forked from mrq/DL-Art-School
784b96c059
- Allow image_folder_dataset to normalize inbound images - ExtensibleTrainer can denormalize images on the output path - Support .webp - an output from LSUN - Support logistic GAN divergence loss - Support stylegan2 TF weight extraction for discriminator - New injector that produces latent noise (with separated paths) - Modify FID evaluator to be operable with rosinality-style GANs
44 lines
2.0 KiB
Python
44 lines
2.0 KiB
Python
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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import trainer.eval.evaluator as evaluator
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from pytorch_fid import fid_score
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# Evaluate that generates uniform noise to feed into a generator, then calculates a FID score on the results.
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from utils.util import opt_get
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class StyleTransferEvaluator(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.batches_per_eval = opt_eval['batches_per_eval']
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self.batch_sz = opt_eval['batch_size']
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self.im_sz = opt_eval['image_size']
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self.fid_real_samples = opt_eval['real_fid_path']
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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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self.noise_type = opt_get(opt_eval, ['noise_type'], 'imgnoise')
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self.latent_dim = opt_get(opt_eval, ['latent_dim'], 512) # Not needed if using 'imgnoise' input.
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def perform_eval(self):
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fid_fake_path = osp.join(self.env['base_path'], "../../models", "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 i in range(self.batches_per_eval):
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if self.noise_type == 'imgnoise':
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batch = torch.FloatTensor(self.batch_sz, 3, self.im_sz, self.im_sz).uniform_(0., 1.).to(self.env['device'])
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elif self.noise_type == 'stylenoise':
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batch = [torch.randn(self.batch_sz, self.latent_dim).to(self.env['device'])]
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gen = self.model(batch)
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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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for b in range(self.batch_sz):
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torchvision.utils.save_image(gen[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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