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| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import os.path as osp | |
| from typing import Optional, Sequence, Tuple, Union | |
| import cv2 | |
| import numpy as np | |
| from mmengine.hooks import Hook | |
| from mmengine.registry import HOOKS | |
| from mmengine.utils.dl_utils import tensor2imgs | |
| DATA_BATCH = Optional[Union[dict, tuple, list]] | |
| # TODO: Due to interface changes, the current class | |
| # functions incorrectly | |
| class NaiveVisualizationHook(Hook): | |
| """Show or Write the predicted results during the process of testing. | |
| Args: | |
| interval (int): Visualization interval. Defaults to 1. | |
| draw_gt (bool): Whether to draw the ground truth. Defaults to True. | |
| draw_pred (bool): Whether to draw the predicted result. | |
| Defaults to True. | |
| """ | |
| priority = 'NORMAL' | |
| def __init__(self, | |
| interval: int = 1, | |
| draw_gt: bool = True, | |
| draw_pred: bool = True): | |
| self.draw_gt = draw_gt | |
| self.draw_pred = draw_pred | |
| self._interval = interval | |
| def _unpad(self, input: np.ndarray, unpad_shape: Tuple[int, | |
| int]) -> np.ndarray: | |
| """Unpad the input image. | |
| Args: | |
| input (np.ndarray): The image to unpad. | |
| unpad_shape (tuple): The shape of image before padding. | |
| Returns: | |
| np.ndarray: The image before padding. | |
| """ | |
| unpad_width, unpad_height = unpad_shape | |
| unpad_image = input[:unpad_height, :unpad_width] | |
| return unpad_image | |
| def before_train(self, runner) -> None: | |
| """Call add_graph method of visualizer. | |
| Args: | |
| runner (Runner): The runner of the training process. | |
| """ | |
| runner.visualizer.add_graph(runner.model, None) | |
| def after_test_iter(self, | |
| runner, | |
| batch_idx: int, | |
| data_batch: DATA_BATCH = None, | |
| outputs: Optional[Sequence] = None) -> None: | |
| """Show or Write the predicted results. | |
| Args: | |
| runner (Runner): The runner of the training process. | |
| batch_idx (int): The index of the current batch in the test loop. | |
| data_batch (dict or tuple or list, optional): Data from dataloader. | |
| outputs (Sequence, optional): Outputs from model. | |
| """ | |
| if self.every_n_inner_iters(batch_idx, self._interval): | |
| for data, output in zip(data_batch, outputs): # type: ignore | |
| input = data['inputs'] | |
| data_sample = data['data_sample'] | |
| input = tensor2imgs(input, | |
| **data_sample.get('img_norm_cfg', | |
| dict()))[0] | |
| # TODO We will implement a function to revert the augmentation | |
| # in the future. | |
| ori_shape = (data_sample.ori_width, data_sample.ori_height) | |
| if 'pad_shape' in data_sample: | |
| input = self._unpad(input, | |
| data_sample.get('scale', ori_shape)) | |
| origin_image = cv2.resize(input, ori_shape) | |
| name = osp.basename(data_sample.img_path) | |
| runner.visualizer.add_datasample(name, origin_image, | |
| data_sample, output, | |
| self.draw_gt, self.draw_pred) | |