how to eliminate detector entirely in CondInst?

Hi, I have trained a CondInst without detector version, which will not concat detections before DynamicMaskHead. it works well, however, DynamicMaskHead actually consume n_insts and mask_feats as well as mask_head_params, but ProposalGenerator will run this enssential part of detection:

def forward_for_single_feature_map(
            self, locations, logits_pred, reg_pred,
            ctrness_pred, image_sizes, top_feat=None
    ):
        N, C, H, W = logits_pred.shape

        # put in the same format as locations
        logits_pred = logits_pred.view(N, C, H, W).permute(0, 2, 3, 1)
        logits_pred = logits_pred.reshape(N, -1, C).sigmoid()
        box_regression = reg_pred.view(N, 4, H, W).permute(0, 2, 3, 1)
        box_regression = box_regression.reshape(N, -1, 4)
        ctrness_pred = ctrness_pred.view(N, 1, H, W).permute(0, 2, 3, 1)
        ctrness_pred = ctrness_pred.reshape(N, -1).sigmoid()
        if top_feat is not None:
            top_feat = top_feat.view(N, -1, H, W).permute(0, 2, 3, 1)
            top_feat = top_feat.reshape(N, H * W, -1)

        # if self.thresh_with_ctr is True, we multiply the classification
        # scores with centerness scores before applying the threshold.
        if self.thresh_with_ctr:
            logits_pred = logits_pred * ctrness_pred[:, :, None]
        candidate_inds = logits_pred > self.pre_nms_thresh
        pre_nms_top_n = candidate_inds.view(N, -1).sum(1)
        pre_nms_top_n = pre_nms_top_n.clamp(max=self.pre_nms_topk)

        if not self.thresh_with_ctr:
            logits_pred = logits_pred * ctrness_pred[:, :, None]

        results = []
        for i in range(N):
            per_box_cls = logits_pred[i]
            per_candidate_inds = candidate_inds[i]
            per_box_cls = per_box_cls[per_candidate_inds]

            per_candidate_nonzeros = per_candidate_inds.nonzero()
            per_box_loc = per_candidate_nonzeros[:, 0]
            per_class = per_candidate_nonzeros[:, 1]

            per_box_regression = box_regression[i]
            per_box_regression = per_box_regression[per_box_loc]
            per_locations = locations[per_box_loc]
            if top_feat is not None:
                per_top_feat = top_feat[i]
                per_top_feat = per_top_feat[per_box_loc]

            per_pre_nms_top_n = pre_nms_top_n[i]

            if per_candidate_inds.sum().item() > per_pre_nms_top_n.item():
                per_box_cls, top_k_indices = \
                    per_box_cls.topk(per_pre_nms_top_n, sorted=False)
                per_class = per_class[top_k_indices]
                per_box_regression = per_box_regression[top_k_indices]
                per_locations = per_locations[top_k_indices]
                if top_feat is not None:
                    per_top_feat = per_top_feat[top_k_indices]

            detections = torch.stack([
                per_locations[:, 0] - per_box_regression[:, 0],
                per_locations[:, 1] - per_box_regression[:, 1],
                per_locations[:, 0] + per_box_regression[:, 2],
                per_locations[:, 1] + per_box_regression[:, 3],
            ], dim=1)

            boxlist = Instances(image_sizes[i])
            boxlist.pred_boxes = Boxes(detections)
            boxlist.scores = torch.sqrt(per_box_cls)
            boxlist.pred_classes = per_class
            boxlist.locations = per_locations
            if top_feat is not None:
                boxlist.top_feat = per_top_feat
            results.append(boxlist)

        return results

My question is: how to make it more simple to get n_insts the rest module need without computate all steps of detection? which is simply eliminate these unnecessary steps of CondInst without detector?

aim-uofa / AdelaiDet

how to eliminate detector entirely in CondInst? #142