add predict pipeline

Link: https://code.alibaba-inc.com/pai-vision/EasyCV/codereview/9828601

    * add predict pipeline

configs/segmentation/segformer/segformer_b0_coco.py

@@ -233,6 +233,8 @@ eval_pipelines = [
     )
 ]
 
+predict = dict(type='SegmentationPredictor')
+
 log_config = dict(
     interval=50,
     hooks=[

configs/segmentation/segformer/segformer_b5_coco.py

@@ -233,6 +233,8 @@ eval_pipelines = [
     )
 ]
 
+predict = dict(type='SegmentationPredictor')
+
 log_config = dict(
     interval=50,
     hooks=[

easycv/datasets/shared/pipelines/__init__.py

@@ -4,4 +4,4 @@ from .dali_transforms import (DaliColorTwist, DaliCropMirrorNormalize,
                               DaliImageDecoder, DaliRandomGrayscale,
                               DaliRandomResizedCrop, DaliResize)
 from .format import Collect, DefaultFormatBundle, ImageToTensor
-from .transforms import Compose
+from .transforms import Compose, LoadImage

easycv/datasets/shared/pipelines/transforms.py

@@ -2,7 +2,10 @@
 import time
 from collections.abc import Sequence
 
+import numpy as np
+
 from easycv.datasets.registry import PIPELINES
+from easycv.file.image import load_image
 from easycv.utils.registry import build_from_cfg
 
 
@@ -48,3 +51,49 @@ class Compose(object):
             format_string += f'\n    {t}'
         format_string += '\n)'
         return format_string
+
+
+@PIPELINES.register_module()
+class LoadImage:
+    """Load an image from file or numpy or PIL object.
+    Args:
+        to_float32 (bool): Whether to convert the loaded image to a float32
+            numpy array. If set to False, the loaded image is an uint8 array.
+            Defaults to False.
+    """
+
+    def __init__(self, to_float32=False, mode='bgr'):
+        self.to_float32 = to_float32
+        self.mode = mode
+
+    def __call__(self, results):
+        """Call functions to load image and get image meta information.
+        Returns:
+            dict: The dict contains loaded image and meta information.
+        """
+        filename = results.get('filename', None)
+        img = results.get('img', None)
+
+        if img is not None:
+            if not isinstance(img, np.ndarray):
+                img = np.asarray(img, dtype=np.uint8)
+        elif filename is None:
+            raise ValueError('Please provide "filename" or "img"!')
+
+        img = load_image(filename, mode=self.mode)
+        if self.to_float32:
+            img = img.astype(np.float32)
+
+        results['filename'] = filename
+        results['img'] = img
+        results['img_shape'] = img.shape
+        results['ori_shape'] = img.shape
+        results['img_fields'] = ['img']
+        return results
+
+    def __repr__(self):
+        repr_str = (f'{self.__class__.__name__}('
+                    f'to_float32={self.to_float32}, '
+                    f"mode='{self.mode}'")
+
+        return repr_str

easycv/predictors/__init__.py

@@ -7,4 +7,5 @@ from .feature_extractor import (TorchFaceAttrExtractor,
                                 TorchFeatureExtractor)
 from .pose_predictor import (TorchPoseTopDownPredictor,
                              TorchPoseTopDownPredictorWithDetector)
-from .segmentation import Mask2formerPredictor, SegFormerPredictor
+from .segmentation import (Mask2formerPredictor, SegFormerPredictor,
+                           SegmentationPredictor)

easycv/predictors/base.py

@@ -1,14 +1,16 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 import os
+import pickle
 
 import numpy as np
 import torch
+from mmcv.parallel import collate, scatter_kwargs
 from PIL import Image
 from torchvision.transforms import Compose
 
