Source code for

@author: Yifei Ji
import os
from typing import Optional
from .imagelist import ImageList
from ._util import download as download_data, check_exits

[docs]class COCO70(ImageList): """COCO-70 dataset is a large-scale classification dataset (1000 images per class) created from `COCO <>`_ Dataset. It is used to explore the effect of fine-tuning with a large amount of data. Args: root (str): Root directory of dataset split (str, optional): The dataset split, supports ``train``, or ``test``. sample_rate (int): The sampling rates to sample random ``training`` images for each category. Choices include 100, 50, 30, 15. Default: 100. download (bool, optional): If true, downloads the dataset from the internet and puts it \ in root directory. If dataset is already downloaded, it is not downloaded again. transform (callable, optional): A function/transform that takes in an PIL image and returns a \ transformed version. E.g, :class:`torchvision.transforms.RandomCrop`. target_transform (callable, optional): A function/transform that takes in the target and transforms it. .. note:: In `root`, there will exist following files after downloading. :: train/ test/ image_list/ train_100.txt train_50.txt train_30.txt train_15.txt test.txt """ download_list = [ ("image_list", "", ""), ("train", "train.tgz", ""), ("test", "test.tgz", ""), ] image_list = { "train": "image_list/train_100.txt", "train100": "image_list/train_100.txt", "train50": "image_list/train_50.txt", "train30": "image_list/train_30.txt", "train15": "image_list/train_15.txt", "test": "image_list/test.txt", "test100": "image_list/test.txt", } CLASSES =['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic_light', 'fire_hydrant', 'stop_sign', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'skis', 'kite', 'baseball_bat', 'skateboard', 'surfboard', 'tennis_racket', 'bottle', 'wine_glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot_dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted_plant', 'bed', 'dining_table', 'toilet', 'tv', 'laptop', 'remote', 'keyboard', 'cell_phone', 'microwave', 'oven', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'teddy_bear'] def __init__(self, root: str, split: str, sample_rate: Optional[int] =100, download: Optional[bool] = False, **kwargs): if split == 'train': list_name = 'train' + str(sample_rate) assert list_name in self.image_list data_list_file = os.path.join(root, self.image_list[list_name]) else: data_list_file = os.path.join(root, self.image_list['test']) if download: list(map(lambda args: download_data(root, *args), self.download_list)) else: list(map(lambda file_name, _: check_exits(root, file_name), self.download_list)) super(COCO70, self).__init__(root, COCO70.CLASSES, data_list_file=data_list_file, **kwargs)


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