#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved

"""
Grid features pre-training script.

This script is a simplified version of the training script in detectron2/tools.
"""

import os
import time
import torch

import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import MetadataCatalog
from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, launch
from detectron2.evaluation import COCOEvaluator, DatasetEvaluators, verify_results

from grid_feats import (
    add_attribute_config,
    build_detection_train_loader_with_attributes,
    build_detection_test_loader_with_attributes,
)


class Trainer(DefaultTrainer):
    """
    A trainer for visual genome dataset.
    """
    def __init__(self, cfg):
        super().__init__(cfg)
        self.rpn_box_lw = cfg.MODEL.RPN.BBOX_LOSS_WEIGHT
        self.rcnn_box_lw = cfg.MODEL.ROI_BOX_HEAD.BBOX_LOSS_WEIGHT

    @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")
        evaluator_list = []
        evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
        if evaluator_type == "coco":
            return COCOEvaluator(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):
        return build_detection_train_loader_with_attributes(cfg)

    @classmethod
    def build_test_loader(cls, cfg, dataset_name):
        return build_detection_test_loader_with_attributes(cfg, dataset_name)

    def run_step(self):
        """
        !!Hack!! for the run_step method in SimpleTrainer to adjust the loss
        """
        assert self.model.training, "[Trainer] model was changed to eval mode!"
        start = time.perf_counter()
        data = next(self._data_loader_iter)
        data_time = time.perf_counter() - start
        loss_dict = self.model(data)
        # RPN box loss:
        loss_dict["loss_rpn_loc"] *= self.rpn_box_lw
        # R-CNN box loss:
        loss_dict["loss_box_reg"] *= self.rcnn_box_lw
        losses = sum(loss_dict.values())
        self._detect_anomaly(losses, loss_dict)

        metrics_dict = loss_dict
        metrics_dict["data_time"] = data_time
        self._write_metrics(metrics_dict)
        self.optimizer.zero_grad()
        losses.backward()
        self.optimizer.step()


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

    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,),
    )