add test_mmdet test_mmocr and test_mmpose

tests/test_codebase/test_mmocr/test_text_detection.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+from tempfile import NamedTemporaryFile, TemporaryDirectory
+
+import mmengine
+import numpy as np
+import pytest
+import torch
+
+import mmdeploy.backend.onnxruntime as ort_apis
+from mmdeploy.apis import build_task_processor
+from mmdeploy.codebase import import_codebase
+from mmdeploy.utils import Codebase, load_config
+from mmdeploy.utils.test import SwitchBackendWrapper
+
+model_cfg_path = 'tests/test_codebase/test_mmocr/data/dbnet.py'
+model_cfg = load_config(model_cfg_path)[0]
+deploy_cfg = mmengine.Config(
+    dict(
+        backend_config=dict(type='onnxruntime'),
+        codebase_config=dict(type='mmocr', task='TextDetection'),
+        onnx_config=dict(
+            type='onnx',
+            export_params=True,
+            keep_initializers_as_inputs=False,
+            opset_version=11,
+            input_shape=None,
+            input_names=['input'],
+            output_names=['output'])))
+
+onnx_file = NamedTemporaryFile(suffix='.onnx').name
+task_processor = None
+img_shape = (32, 32)
+img = np.random.rand(*img_shape, 3).astype(np.uint8)
+
+
+@pytest.fixture(autouse=True)
+def init_task_processor():
+    try:
+        import_codebase(Codebase.MMOCR)
+    except ImportError:
+        pytest.skip(
+            f'{Codebase.MMOCR} is not installed.', allow_module_level=True)
+    global task_processor
+    task_processor = build_task_processor(model_cfg, deploy_cfg, 'cpu')
+
+
+def test_build_pytorch_model():
+    from mmocr.utils.setup_env import register_all_modules
+    register_all_modules()
+    from mmocr.models.textdet.detectors.single_stage_text_detector import \
+        SingleStageTextDetector
+    model = task_processor.build_pytorch_model(None)
+    assert isinstance(model, SingleStageTextDetector)
+
+
+@pytest.fixture
+def backend_model():
+    from mmdeploy.backend.onnxruntime import ORTWrapper
+    ort_apis.__dict__.update({'ORTWrapper': ORTWrapper})
+    wrapper = SwitchBackendWrapper(ORTWrapper)
+    wrapper.set(outputs={
+        'output': torch.rand(1, *img_shape),
+    })
+
+    yield task_processor.build_backend_model([''])
+
+    wrapper.recover()
+
+
+def test_build_backend_model(backend_model):
+    assert isinstance(backend_model, torch.nn.Module)
+
+
+def test_create_input():
+    inputs = task_processor.create_input(img, input_shape=img_shape)
+    assert isinstance(inputs, tuple) and len(inputs) == 2
+
+
+def test_visualize(backend_model):
+    input_dict, _ = task_processor.create_input(img, input_shape=img_shape)
+    results = backend_model.test_step(input_dict)[0]
+    with TemporaryDirectory() as dir:
+        filename = dir + 'tmp.jpg'
+        task_processor.visualize(img, results, filename, 'tmp')
+        assert os.path.exists(filename)
+
+
+def test_get_tensor_from_input():
+    input_data = {'inputs': torch.ones(3, 4, 5)}
+    inputs = task_processor.get_tensor_from_input(input_data)
+    assert torch.equal(inputs, torch.ones(3, 4, 5))
+
+
+def test_get_partition_cfg():
+    with pytest.raises(NotImplementedError):
+        _ = task_processor.get_partition_cfg(partition_type='')

