[Bug]: TypeError: keywords must be strings

[shiyu22]

Is there an existing issue for this?

• [X] I have searched the existing issues

Current Behavior

When I run the following code with the latest (main) towhee version, it randomly shows "TypeError: keywords must be strings", and when I re-run it, it sometimes works, and sometimes it still errors.

import towhee

feat = towhee.glob('/Users/chenshiyu/workspace/data/pic/test/1.jpg') \
            .image_decode() \
            .image_embedding.timm(model_name='resnet50') \
            .tensor_normalize() \
            .to_list()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
File <timed exec>:1, in <module>

Input In [13], in Resnet501.resnet50_extract_feat(self, img_path)
     13 def resnet50_extract_feat(self, img_path):
---> 14     feat = towhee.glob(img_path) \
     15                  .image_decode() \
     16                  .image_embedding.timm(model_name='resnet50') \
     17                  .tensor_normalize() \
     18                  .to_list()
     19     return feat

File ~/workspace/git/towhee/towhee/hparam/hyperparameter.py:200, in DynamicDispatch.__call__(self, *args, **kws)
    198 def __call__(self, *args, **kws) -> Any:
    199     with param_scope(_index=self._index, _name=self._name):
--> 200         return self._func(*args, **kws)

File ~/workspace/git/towhee/towhee/functional/data_collection.py:135, in DataCollection.__getattr__.<locals>.wrapper(*arg, **kws)
    133     op = self.jit_resolve(path, index, *arg, **kws)
    134 else:
--> 135     with param_scope(  #
    136             locals={
    137                 k: v
    138                 for k, v in inspect.stack()[2]
    139                 [0].f_locals.items() if k != 'self'
    140             },
    141             globals={
    142                 k: v
    143                 for k, v in inspect.stack()[2]
    144                 [0].f_globals.items() if k != 'self'
    145             }):
    146         op = self.resolve(path, index, *arg, **kws)
    147 return self.map(op)

File ~/workspace/git/towhee/towhee/hparam/hyperparameter.py:473, in param_scope.__init__(self, *args, **kws)
    470 if hasattr(param_scope.tls,
    471            'history') and len(param_scope.tls.history) > 0:
    472     self.update(param_scope.tls.history[-1])
--> 473 self.update(kws)
    474 for line in args:
    475     if '=' in line:

File ~/workspace/git/towhee/towhee/hparam/hyperparameter.py:270, in HyperParameter.update(self, kws)
    268         v = vv
    269     else:
--> 270         v = HyperParameter(**v)
    271 self[k] = v

File ~/workspace/git/towhee/towhee/hparam/hyperparameter.py:260, in HyperParameter.__init__(self, **kws)
    258 def __init__(self, **kws):
    259     super().__init__()
--> 260     self.update(kws)

File ~/workspace/git/towhee/towhee/hparam/hyperparameter.py:270, in HyperParameter.update(self, kws)
    268         v = vv
    269     else:
--> 270         v = HyperParameter(**v)
    271 self[k] = v

TypeError: keywords must be strings

Expected Behavior

No response

Steps To Reproduce

No response

Environment

- Towhee version(e.g. v0.1.3 or 8b23a93):
- OS(Ubuntu or CentOS):
- CPU/Memory:
- GPU:
- Others:

Anything else?

No response

[shiyu22]

I can reproduce it, instead of random, it shows error when I define a function for the first time and then change it:

import towhee
img_path = 'my_img.png'

def resnet50_extract_feat(img_path):
        feat = towhee.glob(img_path) \
                     .image_decode() \
                     .image_embedding.timm(model_name='resnet50') \
                     .tensor_normalize() \
                     .to_list()
        return feat[0]

resnet50_extract_feat(img_path)


def resnet50_extract_feat(img_path):
        feat = towhee.glob(img_path) \
                     .image_decode() \
                     .image_embedding.timm(model_name='resnet50') \
                     .tensor_normalize() \
                     .to_list()
        return feat

resnet50_extract_feat(img_path)

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