[to #42322933] support finetune on cv/hand_2d_keypoints

添加2d手部关键点检测finetune功能
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10371710

modelscope/metainfo.py

@@ -50,6 +50,7 @@ class Models(object):
     # EasyCV models
     yolox = 'YOLOX'
     segformer = 'Segformer'
+    hand_2d_keypoints = 'HRNet-Hand2D-Keypoints'
     image_object_detection_auto = 'image-object-detection-auto'
 
     # nlp models
@@ -439,6 +440,7 @@ class Datasets(object):
     """
     ClsDataset = 'ClsDataset'
     Face2dKeypointsDataset = 'Face2dKeypointsDataset'
+    HandCocoWholeBodyDataset = 'HandCocoWholeBodyDataset'
     HumanWholeBodyKeypointDataset = 'HumanWholeBodyKeypointDataset'
     SegDataset = 'SegDataset'
     DetDataset = 'DetDataset'

modelscope/models/cv/hand_2d_keypoints/__init__.py

@@ -0,0 +1,20 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .hand_2d_keypoints import Hand2dKeyPoints
+
+else:
+    _import_structure = {'hand_2d_keypoints': ['Hand2dKeyPoints']}
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/cv/hand_2d_keypoints/hand_2d_keypoints.py

@@ -0,0 +1,16 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from easycv.models.pose import TopDown
+
+from modelscope.metainfo import Models
+from modelscope.models.builder import MODELS
+from modelscope.models.cv.easycv_base import EasyCVBaseModel
+from modelscope.utils.constant import Tasks
+
+
+@MODELS.register_module(
+    group_key=Tasks.hand_2d_keypoints, module_name=Models.hand_2d_keypoints)
+class Hand2dKeyPoints(EasyCVBaseModel, TopDown):
+
+    def __init__(self, model_dir=None, *args, **kwargs):
+        EasyCVBaseModel.__init__(self, model_dir, args, kwargs)
+        TopDown.__init__(self, *args, **kwargs)

modelscope/msdatasets/cv/hand_2d_keypoints/__init__.py

@@ -0,0 +1,22 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .hand_2d_keypoints_dataset import Hand2DKeypointDataset
+
+else:
+    _import_structure = {
+        'hand_2d_keypoints_dataset': ['Hand2DKeypointDataset']
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/msdatasets/cv/hand_2d_keypoints/hand_2d_keypoints_dataset.py

@@ -0,0 +1,38 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from easycv.datasets.pose import \
+    HandCocoWholeBodyDataset as _HandCocoWholeBodyDataset
+
+from modelscope.metainfo import Datasets
+from modelscope.msdatasets.cv.easycv_base import EasyCVBaseDataset
+from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.utils.constant import Tasks
+
+
+@TASK_DATASETS.register_module(
+    group_key=Tasks.hand_2d_keypoints,
+    module_name=Datasets.HandCocoWholeBodyDataset)
+class HandCocoWholeBodyDataset(EasyCVBaseDataset, _HandCocoWholeBodyDataset):
+    """EasyCV dataset for human hand 2d keypoints.
+
+    Args:
+        split_config (dict): Dataset root path from MSDataset, e.g.
+            {"train":"local cache path"} or {"evaluation":"local cache path"}.
+        preprocessor (Preprocessor): An optional preprocessor instance, please make sure the preprocessor fits for
+            the model if supplied. Not support yet.
+        mode: Training or Evaluation.
+    """
+
+    def __init__(self,
+                 split_config=None,
+                 preprocessor=None,
+                 mode=None,
+                 *args,
+                 **kwargs) -> None:
+        EasyCVBaseDataset.__init__(
+            self,
+            split_config=split_config,
+            preprocessor=preprocessor,
+            mode=mode,
+            args=args,
+            kwargs=kwargs)
+        _HandCocoWholeBodyDataset.__init__(self, *args, **kwargs)

tests/trainers/easycv/test_easycv_trainer_hand_2d_keypoints.py

@@ -0,0 +1,72 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import glob
+import os
+import shutil
+import tempfile
+import unittest
+
+import torch
+
+from modelscope.metainfo import Trainers
+from modelscope.msdatasets import MsDataset
+from modelscope.trainers import build_trainer
+from modelscope.utils.constant import DownloadMode, LogKeys, Tasks
+from modelscope.utils.logger import get_logger
+from modelscope.utils.test_utils import test_level
+
+
+@unittest.skipIf(not torch.cuda.is_available(), 'cuda unittest')
+class EasyCVTrainerTestHand2dKeypoints(unittest.TestCase):
+    model_id = 'damo/cv_hrnetw18_hand-pose-keypoints_coco-wholebody'
+
+    def setUp(self):
+        self.logger = get_logger()
+        self.logger.info(('Testing %s.%s' %
+                          (type(self).__name__, self._testMethodName)))
+        self.tmp_dir = tempfile.TemporaryDirectory().name
+        if not os.path.exists(self.tmp_dir):
+            os.makedirs(self.tmp_dir)
+
+    def tearDown(self):
+        super().tearDown()
+        shutil.rmtree(self.tmp_dir, ignore_errors=True)
+
+    def _train(self):
+        cfg_options = {'train.max_epochs': 20}
+
+        trainer_name = Trainers.easycv
+
+        train_dataset = MsDataset.load(
+            dataset_name='cv_hand_2d_keypoints_coco_wholebody',
+            namespace='chenhyer',
+            split='subtrain',
+            download_mode=DownloadMode.FORCE_REDOWNLOAD)
+        eval_dataset = MsDataset.load(
+            dataset_name='cv_hand_2d_keypoints_coco_wholebody',
+            namespace='chenhyer',
+            split='subtrain',
+            download_mode=DownloadMode.FORCE_REDOWNLOAD)
+
+        kwargs = dict(
+            model=self.model_id,
+            train_dataset=train_dataset,
+            eval_dataset=eval_dataset,
+            work_dir=self.tmp_dir,
+            cfg_options=cfg_options)
+
+        trainer = build_trainer(trainer_name, kwargs)
+        trainer.train()
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_trainer_single_gpu(self):
+        self._train()
+
+        results_files = os.listdir(self.tmp_dir)
+        json_files = glob.glob(os.path.join(self.tmp_dir, '*.log.json'))
+        self.assertEqual(len(json_files), 1)
+        self.assertIn(f'{LogKeys.EPOCH}_10.pth', results_files)
+        self.assertIn(f'{LogKeys.EPOCH}_20.pth', results_files)
+
+
+if __name__ == '__main__':
+    unittest.main()