tests/test_codebase/test_mmocr/test_text_detection_models.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import mmengine
+import pytest
+import torch
+
+import mmdeploy.backend.onnxruntime as ort_apis
+from mmdeploy.codebase import import_codebase
+from mmdeploy.utils import Backend, Codebase, load_config
+from mmdeploy.utils.test import SwitchBackendWrapper, backend_checker
+
+try:
+    import_codebase(Codebase.MMOCR)
+except ImportError:
+    pytest.skip(f'{Codebase.MMOCR} is not installed.', allow_module_level=True)
+
+IMAGE_SIZE = 32
+
+
+@backend_checker(Backend.ONNXRUNTIME)
+class TestEnd2EndModel:
+
+    @classmethod
+    def setup_class(cls):
+        # force add backend wrapper regardless of plugins
+        from mmdeploy.backend.onnxruntime import ORTWrapper
+        ort_apis.__dict__.update({'ORTWrapper': ORTWrapper})
+
+        # simplify backend inference
+        cls.wrapper = SwitchBackendWrapper(ORTWrapper)
+        cls.outputs = {
+            'outputs': torch.rand(1, IMAGE_SIZE, IMAGE_SIZE),
+        }
+        cls.wrapper.set(outputs=cls.outputs)
+        deploy_cfg = mmengine.Config(
+            {'onnx_config': {
+                'output_names': ['outputs']
+            }})
+        model_cfg_path = 'tests/test_codebase/test_mmocr/data/dbnet.py'
+        model_cfg = load_config(model_cfg_path)[0]
+
+        from mmdeploy.codebase.mmocr.deploy.text_detection_model import \
+            End2EndModel
+        cls.end2end_model = End2EndModel(
+            Backend.ONNXRUNTIME, [''],
+            device='cpu',
+            deploy_cfg=deploy_cfg,
+            model_cfg=model_cfg)
+
+    @classmethod
+    def teardown_class(cls):
+        cls.wrapper.recover()
+
+    @pytest.mark.parametrize(
+        'ori_shape',
+        [[IMAGE_SIZE, IMAGE_SIZE], [2 * IMAGE_SIZE, 2 * IMAGE_SIZE]])
+    def test_forward(self, ori_shape):
+        imgs = torch.rand(1, 3, IMAGE_SIZE, IMAGE_SIZE)
+        img_meta = {
+            'ori_shape': ori_shape,
+            'img_shape': [IMAGE_SIZE, IMAGE_SIZE],
+            'scale_factor': [1., 1.],
+            'img_path': ''
+        }
+        from mmengine.structures import InstanceData
+        from mmocr.structures import TextDetDataSample
+        pred_instances = InstanceData(metainfo=img_meta)
+        data_sample = TextDetDataSample(pred_instances=pred_instances)
+        data_sample.set_metainfo(img_meta)
+        results = self.end2end_model.forward(imgs, [data_sample])
+        assert results is not None, 'failed to get output using '\
+            'End2EndModel'
+
+
+@backend_checker(Backend.ONNXRUNTIME)
+def test_build_text_detection_model():
+    model_cfg_path = 'tests/test_codebase/test_mmocr/data/dbnet.py'
+    model_cfg = load_config(model_cfg_path)[0]
+    deploy_cfg = mmengine.Config(
+        dict(
+            backend_config=dict(type='onnxruntime'),
+            onnx_config=dict(output_names=['outputs']),
+            codebase_config=dict(type='mmocr')))
+
+    from mmdeploy.backend.onnxruntime import ORTWrapper
+    ort_apis.__dict__.update({'ORTWrapper': ORTWrapper})
+
+    # simplify backend inference
+    with SwitchBackendWrapper(ORTWrapper) as wrapper:
+        wrapper.set(model_cfg=model_cfg, deploy_cfg=deploy_cfg)
+        from mmdeploy.codebase.mmocr.deploy.text_detection_model import (
+            End2EndModel, build_text_detection_model)
+        segmentor = build_text_detection_model([''], model_cfg, deploy_cfg,
+                                               'cpu')
+        assert isinstance(segmentor, End2EndModel)

