Postprocessors

Postprocessor is function which processes prediction and/or annotation data after model infer and before metric calculation. For correct work postprocessors require specific representation format. (e. g. clip boxes postprocessor expects detection annotation and detection prediction for processing).

In case when you use complicated representation located in representation container, you can add options annotation_source and prediction_source in configuration file, if you want process only specific representations, another way postprocessor will be used for all suitable representations. annotation_source and prediction_source should contain comma separated list of annotation identifiers and output layer names respectively.

Every postprocessor has parameters available for configuration.

Accuracy Checker supports following set of postprocessors:

• cast_to_int - casting detection bounding box coordinates given in floating point format to integer. Supported representations: DetectionAnotation, DetectionPrediction, TextDetectionAnnotation, TextDetectionPrediction.
  • round_policy - method for rounding: nearest, greater, lower, nearest_to_zero.
• clip_boxes - clipping detection bounding box sizes. Supported representations: DetectionAnotation, DetectionPrediction.
  • dst_width and dst_height - destination width and height for box clipping respectively. You can also use size instead in case when destination sizes are equal. If not provided, image size will be used.
  • apply_to - option which determines target boxes for processing (annotation for ground truth boxes and prediction for detection results, all for both).
  • bboxes_normalized is flag which says that target bounding boxes are in normalized format.
• normalize_boxes - normalizing ground truth detection bounding boxes (cast to range [0, 1]). Supported representations: DetectionAnotation, DetectionPrediction, ActionDetectionAnotation, ActionDetectionPrediction
• correct_yolo_v2_boxes - resizing detection prediction bbox coordinates using specific for Yolo v2 approach. Supported representations: DetectionAnotation, DetectionPrediction.
  • dst_width and dst_height - destination width and height respectively. You can also use size instead in case when destination sizes are equal.
• resize_prediction_boxes - resizing normalized detection prediction boxes according to image size. Supported representations: DetectionAnotation, DetectionPrediction.
• faster_rcnn_postprocessing_resize - resizing normalized detection prediction boxes according to the original image size before preprocessing steps. Supported representations: DetectionAnotation, DetectionPrediction. At the moment works in the following cases only:
  • the preprocessing steps contains only one operation changing input image size, and the operation is resize
  • the preprocessing steps contains only two operations changing input image size, and the operations are resize and then padding.
• nms - non-maximum suppression. Supported representations: DetectionAnotation, DetectionPrediction, ActionDetectionAnnotation, ActionDetectionPrediction.
  • overlap - overlap threshold for merging detections.
• soft_nms - soft non-maximum suppression. Supported representations: DetectionAnotation, DetectionPrediction, ActionDetectionAnnotation, ActionDetectionPrediction.
  • keep_top_k - the maximal number of detections which should be kept.
  • sigma - sigma-value for updated detection score calculation.
  • min_score - break point.
• filter - filtering data using different parameters. Supported representations: DetectionAnotation, DetectionPrediction.
  • apply_to - determines target boxes for processing (annotation for ground truth boxes and prediction for detection results, all for both).
  • remove_filtered - removing filtered data. Annotations support ignoring filtered data without removing as default, in other cases filtered data will be removed automatically.
  • Supported parameters for filtering: labels, min_confidence, height_range, width_range, is_empty, min_visibility, aspect_ratio, area_ratio, area_range. Filtering by height_range, width_range are also available for TextDetectionAnnotation, TextDetectionPrediction, area_range - for PoseEstimationAnnotation, PoseEstimationPrediction and TextDetectionAnnotation, TextDetectionPrediction.
• normalize_landmarks_points - normalizing ground truth landmarks points. Supported representations: FacialLandmarksAnnotation, FacialLandmarksPrediction.
  • use_annotation_rect - allows to use size of rectangle saved in annotation metadata for point scaling instead source image size. encode_segmentation_mask - encoding segmentation label image as segmentation mask. Supported representations: SegmentationAnotation, SegmentationPrediction.
  • apply_to - determines target masks for processing (annotation for ground truth and prediction for detection results, all for both). Note: this postprocessing requires specific dataset meta: segmentation_colors for annotations and prediction_to_gt_labels for predictions.
• resize_segmentation_mask - resizing segmentation mask. Supported representations: SegmentationAnotation, SegmentationPrediction.
  • dst_width and dst_height - destination width and height for box clipping respectively. You can also use size instead in case when destination sizes are equal. If any of these parameters are not specified, image size will be used as default.
  • apply_to - determines target masks for processing (annotation for ground truth and prediction for detection results, all for both).
• extend_segmentation_mask - extending annotation segmentation mask to predicted mask size making border filled by specific value. Supported representations: SegmentationAnotation, SegmentationPrediction.
  • filling_label - value for filling border (default 255).
• zoom_segmentation_mask - zooming segmentation mask. Supported representations: SegmentationAnotation, SegmentationPrediction.
  • zoom - size for zoom operation.
• crop_segmentation_mask - cropping 3-d annotation mask. Supported representations: BrainTumorSegmentationAnnotation, BrainTumorSegmentationPrediction.
  • dst_width, dst_height and dst_volume are destination width, height and volume for cropped 3D-image respectively. You can also use size instead in case when destination sizes are equal for all three dimensions.
• clip_segmentation_mask - clipping segmentation mask values. Supported representations: BrainTumorSegmentationAnnotation, BrainTumorSegmentationPrediction.
  • min_value - lower bound of range.
  • max_value - upper bound of range.
• segmentation-prediction-resample - resamples output prediction in two steps: 1) resizes it to bounding box size; 2) extends to annotation size. Supported representations: BrainTumorSegmentationAnnotation, BrainTumorSegmentationPrediction. For correct bounding box size must be set via tag boxes_file in brats_numpy converter.