 from easycv.datasets.registry import PIPELINES
 from easycv.file import io
-from easycv.models import build_model
+from easycv.models.builder import build_model
 from easycv.utils.checkpoint import load_checkpoint
 from easycv.utils.config_tools import mmcv_config_fromfile
 from easycv.utils.constant import CACHE_DIR
@@ -91,3 +93,175 @@ class Predictor(object):
             output = self.model.forward(
                 image_batch.to(self.device), **forward_kwargs)
         return output
+
+
+class PredictorV2(object):
+    """Base predict pipeline.
+        Args:
+            model_path (str): Path of model path.
+            config_file (Optinal[str]): config file path for model and processor to init. Defaults to None.
+            batch_size (int): batch size for forward.
+            device (str): Support 'cuda' or 'cpu', if is None, detect device automatically.
+            save_results (bool): Whether to save predict results.
+            save_path (str): File path for saving results, only valid when `save_results` is True.
+        """
+
+    def __init__(self,
+                 model_path,
+                 config_file=None,
+                 batch_size=1,
+                 device=None,
+                 save_results=False,
+                 save_path=None,
+                 *args,
+                 **kwargs):
+        self.model_path = model_path
+        self.batch_size = batch_size
+        self.save_results = save_results
+        self.save_path = save_path
+        if self.save_results:
+            assert self.save_path is not None
+        self.device = device
+        if self.device is None:
+            self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+        self.cfg = None
+        if config_file is not None:
+            if isinstance(config_file, str):
+                self.cfg = mmcv_config_fromfile(config_file)
+            else:
+                self.cfg = config_file
+
+        self.model = self.prepare_model()
+        self.processor = self.build_processor()
+        self._load_op = None
+
+    def prepare_model(self):
+        """Build model from config file by default.
+        If the model is not loaded from a configuration file, e.g. torch jit model, you need to reimplement it.
+        """
+        model = self._build_model()
+        model.to(self.device)
+        model.eval()
+        load_checkpoint(model, self.model_path, map_location='cpu')
+        return model
+
+    def _build_model(self):
+        if self.cfg is None:
+            raise ValueError('Please provide "config_file"!')
+
+        model = build_model(self.cfg.model)
+        return model
+
+    def build_processor(self):
+        """Build processor to process loaded input.
+        If you need custom preprocessing ops, you need to reimplement it.
+        """
+        if self.cfg is None:
+            pipeline = []
+        else:
+            pipeline = [
+                build_from_cfg(p, PIPELINES)
+                for p in self.cfg.get('test_pipeline', [])
+            ]
+
+        from easycv.datasets.shared.pipelines.transforms import Compose
+        processor = Compose(pipeline)
+        return processor
+
+    def _load_input(self, input):
+        """Load image from file or numpy or PIL object.
+        Args:
+            input: File path or numpy or PIL object.
+        Returns:
+           {
+                'filename': filename,
+                'img': img,
+                'img_shape': img_shape,
+                'img_fields': ['img']
+            }
+        """
+        if self._load_op is None:
+            load_cfg = dict(type='LoadImage', mode='rgb')
+            self._load_op = build_from_cfg(load_cfg, PIPELINES)
+
+        if not isinstance(input, str):
+            sample = self._load_op({'img': input})
+        else:
+            sample = self._load_op({'filename': input})
+
+        return sample
+
+    def preprocess_single(self, input):
+        """Preprocess single input sample.
+        If you need custom ops to load or process a single input sample, you need to reimplement it.
+        """
+        input = self._load_input(input)
+        return self.processor(input)
+
+    def preprocess(self, inputs, *args, **kwargs):
+        """Process all inputs list. And collate to batch and put to target device.
+        If you need custom ops to load or process a batch samples, you need to reimplement it.
+        """
+        batch_outputs = []
+        for i in inputs:
+            batch_outputs.append(self.preprocess_single(i, *args, **kwargs))
+
+        batch_outputs = self._collate_fn(batch_outputs)
+        batch_outputs = self._to_device(batch_outputs)
+
+        return batch_outputs
+
+    def forward(self, inputs):
+        """Model forward.
+        If you need refactor model forward, you need to reimplement it.
+        """
+        with torch.no_grad():
+            outputs = self.model(**inputs, mode='test')
+        return outputs
+
+    def postprocess(self, inputs, *args, **kwargs):
+        """Process model outputs.
+        If you need add some processing ops to process model outputs, you need to reimplement it.
+        """
+        return inputs
+
+    def _collate_fn(self, inputs):
+        """Prepare the input just before the forward function.
+        Puts each data field into a tensor with outer dimension batch size
+        """
+        return collate(inputs, samples_per_gpu=self.batch_size)
+
+    def _to_device(self, inputs):
+        target_gpus = [-1] if self.device == 'cpu' else [
+            torch.cuda.current_device()
+        ]
+        _, kwargs = scatter_kwargs(None, inputs, target_gpus=target_gpus)
+        return kwargs[0]
+
+    @staticmethod
+    def dump(obj, save_path, mode='wb'):
+        with open(save_path, mode) as f:
+            f.write(pickle.dumps(obj))
+
+    def __call__(self, inputs, keep_inputs=False):
+        # TODO: fault tolerance
+
+        if isinstance(inputs, str):
+            inputs = [inputs]
+
+        results_list = []
+        for i in range(0, len(inputs), self.batch_size):
+            batch = inputs[i:max(len(inputs) - 1, i + self.batch_size)]
+            batch_outputs = self.preprocess(batch)
+            batch_outputs = self.forward(batch_outputs)
+            results = self.postprocess(batch_outputs)
+            if keep_inputs:
+                results = {'inputs': batch, 'results': results}
+            # if dump, the outputs will not added to the return value to prevent taking up too much memory
+            if self.save_results:
+                self.dump([results], self.save_path, mode='ab+')
+            else:
+                results_list.append(results)
+
+        return results_list