tests/test_codebase/test_mmocr/test_text_recognition.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+from tempfile import NamedTemporaryFile, TemporaryDirectory
+
+import mmengine
+import numpy as np
+import pytest
+import torch
+
+import mmdeploy.backend.onnxruntime as ort_apis
+from mmdeploy.apis import build_task_processor
+from mmdeploy.codebase import import_codebase
+from mmdeploy.utils import Codebase, load_config
+from mmdeploy.utils.test import SwitchBackendWrapper
+
+model_cfg_path = 'tests/test_codebase/test_mmocr/data/crnn.py'
+model_cfg = load_config(model_cfg_path)[0]
+deploy_cfg = mmengine.Config(
+    dict(
+        backend_config=dict(type='onnxruntime'),
+        codebase_config=dict(type='mmocr', task='TextRecognition'),
+        onnx_config=dict(
+            type='onnx',
+            export_params=True,
+            keep_initializers_as_inputs=False,
+            opset_version=11,
+            input_shape=None,
+            input_names=['input'],
+            output_names=['output'])))
+
+onnx_file = NamedTemporaryFile(suffix='.onnx').name
+task_processor = None
+img_shape = (32, 32)
+img = np.random.rand(*img_shape, 3).astype(np.uint8)
+
+
+@pytest.fixture(autouse=True)
+def init_task_processor():
+    try:
+        import_codebase(Codebase.MMOCR)
+    except ImportError:
+        pytest.skip(
+            f'{Codebase.MMOCR} is not installed.', allow_module_level=True)
+    global task_processor
+    task_processor = build_task_processor(model_cfg, deploy_cfg, 'cpu')
+
+
+def test_build_pytorch_model():
+    from mmocr.utils.setup_env import register_all_modules
+    register_all_modules()
+    from mmocr.models.textrecog.recognizers import BaseRecognizer
+    model = task_processor.build_pytorch_model(None)
+    assert isinstance(model, BaseRecognizer)
+
+
+@pytest.fixture
+def backend_model():
+    from mmdeploy.backend.onnxruntime import ORTWrapper
+    ort_apis.__dict__.update({'ORTWrapper': ORTWrapper})
+    wrapper = SwitchBackendWrapper(ORTWrapper)
+    wrapper.set(outputs={
+        'output': torch.rand(1, 9, 37),
+    })
+
+    yield task_processor.build_backend_model([''])
+
+    wrapper.recover()
+
+
+def test_build_backend_model(backend_model):
+    assert isinstance(backend_model, torch.nn.Module)
+
+
+def test_create_input():
+    inputs = task_processor.create_input(img, input_shape=img_shape)
+    assert isinstance(inputs, tuple) and len(inputs) == 2
+
+
+def test_visualize(backend_model):
+    input_dict, _ = task_processor.create_input(img, input_shape=img_shape)
+    results = backend_model.test_step(input_dict)[0]
+    with TemporaryDirectory() as dir:
+        filename = dir + 'tmp.jpg'
+        task_processor.visualize(img, results, filename, 'tmp')
+        assert os.path.exists(filename)
+
+
+def test_get_tensor_from_input():
+    input_data = {'inputs': torch.ones(3, 4, 5)}
+    inputs = task_processor.get_tensor_from_input(input_data)
+    assert torch.equal(inputs, torch.ones(3, 4, 5))
+
+
+def test_get_partition_cfg():
+    try:
+        _ = task_processor.get_partition_cfg(partition_type='')
+    except NotImplementedError:
+        pass

