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)
save_networks(networks, epoch, options['result_dir'])
num_workers=8,
pin_memory=True,
drop_last=False)
dataloader_off = torch.utils.data.DataLoader(dataset_off,
batch_size=options['batch_size'],
shuffle=False,
num_workers=8,
pin_memory=True,
drop_last=False)
networks = build_networks(**options)
new_results = evaluate_with_comparison(networks,
dataloader,
comparison_dataloader=dataloader_off,
dataloader_train=dataloader_train,
**options)
pprint(new_results)
if not os.path.exists('evaluation'):
os.mkdir('evaluation')
result = {options['mode']: new_results}
result_dir = os.path.join('evaluation',
'result_{}.json'.format(options['image_size']))
if os.path.exists(result_dir):
old_result = json.load(open(result_dir))
old_result.update(result)
result = old_result.copy()
with open(result_dir, 'w') as fp:
json.dump(result, fp, indent=2, sort_keys=True)