Method _generate_empty_tracks

tutorials/motr/motr.py:453–474

(self)

        self.mem_bank_len = 0 if memory_bank is None else memory_bank.max_his_length

    def _generate_empty_tracks(self):
        track_instances = Instances((1, 1))
        num_queries, dim = self.query_embed.weight.shape  # (300, 512)
        device = self.query_embed.weight.device
        track_instances.ref_pts = self.transformer.reference_points(self.query_embed.weight[:, :dim // 2])
        track_instances.query_pos = self.query_embed.weight
        track_instances.output_embedding = torch.zeros((num_queries, dim >> 1), device=device)
        track_instances.obj_idxes = torch.full((len(track_instances),), -1, dtype=torch.long, device=device)
        track_instances.matched_gt_idxes = torch.full((len(track_instances),), -1, dtype=torch.long, device=device)
        track_instances.disappear_time = torch.zeros((len(track_instances), ), dtype=torch.long, device=device)
        track_instances.iou = torch.zeros((len(track_instances),), dtype=torch.float, device=device)
        track_instances.scores = torch.zeros((len(track_instances),), dtype=torch.float, device=device)
        track_instances.track_scores = torch.zeros((len(track_instances),), dtype=torch.float, device=device)
        track_instances.pred_boxes = torch.zeros((len(track_instances), 4), dtype=torch.float, device=device)
        track_instances.pred_logits = torch.zeros((len(track_instances), self.num_classes), dtype=torch.float, device=device)

        mem_bank_len = self.mem_bank_len
        track_instances.mem_bank = torch.zeros((len(track_instances), mem_bank_len, dim // 2), dtype=torch.float32, device=device)
        track_instances.mem_padding_mask = torch.ones((len(track_instances), mem_bank_len), dtype=torch.bool, device=device)
        track_instances.save_period = torch.zeros((len(track_instances), ), dtype=torch.float32, device=device)

        return track_instances.to(self.query_embed.weight.device)

    def clear(self):
        self.track_base.clear()