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"""Implementation of the Video Panoptic Quality metric. |
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Video Panoptic Quality is an instance-based metric for evaluating the task |
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of video panoptic segmentation. |
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Please see the paper for details: |
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Dahun Kim, Sanghyun Woo, Joon-Young Lee, and In So Kweon. |
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"Video panoptic segmentation." In CVPR, 2020. |
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""" |
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from typing import List, Tuple |
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import numpy as np |
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import tensorflow as tf |
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from deeplab2.evaluation import panoptic_quality |
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class VideoPanopticQuality(panoptic_quality.PanopticQuality): |
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"""Metric class for Video Panoptic Quality. |
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|
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Dahun Kim, Sanghyun Woo, Joon-Young Lee, and In So Kweon. |
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"Video panoptic segmentation." In CVPR, 2020. |
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Video Panoptic Quality can be modeled as Image Panoptic Quality with the |
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sequences of predictions and the ground-truth labels horizontally concatenated |
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as two images, separately. Therefore, this class inherits the image panoptic |
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quality class and changes the implementation to concatenated comparisons. |
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Siyuan Qiao, Yukun Zhu, Hartwig Adam, Alan Yuille, and Liang-Chieh Chen. |
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"ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic |
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Segmentation." In CVPR, 2021. |
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Stand-alone usage: |
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vpq_obj = video_panoptic_quality.VideoPanopticQuality( |
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num_classes, max_instances_per_category, ignored_label) |
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vpq_obj.update_state(y_true_1, y_pred_1) |
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vpq_obj.update_state(y_true_2, y_pred_2) |
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... |
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result = vpq_obj.result().numpy() |
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""" |
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def __init__(self, |
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num_classes: int, |
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ignored_label: int, |
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max_instances_per_category: int, |
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offset: int, |
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name: str = 'video_panoptic_quality', |
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**kwargs): |
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"""Initialization of the VideoPanopticQuality metric. |
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Args: |
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num_classes: Number of classes in the dataset as an integer. |
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ignored_label: The class id to be ignored in evaluation as an integer or |
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integer tensor. |
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max_instances_per_category: The maximum number of instances for each class |
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as an integer or integer tensor. |
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offset: The maximum number of unique labels as an integer or integer |
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tensor. |
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name: An optional variable_scope name. (default: 'video_panoptic_quality') |
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**kwargs: The keyword arguments that are passed on to `fn`. |
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""" |
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super().__init__(num_classes, ignored_label, max_instances_per_category, |
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offset, name, **kwargs) |
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def compare_and_accumulate( |
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self, gt_panoptic_labels: List[tf.Tensor], |
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pred_panoptic_labels: List[tf.Tensor] |
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) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: |
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"""Compares predicted segmentation with groundtruth, accumulates its metric. |
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Args: |
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gt_panoptic_labels: A list of tensors for the ground-truth |
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video panoptic segmentation labels. |
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pred_panoptic_labels: A list of tensors for video panoptic |
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segmentation predictions. |
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Returns: |
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The value of the metrics (iou, tp, fn, fp) over all comparisons, as a |
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float scalar. |
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""" |
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gt_panoptic_label = tf.concat(gt_panoptic_labels, axis=1) |
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pred_panoptic_label = tf.concat(pred_panoptic_labels, axis=1) |
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return super(VideoPanopticQuality, self).compare_and_accumulate( |
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gt_panoptic_label, pred_panoptic_label) |
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