[Feature] Add MultiTaskDataset to support multi-task training.

docs/en/api/datasets.rst

@@ -46,6 +46,11 @@ Base classes
 
 .. autoclass:: MultiLabelDataset
 
+Multi-Task Dataset
+------------------
+
+.. autoclass:: MultiTaskDataset
+
 Dataset Wrappers
 ----------------
 

docs/en/api/transforms.rst

@@ -169,3 +169,7 @@ ToTensor
 Transpose
 ---------------------
 .. autoclass:: Transpose
+
+FormatMultiTaskLabels
+---------------------
+.. autoclass:: FormatMultiTaskLabels

mmcls/datasets/__init__.py

@@ -11,6 +11,7 @@
 from .imagenet21k import ImageNet21k
 from .mnist import MNIST, FashionMNIST
 from .multi_label import MultiLabelDataset
+from .multi_task import MultiTaskDataset
 from .samplers import DistributedSampler, RepeatAugSampler
 from .voc import VOC
 
@@ -19,5 +20,6 @@
     'VOC', 'MultiLabelDataset', 'build_dataloader', 'build_dataset',
     'DistributedSampler', 'ConcatDataset', 'RepeatDataset',
     'ClassBalancedDataset', 'DATASETS', 'PIPELINES', 'ImageNet21k', 'SAMPLERS',
-    'build_sampler', 'RepeatAugSampler', 'KFoldDataset', 'CUB', 'CustomDataset'
+    'build_sampler', 'RepeatAugSampler', 'KFoldDataset', 'CUB',
+    'CustomDataset', 'MultiTaskDataset'
 ]

mmcls/datasets/base_dataset.py

@@ -145,6 +145,24 @@ def evaluate(self,
         Returns:
             dict: evaluation results
         """
+        results = np.vstack(results)
+        gt_labels = self.get_gt_labels()
+        if indices is not None:
+            gt_labels = gt_labels[indices]
+
+        return self.evaluate_single_label(
+            results=results,
+            gt_labels=gt_labels,
+            metric=metric,
+            metric_options=metric_options,
+            logger=logger)
+
+    @staticmethod
+    def evaluate_single_label(results,
+                              gt_labels,
+                              metric='accuracy',
+                              metric_options=None,
+                              logger=None):
         if metric_options is None:
             metric_options = {'topk': (1, 5)}
         if isinstance(metric, str):
@@ -154,11 +172,6 @@ def evaluate(self,
         allowed_metrics = [
             'accuracy', 'precision', 'recall', 'f1_score', 'support'
         ]
-        eval_results = {}
-        results = np.vstack(results)
-        gt_labels = self.get_gt_labels()
-        if indices is not None:
-            gt_labels = gt_labels[indices]
         num_imgs = len(results)
         assert len(gt_labels) == num_imgs, 'dataset testing results should '\
             'be of the same length as gt_labels.'
@@ -171,6 +184,7 @@ def evaluate(self,
         thrs = metric_options.get('thrs')
         average_mode = metric_options.get('average_mode', 'macro')
 
+        eval_results = {}
         if 'accuracy' in metrics:
             if thrs is not None:
                 acc = accuracy(results, gt_labels, topk=topk, thrs=thrs)

mmcls/datasets/multi_label.py

@@ -44,6 +44,23 @@ def evaluate(self,
         Returns:
             dict: evaluation results
         """
+        results = np.vstack(results)
+        gt_labels = self.get_gt_labels()
+        if indices is not None:
+            gt_labels = gt_labels[indices]
+        return self.evaluate_multi_label(
+            results=results,
+            gt_labels=gt_labels,
+            metric=metric,
+            metric_options=metric_options,
+            logger=logger)
+
+    @staticmethod
+    def evaluate_multi_label(results,
+                             gt_labels,
+                             metric='mAP',
+                             metric_options=None,
+                             logger=None):
         if metric_options is None or metric_options == {}:
             metric_options = {'thr': 0.5}
 
@@ -53,10 +70,7 @@ def evaluate(self,
             metrics = metric
         allowed_metrics = ['mAP', 'CP', 'CR', 'CF1', 'OP', 'OR', 'OF1']
         eval_results = {}
-        results = np.vstack(results)
-        gt_labels = self.get_gt_labels()
-        if indices is not None:
-            gt_labels = gt_labels[indices]
+
         num_imgs = len(results)
         assert len(gt_labels) == num_imgs, 'dataset testing results should '\
             'be of the same length as gt_labels.'