# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved """ FCCLIP Training Script. This script is a simplified version of the training script in detectron2/tools. """ try: # ignore ShapelyDeprecationWarning from fvcore from shapely.errors import ShapelyDeprecationWarning import warnings warnings.filterwarnings('ignore', category=ShapelyDeprecationWarning) except: pass import copy import itertools import logging import os from collections import OrderedDict from typing import Any, Dict, List, Set import torch import detectron2.utils.comm as comm from detectron2.checkpoint import DetectionCheckpointer from detectron2.config import get_cfg from detectron2.data import MetadataCatalog, build_detection_train_loader from detectron2.engine import ( DefaultTrainer, default_argument_parser, default_setup, launch, ) from detectron2.evaluation import ( CityscapesInstanceEvaluator, CityscapesSemSegEvaluator, COCOEvaluator, COCOPanopticEvaluator, DatasetEvaluators, LVISEvaluator, SemSegEvaluator, verify_results, ) from detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler from detectron2.solver.build import maybe_add_gradient_clipping from detectron2.utils.logger import setup_logger from fcclip import ( COCOInstanceNewBaselineDatasetMapper, COCOPanopticNewBaselineDatasetMapper, InstanceSegEvaluator, MaskFormerInstanceDatasetMapper, MaskFormerPanopticDatasetMapper, MaskFormerSemanticDatasetMapper, SemanticSegmentorWithTTA, add_maskformer2_config, add_fcclip_config ) class Trainer(DefaultTrainer): """ Extension of the Trainer class adapted to FCCLIP. """ @classmethod def build_evaluator(cls, cfg, dataset_name, output_folder=None): """ Create evaluator(s) for a given dataset. This uses the special metadata "evaluator_type" associated with each builtin dataset. For your own dataset, you can simply create an evaluator manually in your script and do not have to worry about the hacky if-else logic here. """ if output_folder is None: output_folder = os.path.join(cfg.OUTPUT_DIR, "inference") evaluator_list = [] evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type # semantic segmentation if evaluator_type in ["sem_seg", "ade20k_panoptic_seg"]: evaluator_list.append( SemSegEvaluator( dataset_name, distributed=True, output_dir=output_folder, ) ) # instance segmentation if evaluator_type == "coco": evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder)) # panoptic segmentation if evaluator_type in [ "coco_panoptic_seg", "ade20k_panoptic_seg", "cityscapes_panoptic_seg", "mapillary_vistas_panoptic_seg", ]: if cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON: evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder)) # COCO if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON: evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder)) if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON: evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder)) # Mapillary Vistas if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON: evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder)) if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON: evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder)) # Cityscapes if evaluator_type == "cityscapes_instance": assert ( torch.cuda.device_count() > comm.get_rank() ), "CityscapesEvaluator currently do not work with multiple machines." return CityscapesInstanceEvaluator(dataset_name) if evaluator_type == "cityscapes_sem_seg": assert ( torch.cuda.device_count() > comm.get_rank() ), "CityscapesEvaluator currently do not work with multiple machines." return CityscapesSemSegEvaluator(dataset_name) if evaluator_type == "cityscapes_panoptic_seg": if cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON: assert ( torch.cuda.device_count() > comm.get_rank() ), "CityscapesEvaluator currently do not work with multiple machines." evaluator_list.append(CityscapesSemSegEvaluator(dataset_name)) if cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON: assert ( torch.cuda.device_count() > comm.get_rank() ), "CityscapesEvaluator currently do not work with multiple machines." evaluator_list.append(CityscapesInstanceEvaluator(dataset_name)) # ADE20K if evaluator_type == "ade20k_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON: evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder)) # LVIS if evaluator_type == "lvis": return LVISEvaluator(dataset_name, output_dir=output_folder) if len(evaluator_list) == 0: raise NotImplementedError( "no Evaluator for the dataset {} with the type {}".format( dataset_name, evaluator_type ) ) elif len(evaluator_list) == 1: return evaluator_list[0] return DatasetEvaluators(evaluator_list) @classmethod def build_train_loader(cls, cfg): # Semantic segmentation dataset mapper if cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_semantic": mapper = MaskFormerSemanticDatasetMapper(cfg, True) return build_detection_train_loader(cfg, mapper=mapper) # Panoptic segmentation dataset mapper elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_panoptic": mapper = MaskFormerPanopticDatasetMapper(cfg, True) return build_detection_train_loader(cfg, mapper=mapper) # Instance segmentation dataset mapper elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_instance": mapper = MaskFormerInstanceDatasetMapper(cfg, True) return build_detection_train_loader(cfg, mapper=mapper) # coco instance segmentation lsj new baseline elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_instance_lsj": mapper = COCOInstanceNewBaselineDatasetMapper(cfg, True) return build_detection_train_loader(cfg, mapper=mapper) # coco panoptic segmentation lsj new baseline elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_panoptic_lsj": mapper = COCOPanopticNewBaselineDatasetMapper(cfg, True) return build_detection_train_loader(cfg, mapper=mapper) else: mapper = None return build_detection_train_loader(cfg, mapper=mapper) @classmethod def build_lr_scheduler(cls, cfg, optimizer): """ It now calls :func:`detectron2.solver.build_lr_scheduler`. Overwrite it if you'd like a different scheduler. """ return build_lr_scheduler(cfg, optimizer) @classmethod def build_optimizer(cls, cfg, model): weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED defaults = {} defaults["lr"] = cfg.SOLVER.BASE_LR defaults["weight_decay"] = cfg.SOLVER.WEIGHT_DECAY norm_module_types = ( torch.nn.BatchNorm1d, torch.nn.BatchNorm2d, torch.nn.BatchNorm3d, torch.nn.SyncBatchNorm, # NaiveSyncBatchNorm inherits from BatchNorm2d torch.nn.GroupNorm, torch.nn.InstanceNorm1d, torch.nn.InstanceNorm2d, torch.nn.InstanceNorm3d, torch.nn.LayerNorm, torch.nn.LocalResponseNorm, ) params: List[Dict[str, Any]] = [] memo: Set[torch.nn.parameter.Parameter] = set() for module_name, module in model.named_modules(): for module_param_name, value in module.named_parameters(recurse=False): if not value.requires_grad: continue # Avoid duplicating parameters if value in memo: continue memo.add(value) hyperparams = copy.copy(defaults) if "backbone" in module_name: hyperparams["lr"] = hyperparams["lr"] * cfg.SOLVER.BACKBONE_MULTIPLIER if ( "relative_position_bias_table" in module_param_name or "absolute_pos_embed" in module_param_name ): print(module_param_name) hyperparams["weight_decay"] = 0.0 if isinstance(module, norm_module_types): hyperparams["weight_decay"] = weight_decay_norm if isinstance(module, torch.nn.Embedding): hyperparams["weight_decay"] = weight_decay_embed params.append({"params": [value], **hyperparams}) def maybe_add_full_model_gradient_clipping(optim): # detectron2 doesn't have full model gradient clipping now clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE enable = ( cfg.SOLVER.CLIP_GRADIENTS.ENABLED and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model" and clip_norm_val > 0.0 ) class FullModelGradientClippingOptimizer(optim): def step(self, closure=None): all_params = itertools.chain(*[x["params"] for x in self.param_groups]) torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val) super().step(closure=closure) return FullModelGradientClippingOptimizer if enable else optim optimizer_type = cfg.SOLVER.OPTIMIZER if optimizer_type == "SGD": optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)( params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM ) elif optimizer_type == "ADAMW": optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)( params, cfg.SOLVER.BASE_LR ) else: raise NotImplementedError(f"no optimizer type {optimizer_type}") if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model": optimizer = maybe_add_gradient_clipping(cfg, optimizer) return optimizer @classmethod def test_with_TTA(cls, cfg, model): logger = logging.getLogger("detectron2.trainer") # In the end of training, run an evaluation with TTA. logger.info("Running inference with test-time augmentation ...") model = SemanticSegmentorWithTTA(cfg, model) evaluators = [ cls.build_evaluator( cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, "inference_TTA") ) for name in cfg.DATASETS.TEST ] res = cls.test(cfg, model, evaluators) res = OrderedDict({k + "_TTA": v for k, v in res.items()}) return res def setup(args): """ Create configs and perform basic setups. """ cfg = get_cfg() # for poly lr schedule add_deeplab_config(cfg) add_maskformer2_config(cfg) add_fcclip_config(cfg) cfg.merge_from_file(args.config_file) cfg.merge_from_list(args.opts) cfg.freeze() default_setup(cfg, args) # Setup logger for "fcclip" module setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="fcclip") 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 cfg.TEST.AUG.ENABLED: res.update(Trainer.test_with_TTA(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,), )