mmclassification/mmpretrain/visualization/visualizer.py

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence, Tuple, Union

import mmcv
import numpy as np
import torch
import torch.nn.functional as F
from mmengine.dataset import BaseDataset
from mmengine.dist import master_only
from mmengine.visualization import Visualizer
from mmengine.visualization.utils import img_from_canvas

from mmpretrain.registry import VISUALIZERS
from mmpretrain.structures import DataSample
from .utils import create_figure, get_adaptive_scale


@VISUALIZERS.register_module()
class UniversalVisualizer(Visualizer):
    """Universal Visualizer for multiple tasks.

    Args:
        name (str): Name of the instance. Defaults to 'visualizer'.
        image (np.ndarray, optional): the origin image to draw. The format
            should be RGB. Defaults to None.
        vis_backends (list, optional): Visual backend config list.
            Defaults to None.
        save_dir (str, optional): Save file dir for all storage backends.
            If it is None, the backend storage will not save any data.
        fig_save_cfg (dict): Keyword parameters of figure for saving.
            Defaults to empty dict.
        fig_show_cfg (dict): Keyword parameters of figure for showing.
            Defaults to empty dict.
    """
    DEFAULT_TEXT_CFG = {
        'family': 'monospace',
        'color': 'white',
        'bbox': dict(facecolor='black', alpha=0.5, boxstyle='Round'),
        'verticalalignment': 'top',
        'horizontalalignment': 'left',
    }

    @master_only
    def visualize_cls(self,
                      image: np.ndarray,
                      data_sample: DataSample,
                      classes: Optional[Sequence[str]] = None,
                      draw_gt: bool = True,
                      draw_pred: bool = True,
                      draw_score: bool = True,
                      resize: Optional[int] = None,
                      rescale_factor: Optional[float] = None,
                      text_cfg: dict = dict(),
                      show: bool = False,
                      wait_time: float = 0,
                      out_file: Optional[str] = None,
                      name: str = '',
                      step: int = 0) -> None:
        """Visualize image classification result.

        This method will draw an text box on the input image to visualize the
        information about image classification, like the ground-truth label and
        prediction label.

        Args:
            image (np.ndarray): The image to draw. The format should be RGB.
            data_sample (:obj:`DataSample`): The annotation of the image.
            classes (Sequence[str], optional): The categories names.
                Defaults to None.
            draw_gt (bool): Whether to draw ground-truth labels.
                Defaults to True.
            draw_pred (bool): Whether to draw prediction labels.
                Defaults to True.
            draw_score (bool): Whether to draw the prediction scores
                of prediction categories. Defaults to True.
            resize (int, optional): Resize the short edge of the image to the
                specified length before visualization. Defaults to None.
            rescale_factor (float, optional): Rescale the image by the rescale
                factor before visualization. Defaults to None.
            text_cfg (dict): Extra text setting, which accepts
                arguments of :meth:`mmengine.Visualizer.draw_texts`.
                Defaults to an empty dict.
            show (bool): Whether to display the drawn image in a window, please
                confirm your are able to access the graphical interface.
                Defaults to False.
            wait_time (float): The display time (s). Defaults to 0, which means
                "forever".
            out_file (str, optional): Extra path to save the visualization
                result. If specified, the visualizer will only save the result
                image to the out_file and ignore its storage backends.
                Defaults to None.
            name (str): The image identifier. It's useful when using the
                storage backends of the visualizer to save or display the
                image. Defaults to an empty string.
            step (int): The global step value. It's useful to record a
                series of visualization results for the same image with the
                storage backends. Defaults to 0.

        Returns:
            np.ndarray: The visualization image.
        """
        if self.dataset_meta is not None:
            classes = classes or self.dataset_meta.get('classes', None)

        if resize is not None:
            h, w = image.shape[:2]
            if w < h:
                image = mmcv.imresize(image, (resize, resize * h // w))
            else:
                image = mmcv.imresize(image, (resize * w // h, resize))
        elif rescale_factor is not None:
            image = mmcv.imrescale(image, rescale_factor)

        texts = []
        self.set_image(image)

        if draw_gt and 'gt_label' in data_sample:
            idx = data_sample.gt_label.tolist()
            class_labels = [''] * len(idx)
            if classes is not None:
                class_labels = [f' ({classes[i]})' for i in idx]
            labels = [str(idx[i]) + class_labels[i] for i in range(len(idx))]
            prefix = 'Ground truth: '
            texts.append(prefix + ('\n' + ' ' * len(prefix)).join(labels))

