data.py

# Copyright The PyTorch Lightning team.
#
# 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
#
# 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, Callable, Dict, List, Optional, Tuple, Union

import numpy as np
import torch
from pytorch_lightning.trainer.states import RunningStage
from pytorch_lightning.utilities.exceptions import MisconfigurationException

from flash.core.data.base_viz import BaseVisualization  # for viz
from flash.core.data.callback import BaseDataFetcher
from flash.core.data.data_module import DataModule
from flash.core.data.data_source import DefaultDataKeys, DefaultDataSources
from flash.core.data.process import Preprocess
from flash.core.utilities.imports import _IMAGE_AVAILABLE, _MATPLOTLIB_AVAILABLE
from flash.image.classification.transforms import default_transforms, train_default_transforms
from flash.image.data import ImageNumpyDataSource, ImagePathsDataSource, ImageTensorDataSource

if _MATPLOTLIB_AVAILABLE:
    import matplotlib.pyplot as plt
else:
    plt = None

if _IMAGE_AVAILABLE:
    from PIL import Image
else:

    class Image:
        Image = None


class ImageClassificationPreprocess(Preprocess):

    def __init__(
        self,
        train_transform: Optional[Dict[str, Callable]] = None,
        val_transform: Optional[Dict[str, Callable]] = None,
        test_transform: Optional[Dict[str, Callable]] = None,
        predict_transform: Optional[Dict[str, Callable]] = None,
        image_size: Tuple[int, int] = (196, 196),
    ):
        self.image_size = image_size

        super().__init__(
            train_transform=train_transform,
            val_transform=val_transform,
            test_transform=test_transform,
            predict_transform=predict_transform,
            data_sources={
                DefaultDataSources.FILES: ImagePathsDataSource(),
                DefaultDataSources.FOLDERS: ImagePathsDataSource(),
                DefaultDataSources.NUMPY: ImageNumpyDataSource(),
                DefaultDataSources.TENSORS: ImageTensorDataSource(),
                DefaultDataSources.TENSORS: ImageTensorDataSource(),
            },
            default_data_source=DefaultDataSources.FILES,
        )

    def get_state_dict(self) -> Dict[str, Any]:
        return {**self.transforms, "image_size": self.image_size}

    @classmethod
    def load_state_dict(cls, state_dict: Dict[str, Any], strict: bool = False):
        return cls(**state_dict)

    def default_transforms(self) -> Optional[Dict[str, Callable]]:
        return default_transforms(self.image_size)

    def train_default_transforms(self) -> Optional[Dict[str, Callable]]:
        return train_default_transforms(self.image_size)


class ImageClassificationData(DataModule):
    """Data module for image classification tasks."""

    preprocess_cls = ImageClassificationPreprocess

    def set_block_viz_window(self, value: bool) -> None:
        """Setter method to switch on/off matplotlib to pop up windows."""
        self.data_fetcher.block_viz_window = value

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


class MatplotlibVisualization(BaseVisualization):
    """Process and show the image batch and its associated label using matplotlib.
    """
    max_cols: int = 4  # maximum number of columns we accept
    block_viz_window: bool = True  # parameter to allow user to block visualisation windows

    @staticmethod
    def _to_numpy(img: Union[torch.Tensor, Image.Image]) -> np.ndarray:
        out: np.ndarray
        if isinstance(img, Image.Image):
            out = np.array(img)
        elif isinstance(img, torch.Tensor):
            out = img.squeeze(0).permute(1, 2, 0).cpu().numpy()
        else:
            raise TypeError(f"Unknown image type. Got: {type(img)}.")
        return out

    def _show_images_and_labels(self, data: List[Any], num_samples: int, title: str):
        # define the image grid
        cols: int = min(num_samples, self.max_cols)
        rows: int = num_samples // cols

        if not _MATPLOTLIB_AVAILABLE:
            raise MisconfigurationException("You need matplotlib to visualise. Please, pip install matplotlib")

        # create figure and set title
        fig, axs = plt.subplots(rows, cols)
        fig.suptitle(title)

        for i, ax in enumerate(axs.ravel()):
            # unpack images and labels
            if isinstance(data, list):
                _img, _label = data[i][DefaultDataKeys.INPUT], data[i][DefaultDataKeys.TARGET]
            elif isinstance(data, dict):
                _img, _label = data[DefaultDataKeys.INPUT][i], data[DefaultDataKeys.TARGET][i]
            else:
                raise TypeError(f"Unknown data type. Got: {type(data)}.")
            # convert images to numpy
            _img: np.ndarray = self._to_numpy(_img)
            if isinstance(_label, torch.Tensor):
                _label = _label.squeeze().tolist()
            # show image and set label as subplot title
            ax.imshow(_img)
            ax.set_title(str(_label))
            ax.axis('off')
        plt.show(block=self.block_viz_window)

    def show_load_sample(self, samples: List[Any], running_stage: RunningStage):
        win_title: str = f"{running_stage} - show_load_sample"
        self._show_images_and_labels(samples, len(samples), win_title)

    def show_pre_tensor_transform(self, samples: List[Any], running_stage: RunningStage):
        win_title: str = f"{running_stage} - show_pre_tensor_transform"
        self._show_images_and_labels(samples, len(samples), win_title)

    def show_to_tensor_transform(self, samples: List[Any], running_stage: RunningStage):
        win_title: str = f"{running_stage} - show_to_tensor_transform"
        self._show_images_and_labels(samples, len(samples), win_title)

    def show_post_tensor_transform(self, samples: List[Any], running_stage: RunningStage):
        win_title: str = f"{running_stage} - show_post_tensor_transform"
        self._show_images_and_labels(samples, len(samples), win_title)

    def show_per_batch_transform(self, batch: List[Any], running_stage):
        win_title: str = f"{running_stage} - show_per_batch_transform"
        self._show_images_and_labels(batch[0], batch[0][DefaultDataKeys.INPUT].shape[0], win_title)