import os import torch import os.path as osp import torchvision from torch.utils.data import BatchSampler import models.eval.evaluator as evaluator from pytorch_fid import fid_score # Evaluate that feeds a LR structure into the input, then calculates a FID score on the results added to # the interpolated LR structure. from data.stylegan2_dataset import Stylegan2Dataset class SrStyleTransferEvaluator(evaluator.Evaluator): def __init__(self, model, opt_eval, env): super().__init__(model, opt_eval, env) self.batches_per_eval = opt_eval['batches_per_eval'] self.batch_sz = opt_eval['batch_size'] self.im_sz = opt_eval['image_size'] self.scale = opt_eval['scale'] self.fid_real_samples = opt_eval['real_fid_path'] self.gen_output_index = opt_eval['gen_index'] if 'gen_index' in opt_eval.keys() else 0 self.dataset = Stylegan2Dataset({'path': self.fid_real_samples, 'target_size': self.im_sz, 'aug_prob': 0, 'transparent': False}) self.sampler = BatchSampler(self.dataset, self.batch_sz, False) def perform_eval(self): fid_fake_path = osp.join(self.env['base_path'], "..", "fid_fake", str(self.env["step"])) os.makedirs(fid_fake_path, exist_ok=True) fid_real_path = osp.join(self.env['base_path'], "..", "fid_real", str(self.env["step"])) os.makedirs(fid_real_path, exist_ok=True) counter = 0 for batch in self.sampler: noise = torch.FloatTensor(self.batch_sz, 3, self.im_sz, self.im_sz).uniform_(0., 1.).to(self.env['device']) batch_hq = [e['GT'] for e in batch] batch_hq = torch.stack(batch_hq, dim=0).to(self.env['device']) resized_batch = torch.nn.functional.interpolate(batch_hq, scale_factor=1/self.scale, mode="area") gen = self.model(noise, resized_batch) if not isinstance(gen, list) and not isinstance(gen, tuple): gen = [gen] gen = gen[self.gen_output_index] out = gen + torch.nn.functional.interpolate(resized_batch, scale_factor=self.scale, mode='bilinear') for b in range(self.batch_sz): torchvision.utils.save_image(out[b], osp.join(fid_fake_path, "%i_.png" % (counter))) torchvision.utils.save_image(batch_hq[b], osp.join(fid_real_path, "%i_.png" % (counter))) counter += 1 return {"fid": fid_score.calculate_fid_given_paths([fid_real_path, fid_fake_path], self.batch_sz, True, 2048)}