        if draw_pred and 'pred_label' in data_sample:
            idx = data_sample.pred_label.tolist()
            score_labels = [''] * len(idx)
            class_labels = [''] * len(idx)
            if draw_score and 'pred_score' in data_sample:
                score_labels = [
                    f', {data_sample.pred_score[i].item():.2f}' for i in idx
                ]

            if classes is not None:
                class_labels = [f' ({classes[i]})' for i in idx]

            labels = [
                str(idx[i]) + score_labels[i] + class_labels[i]
                for i in range(len(idx))
            ]
            prefix = 'Prediction: '
            texts.append(prefix + ('\n' + ' ' * len(prefix)).join(labels))

        img_scale = get_adaptive_scale(image.shape[:2])
        text_cfg = {
            'size': int(img_scale * 7),
            **self.DEFAULT_TEXT_CFG,
            **text_cfg,
        }
        self.ax_save.text(
            img_scale * 5,
            img_scale * 5,
            '\n'.join(texts),
            **text_cfg,
        )
        drawn_img = self.get_image()

        if show:
            self.show(drawn_img, win_name=name, wait_time=wait_time)

        if out_file is not None:
            # save the image to the target file instead of vis_backends
            mmcv.imwrite(drawn_img[..., ::-1], out_file)
        else:
            self.add_image(name, drawn_img, step=step)

        return drawn_img

    @master_only
    def visualize_image_retrieval(self,
                                  image: np.ndarray,
                                  data_sample: DataSample,
                                  prototype_dataset: BaseDataset,
                                  topk: int = 1,
                                  draw_score: bool = True,
                                  resize: Optional[int] = None,
                                  text_cfg: dict = dict(),
                                  show: bool = False,
                                  wait_time: float = 0,
                                  out_file: Optional[str] = None,
                                  name: Optional[str] = '',
                                  step: int = 0) -> None:
        """Visualize image retrieval result.

        This method will draw the input image and the images retrieved from the
        prototype dataset.

        Args:
            image (np.ndarray): The image to draw. The format should be RGB.
            data_sample (:obj:`DataSample`): The annotation of the image.
            prototype_dataset (:obj:`BaseDataset`): The prototype dataset.
                It should have `get_data_info` method and return a dict
                includes `img_path`.
            draw_score (bool): Whether to draw the match scores of the
                retrieved images. Defaults to True.
            resize (int, optional): Resize the long edge of the image to the
                specified length before visualization. Defaults to None.
            text_cfg (dict): Extra text setting, which accepts arguments of
                :func:`plt.text`. Defaults to an empty dict.
            show (bool): Whether to display the drawn image in a window, please
                confirm your are able to access the graphical interface.
                Defaults to False.
            wait_time (float): The display time (s). Defaults to 0, which means
                "forever".
            out_file (str, optional): Extra path to save the visualization
                result. If specified, the visualizer will only save the result
                image to the out_file and ignore its storage backends.
                Defaults to None.
            name (str): The image identifier. It's useful when using the
                storage backends of the visualizer to save or display the
                image. Defaults to an empty string.
            step (int): The global step value. It's useful to record a
                series of visualization results for the same image with the
                storage backends. Defaults to 0.

        Returns:
            np.ndarray: The visualization image.
        """
        text_cfg = {**self.DEFAULT_TEXT_CFG, **text_cfg}
        if resize is not None:
            image = mmcv.imrescale(image, (resize, resize))

        match_scores, indices = torch.topk(data_sample.pred_score, k=topk)

        figure = create_figure(margin=True)
        gs = figure.add_gridspec(2, topk)
        query_plot = figure.add_subplot(gs[0, :])
        query_plot.axis(False)
        query_plot.imshow(image)

        for k, (score, sample_idx) in enumerate(zip(match_scores, indices)):
            sample = prototype_dataset.get_data_info(sample_idx.item())
            value_image = mmcv.imread(sample['img_path'])[..., ::-1]
            value_plot = figure.add_subplot(gs[1, k])
            value_plot.axis(False)
            value_plot.imshow(value_image)
            if draw_score:
                value_plot.text(
                    5,
                    5,
                    f'{score:.2f}',
                    **text_cfg,
                )
        drawn_img = img_from_canvas(figure.canvas)
        self.set_image(drawn_img)

        if show:
            self.show(drawn_img, win_name=name, wait_time=wait_time)

        if out_file is not None:
            # save the image to the target file instead of vis_backends
            mmcv.imwrite(drawn_img[..., ::-1], out_file)
        else:
            self.add_image(name, drawn_img, step=step)

