# ------------------------------------------------------------------------------
# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/train_net.py
# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
# ------------------------------------------------------------------------------

"""
OneFormer 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 warnings

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 (
    CityscapesSemSegEvaluator,
    COCOPanopticEvaluator,
    DatasetEvaluators,
    SemSegEvaluator,
    DatasetEvaluator,
    inference_on_dataset,
    print_csv_format,
    verify_results,
)

from oneformer.evaluation import (
    COCOEvaluator,
    DetectionCOCOEvaluator,
    CityscapesInstanceEvaluator,
)

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 oneformer import (
    COCOUnifiedNewBaselineDatasetMapper,
    OneFormerUnifiedDatasetMapper,
    InstanceSegEvaluator,
    SemanticSegmentorWithTTA,
    add_oneformer_config,
    add_common_config,
    add_swin_config,
    add_dinat_config,
    add_convnext_config,
)

from detectron2.utils.events import CommonMetricPrinter, JSONWriter
from oneformer.utils.events import WandbWriter, setup_wandb
from time import sleep
from oneformer.data.build import *
from oneformer.data.dataset_mappers.dataset_mapper import DatasetMapper

class Trainer(DefaultTrainer):
    """
    Extension of the Trainer class adapted to OneFormer.
    """

    @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))
            if cfg.MODEL.TEST.DETECTION_ON:
                evaluator_list.append(DetectionCOCOEvaluator(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.TEST.PANOPTIC_ON:
                evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
        # COCO
        if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.TEST.INSTANCE_ON:
            evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
        if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.TEST.SEMANTIC_ON:
            evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))
        if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.TEST.DETECTION_ON:
            evaluator_list.append(DetectionCOCOEvaluator(dataset_name, output_dir=output_folder))
        if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.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.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.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.TEST.INSTANCE_ON:
            evaluator_list.append(InstanceSegEvaluator(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):
        # Unified segmentation dataset mapper
        if cfg.INPUT.DATASET_MAPPER_NAME == "oneformer_unified":
            mapper = OneFormerUnifiedDatasetMapper(cfg, True)
            return build_detection_train_loader(cfg, mapper=mapper)
        # coco unified segmentation lsj new baseline
        elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_unified_lsj":
            mapper = COCOUnifiedNewBaselineDatasetMapper(cfg, True)
            return build_detection_train_loader(cfg, mapper=mapper)
        else:
            mapper = None
            return build_detection_train_loader(cfg, mapper=mapper)

    def build_writers(self):
        """
        Build a list of writers to be used. By default it contains
        writers that write metrics to the screen,
        a json file, and a tensorboard event file respectively.
        If you'd like a different list of writers, you can overwrite it in
        your trainer.
        Returns:
            list[EventWriter]: a list of :class:`EventWriter` objects.
        It is now implemented by:
        ::
            return [
                CommonMetricPrinter(self.max_iter),
                JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, "metrics.json")),
                TensorboardXWriter(self.cfg.OUTPUT_DIR),
            ]
        """
        # Here the default print/log frequency of each writer is used.
        return [
            # It may not always print what you want to see, since it prints "common" metrics only.
            CommonMetricPrinter(self.max_iter),
            JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, "metrics.json")),
            WandbWriter(),
        ]

    @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])
                    for p in all_params:
                        torch.nan_to_num(p.grad, nan=0.0, posinf=1e5, neginf=-1e5, out=p.grad)
                    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_SEMANTIC
        ]
        res = cls.test(cfg, model, evaluators)
        res = OrderedDict({k + "_TTA": v for k, v in res.items()})
        return res

    @classmethod
    def build_test_loader(cls, cfg, dataset_name):
        """
        Returns:
            iterable
        It now calls :func:`detectron2.data.build_detection_test_loader`.
        Overwrite it if you'd like a different data loader.
        """
        mapper = DatasetMapper(cfg, False)
        return build_detection_test_loader(cfg, dataset_name, mapper=mapper)

