├── README.md ├── tinyimagenetloader.py └── LICENSE /README.md: -------------------------------------------------------------------------------- 1 | # TinyImageNetLoader 2 | DataLoader for TinyImageNet Dataset 3 | -------------------------------------------------------------------------------- /tinyimagenetloader.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """TinyImageNetLoader.ipynb 3 | 4 | Automatically generated by Colaboratory. 5 | 6 | 7 | """ 8 | 9 | #loads images as 3*64*64 tensors 10 | 11 | !wget http://cs231n.stanford.edu/tiny-imagenet-200.zip 12 | !unzip -q tiny-imagenet-200.zip 13 | 14 | import torch 15 | import torchvision 16 | import torchvision.transforms as transforms 17 | from torch.utils.data import DataLoader 18 | from torch.utils.data import Dataset 19 | import os, glob 20 | from torchvision.io import read_image, ImageReadMode 21 | 22 | batch_size = 64 23 | 24 | id_dict = {} 25 | for i, line in enumerate(open('/content/tiny-imagenet-200/wnids.txt', 'r')): 26 | id_dict[line.replace('\n', '')] = i 27 | 28 | class TrainTinyImageNetDataset(Dataset): 29 | def __init__(self, id, transform=None): 30 | self.filenames = glob.glob("/content/tiny-imagenet-200/train/*/*/*.JPEG") 31 | self.transform = transform 32 | self.id_dict = id 33 | 34 | def __len__(self): 35 | return len(self.filenames) 36 | 37 | def __getitem__(self, idx): 38 | img_path = self.filenames[idx] 39 | image = read_image(img_path) 40 | if image.shape[0] == 1: 41 | image = read_image(img_path,ImageReadMode.RGB) 42 | label = self.id_dict[img_path.split('/')[4]] 43 | if self.transform: 44 | image = self.transform(image.type(torch.FloatTensor)) 45 | return image, label 46 | 47 | class TestTinyImageNetDataset(Dataset): 48 | def __init__(self, id, transform=None): 49 | self.filenames = glob.glob("/content/tiny-imagenet-200/val/images/*.JPEG") 50 | self.transform = transform 51 | self.id_dict = id 52 | self.cls_dic = {} 53 | for i, line in enumerate(open('/content/tiny-imagenet-200/val/val_annotations.txt', 'r')): 54 | a = line.split('\t') 55 | img, cls_id = a[0],a[1] 56 | self.cls_dic[img] = self.id_dict[cls_id] 57 | 58 | 59 | def __len__(self): 60 | return len(self.filenames) 61 | 62 | def __getitem__(self, idx): 63 | img_path = self.filenames[idx] 64 | image = read_image(img_path) 65 | if image.shape[0] == 1: 66 | image = read_image(img_path,ImageReadMode.RGB) 67 | label = self.cls_dic[img_path.split('/')[-1]] 68 | if self.transform: 69 | image = self.transform(image.type(torch.FloatTensor)) 70 | return image, label 71 | 72 | transform = transforms.Normalize((122.4786, 114.2755, 101.3963), (70.4924, 68.5679, 71.8127)) 73 | 74 | trainset = TrainTinyImageNetDataset(id=id_dict, transform = transform) 75 | trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=2) 76 | 77 | testset = TestTinyImageNetDataset(id=id_dict, transform=transform) 78 | testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=2) 79 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Creative Commons Legal Code 2 | 3 | CC0 1.0 Universal 4 | 5 | CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE 6 | LEGAL SERVICES. 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