def get_composed_augmentations(aug_dict): if aug_dict is None: logger.info("Using No Augmentations") return None augmentations = [] for aug_key, aug_param in aug_dict.items(): augmentations.append(key2aug[aug_key](aug_param)) logger.info("Using {} aug with params {}".format(aug_key, aug_param)) return Compose(augmentations)
def inference(cfg, model, val_loader): logger.info("Start infer") device = torch.device('cuda' if torch.cuda.is_available() else 'cpu' ) if cfg.MODEL.DEVICE == 'cuda' else 'cpu' evaluator = create_supervised_evaluator(model, metrics={'accuracy': Accuracy()}, device=device) # adding handlers using `evaluator.on` decorator API @evaluator.on(Events.EPOCH_COMPLETED) def print_validation_results(engine): metrics = evaluator.state.metrics avg_acc = metrics['accuracy'] logger.info("Validation Results - Accuracy: {:.3f}".format(avg_acc)) evaluator.run(val_loader)
def print_validation_results(engine): metrics = evaluator.state.metrics avg_acc = metrics['accuracy'] logger.info("Validation Results - Accuracy: {:.3f}".format(avg_acc))
from ignite.engine import Events from ignite.engine import create_supervised_evaluator from ignite.metrics import Accuracy sys.path.append('../..') from cvnet.utils.logger import logger def inference(cfg, model, val_loader): logger.info("Start infer") device = torch.device('cuda' if torch.cuda.is_available() else 'cpu' ) if cfg.MODEL.DEVICE == 'cuda' else 'cpu' evaluator = create_supervised_evaluator(model, metrics={'accuracy': Accuracy()}, device=device) # adding handlers using `evaluator.on` decorator API @evaluator.on(Events.EPOCH_COMPLETED) def print_validation_results(engine): metrics = evaluator.state.metrics avg_acc = metrics['accuracy'] logger.info("Validation Results - Accuracy: {:.3f}".format(avg_acc)) evaluator.run(val_loader) if __name__ == '__main__': logger.info('infer')