def build()

in models/detr.py [0:0]

• 40 lines of code
• 8 McCabe index (conditional complexity)

def build(args):
    # the `num_classes` naming here is somewhat misleading.
    # it indeed corresponds to `max_obj_id + 1`, where max_obj_id
    # is the maximum id for a class in your dataset. For example,
    # COCO has a max_obj_id of 90, so we pass `num_classes` to be 91.
    # As another example, for a dataset that has a single class with id 1,
    # you should pass `num_classes` to be 2 (max_obj_id + 1).
    # For more details on this, check the following discussion
    # https://github.com/facebookresearch/detr/issues/108#issuecomment-650269223
    num_classes = 20 if args.dataset_file != 'coco' else 91
    if args.dataset_file == "coco_panoptic":
        # for panoptic, we just add a num_classes that is large enough to hold
        # max_obj_id + 1, but the exact value doesn't really matter
        num_classes = 250
    device = torch.device(args.device)

    backbone = build_backbone(args)

    transformer = build_transformer(args)

    model = DETR(
        backbone,
        transformer,
        num_classes=num_classes,
        num_queries=args.num_queries,
        aux_loss=args.aux_loss,
    )
    if args.masks:
        model = DETRsegm(model, freeze_detr=(args.frozen_weights is not None))
    matcher = build_matcher(args)
    weight_dict = {'loss_ce': 1, 'loss_bbox': args.bbox_loss_coef}
    weight_dict['loss_giou'] = args.giou_loss_coef
    if args.masks:
        weight_dict["loss_mask"] = args.mask_loss_coef
        weight_dict["loss_dice"] = args.dice_loss_coef
    # TODO this is a hack
    if args.aux_loss:
        aux_weight_dict = {}
        for i in range(args.dec_layers - 1):
            aux_weight_dict.update({k + f'_{i}': v for k, v in weight_dict.items()})
        weight_dict.update(aux_weight_dict)

    losses = ['labels', 'boxes', 'cardinality']
    if args.masks:
        losses += ["masks"]
    criterion = SetCriterion(num_classes, matcher=matcher, weight_dict=weight_dict,
                             eos_coef=args.eos_coef, losses=losses)
    criterion.to(device)
    postprocessors = {'bbox': PostProcess()}
    if args.masks:
        postprocessors['segm'] = PostProcessSegm()
        if args.dataset_file == "coco_panoptic":
            is_thing_map = {i: i <= 90 for i in range(201)}
            postprocessors["panoptic"] = PostProcessPanoptic(is_thing_map, threshold=0.85)

    return model, criterion, postprocessors