    @classmethod
    def test(cls, cfg, model, evaluators=None):
        """
        Evaluate the given model. The given model is expected to already contain
        weights to evaluate.
        Args:
            cfg (CfgNode):
            model (nn.Module):
            evaluators (list[DatasetEvaluator] or None): if None, will call
                :meth:`build_evaluator`. Otherwise, must have the same length as
                ``cfg.DATASETS.TEST_{TASK}``.
        Returns:
            dict: a dict of result metrics
        """
        logger = logging.getLogger(__name__)
        if isinstance(evaluators, DatasetEvaluator):
            evaluators = [evaluators]

        if cfg.MODEL.TEST.TASK == "panoptic":
            test_dataset = cfg.DATASETS.TEST_PANOPTIC
        elif cfg.MODEL.TEST.TASK == "instance":
            test_dataset = cfg.DATASETS.TEST_INSTANCE
        elif cfg.MODEL.TEST.TASK == "semantic":
            test_dataset = cfg.DATASETS.TEST_SEMANTIC
        else:
            warnings.warn(f"WARNING: No task provided! Setting task to default value: 'panoptic'")
            test_dataset = cfg.DATASETS.TEST_PANOPTIC

        if evaluators is not None:
            assert len(test_dataset) == len(evaluators), "{} != {}".format(
                len(test_dataset), len(evaluators)
            )

        results = OrderedDict

        results = OrderedDict()
        for idx, dataset_name in enumerate(test_dataset):
            data_loader = cls.build_test_loader(cfg, dataset_name)
            # When evaluators are passed in as arguments,
            # implicitly assume that evaluators can be created before data_loader.
            if evaluators is not None:
                evaluator = evaluators[idx]
            else:
                try:
                    evaluator = cls.build_evaluator(cfg, dataset_name)
                except NotImplementedError:
                    logger.warn(
                        "No evaluator found. Use `DefaultTrainer.test(evaluators=)`, "
                        "or implement its `build_evaluator` method."
                    )
                    results[dataset_name] = {}
                    continue
            results_i = inference_on_dataset(model, data_loader, evaluator)

            results[dataset_name] = results_i
            if comm.is_main_process():
                assert isinstance(
                    results_i, dict
                ), "Evaluator must return a dict on the main process. Got {} instead.".format(
                    results_i
                )
                logger.info("Evaluation results for {} in csv format:".format(dataset_name))
                print_csv_format(results_i)

        if len(results) == 1:
            results = list(results.values())[0]
        return results

def setup(args):
    """
    Create configs and perform basic setups.
    """
    cfg = get_cfg()
    # for poly lr schedule
    add_deeplab_config(cfg)
    add_common_config(cfg)
    add_swin_config(cfg)
    add_dinat_config(cfg)
    add_convnext_config(cfg)
    add_oneformer_config(cfg)
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()
    default_setup(cfg, args)
    if not args.eval_only:
        setup_wandb(cfg, args)
    # Setup logger for "oneformer" module
    setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="oneformer")
    return cfg

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

    if args.eval_only:
        model = Trainer.build_model(cfg)
        net_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
        print("Total Params: {} M".format(net_params/1e6))
        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)
    if args.machine_rank == 0:
        net_params = sum(p.numel() for p in trainer.model.parameters() if p.requires_grad)
        print("Total Params: {} M".format(net_params/1e6))
    sleep(3)
    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,),
    )


Trainer

from detectron2.utils.logger import setup_logger

from oneformer import (
    COCOUnifiedNewBaselineDatasetMapper,
    OneFormerUnifiedDatasetMapper,
    InstanceSegEvaluator,
    SemanticSegmentorWithTTA,
    add_oneformer_config,
    add_common_config,
    add_swin_config,
    add_dinat_config,
    add_convnext_config,
)

from detectron2.utils.events import CommonMetricPrinter, JSONWriter
from oneformer.utils.events import WandbWriter, setup_wandb
from time import sleep
from oneformer.data.build import *
from oneformer.data.dataset_mappers.dataset_mapper import DatasetMapper

class Trainer(DefaultTrainer):
    """
    Extension of the Trainer class adapted to OneFormer.
    """