easycv/predictors/builder.py

@@ -4,5 +4,5 @@ from easycv.utils.registry import Registry, build_from_cfg
 PREDICTORS = Registry('predictor')
 
 
-def build_predictor(cfg):
+def build_predictor(cfg, default_args=None):
-    return build_from_cfg(cfg, PREDICTORS, default_args=None)
+    return build_from_cfg(cfg, PREDICTORS, default_args=default_args)

easycv/predictors/segmentation.py

@@ -16,6 +16,110 @@ from easycv.predictors.interface import PredictorInterface
 from easycv.utils.checkpoint import load_checkpoint
 from easycv.utils.config_tools import mmcv_config_fromfile
 from easycv.utils.registry import build_from_cfg
+from .base import PredictorV2
+
+
+@PREDICTORS.register_module()
+class SegmentationPredictor(PredictorV2):
+
+    def __init__(self,
+                 model_path,
+                 config_file,
+                 batch_size=1,
+                 device=None,
+                 save_results=False,
+                 save_path=None):
+        """Predict pipeline for Segmentation
+
+        Args:
+            model_path (str): Path of model path
+            config_file (str): config file path for model and processor to init. Defaults to None.
+        """
+        super(SegmentationPredictor, self).__init__(
+            model_path,
+            config_file,
+            batch_size=batch_size,
+            device=device,
+            save_results=save_results,
+            save_path=save_path)
+
+        self.CLASSES = self.cfg.CLASSES
+        self.PALETTE = self.cfg.PALETTE
+
+    def show_result(self,
+                    img,
+                    result,
+                    palette=None,
+                    win_name='',
+                    show=False,
+                    wait_time=0,
+                    out_file=None,
+                    opacity=0.5):
+        """Draw `result` over `img`.
+
+        Args:
+            img (str or Tensor): The image to be displayed.
+            result (Tensor): The semantic segmentation results to draw over
+                `img`.
+            palette (list[list[int]]] | np.ndarray | None): The palette of
+                segmentation map. If None is given, random palette will be
+                generated. Default: None
+            win_name (str): The window name.
+            wait_time (int): Value of waitKey param.
+                Default: 0.
+            show (bool): Whether to show the image.
+                Default: False.
+            out_file (str or None): The filename to write the image.
+                Default: None.
+            opacity(float): Opacity of painted segmentation map.
+                Default 0.5.
+                Must be in (0, 1] range.
+        Returns:
+            img (Tensor): Only if not `show` or `out_file`
+        """
+
+        img = mmcv.imread(img)
+        img = img.copy()
+        seg = result[0]
+        if palette is None:
+            if self.PALETTE is None:
+                # Get random state before set seed,
+                # and restore random state later.
+                # It will prevent loss of randomness, as the palette
+                # may be different in each iteration if not specified.
+                # See: https://github.com/open-mmlab/mmdetection/issues/5844
+                state = np.random.get_state()
+                np.random.seed(42)
+                # random palette
+                palette = np.random.randint(
+                    0, 255, size=(len(self.CLASSES), 3))
+                np.random.set_state(state)
+            else:
+                palette = self.PALETTE
+        palette = np.array(palette)
+        assert palette.shape[0] == len(self.CLASSES)
+        assert palette.shape[1] == 3
+        assert len(palette.shape) == 2
+        assert 0 < opacity <= 1.0
+        color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
+        for label, color in enumerate(palette):
+            color_seg[seg == label, :] = color
+        # convert to BGR
+        color_seg = color_seg[..., ::-1]
+
+        img = img * (1 - opacity) + color_seg * opacity
+        img = img.astype(np.uint8)
+        # if out_file specified, do not show image in window
+        if out_file is not None:
+            show = False
+
+        if show:
+            mmcv.imshow(img, win_name, wait_time)
+        if out_file is not None:
+            mmcv.imwrite(img, out_file)
+
+        if not (show or out_file):
+            return img
 