tests/test_codebase/test_mmocr/test_text_recognition_models.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import mmengine
+import pytest
+import torch
+
+import mmdeploy.backend.onnxruntime as ort_apis
+from mmdeploy.codebase import import_codebase
+from mmdeploy.utils import Backend, Codebase, load_config
+from mmdeploy.utils.test import SwitchBackendWrapper, backend_checker
+
+try:
+    import_codebase(Codebase.MMOCR)
+except ImportError:
+    pytest.skip(f'{Codebase.MMOCR} is not installed.', allow_module_level=True)
+
+IMAGE_SIZE = 32
+
+
+@backend_checker(Backend.ONNXRUNTIME)
+class TestEnd2EndModel:
+
+    @classmethod
+    def setup_class(cls):
+        # force add backend wrapper regardless of plugins
+        from mmdeploy.backend.onnxruntime import ORTWrapper
+        ort_apis.__dict__.update({'ORTWrapper': ORTWrapper})
+
+        # simplify backend inference
+        cls.wrapper = SwitchBackendWrapper(ORTWrapper)
+        cls.outputs = {
+            'output': torch.rand(1, 9, 37),
+        }
+        cls.wrapper.set(outputs=cls.outputs)
+        deploy_cfg = mmengine.Config(
+            {'onnx_config': {
+                'output_names': ['output']
+            }})
+        model_cfg_path = 'tests/test_codebase/test_mmocr/data/crnn.py'
+        model_cfg = load_config(model_cfg_path)[0]
+
+        from mmdeploy.codebase.mmocr.deploy.text_recognition_model import \
+            End2EndModel
+        cls.end2end_model = End2EndModel(
+            Backend.ONNXRUNTIME, [''],
+            device='cpu',
+            deploy_cfg=deploy_cfg,
+            model_cfg=model_cfg)
+
+    @classmethod
+    def teardown_class(cls):
+        cls.wrapper.recover()
+
+    @pytest.mark.parametrize(
+        'ori_shape',
+        [[IMAGE_SIZE, IMAGE_SIZE, 3], [2 * IMAGE_SIZE, 2 * IMAGE_SIZE, 3]])
+    def test_forward(self, ori_shape):
+        imgs = [torch.rand(1, 3, IMAGE_SIZE, IMAGE_SIZE)]
+        img_meta = {
+            'ori_shape': ori_shape,
+            'img_shape': [IMAGE_SIZE, IMAGE_SIZE, 3],
+            'scale_factor': [1., 1.]
+        }
+        from mmengine.structures import InstanceData
+        from mmocr.structures import TextRecogDataSample
+        pred_instances = InstanceData(metainfo=img_meta)
+        data_sample = TextRecogDataSample(pred_instances=pred_instances)
+        data_sample.set_metainfo(img_meta)
+        results = self.end2end_model.forward(imgs, [data_sample])
+        assert results is not None, 'failed to get output using '\
+            'End2EndModel'
+
+
+@backend_checker(Backend.ONNXRUNTIME)
+def test_build_text_recognition_model():
+    model_cfg_path = 'tests/test_codebase/test_mmocr/data/crnn.py'
+    model_cfg = load_config(model_cfg_path)[0]
+    deploy_cfg = mmengine.Config(
+        dict(
+            backend_config=dict(type='onnxruntime'),
+            onnx_config=dict(output_names=['outputs']),
+            codebase_config=dict(type='mmocr')))
+
+    from mmdeploy.backend.onnxruntime import ORTWrapper
+    ort_apis.__dict__.update({'ORTWrapper': ORTWrapper})
+
+    # simplify backend inference
+    with SwitchBackendWrapper(ORTWrapper) as wrapper:
+        wrapper.set(model_cfg=model_cfg, deploy_cfg=deploy_cfg)
+        from mmdeploy.codebase.mmocr.deploy.text_recognition_model import (
+            End2EndModel, build_text_recognition_model)
+        segmentor = build_text_recognition_model([''], model_cfg, deploy_cfg,
+                                                 'cpu')
+        assert isinstance(segmentor, End2EndModel)

tests/test_codebase/test_mmpose/__init__.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from .utils import (generate_datasample, generate_mmpose_deploy_config,
+                    generate_mmpose_task_processor)
+
+__all__ = [
+    'generate_datasample', 'generate_mmpose_deploy_config',
+    'generate_mmpose_task_processor'
+]

tests/test_codebase/test_mmpose/conftest.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+
+
+@pytest.fixture(autouse=True)
+def init_test():
+    # init default scope
+    from mmpose.utils import register_all_modules
+
+    register_all_modules(True)

