train_net_auto.py

# Copyright (c) Facebook, Inc. and its affiliates.
import logging
import os
import sys
from collections import OrderedDict
import torch
from torch.nn.parallel import DistributedDataParallel
import time
import datetime
from typing import Any

from fvcore.common.timer import Timer
from iopath.common.file_io import PathManager
import detectron2.utils.comm as comm
from detectron2.checkpoint import (
    DetectionCheckpointer, PeriodicCheckpointer, Checkpointer
)
from detectron2.config import get_cfg
from detectron2.data import (
    MetadataCatalog,
    build_detection_test_loader,
)
from detectron2.engine import default_argument_parser, default_setup, launch

from detectron2.evaluation import (
    inference_on_dataset,
    print_csv_format,
    LVISEvaluator,
    COCOEvaluator,
)
from detectron2.modeling import build_model
from detectron2.solver import build_lr_scheduler, build_optimizer
from detectron2.utils.events import (
    CommonMetricPrinter,
    EventStorage,
    JSONWriter,
    TensorboardXWriter,
)
from detectron2.data.dataset_mapper import DatasetMapper
from detectron2.data.build import build_detection_train_loader
from detectron2.utils.logger import setup_logger
from torch.cuda.amp import GradScaler

sys.path.insert(0, 'third_party/CenterNet2/')
from centernet.config import add_centernet_config

from mmovod.config import add_mmovod_config
from mmovod.data.custom_build_augmentation import build_custom_augmentation
from mmovod.data.custom_dataset_dataloader import build_custom_train_loader
from mmovod.data.custom_dataset_mapper import CustomDatasetMapper
from mmovod.custom_solver import build_custom_optimizer
from mmovod.modeling.utils import reset_cls_test


logger = logging.getLogger("detectron2")


class LatestCheckpointer:
    """
    Save checkpoints periodically. When `.step(iteration)` is called, it will
    execute `checkpointer.save` on the given checkpointer, if iteration is a
    multiple of period or if `max_iter` is reached.

    Attributes:
        checkpointer (Checkpointer): the underlying checkpointer object
    """

    def __init__(
        self,
        checkpointer: Checkpointer,
        period: int,
        file_prefix: str = "model",
    ) -> None:
        """
        Args:
            checkpointer: the checkpointer object used to save checkpoints.
            period (int): the period to save checkpoint.
            max_iter (int): maximum number of iterations. When it is reached,
                a checkpoint named "{file_prefix}_final" will be saved.
            max_to_keep (int): maximum number of most current checkpoints to keep,
                previous checkpoints will be deleted
            file_prefix (str): the prefix of checkpoint's filename
        """
        self.checkpointer = checkpointer
        self.period = int(period)
        self.path_manager: PathManager = checkpointer.path_manager
        self.file_prefix = file_prefix

    def step(self, iteration: int, **kwargs: Any) -> None:
        """
        Perform the appropriate action at the given iteration.

        Args:
            iteration (int): the current iteration, ranged in [0, max_iter-1].
            kwargs (Any): extra data to save, same as in
                :meth:`Checkpointer.save`.
        """
        iteration = int(iteration)
        additional_state = {"iteration": iteration}
        additional_state.update(kwargs)

        if (iteration + 1) % self.period == 0:
            self.checkpointer.save(
                f"{self.file_prefix}_latest", **additional_state
            )

    def save(self, name: str, **kwargs: Any) -> None:
        """
        Same argument as :meth:`Checkpointer.save`.
        Use this method to manually save checkpoints outside the schedule.

