mmagic.engine.hooks.visualization_hook

Module Contents

Classes

BasicVisualizationHook	Basic hook that invoke visualizers during validation and test.
VisualizationHook	MMagic Visualization Hook. Used to visual output samples in training,

class mmagic.engine.hooks.visualization_hook.BasicVisualizationHook(interval: dict = {}, on_train=False, on_val=True, on_test=True)

Bases: mmengine.hooks.Hook

Basic hook that invoke visualizers during validation and test.

Parameters

• interval (int | dict) – Visualization interval. Default: {}.

• on_train (bool) – Whether to call hook during train. Default to False.

• on_val (bool) – Whether to call hook during validation. Default to True.

• on_test (bool) – Whether to call hook during test. Default to True.

priority = NORMAL

_after_iter(runner, batch_idx: int, data_batch: Optional[Sequence[dict]], outputs: Optional[Sequence[mmengine.structures.BaseDataElement]], mode=None) → None

Show or Write the predicted results.

Parameters

• runner (Runner) – The runner of the training process.

• batch_idx (int) – The index of the current batch in the test loop.

• data_batch (Sequence[dict], optional) – Data from dataloader. Defaults to None.

• outputs (Sequence[BaseDataElement], optional) – Outputs from model. Defaults to None.

class mmagic.engine.hooks.visualization_hook.VisualizationHook(interval: int = 1000, vis_kwargs_list: Tuple[List[dict], dict] = None, fixed_input: bool = True, n_samples: Optional[int] = 64, n_row: Optional[int] = None, message_hub_vis_kwargs: Optional[Tuple[str, dict, List[str], List[Dict]]] = None, save_at_test: bool = True, max_save_at_test: int = 100, test_vis_keys: Optional[Union[str, List[str]]] = None, show: bool = False, wait_time: float = 0)

Bases: mmengine.hooks.Hook

MMagic Visualization Hook. Used to visual output samples in training, validation and testing. In this hook, we use a list called sample_kwargs_list to control how to generate samples and how to visualize them. Each element in sample_kwargs_list, called sample_kwargs, may contains the following keywords:

• Required key words:

  • ‘type’: Value must be string. Denotes what kind of sampler is used to

    generate image. Refers to get_sampler().

• Optional key words (If not passed, will use the default value):

  • ‘n_row’: Value must be int. The number of images in one row.

  • ‘num_samples’: Value must be int. The number of samples to visualize.

  • ‘vis_mode’: Value must be string. How to visualize the generated

    samples (e.g. image, gif).

  • ‘fixed_input’: Value must be bool. Whether use the fixed input

    during the loop.

  • ‘draw_gt’: Value must be bool. Whether save the real images.

  • ‘target_keys’: Value must be string or list of string. The keys of

    the target image to visualize.

  • ‘name’: Value must be string. If not passed, will use

    sample_kwargs[‘type’] as default.

For convenience, we also define a group of alias of samplers’ type for models supported in MMagic. Refers to :attr:self.SAMPLER_TYPE_MAPPING.

Example

>>> # for GAN models
>>> custom_hooks = [
>>>     dict(
>>>         type='VisualizationHook',
>>>         interval=1000,
>>>         fixed_input=True,
>>>         vis_kwargs_list=dict(type='GAN', name='fake_img'))]
>>> # for Translation models
>>> custom_hooks = [
>>>     dict(
>>>         type='VisualizationHook',
>>>         interval=10,
>>>         fixed_input=False,
>>>         vis_kwargs_list=[dict(type='Translation',
>>>                                  name='translation_train',
>>>                                  n_samples=6, draw_gt=True,
>>>                                  n_row=3),
>>>                             dict(type='TranslationVal',
>>>                                  name='translation_val',
>>>                                  n_samples=16, draw_gt=True,
>>>                                  n_row=4)])]

# NOTE: user-defined vis_kwargs > vis_kwargs_mapping > hook init args

Parameters

• interval (int) – Visualization interval. Default: 1000.

• sampler_kwargs_list (Tuple[List[dict], dict]) – The list of sampling behavior to generate images.

• fixed_input (bool) – The default action of whether use fixed input to generate samples during the loop. Defaults to True.

• n_samples (Optional[int]) – The default value of number of samples to visualize. Defaults to 64.

• n_row (Optional[int]) – The default value of number of images in each row in the visualization results. Defaults to None.

• (Optional[Tuple[str (message_hub_vis_kwargs) – List[Dict]]]): Key arguments visualize images in message hub. Defaults to None.

• dict – List[Dict]]]): Key arguments visualize images in message hub. Defaults to None.

• List[str] – List[Dict]]]): Key arguments visualize images in message hub. Defaults to None.

:paramList[Dict]]]): Key arguments visualize images in message hub.

Defaults to None.

Parameters

• save_at_test (bool) – Whether save images during test. Defaults to True.

• max_save_at_test (int) – Maximum number of samples saved at test time. If None is passed, all samples will be saved. Defaults to 100.

• show (bool) – Whether to display the drawn image. Default to False.

• wait_time (float) – The interval of show (s). Defaults to 0.

priority = NORMAL

VIS_KWARGS_MAPPING

after_val_iter(runner: mmengine.runner.Runner, batch_idx: int, data_batch: dict, outputs) → None

VisualizationHook do not support visualize during validation.

Parameters

• runner (Runner) – The runner of the training process.

• batch_idx (int) – The index of the current batch in the test loop.

• data_batch (Sequence[dict], optional) – Data from dataloader. Defaults to None.

• outputs – outputs of the generation model

after_test_iter(runner: mmengine.runner.Runner, batch_idx: int, data_batch: dict, outputs)

Visualize samples after test iteration.

Parameters

• runner (Runner) – The runner of the training process.

• batch_idx (int) – The index of the current batch in the test loop.

• data_batch (dict, optional) – Data from dataloader. Defaults to None.

• outputs – outputs of the generation model Defaults to None.

after_train_iter(runner: mmengine.runner.Runner, batch_idx: int, data_batch: dict = None, outputs: Optional[dict] = None) → None

Visualize samples after train iteration.

Parameters

• runner (Runner) – The runner of the training process.

• batch_idx (int) – The index of the current batch in the train loop.

• data_batch (dict) – Data from dataloader. Defaults to None.

• outputs (dict, optional) – Outputs from model. Defaults to None.

vis_sample(runner: mmengine.runner.Runner, batch_idx: int, data_batch: dict, outputs: Optional[dict] = None) → None

Visualize samples.

Parameters

• runner (Runner) – The runner contains model to visualize.

• batch_idx (int) – The index of the current batch in loop.

• data_batch (dict) – Data from dataloader. Defaults to None.

• outputs (dict, optional) – Outputs from model. Defaults to None.

vis_from_message_hub(batch_idx: int)

Visualize samples from message hub.

Parameters

• batch_idx (int) – The index of the current batch in the test loop.

• color_order (str) – The color order of generated images.

• target_mean (Sequence[Union[float, int]]) – The original mean of the image tensor before preprocessing. Image will be re-shifted to target_mean before visualizing.

• target_std (Sequence[Union[float, int]]) – The original std of the image tensor before preprocessing. Image will be re-scaled to target_std before visualizing.