 
 @PREDICTORS.register_module()

easycv/utils/ms_utils.py

@@ -0,0 +1,125 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import os
+
+import jsonplus
+
+from easycv.file import io
+from easycv.utils.config_tools import Config
+
+MODELSCOPE_PREFIX = 'modelscope'
+EASYCV_ARCH = '__easycv_arch__'
+
+
+def to_ms_config(cfg, task, ms_model_name, save_path=None, dump=True):
+    """Convert EasyCV config to ModelScope style.
+
+    Args:
+        cfg (str | Config): Easycv config file or Config object.
+        task (str): Task name in modelscope, refer to: modelscope.utils.constant.Tasks.
+        ms_model_name (str): Model name registered in modelscope, model type will be replaced with `ms_model_name`, used in modelscope.
+        save_path (str): Save path for saving the generated modelscope configuration file. Only valid when dump is True.
+        dump (bool): Whether dump the converted config to `save_path`.
+    """
+    # TODO: support multi eval_pipelines
+    # TODO: support for adding customized required keys to the configuration file
+
+    if isinstance(cfg, str):
+        easycv_cfg = Config.fromfile(cfg)
+        if dump and save_path is None:
+            save_dir = os.path.dirname(cfg)
+            save_name = MODELSCOPE_PREFIX + '_' + os.path.splitext(
+                os.path.basename(cfg))[0] + '.json'
+            save_path = os.path.join(save_dir, save_name)
+    else:
+        easycv_cfg = cfg
+        if dump and save_path is None:
+            raise ValueError('Please provide `save_path`!')
+
+    assert save_path.endswith('json'), 'Only support json file!'
+    optimizer_options = easycv_cfg.optimizer_config
+    optimizer_options.update({'loss_keys': 'total_loss'})
+
+    val_dataset_cfg = easycv_cfg.data.val
+    val_imgs_per_gpu = val_dataset_cfg.pop('imgs_per_gpu',
+                                           easycv_cfg.data.imgs_per_gpu)
+    val_workers_per_gpu = val_dataset_cfg.pop('workers_per_gpu',
+                                              easycv_cfg.data.workers_per_gpu)
+
+    log_config = easycv_cfg.log_config
+    predict_config = easycv_cfg.get('predict', None)
+
+    hooks = [{
+        'type': 'CheckpointHook',
+        'interval': easycv_cfg.checkpoint_config.interval
+    }, {
+        'type': 'EvaluationHook',
+        'interval': easycv_cfg.eval_config.interval
+    }, {
+        'type': 'AddLrLogHook'
+    }, {
+        'type': 'IterTimerHook'
+    }]
+
+    custom_hooks = easycv_cfg.get('custom_hooks', [])
+    hooks.extend(custom_hooks)
+
+    for log_hook_i in log_config.hooks:
+        if log_hook_i['type'] == 'TensorboardLoggerHook':
+            # replace with modelscope api
+            hooks.append({
+                'type': 'TensorboardHook',
+                'interval': log_config.interval
+            })
+        elif log_hook_i['type'] == 'TextLoggerHook':
+            # use modelscope api
+            hooks.append({
+                'type': 'TextLoggerHook',
+                'interval': log_config.interval
+            })
+        else:
+            log_hook_i.update({'interval': log_config.interval})
+            hooks.append(log_hook_i)
+
+    ori_model_type = easycv_cfg.model.pop('type')
+
+    ms_cfg = Config(
+        dict(
+            task=task,
+            framework='pytorch',
+            model={
+                'type': ms_model_name,
+                **easycv_cfg.model, EASYCV_ARCH: {
+                    'type': ori_model_type
+                }
+            },
+            dataset=dict(train=easycv_cfg.data.train, val=val_dataset_cfg),
+            train=dict(
+                work_dir=easycv_cfg.get('work_dir', None),
+                max_epochs=easycv_cfg.total_epochs,
+                dataloader=dict(
+                    batch_size_per_gpu=easycv_cfg.data.imgs_per_gpu,
+                    workers_per_gpu=easycv_cfg.data.workers_per_gpu,
+                ),
+                optimizer=dict(
+                    **easycv_cfg.optimizer, options=optimizer_options),
+                lr_scheduler=easycv_cfg.lr_config,
+                hooks=hooks),
+            evaluation=dict(
+                dataloader=dict(
+                    batch_size_per_gpu=val_imgs_per_gpu,
+                    workers_per_gpu=val_workers_per_gpu,
+                ),
+                metrics={
+                    'type': 'EasyCVMetric',
+                    'evaluators': easycv_cfg.eval_pipelines[0].evaluators
+                }),
+            pipeline=dict(predictor_config=predict_config),
+        ))
+
+    if dump:
+        with io.open(save_path, 'w') as f:
+            res = jsonplus.dumps(
+                ms_cfg._cfg_dict.to_dict(), indent=4, sort_keys=False)
+            f.write(res)
+
+    return ms_cfg

