forked from mrq/DL-Art-School
38 lines
1.3 KiB
Python
38 lines
1.3 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.utils.data import DataLoader
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import models.eval.evaluator as evaluator
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from pytorch_fid import fid_score
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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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with torch.no_grad():
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for batch in self.dataloader:
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z, _, _ = self.model(gt=batch['GT'],
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lr=batch['LQ'],
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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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return {"gaussian_diff": z_loss / total_zs}
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