# 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 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 sys.path.insert(0, 'third_party/CenterNet2/projects/CenterNet2/') from centernet.config import add_centernet_config sys.path.insert(0, 'third_party/Deformable-DETR') from detic.config import add_detic_config from detic.data.custom_build_augmentation import build_custom_augmentation from detic.data.custom_dataset_dataloader import build_custom_train_loader from detic.data.custom_dataset_mapper import CustomDatasetMapper, DetrDatasetMapper from detic.custom_solver import build_custom_optimizer from detic.evaluation.oideval import OIDEvaluator from detic.evaluation.custom_coco_eval import CustomCOCOEvaluator from detic.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" or cfg.GEN_PSEDO_LABELS: 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) elif evaluator_type == 'oid': evaluator = OIDEvaluator(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) 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 ) 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 \ DetrDatasetMapper(cfg, True) if cfg.INPUT.CUSTOM_AUG == 'DETR' 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() 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_detic_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)) cfg.freeze() default_setup(cfg, args) setup_logger(output=cfg.OUTPUT_DIR, \ distributed_rank=comm.get_rank(), name="detic") 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,), )