train_net_diffusion.py

import logging
import os
from collections import OrderedDict
import torch
from torch.nn.parallel import DistributedDataParallel
import time
import datetime

from fvcore.common.timer import Timer
import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer, PeriodicCheckpointer
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

from ovd.config import add_ovd_config
from ovd.transforms.custom_build_augmentation import build_custom_augmentation
from ovd.transforms.custom_dataset_dataloader import build_custom_train_loader
from ovd.transforms.custom_dataset_mapper import CustomDatasetMapper, CustomDatasetMapperMix
from ovd.evaluation.custom_coco_eval import CustomCOCOEvaluator
from ovd.modeling.utils import reset_cls_test

logger = logging.getLogger("detectron2")


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":
            evaluator = LVISEvaluator(dataset_name, cfg, True, output_folder)
        elif evaluator_type == 'coco':
            if dataset_name == 'coco_generalized_zeroshot_val':
                # Additionally plot mAP for 'seen classes' and 'unseen classes'
                evaluator = CustomCOCOEvaluator(dataset_name, cfg, True, output_folder)
            else:
                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()
    # assert cfg.SOLVER.OPTIMIZER == 'SGD'
    assert cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE != 'full_model'

    optimizer = build_optimizer(cfg, model)


    scheduler = build_lr_scheduler(cfg, optimizer)

    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

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

    if cfg.WITH_IMAGE_LABELS:
        MapperClass = CustomDatasetMapperMix
    elif cfg.MODEL.DISTILLATION:
        MapperClass = CustomDatasetMapper
    else:
        MapperClass = 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()
                            
                torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=2.0, norm_type=2.0)
                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()
            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_ovd_config(cfg)
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    if '/auto' in cfg.OUTPUT_DIR:
        file_name = os.path.basename(args.config_file)[:-5]
        cfg.OUTPUT_DIR = cfg.OUTPUT_DIR.replace('/auto', '/{}'.format(file_name))
        logger.info('OUTPUT_DIR: {}'.format(cfg.OUTPUT_DIR))


    if comm.is_main_process():
        import shutil
        import os
        # backup_dir =  cfg.OUTPUT_DIR +'/roi_heads_backup/'
        # if os.path.exists(backup_dir):
        #     shutil.rmtree(backup_dir)
        # shutil.copytree('/home/wuyangli2/code/object-centric-ovd/ovd/modeling/roi_heads/', backup_dir)
        
    cfg.freeze()
    default_setup(cfg, args)
    setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="ovd")
    return cfg


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

    model = build_model(cfg)
    print(model)
    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()

    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,),
    )