requirements/optional.txt

@@ -1,2 +1,4 @@
 http://pai-nni.oss-cn-zhangjiakou.aliyuncs.com/release/2.6.1/pai_nni-2.6.1-py3-none-manylinux1_x86_64.whl
+http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/pkgs/whl/panopticapi/panopticapi-0.1-py3-none-any.whl
 http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/third_party/blade_compression-0.0.2-py3-none-any.whl
+https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda100/nvidia_dali_cuda100-0.25.0-1535750-py3-none-manylinux2014_x86_64.whl

requirements/runtime.txt

@@ -3,21 +3,19 @@ dataclasses
 einops
 future
 h5py
-http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/pkgs/whl/panopticapi/panopticapi-0.1-py3-none-any.whl
-https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda100/nvidia_dali_cuda100-0.25.0-1535750-py3-none-manylinux2014_x86_64.whl
 json_tricks
 numpy
-opencv-python-headless
+opencv-python
 oss2
 packaging
 Pillow
 prettytable
 pycocotools
-pytorch_metric_learning==0.9.89
+pytorch_metric_learning>=0.9.89
 scikit-image
 sklearn
 tensorboard
 thop
-timm==0.4.9
+timm>=0.4.9
 xtcocotools
 yacs

tests/predictors/test_segmentation.py

@@ -0,0 +1,107 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import os
+import pickle
+import shutil
+import tempfile
+import unittest
+
+import numpy as np
+from PIL import Image
+from tests.ut_config import (MODEL_CONFIG_SEGFORMER,
+                             PRETRAINED_MODEL_SEGFORMER, TEST_IMAGES_DIR)
+
+from easycv.predictors.segmentation import SegmentationPredictor
+
+
+class SegmentationPredictorTest(unittest.TestCase):
+
+    def setUp(self):
+        print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
+
+    def test_single(self):
+        segmentation_model_path = PRETRAINED_MODEL_SEGFORMER
+        segmentation_model_config = MODEL_CONFIG_SEGFORMER
+
+        img_path = os.path.join(TEST_IMAGES_DIR, '000000289059.jpg')
+        img = np.asarray(Image.open(img_path))
+
+        predict_pipeline = SegmentationPredictor(
+            model_path=segmentation_model_path,
+            config_file=segmentation_model_config)
+
+        outputs = predict_pipeline(img_path, keep_inputs=True)
+        self.assertEqual(len(outputs), 1)
+        self.assertEqual(outputs[0]['inputs'], [img_path])
+
+        results = outputs[0]['results']
+        self.assertListEqual(
+            list(img.shape)[:2], list(results['seg_pred'][0].shape))
+        self.assertListEqual(results['seg_pred'][0][1, :10].tolist(),
+                             [161 for i in range(10)])
+        self.assertListEqual(results['seg_pred'][0][-1, -10:].tolist(),
+                             [133 for i in range(10)])
+
+    def test_batch(self):
+        segmentation_model_path = PRETRAINED_MODEL_SEGFORMER
+        segmentation_model_config = MODEL_CONFIG_SEGFORMER
+
+        img_path = os.path.join(TEST_IMAGES_DIR, '000000289059.jpg')
+        img = np.asarray(Image.open(img_path))
+
+        predict_pipeline = SegmentationPredictor(
+            model_path=segmentation_model_path,
+            config_file=segmentation_model_config,
+            batch_size=2)
+
+        total_samples = 3
+        outputs = predict_pipeline(
