help='If set to "baseline" use the baseline classifier')
options = vars(parser.parse_args())
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from dataloader import CustomDataloader, FlexibleCustomDataloader
from training import train_classifier
from networks import build_networks, save_networks, get_optimizers
from options import load_options, get_current_epoch
from comparison import evaluate_with_comparison
from evaluation import save_evaluation
options = load_options(options)
dataloader = FlexibleCustomDataloader(fold='train', **options)
networks = build_networks(dataloader.num_classes, **options)
optimizers = get_optimizers(networks, finetune=True, **options)
eval_dataloader = CustomDataloader(last_batch=True,
shuffle=False,
fold='test',
**options)
start_epoch = get_current_epoch(options['result_dir']) + 1
for epoch in range(start_epoch, start_epoch + options['epochs']):
train_classifier(networks, optimizers, dataloader, epoch=epoch, **options)
#print(networks['classifier_kplusone'])
#weights = networks['classifier_kplusone'].fc1.weight
eval_results = evaluate_with_comparison(networks, eval_dataloader,
**options)
pprint(eval_results)
save_evaluation(eval_results, options['result_dir'], epoch)
parser = argparse.ArgumentParser()
parser.add_argument('--result_dir', help='Output directory for images and model checkpoints [default: .]', default='.')
parser.add_argument('--epochs', type=int, default=10, help='number of epochs to train for [default: 10]')
parser.add_argument('--aux_dataset', help='Path to aux_dataset file [default: None]')
options = vars(parser.parse_args())
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from dataloader import CustomDataloader, FlexibleCustomDataloader
from training import train_gan
from networks import build_networks, save_networks, get_optimizers
from options import load_options, get_current_epoch
from counterfactual import generate_counterfactual
from comparison import evaluate_with_comparison
options = load_options(options)
dataloader = FlexibleCustomDataloader(fold='train', **options)
eval_dataloader = CustomDataloader(fold='test', **options)
networks = build_networks(dataloader.num_classes, **options)
optimizers = get_optimizers(networks, **options)
start_epoch = get_current_epoch(options['result_dir']) + 1
for epoch in range(start_epoch, start_epoch + options['epochs']):
train_gan(networks, optimizers, dataloader, epoch=epoch, **options)
#generate_counterfactual(networks, dataloader, **options)
eval_results = evaluate_with_comparison(networks, eval_dataloader, **options)
pprint(eval_results)
save_networks(networks, epoch, options['result_dir'])