# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved """ MaskFormer Training Script. This script is a simplified version of the training script in detectron2/tools. """ 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, SemSegEvaluator, verify_results, \ DatasetEvaluator 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 detectron2.utils.file_io import PathManager import numpy as np from PIL import Image import glob import pycocotools.mask as mask_util from detectron2.data import DatasetCatalog, MetadataCatalog from detectron2.utils.comm import all_gather, is_main_process, synchronize import json # from detectron2.evaluation import SemSegGzeroEvaluator # from mask_former.evaluation.sem_seg_evaluation_gzero import SemSegGzeroEvaluator class VOCbEvaluator(SemSegEvaluator): """ Evaluate semantic segmentation metrics. """ def process(self, inputs, outputs): """ Args: inputs: the inputs to a model. It is a list of dicts. Each dict corresponds to an image and contains keys like "height", "width", "file_name". outputs: the outputs of a model. It is either list of semantic segmentation predictions (Tensor [H, W]) or list of dicts with key "sem_seg" that contains semantic segmentation prediction in the same format. """ for input, output in zip(inputs, outputs): output = output["sem_seg"].argmax(dim=0).to(self._cpu_device) pred = np.array(output, dtype=np.int) pred[pred >= 20] = 20 with PathManager.open(self.input_file_to_gt_file[input["file_name"]], "rb") as f: gt = np.array(Image.open(f), dtype=np.int) gt[gt == self._ignore_label] = self._num_classes self._conf_matrix += np.bincount( (self._num_classes + 1) * pred.reshape(-1) + gt.reshape(-1), minlength=self._conf_matrix.size, ).reshape(self._conf_matrix.shape) self._predictions.extend(self.encode_json_sem_seg(pred, input["file_name"])) # MaskFormer from cat_seg import ( DETRPanopticDatasetMapper, MaskFormerPanopticDatasetMapper, MaskFormerSemanticDatasetMapper, SemanticSegmentorWithTTA, add_cat_seg_config, ) class Trainer(DefaultTrainer): """ Extension of the Trainer class adapted to DETR. """ @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 if evaluator_type in ["sem_seg", "ade20k_panoptic_seg"]: evaluator_list.append( SemSegEvaluator( dataset_name, distributed=True, output_dir=output_folder, ) ) if evaluator_type == "sem_seg_background": evaluator_list.append( VOCbEvaluator( dataset_name, distributed=True, output_dir=output_folder, ) ) if evaluator_type == "coco": evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder)) if evaluator_type in [ "coco_panoptic_seg", "ade20k_panoptic_seg", "cityscapes_panoptic_seg", ]: evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder)) 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": assert ( torch.cuda.device_count() >= comm.get_rank() ), "CityscapesEvaluator currently do not work with multiple machines." evaluator_list.append(CityscapesSemSegEvaluator(dataset_name)) 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) # Panoptic segmentation dataset mapper elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_panoptic": mapper = MaskFormerPanopticDatasetMapper(cfg, True) # DETR-style dataset mapper for COCO panoptic segmentation elif cfg.INPUT.DATASET_MAPPER_NAME == "detr_panoptic": mapper = DETRPanopticDatasetMapper(cfg, True) 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() # import ipdb; # ipdb.set_trace() 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 "clip_model" in module_name: hyperparams["lr"] = hyperparams["lr"] * cfg.SOLVER.CLIP_MULTIPLIER # for deformable detr 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_cat_seg_config(cfg) cfg.merge_from_file(args.config_file) cfg.merge_from_list(args.opts) cfg.freeze() default_setup(cfg, args) # Setup logger for "mask_former" module setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="mask_former") return cfg def main(args): cfg = setup(args) torch.set_float32_matmul_precision("high") 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,), )