        return drawn_img

    def add_mask_to_image(
        self,
        image: np.ndarray,
        data_sample: DataSample,
        resize: Union[int, Tuple[int]] = 224,
        color: Union[str, Tuple[int]] = 'black',
        alpha: Union[int, float] = 0.8,
    ) -> np.ndarray:
        if isinstance(resize, int):
            resize = (resize, resize)

        image = mmcv.imresize(image, resize)
        self.set_image(image)

        if isinstance(data_sample.mask, np.ndarray):
            data_sample.mask = torch.tensor(data_sample.mask)
        mask = data_sample.mask.float()[None, None, ...]
        mask_ = F.interpolate(mask, image.shape[:2], mode='nearest')[0, 0]

        self.draw_binary_masks(mask_.bool(), colors=color, alphas=alpha)

        drawn_img = self.get_image()
        return drawn_img

    @master_only
    def visualize_masked_image(self,
                               image: np.ndarray,
                               data_sample: DataSample,
                               resize: Union[int, Tuple[int]] = 224,
                               color: Union[str, Tuple[int]] = 'black',
                               alpha: Union[int, float] = 0.8,
                               show: bool = False,
                               wait_time: float = 0,
                               out_file: Optional[str] = None,
                               name: str = '',
                               step: int = 0) -> None:
        """Visualize masked image.

        This method will draw an image with binary mask.

        Args:
            image (np.ndarray): The image to draw. The format should be RGB.
            data_sample (:obj:`DataSample`): The annotation of the image.
            resize (int | Tuple[int]): Resize the input image to the specified
                shape. Defaults to 224.
            color (str | Tuple[int]): The color of the binary mask.
                Defaults to "black".
            alpha (int | float): The transparency of the mask. Defaults to 0.8.
            show (bool): Whether to display the drawn image in a window, please
                confirm your are able to access the graphical interface.
                Defaults to False.
            wait_time (float): The display time (s). Defaults to 0, which means
                "forever".
            out_file (str, optional): Extra path to save the visualization
                result. If specified, the visualizer will only save the result
                image to the out_file and ignore its storage backends.
                Defaults to None.
            name (str): The image identifier. It's useful when using the
                storage backends of the visualizer to save or display the
                image. Defaults to an empty string.
            step (int): The global step value. It's useful to record a
                series of visualization results for the same image with the
                storage backends. Defaults to 0.

        Returns:
            np.ndarray: The visualization image.
        """
        drawn_img = self.add_mask_to_image(
            image=image,
            data_sample=data_sample,
            resize=resize,
            color=color,
            alpha=alpha)

        if show:
            self.show(drawn_img, win_name=name, wait_time=wait_time)

        if out_file is not None:
            # save the image to the target file instead of vis_backends
            mmcv.imwrite(drawn_img[..., ::-1], out_file)
        else:
            self.add_image(name, drawn_img, step=step)

        return drawn_img

    @master_only
    def visualize_image_caption(self,
                                image: np.ndarray,
                                data_sample: DataSample,
                                resize: Optional[int] = None,
                                text_cfg: dict = dict(),
                                show: bool = False,
                                wait_time: float = 0,
                                out_file: Optional[str] = None,
                                name: Optional[str] = '',
                                step: int = 0) -> None:
        """Visualize image caption result.

        This method will draw the input image and the images caption.

        Args:
            image (np.ndarray): The image to draw. The format should be RGB.
            data_sample (:obj:`DataSample`): The annotation of the image.
            resize (int, optional): Resize the long edge of the image to the
                specified length before visualization. Defaults to None.
            text_cfg (dict): Extra text setting, which accepts arguments of
                :func:`plt.text`. Defaults to an empty dict.
            show (bool): Whether to display the drawn image in a window, please
                confirm your are able to access the graphical interface.
                Defaults to False.
            wait_time (float): The display time (s). Defaults to 0, which means
                "forever".
            out_file (str, optional): Extra path to save the visualization
                result. If specified, the visualizer will only save the result
                image to the out_file and ignore its storage backends.
                Defaults to None.
            name (str): The image identifier. It's useful when using the
                storage backends of the visualizer to save or display the
                image. Defaults to an empty string.
            step (int): The global step value. It's useful to record a
                series of visualization results for the same image with the
                storage backends. Defaults to 0.

