AimVisBackend¶

class mmengine.visualization.AimVisBackend(save_dir=None, init_kwargs=None)[source]¶

Aim visualization backend class.

Examples

>>> from mmengine.visualization import AimVisBackend
>>> import numpy as np
>>> aim_vis_backend = AimVisBackend()
>>> img=np.random.randint(0, 256, size=(10, 10, 3))
>>> aim_vis_backend.add_image('img', img)
>>> aim_vis_backend.add_scalar('mAP', 0.6)
>>> aim_vis_backend.add_scalars({'loss': 0.1, 'acc': 0.8})
>>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
>>> aim_vis_backend.add_config(cfg)

Note

New in version 0.9.0.
Refer to Github issue , Aim is not unable to be install on Windows for now.

Parameters:

save_dir (str, optional) – The root directory to save the files produced by the visualizer.
init_kwargs (dict, optional) – Aim initialization parameters. See Aim for details. Defaults to None.

add_config(config, **kwargs)[source]¶

Record the config to Aim.

Parameters:: config (Config) – The Config object
Return type:: None

add_image(name, image, step=0, **kwargs)[source]¶

Record the image.

Parameters:

name (str) – The image identifier.
image (np.ndarray) – The image to be saved. The format should be RGB. Defaults to None.
step (int) – Global step value to record. Defaults to 0.

Return type:

None

add_scalar(name, value, step=0, **kwargs)[source]¶

Record the scalar data to Aim.

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.

Return type:

None

add_scalars(scalar_dict, step=0, file_path=None, **kwargs)[source]¶

Record the scalar’s data to wandb.

Parameters:

scalar_dict (dict) – Key-value pair storing the tag and corresponding values.
step (int) – Global step value to record. Default to 0.
file_path (str, optional) – Useless parameter. Just for interface unification. Defaults to None.

Return type:

None

close()[source]¶

Close the Aim.

Return type:: None

property experiment¶: Return Aim object.