Method extract

PATH/core/testers/reid_tester.py:167–203

(self, data_loader)

        pass

    def extract(self, data_loader):
        config = self.config
        ginfo = self.ginfo
        tmp = self.tmp

        self.pre_run()
        end = time.time()
        self.model.eval()

        for i, tmp.input in enumerate(data_loader):
            tmp.vdata_time.update(time.time() - end)
            self.prepare_data()

            tmp.current_step = i + 1
            with torch.no_grad():
                self.forward()
            self.gather_result()

            tmp.vbatch_time.update(time.time() - end)
            end = time.time()

            if tmp.current_step % config.print_freq == 0 and ginfo.task_rank == 0:
                print(
                'Extract Features: [{0}/{1}]\t'
                'task{task_id:<2}: {task_name}\t'
                'Time {batch_time.avg:.3f} (ETA:{eta:.2f}h) ({data_time.avg:.3f})\t'
                .format(i + 1, len(data_loader),
                task_id=ginfo.task_id, task_name=ginfo.task_name,
                batch_time=tmp.vbatch_time,
                eta=(config.max_iter-tmp.current_step)*tmp.vbatch_time.avg/3600,
                data_time=tmp.vdata_time))

        all_features = torch.cat(tmp.features_list)
        all_labels = torch.cat(tmp.labels_list)
        all_camera_ids = torch.cat(tmp.camera_ids_list)
        # all_features = F.normalize(all_features)
        return all_features, all_labels, all_camera_ids

    def run(self):
        config = self.config