        Returns:
            np.ndarray: The visualization image.
        """
        text_cfg = {**self.DEFAULT_TEXT_CFG, **text_cfg}

        if resize is not None:
            h, w = image.shape[:2]
            if w < h:
                image = mmcv.imresize(image, (resize, resize * h // w))
            else:
                image = mmcv.imresize(image, (resize * w // h, resize))

        self.set_image(image)

        img_scale = get_adaptive_scale(image.shape[:2])
        text_cfg = {
            'size': int(img_scale * 7),
            **self.DEFAULT_TEXT_CFG,
            **text_cfg,
        }
        self.ax_save.text(
            img_scale * 5,
            img_scale * 5,
            data_sample.get('pred_caption'),
            wrap=True,
            **text_cfg,
        )
        drawn_img = self.get_image()

        if show:
            self.show(drawn_img, win_name=name, wait_time=wait_time)

        if out_file is not None:
            # save the image to the target file instead of vis_backends
            mmcv.imwrite(drawn_img[..., ::-1], out_file)
        else:
            self.add_image(name, drawn_img, step=step)

        return drawn_img

    @master_only
    def visualize_vqa(self,
                      image: np.ndarray,
                      data_sample: DataSample,
                      resize: Optional[int] = None,
                      text_cfg: dict = dict(),
                      show: bool = False,
                      wait_time: float = 0,
                      out_file: Optional[str] = None,
                      name: Optional[str] = '',
                      step: int = 0) -> None:
        """Visualize visual question answering result.

        This method will draw the input image, question and answer.

        Args:
            image (np.ndarray): The image to draw. The format should be RGB.
            data_sample (:obj:`DataSample`): The annotation of the image.
            resize (int, optional): Resize the long edge of the image to the
                specified length before visualization. Defaults to None.
            text_cfg (dict): Extra text setting, which accepts arguments of
                :func:`plt.text`. Defaults to an empty dict.
            show (bool): Whether to display the drawn image in a window, please
                confirm your are able to access the graphical interface.
                Defaults to False.
            wait_time (float): The display time (s). Defaults to 0, which means
                "forever".
            out_file (str, optional): Extra path to save the visualization
                result. If specified, the visualizer will only save the result
                image to the out_file and ignore its storage backends.
                Defaults to None.
            name (str): The image identifier. It's useful when using the
                storage backends of the visualizer to save or display the
                image. Defaults to an empty string.
            step (int): The global step value. It's useful to record a
                series of visualization results for the same image with the
                storage backends. Defaults to 0.

        Returns:
            np.ndarray: The visualization image.
        """
        text_cfg = {**self.DEFAULT_TEXT_CFG, **text_cfg}

        if resize is not None:
            h, w = image.shape[:2]
            if w < h:
                image = mmcv.imresize(image, (resize, resize * h // w))
            else:
                image = mmcv.imresize(image, (resize * w // h, resize))

        self.set_image(image)

        img_scale = get_adaptive_scale(image.shape[:2])
        text_cfg = {
            'size': int(img_scale * 7),
            **self.DEFAULT_TEXT_CFG,
            **text_cfg,
        }
        text = (f'Q: {data_sample.get("question")}\n'
                f'A: {data_sample.get("pred_answer")}')
        self.ax_save.text(
            img_scale * 5,
            img_scale * 5,
            text,
            wrap=True,
            **text_cfg,
        )
        drawn_img = self.get_image()

        if show:
            self.show(drawn_img, win_name=name, wait_time=wait_time)

        if out_file is not None:
            # save the image to the target file instead of vis_backends
            mmcv.imwrite(drawn_img[..., ::-1], out_file)
        else:
            self.add_image(name, drawn_img, step=step)

        return drawn_img

    @master_only
    def visualize_visual_grounding(self,
                                   image: np.ndarray,
                                   data_sample: DataSample,
                                   resize: Optional[int] = None,
                                   text_cfg: dict = dict(),
                                   show: bool = False,
                                   wait_time: float = 0,
                                   out_file: Optional[str] = None,
                                   name: Optional[str] = '',
                                   line_width: Union[int, float] = 3,
                                   bbox_color: Union[str, tuple] = 'green',
                                   step: int = 0) -> None:
        """Visualize visual grounding result.

        This method will draw the input image, bbox and the object.

