def forward_for_single_feature_map()

in evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/rpn/retinanet_fa/retinanet_infer.py [0:0]

• 55 lines of code
• 7 McCabe index (conditional complexity)

    def forward_for_single_feature_map(self, anchors, box_cls, box_regression,
                                      pre_nms_thresh):
        """
        Arguments:
            anchors: list[BoxList]
            box_cls: tensor of size N, A * C, H, W
            box_regression: tensor of size N, A * 4, H, W
        """
        device = box_cls.device
        N, _ , H, W = box_cls.shape
        A = int(box_regression.size(1) / 4)
        C = int(box_cls.size(1) / A)

        # put in the same format as anchors
        box_cls = box_cls.view(N, -1, C, H, W).permute(0, 3, 4, 1, 2)
        box_cls = box_cls.reshape(N, -1, C)
        box_cls = box_cls.sigmoid()

        box_regression = box_regression.view(N, -1, 4, H, W)
        box_regression = box_regression.permute(0, 3, 4, 1, 2)
        box_regression = box_regression.reshape(N, -1, 4)

        num_anchors = A * H * W

        results = [[] for _ in range(N)]
        candidate_inds = box_cls > pre_nms_thresh
        if candidate_inds.sum().item() == 0:
            empty_boxlists = []
            for a in anchors:
                empty_boxlist = BoxList(torch.Tensor(0, 4).to(device), a.size)
                empty_boxlist.add_field(
                    "labels", torch.LongTensor([]).to(device))
                empty_boxlist.add_field(
                    "scores", torch.Tensor([]).to(device))
                empty_boxlists.append(empty_boxlist)
            return empty_boxlists

        pre_nms_top_n = candidate_inds.view(N, -1).sum(1)
        pre_nms_top_n = pre_nms_top_n.clamp(max=self.pre_nms_top_n)

        for batch_idx, (per_box_cls, per_box_regression, per_pre_nms_top_n, \
        per_candidate_inds, per_anchors) in enumerate(zip(
            box_cls,
            box_regression,
            pre_nms_top_n,
            candidate_inds,
            anchors)):

            # Sort and select TopN
            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_class += 1
            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_box_loc = per_box_loc[top_k_indices]
                per_class = per_class[top_k_indices]

            detections = self.box_coder.decode(
                per_box_regression[per_box_loc, :].view(-1, 4),
                per_anchors.bbox[per_box_loc, :].view(-1, 4)
            )

            boxlist = BoxList(detections, per_anchors.size, mode="xyxy")
            boxlist.add_field("labels", per_class)
            boxlist.add_field("scores", per_box_cls)
            boxlist = boxlist.clip_to_image(remove_empty=False)
            boxlist = remove_small_boxes(boxlist, self.min_size)
            results[batch_idx] = boxlist

        return results