    @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))
            if cfg.MODEL.TEST.DETECTION_ON:
                evaluator_list.append(DetectionCOCOEvaluator(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.TEST.PANOPTIC_ON:
                evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
        # COCO
        if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.TEST.INSTANCE_ON:
            evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
        if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.TEST.SEMANTIC_ON:
            evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))
        if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.TEST.DETECTION_ON:
            evaluator_list.append(DetectionCOCOEvaluator(dataset_name, output_dir=output_folder))
        if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.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.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.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.TEST.INSTANCE_ON:
            evaluator_list.append(InstanceSegEvaluator(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):
        # Unified segmentation dataset mapper
        if cfg.INPUT.DATASET_MAPPER_NAME == "oneformer_unified":
            mapper = OneFormerUnifiedDatasetMapper(cfg, True)
            return build_detection_train_loader(cfg, mapper=mapper)
        # coco unified segmentation lsj new baseline
        elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_unified_lsj":
            mapper = COCOUnifiedNewBaselineDatasetMapper(cfg, True)
            return build_detection_train_loader(cfg, mapper=mapper)
        else:
            mapper = None
            return build_detection_train_loader(cfg, mapper=mapper)

    def build_writers(self):
        """
        Build a list of writers to be used. By default it contains
        writers that write metrics to the screen,
        a json file, and a tensorboard event file respectively.
        If you'd like a different list of writers, you can overwrite it in
        your trainer.
        Returns:
            list[EventWriter]: a list of :class:`EventWriter` objects.
        It is now implemented by:
        ::
            return [
                CommonMetricPrinter(self.max_iter),
                JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, "metrics.json")),
                TensorboardXWriter(self.cfg.OUTPUT_DIR),
            ]
        """
        # Here the default print/log frequency of each writer is used.
        return [
            # It may not always print what you want to see, since it prints "common" metrics only.
            CommonMetricPrinter(self.max_iter),
            JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, "metrics.json")),
            WandbWriter(),
        ]

    @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])
                    for p in all_params:
                        torch.nan_to_num(p.grad, nan=0.0, posinf=1e5, neginf=-1e5, out=p.grad)
                    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_SEMANTIC
        ]
        res = cls.test(cfg, model, evaluators)
        res = OrderedDict({k + "_TTA": v for k, v in res.items()})
        return res

    @classmethod
    def build_test_loader(cls, cfg, dataset_name):
        """
        Returns:
            iterable
        It now calls :func:`detectron2.data.build_detection_test_loader`.
        Overwrite it if you'd like a different data loader.
        """
        mapper = DatasetMapper(cfg, False)
        return build_detection_test_loader(cfg, dataset_name, mapper=mapper)

    @classmethod
    def test(cls, cfg, model, evaluators=None):
        """
        Evaluate the given model. The given model is expected to already contain
        weights to evaluate.
        Args:
            cfg (CfgNode):
            model (nn.Module):
            evaluators (list[DatasetEvaluator] or None): if None, will call
                :meth:`build_evaluator`. Otherwise, must have the same length as
                ``cfg.DATASETS.TEST_{TASK}``.
        Returns:
            dict: a dict of result metrics
        """
        logger = logging.getLogger(__name__)
        if isinstance(evaluators, DatasetEvaluator):
            evaluators = [evaluators]

        if cfg.MODEL.TEST.TASK == "panoptic":
            test_dataset = cfg.DATASETS.TEST_PANOPTIC
        elif cfg.MODEL.TEST.TASK == "instance":
            test_dataset = cfg.DATASETS.TEST_INSTANCE
        elif cfg.MODEL.TEST.TASK == "semantic":
            test_dataset = cfg.DATASETS.TEST_SEMANTIC
        else:
            warnings.warn(f"WARNING: No task provided! Setting task to default value: 'panoptic'")
            test_dataset = cfg.DATASETS.TEST_PANOPTIC

        if evaluators is not None:
            assert len(test_dataset) == len(evaluators), "{} != {}".format(
                len(test_dataset), len(evaluators)
            )

