.".center(60, "#")) #通过MCD训练得到模型后，经过test得到伪标签
fake_mcd_u_1 = trainer_mcd_1.get_pl(dataloader_u_1)
del dataloader_u_1
# Train Co-teach0ing
print("Training CO-Teaching.".center(100, "#"))
train_data = impath_label_x + fake_mcd_u_1 #数据为路径加标签，即文本中的一行
dg_transform_test = simple_transform_test(input_size=input_size, type = config['data']['type'])
dg_transform_train = simple_transform_train(input_size=input_size, type = config['data']['type'])
dataloader_train = build_dataloader(train_data, batch_size, config, dg_transform_train)
print('dataset dg train dataloader: {}'.format(len(dataloader_train)))
dataloader_test = build_dataloader(impath_label_t, batch_size, config, dg_transform_test)
print('dataset dg test dataloader: {}'.format(len(dataloader_test)))
source_domain = config['data']['source_domain_x']
trainer_dg = CoTeachingTrainer(dg_model, dg_opt, device, 1, source_domain, **config['trainer'])
#train
trainer_dg.update_lr(index + 1)
trainer_dg.train_dg(dataloader_train,dataloader_test,15)
#test dataloader
dataloader_u_1 = build_dataloader(impath_label_u_1, batch_size, config, mcd_transform_test, False)
#get pseudo label
ratio = config['ratio']
print("get dg pseudo label.".center(60, "#"))
fake_dg_u_1 = trainer_dg.get_pl(dataloader_u_1,ratio = ratio)
del dataloader_u_1
if __name__ == '__main__':
main()