        Args:
            image (np.ndarray): The image to draw. The format should be RGB.
            data_sample (:obj:`DataSample`): The annotation of the image.
            resize (int, optional): Resize the long edge of the image to the
                specified length before visualization. Defaults to None.
            text_cfg (dict): Extra text setting, which accepts arguments of
                :func:`plt.text`. Defaults to an empty dict.
            show (bool): Whether to display the drawn image in a window, please
                confirm your are able to access the graphical interface.
                Defaults to False.
            wait_time (float): The display time (s). Defaults to 0, which means
                "forever".
            out_file (str, optional): Extra path to save the visualization
                result. If specified, the visualizer will only save the result
                image to the out_file and ignore its storage backends.
                Defaults to None.
            name (str): The image identifier. It's useful when using the
                storage backends of the visualizer to save or display the
                image. Defaults to an empty string.
            step (int): The global step value. It's useful to record a
                series of visualization results for the same image with the
                storage backends. Defaults to 0.

        Returns:
            np.ndarray: The visualization image.
        """
        text_cfg = {**self.DEFAULT_TEXT_CFG, **text_cfg}

        gt_bboxes = data_sample.get('gt_bboxes')
        pred_bboxes = data_sample.get('pred_bboxes')
        if resize is not None:
            h, w = image.shape[:2]
            if w < h:
                image, w_scale, h_scale = mmcv.imresize(
                    image, (resize, resize * h // w), return_scale=True)
            else:
                image, w_scale, h_scale = mmcv.imresize(
                    image, (resize * w // h, resize), return_scale=True)
            pred_bboxes[:, ::2] *= w_scale
            pred_bboxes[:, 1::2] *= h_scale
            if gt_bboxes is not None:
                gt_bboxes[:, ::2] *= w_scale
                gt_bboxes[:, 1::2] *= h_scale

        self.set_image(image)
        # Avoid the line-width limit in the base classes.
        self._default_font_size = 1e3
        self.draw_bboxes(
            pred_bboxes, line_widths=line_width, edge_colors=bbox_color)
        if gt_bboxes is not None:
            self.draw_bboxes(
                gt_bboxes, line_widths=line_width, edge_colors='blue')

        img_scale = get_adaptive_scale(image.shape[:2])
        text_cfg = {
            'size': int(img_scale * 7),
            **self.DEFAULT_TEXT_CFG,
            **text_cfg,
        }

        text_positions = pred_bboxes[:, :2] + line_width
        for i in range(pred_bboxes.size(0)):
            self.ax_save.text(
                text_positions[i, 0] + line_width,
                text_positions[i, 1] + line_width,
                data_sample.get('text'),
                **text_cfg,
            )
        drawn_img = self.get_image()

        if show:
            self.show(drawn_img, win_name=name, wait_time=wait_time)

        if out_file is not None:
            # save the image to the target file instead of vis_backends
            mmcv.imwrite(drawn_img[..., ::-1], out_file)
        else:
            self.add_image(name, drawn_img, step=step)

        return drawn_img

    @master_only
    def visualize_t2i_retrieval(self,
                                text: str,
                                data_sample: DataSample,
                                prototype_dataset: BaseDataset,
                                topk: int = 1,
                                draw_score: bool = True,
                                text_cfg: dict = dict(),
                                fig_cfg: dict = dict(),
                                show: bool = False,
                                wait_time: float = 0,
                                out_file: Optional[str] = None,
                                name: Optional[str] = '',
                                step: int = 0) -> None:
        """Visualize Text-To-Image retrieval result.

        This method will draw the input text and the images retrieved from the
        prototype dataset.

        Args:
            image (np.ndarray): The image to draw. The format should be RGB.
            data_sample (:obj:`DataSample`): The annotation of the image.
            prototype_dataset (:obj:`BaseDataset`): The prototype dataset.
                It should have `get_data_info` method and return a dict
                includes `img_path`.
            topk (int): To visualize the topk matching items. Defaults to 1.
            draw_score (bool): Whether to draw the match scores of the
                retrieved images. Defaults to True.
            text_cfg (dict): Extra text setting, which accepts arguments of
                :func:`plt.text`. Defaults to an empty dict.
            fig_cfg (dict): Extra figure setting, which accepts arguments of
                :func:`plt.Figure`. Defaults to an empty dict.
            show (bool): Whether to display the drawn image in a window, please
                confirm your are able to access the graphical interface.
                Defaults to False.
            wait_time (float): The display time (s). Defaults to 0, which means
                "forever".
            out_file (str, optional): Extra path to save the visualization
                result. If specified, the visualizer will only save the result
                image to the out_file and ignore its storage backends.
                Defaults to None.
            name (str): The image identifier. It's useful when using the
                storage backends of the visualizer to save or display the
                image. Defaults to an empty string.
            step (int): The global step value. It's useful to record a
                series of visualization results for the same image with the
                storage backends. Defaults to 0.

