Method get_eval_loader

exps/example/mot/yolox_x_ch.py:94–124

(self, batch_size, is_distributed, testdev=False)

        return train_loader

    def get_eval_loader(self, batch_size, is_distributed, testdev=False):
        from yolox.data import MOTDataset, ValTransform

        valdataset = MOTDataset(
            data_dir=os.path.join(get_yolox_datadir(), "mot"),
            json_file=self.val_ann,
            img_size=self.test_size,
            name='train',
            preproc=ValTransform(
                rgb_means=(0.485, 0.456, 0.406),
                std=(0.229, 0.224, 0.225),
            ),
        )

        if is_distributed:
            batch_size = batch_size // dist.get_world_size()
            sampler = torch.utils.data.distributed.DistributedSampler(
                valdataset, shuffle=False
            )
        else:
            sampler = torch.utils.data.SequentialSampler(valdataset)

        dataloader_kwargs = {
            "num_workers": self.data_num_workers,
            "pin_memory": True,
            "sampler": sampler,
        }
        dataloader_kwargs["batch_size"] = batch_size
        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)

        return val_loader

    def get_evaluator(self, batch_size, is_distributed, testdev=False):
        from yolox.evaluators import COCOEvaluator