# Copyright (c) Facebook, Inc. and its affiliates. # Modified by Jialian Wu from https://github.com/facebookresearch/Detic/blob/main/train_net.py 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.utils.logger import setup_logger sys.path.insert(0, 'third_party/CenterNet2/projects/CenterNet2/') from centernet.config import add_centernet_config from grit.config import add_grit_config from grit.data.custom_build_augmentation import build_custom_augmentation from grit.data.custom_dataset_dataloader import build_custom_train_loader from grit.data.custom_dataset_mapper import CustomDatasetMapper from grit.custom_solver import build_custom_optimizer from grit.evaluation.eval import GRiTCOCOEvaluator, GRiTVGEvaluator logger = logging.getLogger("detectron2") def do_test(cfg, model): results = OrderedDict() for d, dataset_name in enumerate(cfg.DATASETS.TEST): 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 == 'coco': evaluator = GRiTCOCOEvaluator(dataset_name, cfg, True, output_folder) elif evaluator_type == 'vg': evaluator = GRiTVGEvaluator(dataset_name, cfg, True, output_folder) else: raise NotImplementedError('We have not implemented the evaluator for {}'.format(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' 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 [] ) mapper = CustomDatasetMapper(cfg, True, augmentations=build_custom_augmentation(cfg, True)) data_loader = build_custom_train_loader(cfg, mapper=mapper) 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() 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_grit_config(cfg) cfg.merge_from_file(args.config_file) cfg.merge_from_list(args.opts) if args.output_dir_name: cfg.OUTPUT_DIR = args.output_dir_name logger.info('OUTPUT_DIR: {}'.format(cfg.OUTPUT_DIR)) if args.test_task: cfg.MODEL.TEST_TASK = args.test_task cfg.freeze() default_setup(cfg, args) setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), color=False, name="grit") 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 if __name__ == "__main__": args = default_argument_parser() args.add_argument("--output-dir-name", type=str, default='./output/GRiT') args.add_argument("--num-gpus-per-machine", type=int, default=8) args.add_argument("--test-task", type=str, default='', help="Choose a task to have GRiT perform") 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: raise NotImplementedError('Use train_deepspeed.py for multi-node training') print("Command Line Args:", args) launch( main, args.num_gpus_per_machine, num_machines=args.num_machines, machine_rank=args.machine_rank, dist_url=args.dist_url, args=(args,), )