mmagic.visualization.visualizer

Module Contents

Classes

Visualizer

MMagic Visualizer.

Attributes

mean_std_type

mmagic.visualization.visualizer.mean_std_type

class mmagic.visualization.visualizer.Visualizer(name='visualizer', vis_backends: Optional[List[Dict]] = None, save_dir: Optional[str] = None)

Bases: mmengine.visualization.Visualizer

MMagic Visualizer.

Parameters

• name (str) – Name of the instance. Defaults to ‘visualizer’.

• vis_backends (list, optional) – Visual backend config list. Defaults to None.

• save_dir (str, optional) – Save file dir for all storage backends. If it is None, the backend storage will not save any data.

Examples:

>>> # Draw image
>>> vis = Visualizer()
>>> vis.add_datasample(
>>>     'random_noise',
>>>     gen_samples=torch.rand(2, 3, 10, 10),
>>>     gt_samples=dict(imgs=torch.randn(2, 3, 10, 10)),
>>>     gt_keys='imgs',
>>>     vis_mode='image',
>>>     n_rows=2,
>>>     step=10)

static _post_process_image(image: torch.Tensor) → torch.Tensor

Post process images.

Parameters

image (Tensor) – Image to post process. The value range of image should be in [0, 255] and the channel order should be BGR.

Returns

Image in RGB color order.

Return type

Tensor

static _get_n_row_and_padding(samples: Tuple[dict, torch.Tensor], n_row: Optional[int] = None) → Tuple[int, Optional[torch.Tensor]]

Get number of sample in each row and tensor for padding the empty position.

Parameters

• samples (Tuple[dict, Tensor]) – Samples to visualize.

• n_row (int, optional) – Number of images displayed in each row of. If not passed, n_row will be set as int(sqrt(batch_size)).

Returns

Number of sample in each row and tensor

for padding the empty position.

Return type

Tuple[int, Optional[int]]

_vis_gif_sample(gen_samples: mmagic.utils.typing.SampleList, target_keys: Union[str, List[str], None], n_row: int) → numpy.ndarray

Visualize gif samples.

Parameters

• gen_samples (SampleList) – List of data samples to visualize

• target_keys (Union[str, List[str], None]) – Keys of the visualization target in data samples.

• n_rows (int, optional) – Number of images in one row.

Returns

The visualization results.

Return type

np.ndarray

_vis_image_sample(gen_samples: mmagic.utils.typing.SampleList, target_keys: Union[str, List[str], None], n_row: int) → numpy.ndarray

Visualize image samples.

Parameters

• gen_samples (SampleList) – List of data samples to visualize

• target_keys (Union[str, List[str], None]) – Keys of the visualization target in data samples.

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

• target_mean (Sequence[Union[float, int]]) – The target mean of the visualization results.

• target_std (Sequence[Union[float, int]]) – The target std of the visualization results.

• n_rows (int, optional) – Number of images in one row.

Returns

The visualization results.

Return type

np.ndarray

_get_vis_data_by_key(sample: mmagic.structures.DataSample, key: str) → torch.Tensor

Get tensor in DataSample by the given key.

Parameters

• sample (DataSample) – Input data sample.

• key (str) – Name of the target tensor.

Returns

Tensor from the data sample.

Return type

Tensor

add_datasample(name: str, *, gen_samples: Sequence[mmagic.structures.DataSample], target_keys: Optional[Tuple[str, List[str]]] = None, vis_mode: Optional[str] = None, n_row: Optional[int] = None, show: bool = False, wait_time: int = 0, step: int = 0, **kwargs) → None

Draw datasample and save to all backends.

If GT and prediction are plotted at the same time, they are displayed in a stitched image where the left image is the ground truth and the right image is the prediction.

If show is True, all storage backends are ignored, and the images will be displayed in a local window.

Parameters

• name (str) – The image identifier.

• gen_samples (List[DataSample]) – Data samples to visualize.

• vis_mode (str, optional) – Visualization mode. If not passed, will visualize results as image. Defaults to None.

• n_rows (int, optional) – Number of images in one row. Defaults to None.

• color_order (str) – The color order of the passed images. Defaults to ‘bgr’.

• target_mean (Sequence[Union[float, int]]) – The target mean of the visualization results. Defaults to 127.5.

• target_std (Sequence[Union[float, int]]) – The target std of the visualization results. Defaults to 127.5.

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

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

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

add_image(name: str, image: numpy.ndarray, step: int = 0, **kwargs) → None

Record the image. Support input kwargs.

Parameters

• name (str) – The image identifier.

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

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