[Feature]Add Decathlon dataset (#2227)

* [Feature]Add Decathlon dataset

* fix test data

* add file

* remove order

* revise default value for prefix

* modify example

* revise based on  comments

* add comments for ut

mmseg/datasets/__init__.py

@@ -6,6 +6,7 @@ from .cityscapes import CityscapesDataset
 from .coco_stuff import COCOStuffDataset
 from .dark_zurich import DarkZurichDataset
 from .dataset_wrappers import MultiImageMixDataset
+from .decathlon import DecathlonDataset
 from .drive import DRIVEDataset
 from .hrf import HRFDataset
 from .isaid import iSAIDDataset
@@ -33,5 +34,6 @@ __all__ = [
     'RandomRotate', 'AdjustGamma', 'CLAHE', 'Rerange', 'RGB2Gray',
     'RandomCutOut', 'RandomMosaic', 'PackSegInputs', 'ResizeToMultiple',
     'LoadImageFromNDArray', 'LoadBiomedicalImageFromFile',
-    'LoadBiomedicalAnnotation', 'LoadBiomedicalData', 'GenerateEdge'
+    'LoadBiomedicalAnnotation', 'LoadBiomedicalData', 'GenerateEdge',
+    'DecathlonDataset'
 ]

mmseg/datasets/basesegdataset.py

@@ -85,7 +85,7 @@ class BaseSegDataset(BaseDataset):
         seg_map_suffix='.png',
         metainfo: Optional[dict] = None,
         data_root: Optional[str] = None,
-        data_prefix: dict = dict(img_path=None, seg_map_path=None),
+        data_prefix: dict = dict(img_path='', seg_map_path=''),
         filter_cfg: Optional[dict] = None,
         indices: Optional[Union[int, Sequence[int]]] = None,
         serialize_data: bool = True,
@@ -132,9 +132,6 @@ class BaseSegDataset(BaseDataset):
         # if it is not defined
         updated_palette = self._update_palette()
         self._metainfo.update(dict(palette=updated_palette))
-        if test_mode:
-            assert self._metainfo.get('classes') is not None, \
-                'dataset metainfo `classes` should be specified when testing'
 
         # Join paths.
         if self.data_root is not None:
@@ -146,6 +143,10 @@ class BaseSegDataset(BaseDataset):
         if not lazy_init:
             self.full_init()
 
+        if test_mode:
+            assert self._metainfo.get('classes') is not None, \
+                'dataset metainfo `classes` should be specified when testing'
+
     @classmethod
     def get_label_map(cls,
                       new_classes: Optional[Sequence] = None

