forked from mrq/DL-Art-School
52 lines
2.2 KiB
Python
52 lines
2.2 KiB
Python
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import os
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import torch
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import os.path as osp
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import torchvision
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from torch.nn.functional import interpolate
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from tqdm import tqdm
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import trainer.eval.evaluator as evaluator
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from pytorch_fid import fid_score
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from data import create_dataset
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from torch.utils.data import DataLoader
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# Computes the SR FID score for a network.
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class SrFidEvaluator(evaluator.Evaluator):
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def __init__(self, model, opt_eval, env):
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super().__init__(model, opt_eval, env)
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self.batch_sz = opt_eval['batch_size']
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assert self.batch_sz is not None
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self.dataset = create_dataset(opt_eval['dataset'])
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self.scale = opt_eval['scale']
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self.fid_real_samples = opt_eval['dataset']['paths'] # This is assumed to exist for the given dataset.
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assert isinstance(self.fid_real_samples, str)
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self.dataloader = DataLoader(self.dataset, self.batch_sz, shuffle=False, num_workers=1)
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self.gen_output_index = opt_eval['gen_index'] if 'gen_index' in opt_eval.keys() else 0
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def perform_eval(self):
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fid_fake_path = osp.join(self.env['base_path'], "..", "fid", str(self.env["step"]))
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os.makedirs(fid_fake_path, exist_ok=True)
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counter = 0
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for batch in tqdm(self.dataloader):
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lq = batch['lq'].to(self.env['device'])
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gen = self.model(lq)
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if not isinstance(gen, list) and not isinstance(gen, tuple):
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gen = [gen]
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gen = gen[self.gen_output_index]
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# Remove low-frequency differences
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gen_lf = interpolate(interpolate(gen, scale_factor=1/self.scale, mode="area"), scale_factor=self.scale,
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mode="nearest")
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gen_hf = gen - gen_lf
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hq_lf = interpolate(lq, scale_factor=self.scale, mode="nearest")
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hq_gen_hf_applied = hq_lf + gen_hf
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for b in range(self.batch_sz):
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torchvision.utils.save_image(hq_gen_hf_applied[b], osp.join(fid_fake_path, "%i_.png" % (counter)))
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counter += 1
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return {"fid": fid_score.calculate_fid_given_paths([self.fid_real_samples, fid_fake_path], self.batch_sz, True,
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2048)}
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