Class TensorBoardImageHandler

monai/handlers/tensorboard_handlers.py:288–453

TensorBoardImageHandler is an Ignite Event handler that can visualize images, labels and outputs as 2D/3D images. 2D output (shape in Batch, channel, H, W) will be shown as simple image using the first element in the batch, for 3D to ND output (shape in Batch, channel, H, W, D) input, e

class TensorBoardImageHandler(TensorBoardHandler):
    """
    TensorBoardImageHandler is an Ignite Event handler that can visualize images, labels and outputs as 2D/3D images.
    2D output (shape in Batch, channel, H, W) will be shown as simple image using the first element in the batch,
    for 3D to ND output (shape in Batch, channel, H, W, D) input, each of ``self.max_channels`` number of images'
    last three dimensions will be shown as animated GIF along the last axis (typically Depth).
    And if writer is from TensorBoardX, data has 3 channels and `max_channels=3`, will plot as RGB video.

    It can be used for any Ignite Engine (trainer, validator and evaluator).
    User can easily add it to engine for any expected Event, for example: ``EPOCH_COMPLETED``,
    ``ITERATION_COMPLETED``. The expected data source is ignite's ``engine.state.batch`` and ``engine.state.output``.

    Default behavior:
        - Show y_pred as images (GIF for 3D) on TensorBoard when Event triggered,
        - Need to use ``batch_transform`` and ``output_transform`` to specify
          how many images to show and show which channel.
        - Expects ``batch_transform(engine.state.batch)`` to return data
          format: (image[N, channel, ...], label[N, channel, ...]).
        - Expects ``output_transform(engine.state.output)`` to return a torch
          tensor in format (y_pred[N, channel, ...], loss).

    Usage example is available in the tutorial:
    https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/unet_segmentation_3d_ignite.ipynb.

    """

    def __init__(
        self,
        summary_writer: SummaryWriter | SummaryWriterX | None = None,
        log_dir: str = "./runs",
        interval: int = 1,
        epoch_level: bool = True,
        batch_transform: Callable = lambda x: x,
        output_transform: Callable = lambda x: x,
        global_iter_transform: Callable = lambda x: x,
        index: int = 0,
        max_channels: int = 1,
        frame_dim: int = -3,
        max_frames: int = 64,
    ) -> None:
        """
        Args:
            summary_writer: user can specify TensorBoard or TensorBoardX SummaryWriter,
                default to create a new TensorBoard writer.
            log_dir: if using default SummaryWriter, write logs to this directory, default is `./runs`.
            interval: plot content from engine.state every N epochs or every N iterations, default is 1.
            epoch_level: plot content from engine.state every N epochs or N iterations. `True` is epoch level,
                `False` is iteration level.
            batch_transform: a callable that is used to extract `image` and `label` from `ignite.engine.state.batch`,
                then construct `(image, label)` pair. for example: if `ignite.engine.state.batch` is `{"image": xxx,
                "label": xxx, "other": xxx}`, `batch_transform` can be `lambda x: (x["image"], x["label"])`.
                will use the result to plot image from `result[0][index]` and plot label from `result[1][index]`.
                `engine.state` and `batch_transform` inherit from the ignite concept:
                https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
                https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
            output_transform: a callable that is used to extract the `predictions` data from
                `ignite.engine.state.output`, will use the result to plot output from `result[index]`.
                `engine.state` and `output_transform` inherit from the ignite concept: