Method get_eval_loader

yolox/exp/yolox_base.py:202–231

(self, batch_size, is_distributed, testdev=False)

        return scheduler

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

        valdataset = COCODataset(
            data_dir=None,
            json_file=self.val_ann if not testdev else "image_info_test-dev2017.json",
            name="val2017" if not testdev else "test2017",
            img_size=self.test_size,
            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