48 lines
1.7 KiB
Python
48 lines
1.7 KiB
Python
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import random
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import torch
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from torch.utils.data import Dataset
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from kornia import augmentation as augs
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from kornia import filters
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import torch.nn as nn
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# Wrapper for a DLAS Dataset class that applies random augmentations from the BYOL paper to BOTH the 'lq' and 'hq'
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# inputs. These are then outputted as 'aug1' and 'aug2'.
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from data import create_dataset
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class RandomApply(nn.Module):
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def __init__(self, fn, p):
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super().__init__()
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self.fn = fn
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self.p = p
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def forward(self, x):
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if random.random() > self.p:
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return x
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return self.fn(x)
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class ByolDatasetWrapper(Dataset):
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def __init__(self, opt):
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super().__init__()
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self.wrapped_dataset = create_dataset(opt['dataset'])
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self.cropped_img_size = opt['crop_size']
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augmentations = [ \
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RandomApply(augs.ColorJitter(0.8, 0.8, 0.8, 0.2), p=0.8),
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augs.RandomGrayscale(p=0.2),
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augs.RandomHorizontalFlip(),
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RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1),
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augs.RandomResizedCrop((self.cropped_img_size, self.cropped_img_size))]
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if opt['normalize']:
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# The paper calls for normalization. Recommend setting true if you want exactly like the paper.
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augmentations.append(augs.Normalize(mean=torch.tensor([0.485, 0.456, 0.406]), std=torch.tensor([0.229, 0.224, 0.225])))
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self.aug = nn.Sequential(*augmentations)
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def __getitem__(self, item):
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item = self.wrapped_dataset[item]
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item.update({'aug1': self.aug(item['hq']).squeeze(dim=0), 'aug2': self.aug(item['lq']).squeeze(dim=0)})
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return item
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def __len__(self):
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return len(self.wrapped_dataset)
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