import torch from torch.utils.data import DataLoader from tqdm import tqdm import trainer.eval.evaluator as evaluator # Evaluate how close to true Gaussian a flow network predicts in a "normal" pass given a LQ/HQ image pair. from data.images.image_folder_dataset import ImageFolderDataset from models.image_generation.srflow.flow import GaussianDiag class FlowGaussianNll(evaluator.Evaluator): def __init__(self, model, opt_eval, env): super().__init__(model, opt_eval, env, uses_all_ddp=False) self.batch_sz = opt_eval['batch_size'] self.dataset = ImageFolderDataset(opt_eval['dataset']) self.dataloader = DataLoader(self.dataset, self.batch_sz) def perform_eval(self): total_zs = 0 z_loss = 0 self.model.eval() with torch.no_grad(): print("Evaluating FlowGaussianNll..") for batch in tqdm(self.dataloader): dev = self.env['device'] z, _, _ = self.model(gt=batch['hq'].to(dev), lr=batch['lq'].to(dev), epses=[], reverse=False, add_gt_noise=False) for z_ in z: z_loss += GaussianDiag.logp(None, None, z_).mean() total_zs += 1 self.model.train() return {"gaussian_diff": z_loss / total_zs}