# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Detection Training Script.

This scripts reads a given config file and runs the training or evaluation.
It is an entry point that is made to train standard models in FsDet.

In order to let one script support training of many models,
this script contains logic that are specific to these built-in models and
therefore may not be suitable for your own project.
For example, your research project perhaps only needs a single "evaluator".

Therefore, we recommend you to use FsDet as an library and take
this file as an example of how to use the library.
You may want to write your own script with your datasets and other customizations.
"""

import os

import fsdet.utils.comm as comm
from fsdet.checkpoint import DetectionCheckpointer
from fsdet.config import get_cfg, set_global_cfg
from fsdet.data import MetadataCatalog, build_detection_train_loader
from fsdet.engine import (
    DefaultTrainer,
    default_argument_parser,
    default_setup,
    launch,
)
from fsdet.evaluation import (
    COCOEvaluator,
    DatasetEvaluators,
    LVISEvaluator,
    PascalVOCDetectionEvaluator,
    verify_results,
)

# from fsdet.data.dataset_mapper import AlbumentationMapper


class Trainer(DefaultTrainer):
    """
    We use the "DefaultTrainer" which contains a number pre-defined logic for
    standard training workflow. They may not work for you, especially if you
    are working on a new research project. In that case you can use the cleaner
    "SimpleTrainer", or write your own training loop.
    """

    @classmethod
    def build_evaluator(cls, cfg, dataset_name, output_folder=None):
        """
        Create evaluator(s) for a given dataset.
        This uses the special metadata "evaluator_type" associated with each builtin dataset.
        For your own dataset, you can simply create an evaluator manually in your
        script and do not have to worry about the hacky if-else logic here.
        """
        if output_folder is None:
            output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
        evaluator_list = []
        evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
        if evaluator_type == "coco":
            evaluator_list.append(COCOEvaluator(dataset_name, cfg, True, output_folder))
        if evaluator_type == "pascal_voc":
            return PascalVOCDetectionEvaluator(dataset_name)
        if evaluator_type == "lvis":
            return LVISEvaluator(dataset_name, cfg, True, output_folder)
        if len(evaluator_list) == 0:
            raise NotImplementedError(
                "no Evaluator for the dataset {} with the type {}".format(
                    dataset_name, evaluator_type
                )
            )
        if len(evaluator_list) == 1:
            return evaluator_list[0]
        return DatasetEvaluators(evaluator_list)

    @classmethod
    def build_train_loader(cls, cfg):
        mapper = None
        # if cfg.INPUT.USE_ALBUMENTATIONS:
        #     mapper = AlbumentationMapper(cfg, is_train=True)
        return build_detection_train_loader(cfg, mapper=mapper)

def setup(args):
    """
    Create configs and perform basic setups.
    """
    cfg = get_cfg()
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()
    set_global_cfg(cfg)
    default_setup(cfg, args)
    return cfg


def main(args):
    cfg = setup(args)

    if args.eval_only:
        model = Trainer.build_model(cfg)
        DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
            cfg.MODEL.WEIGHTS, resume=args.resume
        )
        res = Trainer.test(cfg, model)
        if comm.is_main_process():
            verify_results(cfg, res)
        return res

    """
    If you'd like to do anything fancier than the standard training logic,
    consider writing your own training loop or subclassing the trainer.
    """
    trainer = Trainer(cfg)
    trainer.resume_or_load(resume=args.resume)
    return trainer.train()


if __name__ == "__main__":
    args = default_argument_parser().parse_args()
    print("Command Line Args:", args)
    launch(
        main,
        args.num_gpus,
        num_machines=args.num_machines,
        machine_rank=args.machine_rank,
        dist_url=args.dist_url,
        args=(args,),
    )
