Unable to replicate default augmentations on Resnet 50

[ExtReMLapin]

Hello,

According to the default training code, default augmentations seems to be :

[ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip()]

When I added my own angmentations I had a lower final score than when I don't manually add my own augmentations.
So I decided to try to understand where is was comming from.

So with this training code :

 if augmentations is False:

        class EZTrainer(DefaultTrainer):
            @classmethod
            def build_evaluator(cls, cfg, dataset_name, output_folder=None):
                if output_folder is None:
                    output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
                return COCOEvaluator(dataset_name, output_dir=output_folder)

        trainer_class = EZTrainer
    else:

        class AugTrainer(DefaultTrainer):
            @classmethod
            def build_train_loader(cls, cfg):
                mapper = DatasetMapper(
                    cfg,
                    is_train=True,
                    augmentations=[
                        T.ResizeShortestEdge(
                            short_edge_length=(640, 672, 704, 736, 768, 800),
                            max_size=1333,
                            sample_style="choice",
                        ),
                        T.RandomFlip(),),
                    ],
                )
                return build_detection_train_loader(cfg, mapper=mapper)

            @classmethod
            def build_evaluator(cls, cfg, dataset_name, output_folder=None):
                if output_folder is None:
                    output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
                return COCOEvaluator(dataset_name, output_dir=output_folder)

        trainer_class = AugTrainer
    print("cfg.MODEL.WEIGHTS = " + str(cfg.MODEL.WEIGHTS))
    _cfr = copy.deepcopy(cfg)
    print("cfg", cfg)
    trainer = trainer_class(_cfr)
    # todo a ajuster avec des settings
    trainer.resume_or_load(resume=False)

    best = BestCheckpointer(
        eval_period=cfg.TEST.EVAL_PERIOD,
        checkpointer=trainer.checkpointer,
        val_metric="bbox/AP50" if training_type == "detection" else "segm/AP50",
    )
    trainer.register_hooks([best])

    print("MAX_ITER = ", max_iter)
    trainer.train()

I would expect the trained to behave the same as if augmentations is False or True

But it's not, with augmentations at True I get a score of 15/100 (IoU) instead of 20

[ExtReMLapin]

bump