def im_detect_bbox_aug()

in maskrcnn_benchmark/engine/bbox_aug.py [0:0]

• 42 lines of code
• 13 McCabe index (conditional complexity)

def im_detect_bbox_aug(model, images, device):
    # Collect detections computed under different transformations
    boxlists_ts = []
    for _ in range(len(images)):
        boxlists_ts.append([])

    def add_preds_t(boxlists_t):
        for i, boxlist_t in enumerate(boxlists_t):
            if len(boxlists_ts[i]) == 0:
                # The first one is identity transform, no need to resize the boxlist
                boxlists_ts[i].append(boxlist_t)
            else:
                # Resize the boxlist as the first one
                boxlists_ts[i].append(boxlist_t.resize(boxlists_ts[i][0].size))

    # Compute detections for the original image (identity transform)
    boxlists_i = im_detect_bbox(
        model, images, cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MAX_SIZE_TEST, device
    )
    add_preds_t(boxlists_i)

    # Perform detection on the horizontally flipped image
    if cfg.TEST.BBOX_AUG.H_FLIP:
        boxlists_hf = im_detect_bbox_hflip(
            model, images, cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MAX_SIZE_TEST, device
        )
        add_preds_t(boxlists_hf)

    # Compute detections at different scales
    for scale in cfg.TEST.BBOX_AUG.SCALES:
        max_size = cfg.TEST.BBOX_AUG.MAX_SIZE
        boxlists_scl = im_detect_bbox_scale(
            model, images, scale, max_size, device
        )
        add_preds_t(boxlists_scl)

        if cfg.TEST.BBOX_AUG.SCALE_H_FLIP:
            boxlists_scl_hf = im_detect_bbox_scale(
                model, images, scale, max_size, device, hflip=True
            )
            add_preds_t(boxlists_scl_hf)

    # Merge boxlists detected by different bbox aug params
    boxlists = []
    for i, boxlist_ts in enumerate(boxlists_ts):
        bbox = torch.cat([boxlist_t.bbox for boxlist_t in boxlist_ts])
        scores = torch.cat([boxlist_t.get_field('scores') for boxlist_t in boxlist_ts])
        boxlist = BoxList(bbox, boxlist_ts[0].size, boxlist_ts[0].mode)
        boxlist.add_field('scores', scores)
        boxlists.append(boxlist)

    # Apply NMS and limit the final detections
    results = []
    post_processor = make_roi_box_post_processor(cfg)
    for boxlist in boxlists:
        results.append(post_processor.filter_results(boxlist, cfg.MODEL.ROI_BOX_HEAD.NUM_CLASSES))

    return results