monailabel.deepedit.handlers module

class monailabel.deepedit.handlers.TensorBoard2DImageHandler(summary_writer=None, log_dir='./runs', tag_prefix='', interval=1, batch_transform=<function TensorBoard2DImageHandler.<lambda>>, output_transform=<function TensorBoard2DImageHandler.<lambda>>, batch_limit=1, device=None)[source]

Bases: object

attach(engine)[source]
Return type

None

write_images(batch_data, output_data, epoch)[source]
class monailabel.deepedit.handlers.TensorBoardImageHandler(summary_writer=None, log_dir='./runs', interval=1, batch_transform=<function TensorBoardImageHandler.<lambda>>, output_transform=<function TensorBoardImageHandler.<lambda>>, global_iter_transform=<function TensorBoardImageHandler.<lambda>>, index=0, max_channels=1, max_frames=64)[source]

Bases: object

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).

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).

Parameters

  • summary_writer (Optional[SummaryWriter, None]) – user can specify TensorBoard SummaryWriter, default to create a new writer.

  • log_dir (str) – if using default SummaryWriter, write logs to this directory, default is ./runs.

  • interval (int) – plot content from engine.state every N epochs or every N iterations, default is 1.

  • batch_transform (Callable) – 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].

  • output_transform (Callable) – 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].

  • global_iter_transform (Callable) – a callable that is used to customize global step number for TensorBoard. For example, in evaluation, the evaluator engine needs to know current epoch from trainer.

  • index (int) – plot which element in a data batch, default is the first element.

  • max_channels (int) – number of channels to plot.

  • max_frames (int) – number of frames for 2D-t plot.

__init__(summary_writer=None, log_dir='./runs', interval=1, batch_transform=<function TensorBoardImageHandler.<lambda>>, output_transform=<function TensorBoardImageHandler.<lambda>>, global_iter_transform=<function TensorBoardImageHandler.<lambda>>, index=0, max_channels=1, max_frames=64)[source]
Parameters

  • summary_writer (Optional[SummaryWriter, None]) – user can specify TensorBoard SummaryWriter, default to create a new writer.

  • log_dir (str) – if using default SummaryWriter, write logs to this directory, default is ./runs.

  • interval (int) – plot content from engine.state every N epochs or every N iterations, default is 1.

  • batch_transform (Callable) – 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].

  • output_transform (Callable) – 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].

  • global_iter_transform (Callable) – a callable that is used to customize global step number for TensorBoard. For example, in evaluation, the evaluator engine needs to know current epoch from trainer.

  • index (int) – plot which element in a data batch, default is the first element.

  • max_channels (int) – number of channels to plot.

  • max_frames (int) – number of frames for 2D-t plot.

attach(engine)[source]
Parameters

engine (Engine) – Ignite Engine, it can be a trainer, validator or evaluator.

Return type

None

close()[source]

Close the summary writer if created in this TensorBoard handler.

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