tests/test_codebase/test_mmpose/data/annotations/person_keypoints_val2017.json

+{"info": {"description": "COCO 2017 Dataset","url": "http://cocodataset.org","version": "1.0","year": 2017,"contributor": "COCO Consortium","date_created": "2017/09/01"},"licenses": [{"url": "http://creativecommons.org/licenses/by-nc-sa/2.0/","id": 1,"name": "Attribution-NonCommercial-ShareAlike License"},{"url": "http://creativecommons.org/licenses/by-nc/2.0/","id": 2,"name": "Attribution-NonCommercial License"},{"url": "http://creativecommons.org/licenses/by-nc-nd/2.0/","id": 3,"name": "Attribution-NonCommercial-NoDerivs License"},{"url": "http://creativecommons.org/licenses/by/2.0/","id": 4,"name": "Attribution License"},{"url": "http://creativecommons.org/licenses/by-sa/2.0/","id": 5,"name": "Attribution-ShareAlike License"},{"url": "http://creativecommons.org/licenses/by-nd/2.0/","id": 6,"name": "Attribution-NoDerivs License"},{"url": "http://flickr.com/commons/usage/","id": 7,"name": "No known copyright restrictions"},{"url": "http://www.usa.gov/copyright.shtml","id": 8,"name": "United States Government Work"}],"images": [{"license": 4,"file_name": "000000397133.jpg","coco_url": "http://images.cocodataset.org/val2017/000000397133.jpg","height": 427,"width": 640,"date_captured": "2013-11-14 17:02:52","flickr_url": "http://farm7.staticflickr.com/6116/6255196340_da26cf2c9e_z.jpg","id": 397133}], "annotations": [{"segmentation": [[125.12,539.69,140.94,522.43,100.67,496.54,84.85,469.21,73.35,450.52,104.99,342.65,168.27,290.88,179.78,288,189.84,286.56,191.28,260.67,202.79,240.54,221.48,237.66,248.81,243.42,257.44,256.36,253.12,262.11,253.12,275.06,299.15,233.35,329.35,207.46,355.24,206.02,363.87,206.02,365.3,210.34,373.93,221.84,363.87,226.16,363.87,237.66,350.92,237.66,332.22,234.79,314.97,249.17,271.82,313.89,253.12,326.83,227.24,352.72,214.29,357.03,212.85,372.85,208.54,395.87,228.67,414.56,245.93,421.75,266.07,424.63,276.13,437.57,266.07,450.52,284.76,464.9,286.2,479.28,291.96,489.35,310.65,512.36,284.76,549.75,244.49,522.43,215.73,546.88,199.91,558.38,204.22,565.57,189.84,568.45,184.09,575.64,172.58,578.52,145.26,567.01,117.93,551.19,133.75,532.49]],"num_keypoints": 10,"area": 47803.27955,"iscrowd": 0,"keypoints": [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,142,309,1,177,320,2,191,398,2,237,317,2,233,426,2,306,233,2,92,452,2,123,468,2,0,0,0,251,469,2,0,0,0,162,551,2],"image_id": 425226,"bbox": [73.35,206.02,300.58,372.5],"category_id": 1,"id": 183126}], "categories": [{"supercategory": "person","id": 1,"name": "person","keypoints": ["nose","left_eye","right_eye","left_ear","right_ear","left_shoulder","right_shoulder","left_elbow","right_elbow","left_wrist","right_wrist","left_hip","right_hip","left_knee","right_knee","left_ankle","right_ankle"],"skeleton": [[16,14],[14,12],[17,15],[15,13],[12,13],[6,12],[7,13],[6,7],[6,8],[7,9],[8,10],[9,11],[2,3],[1,2],[1,3],[2,4],[3,5],[4,6],[5,7]]}]}

tests/test_codebase/test_mmpose/data/model.py

+# Copyright (c) OpenMMLab. All rights reserved.
+# model settings
+codec = dict(
+    type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)
+test_cfg = dict(
+    flip_test=False,
+    flip_mode='heatmap',
+    shift_heatmap=True,
+)
+model = dict(
+    type='TopdownPoseEstimator',
+    data_preprocessor=dict(
+        type='PoseDataPreprocessor',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        bgr_to_rgb=True),
+    backbone=dict(type='ResNet', depth=18),
+    head=dict(
+        type='HeatmapHead',
+        in_channels=512,
+        out_channels=17,
+        deconv_out_channels=None,
+        loss=dict(type='KeypointMSELoss', use_target_weight=True),
+        decoder=codec),
+    test_cfg=test_cfg)
+
+# dataset settings
+dataset_type = 'CocoDataset'
+data_mode = 'topdown'
+data_root = 'tests/test_codebase/test_mmpose/data/'
+file_client_args = dict(backend='disk')
+
+test_pipeline = [
+    dict(type='LoadImage', file_client_args=file_client_args),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='PackPoseInputs')
+]
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='annotations/person_keypoints_val2017.json',
+        data_prefix=dict(img='val2017/'),
+        test_mode=True,
+        lazy_init=True,
+        serialize_data=False,
+        pipeline=test_pipeline,
+    ))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type='CocoMetric',
+    ann_file=data_root + 'annotations/person_keypoints_val2017.json')
+test_evaluator = val_evaluator
+
+# default_runtime
+default_scope = 'mmpose'
+default_hooks = dict()
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='PoseLocalVisualizer', vis_backends=vis_backends, name='visualizer')

tests/test_codebase/test_mmpose/test_pose_detection_model.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+
+import mmdeploy.backend.onnxruntime as ort_apis
+from mmdeploy.codebase import import_codebase
+from mmdeploy.utils import Backend, Codebase, load_config
+from mmdeploy.utils.test import SwitchBackendWrapper, backend_checker
+
+IMAGE_H = 192
+IMAGE_W = 256
+
+try:
+    import_codebase(Codebase.MMPOSE)
+except ImportError:
+    pytest.skip(
+        f'{Codebase.MMPOSE} is not installed.', allow_module_level=True)
+
+from .utils import generate_datasample  # noqa: E402
+from .utils import generate_mmpose_deploy_config  # noqa: E402
+
+
+@backend_checker(Backend.ONNXRUNTIME)
+class TestEnd2EndModel:
+
+    @classmethod
+    def setup_class(cls):
+        # force add backend wrapper regardless of plugins
+        from mmdeploy.backend.onnxruntime import ORTWrapper
+        ort_apis.__dict__.update({'ORTWrapper': ORTWrapper})
+
+        # simplify backend inference
+        cls.wrapper = SwitchBackendWrapper(ORTWrapper)
+        cls.outputs = {
+            'output': torch.rand(1, 1, IMAGE_H, IMAGE_W),
+        }
+        cls.wrapper.set(outputs=cls.outputs)
+
+        from mmdeploy.codebase.mmpose.deploy.pose_detection_model import \
+            End2EndModel
+        model_cfg_path = 'tests/test_codebase/test_mmpose/data/model.py'
+        model_cfg = load_config(model_cfg_path)[0]
+        deploy_cfg = generate_mmpose_deploy_config()
+        cls.end2end_model = End2EndModel(
+            Backend.ONNXRUNTIME, [''],
+            device='cpu',
+            deploy_cfg=deploy_cfg,
+            model_cfg=model_cfg)
+
+    @classmethod
+    def teardown_class(cls):
+        cls.wrapper.recover()
+
+    def test_forward(self):
+        img = torch.rand(1, 3, IMAGE_H, IMAGE_W)
+        data_samples = [generate_datasample((IMAGE_H, IMAGE_W))]
+        results = self.end2end_model.forward(img, data_samples)
+        assert results is not None, 'failed to get output using '\
+            'End2EndModel'
+
+
+@backend_checker(Backend.ONNXRUNTIME)
+def test_build_pose_detection_model():
+    model_cfg_path = 'tests/test_codebase/test_mmpose/data/model.py'
+    model_cfg = load_config(model_cfg_path)[0]
+    deploy_cfg = generate_mmpose_deploy_config()
+
+    from mmdeploy.backend.onnxruntime import ORTWrapper
+    ort_apis.__dict__.update({'ORTWrapper': ORTWrapper})
+
+    # simplify backend inference
+    with SwitchBackendWrapper(ORTWrapper) as wrapper:
+        wrapper.set(model_cfg=model_cfg, deploy_cfg=deploy_cfg)
+        from mmdeploy.codebase.mmpose.deploy.pose_detection_model import (
+            End2EndModel, build_pose_detection_model)
+        posedetector = build_pose_detection_model([''], model_cfg, deploy_cfg,
+                                                  'cpu')
+        assert isinstance(posedetector, End2EndModel)