+            [img_path] * total_samples, keep_inputs=True)
+        self.assertEqual(len(outputs), 2)
+
+        self.assertEqual(outputs[0]['inputs'], [img_path] * 2)
+        self.assertEqual(outputs[1]['inputs'], [img_path] * 1)
+        self.assertEqual(len(outputs[0]['results']['seg_pred']), 2)
+        self.assertEqual(len(outputs[1]['results']['seg_pred']), 1)
+
+        for result in [outputs[0]['results'], outputs[1]['results']]:
+            self.assertListEqual(
+                list(img.shape)[:2], list(result['seg_pred'][0].shape))
+            self.assertListEqual(result['seg_pred'][0][1, :10].tolist(),
+                                 [161 for i in range(10)])
+            self.assertListEqual(result['seg_pred'][0][-1, -10:].tolist(),
+                                 [133 for i in range(10)])
+
+    def test_dump(self):
+        segmentation_model_path = PRETRAINED_MODEL_SEGFORMER
+        segmentation_model_config = MODEL_CONFIG_SEGFORMER
+
+        img_path = os.path.join(TEST_IMAGES_DIR, '000000289059.jpg')
+
+        temp_dir = tempfile.TemporaryDirectory().name
+        if not os.path.exists(temp_dir):
+            os.makedirs(temp_dir)
+        tmp_path = os.path.join(temp_dir, 'results.pkl')
+
+        predict_pipeline = SegmentationPredictor(
+            model_path=segmentation_model_path,
+            config_file=segmentation_model_config,
+            batch_size=2,
+            save_results=True,
+            save_path=tmp_path)
+
+        total_samples = 3
+        outputs = predict_pipeline(
+            [img_path] * total_samples, keep_inputs=True)
+        self.assertEqual(outputs, [])
+
+        with open(tmp_path, 'rb') as f:
+            results = pickle.loads(f.read())
+
+        self.assertIn('inputs', results[0])
+        self.assertIn('results', results[0])
+
+        shutil.rmtree(temp_dir, ignore_errors=True)
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/utils/test_ms_utils.py

@@ -0,0 +1,47 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import os
+import shutil
+import tempfile
+import unittest
+
+import easycv
+from easycv.utils.config_tools import Config
+from easycv.utils.ms_utils import to_ms_config
+
+
+class MsConfigTest(unittest.TestCase):
+
+    def setUp(self):
+        print(('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)
+
+    def test_to_ms_config(self):
+
+        config_path = os.path.join(
+            os.path.dirname(os.path.dirname(easycv.__file__)),
+            'configs/detection/yolox/yolox_s_8xb16_300e_coco.py')
+
+        ms_cfg_file = os.path.join(self.tmp_dir,
+                                   'ms_yolox_s_8xb16_300e_coco.json')
+        to_ms_config(
+            config_path,
+            task='image-object-detection',
+            ms_model_name='yolox',
+            save_path=ms_cfg_file)
+        cfg = Config.fromfile(ms_cfg_file)
+        self.assertIn('task', cfg)
+        self.assertIn('framework', cfg)
+        self.assertEqual(cfg.model.type, 'yolox')
+        self.assertIn('dataset', cfg)
+        self.assertIn('batch_size_per_gpu', cfg.train.dataloader)
+        self.assertIn('batch_size_per_gpu', cfg.evaluation.dataloader)
+
+
+if __name__ == '__main__':
+    unittest.main()