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