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Source code for flash.core.data.base_viz

# Copyright The PyTorch Lightning team.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Dict, List, Set, Tuple

from pytorch_lightning.utilities.exceptions import MisconfigurationException

from flash.core.data.callback import BaseDataFetcher
from flash.core.data.utils import _CALLBACK_FUNCS
from flash.core.utilities.apply_func import _is_overridden
from flash.core.utilities.stages import RunningStage


[docs]class BaseVisualization(BaseDataFetcher): """This Base Class is used to create visualization tool on top of :class:`~flash.core.data.io.input_transform.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 :class:`~flash.core.data.io.input_transform.InputTransform` hooks are injected within the threaded workers of the DataLoader, the data won't be accessible when using ``num_workers > 0``. """ def _show( self, running_stage: RunningStage, func_names_list: List[str], limit_nb_samples: int = None, figsize: Tuple[int, int] = (6.4, 4.8), ) -> None: self.show(self.batches[running_stage], running_stage, func_names_list, limit_nb_samples, figsize)
[docs] def show( self, batch: Dict[str, Any], running_stage: RunningStage, func_names_list: List[str], limit_nb_samples: int = None, figsize: Tuple[int, int] = (6.4, 4.8), ) -> None: """Override this function when you want to visualize a composition.""" # filter out the functions to visualise func_names_set: Set[str] = set(func_names_list) & set(_CALLBACK_FUNCS) if len(func_names_set) == 0: raise MisconfigurationException(f"Invalid function names: {func_names_list}.") for func_name in func_names_set: hook_name = f"show_{func_name}" if _is_overridden(hook_name, self, BaseVisualization): getattr(self, hook_name)(batch[func_name], running_stage, limit_nb_samples, figsize)
[docs] def show_load_sample( self, samples: List[Any], running_stage: RunningStage, limit_nb_samples: int = None, figsize: Tuple[int, int] = (6.4, 4.8), ): """Override to visualize ``load_sample`` output data."""
[docs] def show_per_sample_transform( self, samples: List[Any], running_stage: RunningStage, limit_nb_samples: int = None, figsize: Tuple[int, int] = (6.4, 4.8), ): """Override to visualize ``per_sample_transform`` output data."""
[docs] def show_collate( self, batch: List[Any], running_stage: RunningStage, limit_nb_samples: int = None, figsize: Tuple[int, int] = (6.4, 4.8), ) -> None: """Override to visualize ``collate`` output data."""
[docs] def show_per_batch_transform( self, batch: List[Any], running_stage: RunningStage, limit_nb_samples: int = None, figsize: Tuple[int, int] = (6.4, 4.8), ) -> None: """Override to visualize ``per_batch_transform`` output data."""
[docs] def show_per_sample_transform_on_device( self, samples: List[Any], running_stage: RunningStage, limit_nb_samples: int = None, figsize: Tuple[int, int] = (6.4, 4.8), ) -> None: """Override to visualize ``per_sample_transform_on_device`` output data."""
[docs] def show_per_batch_transform_on_device( self, batch: List[Any], running_stage: RunningStage, limit_nb_samples: int = None, figsize: Tuple[int, int] = (6.4, 4.8), ) -> None: """Override to visualize ``per_batch_transform_on_device`` output data."""

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