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GenVisBackend

class mmedit.visualization.GenVisBackend(save_dir: str, img_save_dir: str = 'vis_image', config_save_file: str = 'config.py', scalar_save_file: str = 'scalars.json', ceph_path: Optional[str] = None, delete_local_image: bool = True)[source]

Generation visualization backend class. It can write image, config, scalars, etc. to the local hard disk and ceph path. You can get the drawing backend through the experiment property for custom drawing.

Examples

>>> from mmgen.visualization import GenVisBackend
>>> import numpy as np
>>> vis_backend = GenVisBackend(save_dir='temp_dir',
>>>                             ceph_path='s3://temp-bucket')
>>> img = np.random.randint(0, 256, size=(10, 10, 3))
>>> vis_backend.add_image('img', img)
>>> vis_backend.add_scalar('mAP', 0.6)
>>> vis_backend.add_scalars({'loss': [1, 2, 3], 'acc': 0.8})
>>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
>>> vis_backend.add_config(cfg)
Parameters
  • save_dir (str) – The root directory to save the files produced by the visualizer.

  • img_save_dir (str) – The directory to save images. Default to ‘vis_image’.

  • config_save_file (str) – The file name to save config. Default to ‘config.py’.

  • scalar_save_file (str) – The file name to save scalar values. Default to ‘scalars.json’.

  • ceph_path (Optional[str]) – The remote path of Ceph cloud storage. Defaults to None.

  • delete_local (bool) – Whether eelete local after uploading to ceph or not. If ceph_path is None, this will be ignored. Defaults to True.

add_config(config: mmengine.config.config.Config, **kwargs) None[source]

Record the config to disk.

Parameters

config (Config) – The Config object

add_image(name: str, image: numpy.array, step: int = 0, **kwargs) None[source]

Record the image to disk.

Parameters
  • name (str) – The image identifier.

  • image (np.ndarray) – The image to be saved. The format should be RGB. Default to None.

  • step (int) – Global step value to record. Default to 0.

add_scalar(name: str, value: Union[int, float, torch.Tensor, numpy.ndarray], step: int = 0, **kwargs) None[source]

Record the scalar data to disk.

Parameters
  • name (str) – The scalar identifier.

  • value (int, float, torch.Tensor, np.ndarray) – Value to save.

  • step (int) – Global step value to record. Default to 0.

add_scalars(scalar_dict: dict, step: int = 0, file_path: Optional[str] = None, **kwargs) None[source]

Record the scalars to disk.

The scalar dict will be written to the default and specified files if file_path is specified.

Parameters
  • scalar_dict (dict) – Key-value pair storing the tag and corresponding values. The value must be dumped into json format.

  • step (int) – Global step value to record. Default to 0.

  • file_path (str, optional) – The scalar’s data will be saved to the file_path file at the same time if the file_path parameter is specified. Default to None.

property experiment: mmedit.visualization.vis_backend.GenVisBackend

Return the experiment object associated with this visualization backend.

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