mmseg/datasets/decathlon.py

@@ -0,0 +1,96 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import os.path as osp
+from typing import List
+
+from mmengine.fileio import load
+
+from mmseg.registry import DATASETS
+from .basesegdataset import BaseSegDataset
+
+
+@DATASETS.register_module()
+class DecathlonDataset(BaseSegDataset):
+    """Dataset for Dacathlon dataset.
+
+    The dataset.json format is shown as follows
+
+    .. code-block:: none
+
+        {
+            "name": "BRATS",
+            "tensorImageSize": "4D",
+            "modality":
+            {
+                "0": "FLAIR",
+                "1": "T1w",
+                "2": "t1gd",
+                "3": "T2w"
+            },
+            "labels": {
+                "0": "background",
+                "1": "edema",
+                "2": "non-enhancing tumor",
+                "3": "enhancing tumour"
+            },
+            "numTraining": 484,
+            "numTest": 266,
+            "training":
+            [
+                {
+                    "image": "./imagesTr/BRATS_306.nii.gz"
+                    "label": "./labelsTr/BRATS_306.nii.gz"
+                    ...
+                }
+            ]
+            "test":
+            [
+                "./imagesTs/BRATS_557.nii.gz"
+                ...
+            ]
+        }
+    """
+
+    def load_data_list(self) -> List[dict]:
+        """Load annotation from directory or annotation file.
+
+        Returns:
+            list[dict]: All data info of dataset.
+        """
+        # `self.ann_file` denotes the absolute annotation file path if
+        # `self.root=None` or relative path if `self.root=/path/to/data/`.
+        annotations = load(self.ann_file)
+        if not isinstance(annotations, dict):
+            raise TypeError(f'The annotations loaded from annotation file '
+                            f'should be a dict, but got {type(annotations)}!')
+        raw_data_list = annotations[
+            'training'] if not self.test_mode else annotations['test']
+        data_list = []
+        for raw_data_info in raw_data_list:
+            # `2:` works for removing './' in file path, which will break
+            # loading from cloud storage.
+            if isinstance(raw_data_info, dict):
+                data_info = dict(
+                    img_path=osp.join(self.data_root, raw_data_info['image']
+                                      [2:]))
+                data_info['seg_map_path'] = osp.join(
+                    self.data_root, raw_data_info['label'][2:])
+            else:
+                data_info = dict(
+                    img_path=osp.join(self.data_root, raw_data_info)[2:])
+            data_info['label_map'] = self.label_map
+            data_info['reduce_zero_label'] = self.reduce_zero_label
+            data_info['seg_fields'] = []
+            data_list.append(data_info)
+        annotations.pop('training')
+        annotations.pop('test')
+
+        metainfo = copy.deepcopy(annotations)
+        metainfo['classes'] = [*metainfo['labels'].values()]
+        # Meta information load from annotation file will not influence the
+        # existed meta information load from `BaseDataset.METAINFO` and
+        # `metainfo` arguments defined in constructor.
+        for k, v in metainfo.items():
+            self._metainfo.setdefault(k, v)
+
+        return data_list

tests/data/dataset.json

@@ -0,0 +1,30 @@
+{
+	"name": "BRATS",
+	"description": "Gliomas segmentation tumour and oedema in on brain images",
+	"tensorImageSize": "4D",
+	"modality": {
+		"0": "FLAIR",
+		"1": "T1w",
+		"2": "t1gd",
+		"3": "T2w"
+	},
+	"labels": {
+		"0": "background",
+		"1": "edema",
+		"2": "non-enhancing tumor",
+		"3": "enhancing tumour"
+	},
+	"numTraining": 484,
+	"numTest": 266,
+	"training": [
+		{
+			"image": "./imagesTr/BRATS_457.nii.gz",
+			"label": "./labelsTr/BRATS_457.nii.gz"
+		}
+	],
+	"test": [
+		"./imagesTs/BRATS_568.nii.gz",
+		"./imagesTs/BRATS_515.nii.gz",
+		"./imagesTs/BRATS_576.nii.gz"
+	]
+}

tests/test_datasets/test_dataset.py

@@ -7,8 +7,9 @@ from unittest.mock import MagicMock
 import pytest
 
 from mmseg.datasets import (ADE20KDataset, BaseSegDataset, CityscapesDataset,
-                            COCOStuffDataset, ISPRSDataset, LoveDADataset,
-                            PascalVOCDataset, PotsdamDataset, iSAIDDataset)
+                            COCOStuffDataset, DecathlonDataset, ISPRSDataset,
+                            LoveDADataset, PascalVOCDataset, PotsdamDataset,
+                            iSAIDDataset)
 from mmseg.registry import DATASETS
 from mmseg.utils import get_classes, get_palette
 
@@ -242,6 +243,22 @@ def test_isaid():
     assert len(test_dataset) == 1
 
 
+def test_decathlon():
+    data_root = osp.join(osp.dirname(__file__), '../data')
+    # test load training dataset
+    test_dataset = DecathlonDataset(
+        pipeline=[], data_root=data_root, ann_file='dataset.json')
+    assert len(test_dataset) == 1
+
+    # test load test dataset
+    test_dataset = DecathlonDataset(
+        pipeline=[],
+        data_root=data_root,
+        ann_file='dataset.json',
+        test_mode=True)
+    assert len(test_dataset) == 3
+
+
 @pytest.mark.parametrize('dataset, classes', [
     ('ADE20KDataset', ('wall', 'building')),
     ('CityscapesDataset', ('road', 'sidewalk')),