[Feature] Support InShop Dataset (Image Retrieval). (#1019)
* rebase * feat: add inshop dataset (retrieval) * update fileIO * update unit tests * fix windows ci * fix windows ci * fix lint * update unit tests * update docs * update docs Co-authored-by: Ezra-Yu <18586273+Ezra-Yu@users.noreply.github.com>pull/1314/merge
zzc98
GitHub
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@ -49,6 +49,11 @@ CUB
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.. autoclass:: CUB
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Retrieval
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---------
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.. autoclass:: InShop
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Base classes
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------------
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@ -6,6 +6,7 @@ from .cub import CUB
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from .custom import CustomDataset
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from .dataset_wrappers import KFoldDataset
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from .imagenet import ImageNet, ImageNet21k
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from .inshop import InShop
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from .mnist import MNIST, FashionMNIST
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from .multi_label import MultiLabelDataset
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from .multi_task import MultiTaskDataset
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@ -16,5 +17,5 @@ from .voc import VOC
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__all__ = [
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'BaseDataset', 'ImageNet', 'CIFAR10', 'CIFAR100', 'MNIST', 'FashionMNIST',
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'VOC', 'build_dataset', 'ImageNet21k', 'KFoldDataset', 'CUB',
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'CustomDataset', 'MultiLabelDataset', 'MultiTaskDataset'
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'CustomDataset', 'MultiLabelDataset', 'MultiTaskDataset', 'InShop'
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]
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@ -0,0 +1,160 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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from mmengine import get_file_backend, list_from_file
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from mmcls.registry import DATASETS
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from .base_dataset import BaseDataset
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@DATASETS.register_module()
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class InShop(BaseDataset):
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"""InShop Dataset for Image Retrieval.
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Please download the images from the homepage
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'https://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/InShopRetrieval.html'
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(In-shop Clothes Retrieval Benchmark -> Img -> img.zip,
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Eval/list_eval_partition.txt), and organize them as follows way: ::
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In-shop Clothes Retrieval Benchmark (data_root)/
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├── Eval /
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│ └── list_eval_partition.txt (ann_file)
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├── Img
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│ └── img/ (img_prefix)
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├── README.txt
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└── .....
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Args:
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data_root (str): The root directory for dataset.
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split (str): Choose from 'train', 'query' and 'gallery'.
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Defaults to 'train'.
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data_prefix (str | dict): Prefix for training data.
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Defaults to 'Img/img'.
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ann_file (str): Annotation file path, path relative to
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``data_root``. Defaults to 'Eval/list_eval_partition.txt'.
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**kwargs: Other keyword arguments in :class:`BaseDataset`.
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Examples:
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>>> from mmcls.datasets import InShop
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>>>
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>>> # build train InShop dataset
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>>> inshop_train_cfg = dict(data_root='data/inshop', split='train')
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>>> inshop_train = InShop(**inshop_train_cfg)
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>>> inshop_train
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Dataset InShop
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Number of samples: 25882
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The `CLASSES` meta info is not set.
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Root of dataset: data/inshop
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>>>
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>>> # build query InShop dataset
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>>> inshop_query_cfg = dict(data_root='data/inshop', split='query')
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>>> inshop_query = InShop(**inshop_query_cfg)
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>>> inshop_query
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Dataset InShop
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Number of samples: 14218
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The `CLASSES` meta info is not set.
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Root of dataset: data/inshop
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>>>
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>>> # build gallery InShop dataset
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>>> inshop_gallery_cfg = dict(data_root='data/inshop', split='gallery')
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>>> inshop_gallery = InShop(**inshop_gallery_cfg)
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>>> inshop_gallery
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Dataset InShop
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Number of samples: 12612
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The `CLASSES` meta info is not set.
