mirror of https://github.com/facebookresearch/deit
110 lines
4.0 KiB
Python
110 lines
4.0 KiB
Python
# Copyright (c) 2015-present, Facebook, Inc.
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# All rights reserved.
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import os
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import json
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from torchvision import datasets, transforms
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from torchvision.datasets.folder import ImageFolder, default_loader
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from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from timm.data import create_transform
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class INatDataset(ImageFolder):
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def __init__(self, root, train=True, year=2018, transform=None, target_transform=None,
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category='name', loader=default_loader):
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self.transform = transform
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self.loader = loader
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self.target_transform = target_transform
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self.year = year
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# assert category in ['kingdom','phylum','class','order','supercategory','family','genus','name']
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path_json = os.path.join(root, f'{"train" if train else "val"}{year}.json')
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with open(path_json) as json_file:
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data = json.load(json_file)
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with open(os.path.join(root, 'categories.json')) as json_file:
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data_catg = json.load(json_file)
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path_json_for_targeter = os.path.join(root, f"train{year}.json")
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with open(path_json_for_targeter) as json_file:
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data_for_targeter = json.load(json_file)
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targeter = {}
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indexer = 0
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for elem in data_for_targeter['annotations']:
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king = []
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king.append(data_catg[int(elem['category_id'])][category])
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if king[0] not in targeter.keys():
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targeter[king[0]] = indexer
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indexer += 1
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self.nb_classes = len(targeter)
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self.samples = []
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for elem in data['images']:
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cut = elem['file_name'].split('/')
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target_current = int(cut[2])
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path_current = os.path.join(root, cut[0], cut[2], cut[3])
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categors = data_catg[target_current]
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target_current_true = targeter[categors[category]]
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self.samples.append((path_current, target_current_true))
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# __getitem__ and __len__ inherited from ImageFolder
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def build_dataset(is_train, args):
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transform = build_transform(is_train, args)
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if args.data_set == 'CIFAR':
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dataset = datasets.CIFAR100(args.data_path, train=is_train, transform=transform)
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nb_classes = 100
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elif args.data_set == 'IMNET':
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root = os.path.join(args.data_path, 'train' if is_train else 'val')
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dataset = datasets.ImageFolder(root, transform=transform)
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nb_classes = 1000
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elif args.data_set == 'INAT':
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dataset = INatDataset(args.data_path, train=is_train, year=2018,
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category=args.inat_category, transform=transform)
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nb_classes = dataset.nb_classes
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elif args.data_set == 'INAT19':
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dataset = INatDataset(args.data_path, train=is_train, year=2019,
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category=args.inat_category, transform=transform)
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nb_classes = dataset.nb_classes
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return dataset, nb_classes
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def build_transform(is_train, args):
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resize_im = args.input_size > 32
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if is_train:
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# this should always dispatch to transforms_imagenet_train
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transform = create_transform(
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input_size=args.input_size,
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is_training=True,
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color_jitter=args.color_jitter,
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auto_augment=args.aa,
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interpolation=args.train_interpolation,
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re_prob=args.reprob,
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re_mode=args.remode,
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re_count=args.recount,
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)
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if not resize_im:
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# replace RandomResizedCropAndInterpolation with
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# RandomCrop
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transform.transforms[0] = transforms.RandomCrop(
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args.input_size, padding=4)
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return transform
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t = []
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if resize_im:
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size = int(args.input_size / args.eval_crop_ratio)
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t.append(
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transforms.Resize(size, interpolation=3), # to maintain same ratio w.r.t. 224 images
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)
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t.append(transforms.CenterCrop(args.input_size))
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t.append(transforms.ToTensor())
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t.append(transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD))
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return transforms.Compose(t)
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