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mmedit.visualization.vis_backend

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Classes

GenVisBackend

Generation visualization backend class. It can write image, config,

TensorboardGenVisBackend

Tensorboard visualization backend class.

PaviGenVisBackend

Visualization backend for Pavi.

WandbGenVisBackend

Wandb visualization backend for MMEditing.

class mmedit.visualization.vis_backend.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]

Bases: mmengine.visualization.BaseVisBackend

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.

property experiment: GenVisBackend[source]

Return the experiment object associated with this visualization backend.

_init_env()[source]

Setup env for VisBackend.

add_config(config: mmengine.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.

_dump(value_dict: dict, file_path: str, file_format: str) None[source]

dump dict to file.

Parameters
  • value_dict (dict) – The dict data to saved.

  • file_path (str) – The file path to save data.

  • file_format (str) – The file format to save data.

_upload(path: str, delete_local=False) None[source]

Upload file at path to remote.

Parameters

path (str) – Path of file to upload.

class mmedit.visualization.vis_backend.TensorboardGenVisBackend(save_dir: str)[source]

Bases: mmengine.visualization.TensorboardVisBackend

Tensorboard visualization backend class.

It can write images, config, scalars, etc. to a tensorboard file.

Examples

>>> from mmengine.visualization import TensorboardVisBackend
>>> import numpy as np
>>> vis_backend = TensorboardVisBackend(save_dir='temp_dir')
>>> img = np.random.randint(0, 256, size=(10, 10, 3))
>>> vis_backend.add_image('img', img)
>>> vis_backend.add_scaler('mAP', 0.6)
>>> vis_backend.add_scalars({'loss': 0.1,'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 backend.

add_image(name: str, image: numpy.array, step: int = 0, **kwargs)[source]

Record the image to Tensorboard. Additional support upload gif files.

Parameters
  • name (str) – The image identifier.

  • image (np.ndarray) – The image to be saved. The format should be RGB.

  • step (int) – Useless parameter. Wandb does not need this parameter. Default to 0.

class mmedit.visualization.vis_backend.PaviGenVisBackend(save_dir: str, exp_name: Optional[str] = None, labels: Optional[str] = None, project: Optional[str] = None, model: Optional[str] = None, description: Optional[str] = None)[source]

Bases: mmengine.visualization.BaseVisBackend

Visualization backend for Pavi.

property experiment: GenVisBackend[source]

Return the experiment object associated with this visualization backend.

_init_env()[source]

Init save dir.

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

Record the image to Pavi.

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 Pavi.

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 Pavi.

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.

class mmedit.visualization.vis_backend.WandbGenVisBackend(save_dir: str, init_kwargs: Optional[dict] = None, define_metric_cfg: Optional[dict] = None, commit: Optional[bool] = True, log_code_name: Optional[str] = None, watch_kwargs: Optional[dict] = None)[source]

Bases: mmengine.visualization.WandbVisBackend

Wandb visualization backend for MMEditing.

_init_env()[source]

Setup env for wandb.

add_image(name: str, image: numpy.array, step: int = 0, **kwargs)[source]

Record the image to wandb. Additional support upload gif files.

Parameters
  • name (str) – The image identifier.

  • image (np.ndarray) – The image to be saved. The format should be RGB.

  • step (int) – Useless parameter. Wandb does not need this parameter. Default to 0.

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