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Root of dataset: data/inshop
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"""
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def __init__(self,
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data_root: str,
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split: str = 'train',
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data_prefix: str = 'Img/img',
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ann_file: str = 'Eval/list_eval_partition.txt',
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**kwargs):
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assert split in ('train', 'query', 'gallery'), "'split' of `InShop`" \
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f" must be one of ['train', 'query', 'gallery'], bu get '{split}'"
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self.backend = get_file_backend(data_root, enable_singleton=True)
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self.split = split
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super().__init__(
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data_root=data_root,
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data_prefix=data_prefix,
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ann_file=ann_file,
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**kwargs)
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def _process_annotations(self):
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lines = list_from_file(self.ann_file)
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anno_train = dict(metainfo=dict(), data_list=list())
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anno_gallery = dict(metainfo=dict(), data_list=list())
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# item_id to label, each item corresponds to one class label
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class_num = 0
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gt_label_train = {}
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# item_id to label, each label corresponds to several items
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gallery_num = 0
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gt_label_gallery = {}
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# (lines[0], lines[1]) is the image number and the field name;
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# Each line format as 'image_name, item_id, evaluation_status'
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for line in lines[2:]:
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img_name, item_id, status = line.split()
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img_path = self.backend.join_path(self.img_prefix, img_name)
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if status == 'train':
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if item_id not in gt_label_train:
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gt_label_train[item_id] = class_num
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class_num += 1
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# item_id to class_id (for the training set)
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anno_train['data_list'].append(
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dict(img_path=img_path, gt_label=gt_label_train[item_id]))
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elif status == 'gallery':
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if item_id not in gt_label_gallery:
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gt_label_gallery[item_id] = []
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# Since there are multiple images for each item,
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# record the corresponding item for each image.
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gt_label_gallery[item_id].append(gallery_num)
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anno_gallery['data_list'].append(
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dict(img_path=img_path, sample_idx=gallery_num))
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gallery_num += 1
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if self.split == 'train':
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anno_train['metainfo']['class_number'] = class_num
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anno_train['metainfo']['sample_number'] = \
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len(anno_train['data_list'])
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return anno_train
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elif self.split == 'gallery':
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anno_gallery['metainfo']['sample_number'] = gallery_num
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return anno_gallery
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# Generate the label for the query(val) set
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anno_query = dict(metainfo=dict(), data_list=list())
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query_num = 0
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for line in lines[2:]:
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img_name, item_id, status = line.split()
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img_path = self.backend.join_path(self.img_prefix, img_name)
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if status == 'query':
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anno_query['data_list'].append(
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dict(
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img_path=img_path, gt_label=gt_label_gallery[item_id]))
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query_num += 1
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anno_query['metainfo']['sample_number'] = query_num
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return anno_query
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def load_data_list(self):
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"""load data list.
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For the train set, return image and ground truth label. For the query
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set, return image and ids of images in gallery. For the gallery set,
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return image and its id.
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"""
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data_info = self._process_annotations()
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data_list = data_info['data_list']
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for data in data_list:
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data['img_path'] = self.backend.join_path(self.data_root,
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data['img_path'])
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return data_list
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def extra_repr(self):
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"""The extra repr information of the dataset."""