        Returns:
            np.ndarray: The visualization image.
        """
        text_cfg = {**self.DEFAULT_TEXT_CFG, **text_cfg}

        match_scores, indices = torch.topk(data_sample.pred_score, k=topk)

        figure = create_figure(margin=True, **fig_cfg)
        figure.suptitle(text)
        gs = figure.add_gridspec(1, topk)

        for k, (score, sample_idx) in enumerate(zip(match_scores, indices)):
            sample = prototype_dataset.get_data_info(sample_idx.item())
            value_image = mmcv.imread(sample['img_path'])[..., ::-1]
            value_plot = figure.add_subplot(gs[0, k])
            value_plot.axis(False)
            value_plot.imshow(value_image)
            if draw_score:
                value_plot.text(
                    5,
                    5,
                    f'{score:.2f}',
                    **text_cfg,
                )
        drawn_img = img_from_canvas(figure.canvas)
        self.set_image(drawn_img)

        if show:
            self.show(drawn_img, win_name=name, wait_time=wait_time)

        if out_file is not None:
            # save the image to the target file instead of vis_backends
            mmcv.imwrite(drawn_img[..., ::-1], out_file)
        else:
            self.add_image(name, drawn_img, step=step)

        return drawn_img

    @master_only
    def visualize_i2t_retrieval(self,
                                image: np.ndarray,
                                data_sample: DataSample,
                                prototype_dataset: Sequence[str],
                                topk: int = 1,
                                draw_score: bool = True,
                                resize: Optional[int] = None,
                                text_cfg: dict = dict(),
                                show: bool = False,
                                wait_time: float = 0,
                                out_file: Optional[str] = None,
                                name: str = '',
                                step: int = 0) -> None:
        """Visualize Image-To-Text retrieval result.

        This method will draw the input image and the texts retrieved from the
        prototype dataset.

        Args:
            image (np.ndarray): The image to draw. The format should be RGB.
            data_sample (:obj:`DataSample`): The annotation of the image.
            prototype_dataset (Sequence[str]): The prototype dataset.
                It should be a list of texts.
            topk (int): To visualize the topk matching items. Defaults to 1.
            draw_score (bool): Whether to draw the prediction scores
                of prediction categories. Defaults to True.
            resize (int, optional): Resize the short edge of the image to the
                specified length before visualization. Defaults to None.
            text_cfg (dict): Extra text setting, which accepts
                arguments of :meth:`mmengine.Visualizer.draw_texts`.
                Defaults to an empty dict.
            show (bool): Whether to display the drawn image in a window, please
                confirm your are able to access the graphical interface.
                Defaults to False.
            wait_time (float): The display time (s). Defaults to 0, which means
                "forever".
            out_file (str, optional): Extra path to save the visualization
                result. If specified, the visualizer will only save the result
                image to the out_file and ignore its storage backends.
                Defaults to None.
            name (str): The image identifier. It's useful when using the
                storage backends of the visualizer to save or display the
                image. Defaults to an empty string.
            step (int): The global step value. It's useful to record a
                series of visualization results for the same image with the
                storage backends. Defaults to 0.

        Returns:
            np.ndarray: The visualization image.
        """
        if resize is not None:
            h, w = image.shape[:2]
            if w < h:
                image = mmcv.imresize(image, (resize, resize * h // w))
            else:
                image = mmcv.imresize(image, (resize * w // h, resize))

        self.set_image(image)

        match_scores, indices = torch.topk(data_sample.pred_score, k=topk)
        texts = []
        for score, sample_idx in zip(match_scores, indices):
            text = prototype_dataset[sample_idx.item()]
            if draw_score:
                text = f'{score:.2f} ' + text
            texts.append(text)

        img_scale = get_adaptive_scale(image.shape[:2])
        text_cfg = {
            'size': int(img_scale * 7),
            **self.DEFAULT_TEXT_CFG,
            **text_cfg,
        }
        self.ax_save.text(
            img_scale * 5,
            img_scale * 5,
            '\n'.join(texts),
            **text_cfg,
        )
        drawn_img = self.get_image()

        if show:
            self.show(drawn_img, win_name=name, wait_time=wait_time)

        if out_file is not None:
            # save the image to the target file instead of vis_backends
            mmcv.imwrite(drawn_img[..., ::-1], out_file)
        else:
            self.add_image(name, drawn_img, step=step)

        return drawn_img