Function eval_on_epoch

script/feature/direct_feature_matching.py:215–233

Perform 1 epoch of training with batch

(args, data_loaders, model, feat_model, hwf, half_res, device, world_setup_dict, **render_kwargs_test)

    return iter_loss, iter_psnr

def eval_on_epoch(args, data_loaders, model, feat_model, hwf, half_res, device, world_setup_dict, **render_kwargs_test):
    ''' Perform 1 epoch of training with batch '''
    model.eval()
    batch_size = 1
    
    train_dl, val_dl, test_dl = data_loaders

    total_loss = []
    total_psnr = []
    
    ####  Core optimization loop  #####
    for data, pose, img_idx in val_dl:
        # training one step with batch_size = args.batch_size
        loss, psnr = eval_on_batch(args, data, model, feat_model, pose, img_idx, hwf, half_res, device, world_setup_dict, **render_kwargs_test)
        total_loss.append(loss.item())
        total_psnr.append(psnr.item())
    total_loss_mean = np.mean(total_loss)
    total_psnr_mean = np.mean(total_psnr)
    return total_loss_mean, total_psnr_mean

def train_on_feature_batch(args, data, model, feat_model, pose, img_idx, hwf, optimizer, device, world_setup_dict, **render_kwargs_test):
    ''' Perform 1 step of training using scheme1 '''