eval_mu.py

import algorithms
import os
from utils.validate import *
from utils.args import *
from utils.misc import *
from dataset.data_manager import get_dataset
from tqdm import tqdm

if __name__ == "__main__":

    args = get_args()
    cfg = setup_cfg(args)
    # log_path = os.path.join('./result/fundusaug', cfg.OUTPUT_PATH)
    log_path = cfg.OUTPUT_PATH
       
    # init
    train_loader, val_loader, test_loader, dataset_size = get_dataset(cfg)
    # writer = init_log(args, cfg, log_path, len(train_loader), dataset_size)
    algorithm_class = algorithms.get_algorithm_class(cfg.ALGORITHM)
    algorithm = algorithm_class(cfg.DATASET.NUM_CLASSES, cfg)
    algorithm.cuda()

    model_path = args.model_path
    algorithm.load_model(model_path)
    algorithm.eval()
    
    # # train
    # iterator = tqdm(range(cfg.EPOCHS))
    # scheduler = get_scheduler(algorithm.optimizer, cfg.EPOCHS)
    # best_performance = 0.0
    # for i in iterator:
        
    #     epoch = i + 1
    #     loss_avg = LossCounter()
    #     for image, mask, label, domain, img_index in train_loader:

    #         algorithm.train()
    #         minibatch = [image.cuda(), mask.cuda(), label.cuda().long(), domain.cuda().long()]
    #         loss_dict_iter = algorithm.update(minibatch)   
    #         loss_avg.update(loss_dict_iter['loss'])

    #     alpha = algorithm.update_epoch(epoch)
    #     update_writer(writer, epoch, scheduler, loss_avg)
    #     scheduler.step()

    #     # validation
    #     if epoch % cfg.VAL_EPOCH == 0:
    #         val_auc, test_auc = algorithm.validate(val_loader, test_loader, writer)
    #         if val_auc > best_performance and epoch > cfg.EPOCHS * 0.3:
    #             best_performance = val_auc
    #             algorithm.save_model(log_path)
    
    # algorithm.renew_model(log_path)
    mu, sigma = algorithm.eval_mu(val_loader, test_loader)
    d_name = cfg.DATASET.TARGET_DOMAINS
    d_name = d_name[0]
    print(d_name)
    print(mu.shape)
    np.save('D:/Med/DGDR/stat/mu_{}.npy'.format(d_name), mu)
    print(sigma.shape)
    np.save('D:/Med/DGDR/stat/sigma_{}.npy'.format(d_name), sigma)
    # os.mknod(os.path.join(log_path, 'done'))
    # writer.close()