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body = [f'Root of dataset: \t{self.data_root}']
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return body
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@ -929,7 +929,6 @@ class TestMultiTaskDataset(TestCase):
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# Test default behavior
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dataset = dataset_class(**self.DEFAULT_ARGS)
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data = dataset.load_data_list(self.DEFAULT_ARGS['ann_file'])
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self.assertIsInstance(data, list)
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np.testing.assert_equal(len(data), 3)
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@ -938,3 +937,103 @@ class TestMultiTaskDataset(TestCase):
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'gender': 0,
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'wear': [1, 0, 1, 0]
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})
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class TestInShop(TestBaseDataset):
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DATASET_TYPE = 'InShop'
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@classmethod
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def setUpClass(cls) -> None:
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super().setUpClass()
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tmpdir = tempfile.TemporaryDirectory()
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cls.tmpdir = tmpdir
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cls.root = tmpdir.name
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cls.list_eval_partition = 'Eval/list_eval_partition.txt'
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cls.DEFAULT_ARGS = dict(data_root=cls.root, split='train')
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cls.ann_file = osp.join(cls.root, cls.list_eval_partition)
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os.makedirs(osp.join(cls.root, 'Eval'))
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with open(cls.ann_file, 'w') as f:
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f.write('\n'.join([
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'8',
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'image_name item_id evaluation_status',
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'02_1_front.jpg id_00000002 train',
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'02_2_side.jpg id_00000002 train',
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'12_3_back.jpg id_00007982 gallery',
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'12_7_additional.jpg id_00007982 gallery',
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'13_1_front.jpg id_00007982 query',
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'13_2_side.jpg id_00007983 gallery',
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'13_3_back.jpg id_00007983 query ',
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'13_7_additional.jpg id_00007983 query',
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]))
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def test_initialize(self):
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dataset_class = DATASETS.get(self.DATASET_TYPE)
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# Test with mode=train
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cfg = {**self.DEFAULT_ARGS}
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dataset = dataset_class(**cfg)
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self.assertEqual(dataset.split, 'train')
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self.assertEqual(dataset.data_root, self.root)
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self.assertEqual(dataset.ann_file, self.ann_file)
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# Test with mode=query
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cfg = {**self.DEFAULT_ARGS, 'split': 'query'}
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dataset = dataset_class(**cfg)
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self.assertEqual(dataset.split, 'query')
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self.assertEqual(dataset.data_root, self.root)
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self.assertEqual(dataset.ann_file, self.ann_file)
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# Test with mode=gallery
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cfg = {**self.DEFAULT_ARGS, 'split': 'gallery'}
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dataset = dataset_class(**cfg)
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self.assertEqual(dataset.split, 'gallery')
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self.assertEqual(dataset.data_root, self.root)
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self.assertEqual(dataset.ann_file, self.ann_file)
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# Test with mode=other
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cfg = {**self.DEFAULT_ARGS, 'split': 'other'}
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with self.assertRaisesRegex(AssertionError, "'split' of `InS"):
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dataset_class(**cfg)
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def test_load_data_list(self):
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dataset_class = DATASETS.get(self.DATASET_TYPE)
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# Test with mode=train
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cfg = {**self.DEFAULT_ARGS}
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dataset = dataset_class(**cfg)
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self.assertEqual(len(dataset), 2)
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data_info = dataset[0]
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self.assertEqual(data_info['img_path'],
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os.path.join(self.root, 'Img/img', '02_1_front.jpg'))
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self.assertEqual(data_info['gt_label'], 0)
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# Test with mode=query
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cfg = {**self.DEFAULT_ARGS, 'split': 'query'}
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dataset = dataset_class(**cfg)
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self.assertEqual(len(dataset), 3)
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data_info = dataset[0]
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self.assertEqual(data_info['img_path'],
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os.path.join(self.root, 'Img/img', '13_1_front.jpg'))
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self.assertEqual(data_info['gt_label'], [0, 1])
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# Test with mode=gallery
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cfg = {**self.DEFAULT_ARGS, 'split': 'gallery'}
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dataset = dataset_class(**cfg)
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self.assertEqual(len(dataset), 3)
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data_info = dataset[0]
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self.assertEqual(
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data_info['img_path'],
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os.path.join(self.root, self.root, 'Img/img', '12_3_back.jpg'))
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self.assertEqual(data_info['sample_idx'], 0)
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def test_extra_repr(self):
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dataset_class = DATASETS.get(self.DATASET_TYPE)
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cfg = {**self.DEFAULT_ARGS}
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dataset = dataset_class(**cfg)
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self.assertIn(f'Root of dataset: \t{dataset.data_root}', repr(dataset))
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@classmethod
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def tearDownClass(cls):
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cls.tmpdir.cleanup()
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