        Args:
            name (str): file name.
            kwargs (Any): extra data to save, same as in
                :meth:`Checkpointer.save`.
        """
        self.checkpointer.save(name, **kwargs)


def do_test(cfg, model):
    results = OrderedDict()
    for d, dataset_name in enumerate(cfg.DATASETS.TEST):
        if cfg.MODEL.RESET_CLS_TESTS:
            reset_cls_test(
                model,
                cfg.MODEL.TEST_CLASSIFIERS[d],
                cfg.MODEL.TEST_NUM_CLASSES[d])
        mapper = None if cfg.INPUT.TEST_INPUT_TYPE == 'default' \
            else DatasetMapper(
                cfg, False, augmentations=build_custom_augmentation(cfg, False))
        data_loader = build_detection_test_loader(cfg, dataset_name, mapper=mapper)
        output_folder = os.path.join(
            cfg.OUTPUT_DIR, "inference_{}".format(dataset_name))
        evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type

        if evaluator_type == "lvis" or cfg.GEN_PSEDO_LABELS:
            evaluator = LVISEvaluator(dataset_name, cfg, True, output_folder)
        elif evaluator_type == 'coco':
            evaluator = COCOEvaluator(dataset_name, cfg, True, output_folder)
        else:
            assert 0, evaluator_type

        results[dataset_name] = inference_on_dataset(
            model, data_loader, evaluator)
        if comm.is_main_process():
            logger.info("Evaluation results for {} in csv format:".format(
                dataset_name))
            print_csv_format(results[dataset_name])
    if len(results) == 1:
        results = list(results.values())[0]
    return results


def do_train(cfg, model, resume=False):
    model.train()
    if cfg.SOLVER.USE_CUSTOM_SOLVER:
        optimizer = build_custom_optimizer(cfg, model)
    else:
        assert cfg.SOLVER.OPTIMIZER == 'SGD'
        assert cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE != 'full_model'
        assert cfg.SOLVER.BACKBONE_MULTIPLIER == 1.
        optimizer = build_optimizer(cfg, model)
    scheduler = build_lr_scheduler(cfg, optimizer)
    logger.info("Following parameters will be trained:")
    for n, p in model.named_parameters():
        if p.requires_grad:
            logger.info("{}".format(n))

    checkpointer = DetectionCheckpointer(
        model, cfg.OUTPUT_DIR, optimizer=optimizer, scheduler=scheduler
    )

    start_iter = checkpointer.resume_or_load(
        cfg.MODEL.WEIGHTS, resume=resume).get("iteration", -1) + 1
    if not resume:
        start_iter = 0
    max_iter = cfg.SOLVER.MAX_ITER if cfg.SOLVER.TRAIN_ITER < 0 else cfg.SOLVER.TRAIN_ITER

    periodic_checkpointer = PeriodicCheckpointer(
        checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD, max_iter=max_iter
    )

    latest_checkpointer = LatestCheckpointer(
        checkpointer, 15000,)

    writers = (
        [
            CommonMetricPrinter(max_iter),
            JSONWriter(os.path.join(cfg.OUTPUT_DIR, "metrics.json")),
            TensorboardXWriter(cfg.OUTPUT_DIR),
        ]
        if comm.is_main_process()
        else []
    )

    use_custom_mapper = cfg.WITH_IMAGE_LABELS
    MapperClass = CustomDatasetMapper if use_custom_mapper else DatasetMapper
    mapper = MapperClass(cfg, True) if cfg.INPUT.CUSTOM_AUG == '' else \
        MapperClass(cfg, True, augmentations=build_custom_augmentation(cfg, True))
    if cfg.DATALOADER.SAMPLER_TRAIN in ['TrainingSampler', 'RepeatFactorTrainingSampler']:
        data_loader = build_detection_train_loader(cfg, mapper=mapper)
    else:
        data_loader = build_custom_train_loader(cfg, mapper=mapper)

    if cfg.FP16:
        scaler = GradScaler()

    logger.info("Starting training from iteration {}".format(start_iter))
    with EventStorage(start_iter) as storage:
        step_timer = Timer()
        data_timer = Timer()
        start_time = time.perf_counter()
        for data, iteration in zip(data_loader, range(start_iter, max_iter)):
            data_time = data_timer.seconds()
            storage.put_scalars(data_time=data_time)
            step_timer.reset()
            iteration = iteration + 1
            storage.step()
            loss_dict = model(data)

