67 lines
3.1 KiB
Python
67 lines
3.1 KiB
Python
import torch
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from torch.nn import MSELoss
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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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from data.images.image_pair_with_corresponding_points_dataset import ImagePairWithCorrespondingPointsDataset
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# Uses two datasets: a "similar" and "dissimilar" dataset, each of which contains pairs of images and similar/dissimilar
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# points in those datasets. Uses the provided network to compute a latent vector for both similar and dissimilar.
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# Reports a score for the l2 distance of both. A properly trained network will show similar points getting closer while
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# dissimilar points remain constant or get further apart.
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class SinglePointPairContrastiveEval(evaluator.Evaluator):
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def __init__(self, model, opt_eval, env):
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super().__init__(model, opt_eval, env, uses_all_ddp=False)
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self.batch_sz = opt_eval['batch_size']
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self.eval_qty = opt_eval['quantity']
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assert self.eval_qty % self.batch_sz == 0
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self.similar_set = DataLoader(ImagePairWithCorrespondingPointsDataset(opt_eval['similar_set_args']), shuffle=False, batch_size=self.batch_sz)
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self.dissimilar_set = DataLoader(ImagePairWithCorrespondingPointsDataset(opt_eval['dissimilar_set_args']), shuffle=False, batch_size=self.batch_sz)
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# Hack to make this work with the BYOL generator. TODO: fix
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self.model = self.model.online_encoder.net
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def get_l2_score(self, dl, dev):
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distances = []
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l2 = MSELoss()
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for i, data in tqdm(enumerate(dl)):
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latent1 = self.model(img=data['img1'].to(dev), pos=torch.stack(data['coords1'], dim=1).to(dev))
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latent2 = self.model(img=data['img2'].to(dev), pos=torch.stack(data['coords2'], dim=1).to(dev))
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distances.append(l2(latent1, latent2))
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if i * self.batch_sz >= self.eval_qty:
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break
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return torch.stack(distances).mean()
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def perform_eval(self):
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self.model.eval()
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with torch.no_grad():
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dev = next(self.model.parameters()).device
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print("Computing contrastive eval on similar set")
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similars = self.get_l2_score(self.similar_set, dev)
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print("Computing contrastive eval on dissimilar set")
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dissimilars = self.get_l2_score(self.dissimilar_set, dev)
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diff = dissimilars.item() - similars.item()
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print(f"Eval done. val_similar_lq: {similars.item()}; val_dissimilar_l2: {dissimilars.item()}; val_diff: {diff}")
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self.model.train()
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return {"val_similar_l2": similars.item(), "val_dissimilar_l2": dissimilars.item(), "val_diff": diff}
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if __name__ == '__main__':
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model = Segformer(1024, 4).cuda()
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eval = SinglePointPairContrastiveEval(model, {
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'batch_size': 8,
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'quantity': 32,
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'similar_set_args': {
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'path': 'E:\\4k6k\\datasets\\ns_images\\segformer_validation\\similar',
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'size': 256
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},
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'dissimilar_set_args': {
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'path': 'E:\\4k6k\\datasets\\ns_images\\segformer_validation\\dissimilar',
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'size': 256
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},
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}, {})
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eval.perform_eval()
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