[Feature] support mscoco dataset

mmedit/datasets/__init__.py

@@ -6,19 +6,14 @@
 from .comp1k_dataset import AdobeComp1kDataset
 from .grow_scale_image_dataset import GrowScaleImgDataset
 from .imagenet_dataset import ImageNet
+from .mscoco_dataset import MSCoCoDataset
 from .paired_image_dataset import PairedImageDataset
 from .singan_dataset import SinGANDataset
 from .unpaired_image_dataset import UnpairedImageDataset
 
 __all__ = [
-    'AdobeComp1kDataset',
-    'BasicImageDataset',
-    'BasicFramesDataset',
-    'BasicConditionalDataset',
-    'UnpairedImageDataset',
-    'PairedImageDataset',
-    'ImageNet',
-    'CIFAR10',
-    'GrowScaleImgDataset',
-    'SinGANDataset',
+    'AdobeComp1kDataset', 'BasicImageDataset', 'BasicFramesDataset',
+    'BasicConditionalDataset', 'UnpairedImageDataset', 'PairedImageDataset',
+    'ImageNet', 'CIFAR10', 'GrowScaleImgDataset', 'SinGANDataset',
+    'MSCoCoDataset'
 ]

mmedit/datasets/mscoco_dataset.py

@@ -0,0 +1,101 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+import random
+from typing import Optional, Sequence, Union
+
+import mmengine
+from mmengine import FileClient
+
+from mmedit.registry import DATASETS
+from .basic_conditional_dataset import BasicConditionalDataset
+
+
+@DATASETS.register_module()
+@DATASETS.register_module('MSCOCO')
+class MSCoCoDataset(BasicConditionalDataset):
+    """MSCoCo 2014 dataset.
+
+    Args:
+        ann_file (str): Annotation file path. Defaults to ''.
+        metainfo (dict, optional): Meta information for dataset, such as class
+            information. Defaults to None.
+        data_root (str): The root directory for ``data_prefix`` and
+            ``ann_file``. Defaults to ''.
+        drop_caption_rate (float, optional): Rate of dropping caption,
+            used for training. Defaults to 0.0.
+        phase (str, optional): Subdataset used for certain phase, can be set
+            to `train`, `test` and `val`. Defaults to 'train'.
+        year (int, optional): Version of CoCo dataset, can be set to 2014
+            and 2017. Defaults to 2014.
+        data_prefix (str | dict): Prefix for the data. Defaults to ''.
+        extensions (Sequence[str]): A sequence of allowed extensions. Defaults
+            to ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif').
+        lazy_init (bool): Whether to load annotation during instantiation.
+            In some cases, such as visualization, only the meta information of
+            the dataset is needed, which is not necessary to load annotation
+            file. ``Basedataset`` can skip load annotations to save time by set
+            ``lazy_init=False``. Defaults to False.
+        **kwargs: Other keyword arguments in :class:`BaseDataset`.
+    """
+    METAINFO = dict(dataset_type='text_image_dataset', task_name='editing')
+
+    def __init__(self,
+                 ann_file: str = '',
+                 metainfo: Optional[dict] = None,
+                 data_root: str = '',
+                 drop_caption_rate=0.0,
+                 phase='train',
+                 year=2014,
+                 data_prefix: Union[str, dict] = '',
+                 extensions: Sequence[str] = ('.jpg', '.jpeg', '.png', '.ppm',
+                                              '.bmp', '.pgm', '.tif'),
+                 lazy_init: bool = False,
+                 classes: Union[str, Sequence[str], None] = None,
+                 **kwargs):
+        ann_file = os.path.join('annotations', 'captions_' + phase +
+                                f'{year}.json') if ann_file == '' else ann_file
+        self.image_prename = 'COCO_' + phase + f'{year}_'
+        self.phase = phase
+        self.drop_rate = drop_caption_rate
+        self.year = year
+        assert self.year == 2014, 'We only support CoCo2014 now.'
+
+        super().__init__(
+            ann_file=ann_file,
+            metainfo=metainfo,
+            data_root=data_root,
+            data_prefix=data_prefix,
+            extensions=extensions,
+            lazy_init=lazy_init,
+            classes=classes,
+            **kwargs)
+
+    def load_data_list(self):
+        """Load image paths and gt_labels."""
+        if self.img_prefix:
+            file_client = FileClient.infer_client(uri=self.img_prefix)
+        json_file = mmengine.fileio.io.load(self.ann_file)
+
+        def add_prefix(filename, prefix=''):
+            if not prefix:
+                return filename
+            else:
+                return file_client.join_path(prefix, filename)
+
+        data_list = []
+        for item in json_file['annotations']:
+            image_name = self.image_prename + str(
+                item['image_id']).zfill(12) + '.jpg'
+            img_path = add_prefix(
+                os.path.join(self.phase + str(self.year), image_name),
+                self.img_prefix)
+            caption = item['caption'].lower()
+            info = {
+                'img_path':
+                img_path,
+                'gt_label':
+                caption if (self.phase != 'train' or self.drop_rate < 1e-6
+                            or random.random() >= self.drop_rate) else ''
+            }
+            data_list.append(info)
+        return data_list

tests/data/coco/annotations/captions_train2014.json

@@ -0,0 +1,3 @@
+{
+    "annotations": [{"image_id": 9, "caption": "a good meal"}]
+}

tests/data/coco/annotations/captions_val2014.json

@@ -0,0 +1,3 @@
+{
+    "annotations": [{"image_id": 42, "caption": "a pair of slippers"}]
+}

tests/test_datasets/test_mscoco_dataset.py

@@ -0,0 +1,31 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+from pathlib import Path
+
+from mmedit.datasets import MSCoCoDataset
+
+
+class TestMSCoCoDatasets:
+
+    @classmethod
+    def setup_class(cls):
+        cls.data_root = Path(__file__).parent.parent / 'data' / 'coco'
+
+    def test_mscoco(self):
+
+        # test basic usage
+        dataset = MSCoCoDataset(data_root=self.data_root, pipeline=[])
+        assert dataset[0] == dict(
+            gt_label='a good meal',
+            img_path=os.path.join(self.data_root, 'train2014',
+                                  'COCO_train2014_000000000009.jpg'),
+            sample_idx=0)
+
+        # test with different phase
+        dataset = MSCoCoDataset(
+            data_root=self.data_root, phase='val', pipeline=[])
+        assert dataset[0] == dict(
+            gt_label='a pair of slippers',
+            img_path=os.path.join(self.data_root, 'val2014',
+                                  'COCO_val2014_000000000042.jpg'),
+            sample_idx=0)