            losses = sum(
                loss for k, loss in loss_dict.items())
            assert torch.isfinite(losses).all(), loss_dict

            loss_dict_reduced = {
                k: v.item()
                for k, v in comm.reduce_dict(loss_dict).items()
            }
            losses_reduced = sum(loss for loss in loss_dict_reduced.values())
            if comm.is_main_process():
                storage.put_scalars(
                    total_loss=losses_reduced, **loss_dict_reduced)

            optimizer.zero_grad()
            if cfg.FP16:
                scaler.scale(losses).backward()
                scaler.step(optimizer)
                scaler.update()
            else:
                losses.backward()
                optimizer.step()

            storage.put_scalar(
                "lr", optimizer.param_groups[0]["lr"], smoothing_hint=False)

            step_time = step_timer.seconds()
            storage.put_scalars(time=step_time)
            data_timer.reset()
            scheduler.step()

            if (cfg.TEST.EVAL_PERIOD > 0
                    and iteration % cfg.TEST.EVAL_PERIOD == 0
                    and iteration != max_iter):
                do_test(cfg, model)
                comm.synchronize()

            if (iteration - start_iter > 5
                    and (iteration % 20 == 0 or iteration == max_iter)):
                for writer in writers:
                    writer.write()
            latest_checkpointer.step(iteration)
            periodic_checkpointer.step(iteration)

        total_time = time.perf_counter() - start_time
        logger.info(
            "Total training time: {}".format(
                str(datetime.timedelta(seconds=int(total_time)))))


def setup(args):
    """
    Create configs and perform basic setups.
    """
    cfg = get_cfg()
    add_centernet_config(cfg)
    add_mmovod_config(cfg)
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    if '/auto' in cfg.OUTPUT_DIR:
        if "configs/" in args.config_file:
            new_sub_folder = args.config_file.replace("configs", "")[:-5]
            new_output_dir = cfg.OUTPUT_DIR.replace("/auto", new_sub_folder)
            cfg.OUTPUT_DIR = new_output_dir
        else:
            file_name = os.path.basename(args.config_file)[:-5]
            cfg.OUTPUT_DIR = cfg.OUTPUT_DIR.replace('/auto', '/{}'.format(file_name))
        print(cfg.OUTPUT_DIR)
    cfg.freeze()
    default_setup(cfg, args)
    setup_logger(
        output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="mmovod")
    return cfg


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

    model = build_model(cfg)
    logger.info("Model:\n{}".format(model))
    if args.eval_only:
        DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
            cfg.MODEL.WEIGHTS, resume=args.resume
        )

        return do_test(cfg, model)

    distributed = comm.get_world_size() > 1
    if distributed:
        model = DistributedDataParallel(
            model, device_ids=[comm.get_local_rank()], broadcast_buffers=False,
            find_unused_parameters=cfg.FIND_UNUSED_PARAM
        )

    do_train(cfg, model, resume=args.resume)
    return do_test(cfg, model)


if __name__ == "__main__":
    args = default_argument_parser()
    args = args.parse_args()
    if args.num_machines == 1:
        args.dist_url = 'tcp://127.0.0.1:{}'.format(
            torch.randint(11111, 60000, (1,))[0].item())
    else:
        if args.dist_url == 'host':
            args.dist_url = 'tcp://{}:12345'.format(
                os.environ['SLURM_JOB_NODELIST'])
        elif not args.dist_url.startswith('tcp'):
            tmp = os.popen(
                'echo $(scontrol show job {} | grep BatchHost)'.format(
                    args.dist_url)
            ).read()
            tmp = tmp[tmp.find('=') + 1: -1]
            args.dist_url = 'tcp://{}:12345'.format(tmp)
    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,),
    )