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#!/usr/bin/env python3 | |
import os | |
import pickle | |
import zipfile | |
import torch.utils.data as data | |
from PIL import Image | |
from learn2learn.data.utils import download_file_from_google_drive, download_file | |
GOOGLE_DRIVE_FILE_ID = '1_ZsLyqI487NRDQhwvI7rg86FK3YAZvz1' | |
DROPBOX_LINK = 'https://www.dropbox.com/s/ftsjuwsu6lfp0fz/FC100.zip?dl=1' | |
class FC100(data.Dataset): | |
""" | |
[[Source]](https://github.com/learnables/learn2learn/blob/master/learn2learn/vision/datasets/fc100.py) | |
**Description** | |
The FC100 dataset was originally introduced by Oreshkin et al., 2018. | |
It is based on CIFAR100, but unlike CIFAR-FS training, validation, and testing classes are | |
split so as to minimize the information overlap between splits. | |
The 100 classes are grouped into 20 superclasses of which 12 (60 classes) are used for training, | |
4 (20 classes) for validation, and 4 (20 classes) for testing. | |
Each class contains 600 images. | |
The specific splits are provided in the Supplementary Material of the paper. | |
Our data is downloaded from the link provided by [2]. | |
**References** | |
1. Oreshkin et al. 2018. "TADAM: Task Dependent Adaptive Metric for Improved Few-Shot Learning." NeurIPS. | |
2. Kwoonjoon Lee. 2019. "MetaOptNet." [https://github.com/kjunelee/MetaOptNet](https://github.com/kjunelee/MetaOptNet) | |
**Arguments** | |
* **root** (str) - Path to download the data. | |
* **mode** (str, *optional*, default='train') - Which split to use. | |
Must be 'train', 'validation', or 'test'. | |
* **transform** (Transform, *optional*, default=None) - Input pre-processing. | |
* **target_transform** (Transform, *optional*, default=None) - Target pre-processing. | |
**Example** | |
~~~python | |
train_dataset = l2l.vision.datasets.FC100(root='./data', mode='train') | |
train_dataset = l2l.data.MetaDataset(train_dataset) | |
train_generator = l2l.data.TaskDataset(dataset=train_dataset, num_tasks=1000) | |
~~~ | |
""" | |
def __init__(self, | |
root, | |
mode='train', | |
transform=None, | |
target_transform=None, | |
download=False): | |
super(FC100, self).__init__() | |
self.root = os.path.expanduser(root) | |
os.makedirs(self.root, exist_ok=True) | |
self.transform = transform | |
self.target_transform = target_transform | |
if mode not in ['train', 'validation', 'test']: | |
raise ValueError('mode must be train, validation, or test.') | |
self.mode = mode | |
self._bookkeeping_path = os.path.join(self.root, 'fc100-bookkeeping-' + mode + '.pkl') | |
if not self._check_exists() and download: | |
self.download() | |
short_mode = 'val' if mode == 'validation' else mode | |
fc100_path = os.path.join(self.root, 'FC100_' + short_mode + '.pickle') | |
with open(fc100_path, 'rb') as f: | |
u = pickle._Unpickler(f) | |
u.encoding = 'latin1' | |
archive = u.load() | |
self.images = archive['data'] | |
self.labels = archive['labels'] | |
def download(self): | |
archive_path = os.path.join(self.root, 'fc100.zip') | |
print('Downloading FC100. (160Mb)') | |
try: # Download from Google Drive first | |
download_file_from_google_drive(GOOGLE_DRIVE_FILE_ID, | |
archive_path) | |
archive_file = zipfile.ZipFile(archive_path) | |
archive_file.extractall(self.root) | |
os.remove(archive_path) | |
except zipfile.BadZipFile: | |
download_file(DROPBOX_LINK, archive_path) | |
archive_file = zipfile.ZipFile(archive_path) | |
archive_file.extractall(self.root) | |
os.remove(archive_path) | |
def __getitem__(self, idx): | |
image = self.images[idx] | |
image = Image.fromarray(image) | |
label = self.labels[idx] | |
if self.transform is not None: | |
image = self.transform(image) | |
if self.target_transform is not None: | |
label = self.target_transform(label) | |
return image, label | |
def __len__(self): | |
return len(self.labels) | |
def _check_exists(self): | |
return os.path.exists(os.path.join(self.root, | |
'FC100_train.pickle')) | |
if __name__ == '__main__': | |
dataset = FC100(root='~/data') | |
img, tgt = dataset[43] | |
dataset = FC100(root='~/data', mode='validation') | |
img, tgt = dataset[43] | |
dataset = FC100(root='~/data', mode='test') | |
img, tgt = dataset[43] |