[Feature] Support Multi-task.

configs/_base_/datasets/multi_task_medic_data.py

@@ -0,0 +1,61 @@
+# dataset settings
+dataset_type = 'MultiTaskDataset'
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', scale=(256, 200), backend='pillow'),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='PackMultiTaskInputs'),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', scale=(256, 200), backend='pillow'),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='PackMultiTaskInputs')
+]
+
+train_dataloader = dict(
+    batch_size=16,
+    num_workers=16,
+    dataset=dict(
+        type=dataset_type,
+        ann_file='data/MEDIC_train.json',
+        data_root='../dataset',
+        pipeline=train_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    persistent_workers=True,
+)
+
+val_dataloader = dict(
+    batch_size=16,
+    num_workers=16,
+    dataset=dict(
+        type=dataset_type,
+        ann_file='data/MEDIC_test.json',
+        data_root='../dataset',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    persistent_workers=True,
+)
+test_dataloader = dict(
+    batch_size=16,
+    num_workers=16,
+    dataset=dict(
+        type=dataset_type,
+        ann_file='data/MEDIC_test.json',
+        data_root='../dataset',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    persistent_workers=True,
+)
+
+val_evaluator = dict(
+    type='MultiTasksMetric',
+    task_metrics={
+        'damage_severity': [dict(type='Accuracy', topk=(1, ))],
+        'informative': [dict(type='Accuracy', topk=(1, ))],
+        'disaster_types': [dict(type='Accuracy', topk=(1, ))],
+        'humanitarian': [dict(type='Accuracy', topk=(1, ))]
+    })
+
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator

configs/_base_/models/mobilenet_v2_1x_multitask.py

@@ -0,0 +1,23 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='MobileNetV2',
+        widen_factor=1.0,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='https://download.openmmlab.com/mmclassification/v0/mobi\
+            lenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth',
+            prefix='backbone')),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='MultiTaskHead',
+        task_heads={
+            'damage_severity': dict(type='LinearClsHead', num_classes=3),
+            'informative': dict(type='LinearClsHead', num_classes=2),
+            'humanitarian': dict(type='LinearClsHead', num_classes=4),
+            'disaster_types': dict(type='LinearClsHead', num_classes=7)
+        },
+        in_channels=1280,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))

configs/mobilenet_v2/mobilenet_multi_task.py

@@ -0,0 +1,6 @@
+_base_ = [
+    '../_base_/datasets/multi_task_medic_data.py',
+    '../_base_/models/mobilenet_v2_1x_multitask.py',
+    '../_base_/schedules/imagenet_bs256_epochstep.py',
+    '../_base_/default_runtime.py'
+]

mmcls/datasets/__init__.py

@@ -8,12 +8,13 @@
 from .imagenet import ImageNet, ImageNet21k
 from .mnist import MNIST, FashionMNIST
 from .multi_label import MultiLabelDataset
+from .multi_task import MultiTaskDataset
 from .samplers import *  # noqa: F401,F403
 from .transforms import *  # noqa: F401,F403
 from .voc import VOC
 
 __all__ = [
     'BaseDataset', 'ImageNet', 'CIFAR10', 'CIFAR100', 'MNIST', 'FashionMNIST',
     'VOC', 'build_dataset', 'ImageNet21k', 'KFoldDataset', 'CUB',
-    'CustomDataset', 'MultiLabelDataset'
+    'CustomDataset', 'MultiLabelDataset', 'MultiTaskDataset'
 ]