BaseVisualization

class flash.core.data.base_viz.BaseVisualization(enabled=False)

This Base Class is used to create visualization tool on top of InputTransform hooks.

Override any of the show_{_hook_name} to receive the associated data and visualize them.

Example:

from flash.image import ImageClassificationData
from flash.core.data.base_viz import BaseVisualization

class CustomBaseVisualization(BaseVisualization):

    def show_load_sample(self, samples: List[Any], running_stage):
        # plot samples

    def show_per_sample_transform(self, samples: List[Any], running_stage):
        # plot samples

    def show_collate(self, batch: List[Any], running_stage):
        # plot batch

    def show_per_batch_transform(self, batch: List[Any], running_stage):
        # plot batch

class CustomImageClassificationData(ImageClassificationData):

    @staticmethod
    def configure_data_fetcher(*args, **kwargs) -> BaseDataFetcher:
        return CustomBaseVisualization(*args, **kwargs)

dm = CustomImageClassificationData.from_folders(
    train_folder="./data/train",
    val_folder="./data/val",
    test_folder="./data/test",
    predict_folder="./data/predict")

# visualize a ``train`` batch
dm.show_train_batches()

# visualize next ``train`` batch
dm.show_train_batches()

# visualize a ``val`` batch
dm.show_val_batches()

# visualize a ``test`` batch
dm.show_test_batches()

# visualize a ``predict`` batch
dm.show_predict_batches()

Note

If the user wants to plot all different transformation stages at once, override the show function directly.

Example:

class CustomBaseVisualization(BaseVisualization):

    def show(self, batch: Dict[str, Any], running_stage: RunningStage):
        print(batch)
        # out
        {
            'load_sample': [...],
            'per_sample_transform': [...],
            'collate': [...],
            'per_batch_transform': [...],
        }

Note

As the InputTransform hooks are injected within the threaded workers of the DataLoader, the data won’t be accessible when using num_workers > 0.

show(batch, running_stage, func_names_list, limit_nb_samples=None, figsize=(6.4, 4.8))

Override this function when you want to visualize a composition.

Return type

None

show_collate(batch, running_stage, limit_nb_samples=None, figsize=(6.4, 4.8))

Override to visualize collate output data.

Return type

None

show_load_sample(samples, running_stage, limit_nb_samples=None, figsize=(6.4, 4.8))

Override to visualize load_sample output data.

show_per_batch_transform(batch, running_stage, limit_nb_samples=None, figsize=(6.4, 4.8))

Override to visualize per_batch_transform output data.

Return type

None

show_per_batch_transform_on_device(batch, running_stage, limit_nb_samples=None, figsize=(6.4, 4.8))

Override to visualize per_batch_transform_on_device output data.

Return type

None

show_per_sample_transform(samples, running_stage, limit_nb_samples=None, figsize=(6.4, 4.8))

Override to visualize per_sample_transform output data.

show_per_sample_transform_on_device(samples, running_stage, limit_nb_samples=None, figsize=(6.4, 4.8))

Override to visualize per_sample_transform_on_device output data.

Return type

None