DL-Art-School/codes/models/eval/sr_style.py

58 lines
2.8 KiB
Python

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.embedding_generator = opt_eval['embedding_generator']
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):
embedding_generator = self.env['generators'][self.embedding_generator]
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")
embedding = embedding_generator(resized_batch)
gen = self.model(noise, embedding)
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)}