        results = OrderedDict

        results = OrderedDict()
        for idx, dataset_name in enumerate(test_dataset):
            data_loader = cls.build_test_loader(cfg, dataset_name)
            # When evaluators are passed in as arguments,
            # implicitly assume that evaluators can be created before data_loader.
            if evaluators is not None:
                evaluator = evaluators[idx]
            else:
                try:
                    evaluator = cls.build_evaluator(cfg, dataset_name)
                except NotImplementedError:
                    logger.warn(
                        "No evaluator found. Use `DefaultTrainer.test(evaluators=)`, "
                        "or implement its `build_evaluator` method."
                    )
                    results[dataset_name] = {}
                    continue
            results_i = inference_on_dataset(model, data_loader, evaluator)

            results[dataset_name] = results_i
            if comm.is_main_process():
                assert isinstance(
                    results_i, dict
                ), "Evaluator must return a dict on the main process. Got {} instead.".format(
                    results_i
                )
                logger.info("Evaluation results for {} in csv format:".format(dataset_name))
                print_csv_format(results_i)

        if len(results) == 1:
            results = list(results.values())[0]
        return results

def setup(args):
    """
    Create configs and perform basic setups.
    """
    cfg = get_cfg()
    # for poly lr schedule
    add_deeplab_config(cfg)
    add_common_config(cfg)
    add_swin_config(cfg)
    add_dinat_config(cfg)
    add_convnext_config(cfg)
    add_oneformer_config(cfg)
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()
    default_setup(cfg, args)
    if not args.eval_only:
        setup_wandb(cfg, args)


setup

                    )
                    results[dataset_name] = {}
                    continue
            results_i = inference_on_dataset(model, data_loader, evaluator)

            results[dataset_name] = results_i
            if comm.is_main_process():
                assert isinstance(
                    results_i, dict
                ), "Evaluator must return a dict on the main process. Got {} instead.".format(
                    results_i
                )
                logger.info("Evaluation results for {} in csv format:".format(dataset_name))
                print_csv_format(results_i)

        if len(results) == 1:
            results = list(results.values())[0]
        return results

def setup(args):
    """
    Create configs and perform basic setups.
    """
    cfg = get_cfg()
    # for poly lr schedule
    add_deeplab_config(cfg)
    add_common_config(cfg)
    add_swin_config(cfg)
    add_dinat_config(cfg)
    add_convnext_config(cfg)
    add_oneformer_config(cfg)
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()
    default_setup(cfg, args)
    if not args.eval_only:
        setup_wandb(cfg, args)
    # Setup logger for "oneformer" module
    setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="oneformer")
    return cfg

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

    if args.eval_only:
        model = Trainer.build_model(cfg)
        net_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
        print("Total Params: {} M".format(net_params/1e6))
        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)


main

    """
    cfg = get_cfg()
    # for poly lr schedule
    add_deeplab_config(cfg)
    add_common_config(cfg)
    add_swin_config(cfg)
    add_dinat_config(cfg)
    add_convnext_config(cfg)
    add_oneformer_config(cfg)
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()
    default_setup(cfg, args)
    if not args.eval_only:
        setup_wandb(cfg, args)
    # Setup logger for "oneformer" module
    setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="oneformer")
    return cfg

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

    if args.eval_only:
        model = Trainer.build_model(cfg)
        net_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
        print("Total Params: {} M".format(net_params/1e6))
        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)
    if args.machine_rank == 0:
        net_params = sum(p.numel() for p in trainer.model.parameters() if p.requires_grad)
        print("Total Params: {} M".format(net_params/1e6))
    sleep(3)
    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,),
    )


    SemanticSegmentorWithTTA,
    add_oneformer_config,
    add_common_config,
    add_swin_config,
    add_dinat_config,
    add_convnext_config,
)

from detectron2.utils.events import CommonMetricPrinter, JSONWriter
from oneformer.utils.events import WandbWriter, setup_wandb
from time import sleep
from oneformer.data.build import *
from oneformer.data.dataset_mappers.dataset_mapper import DatasetMapper

class Trainer(DefaultTrainer):
    """
    Extension of the Trainer class adapted to OneFormer.
    """

    @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))
            if cfg.MODEL.TEST.DETECTION_ON:
                evaluator_list.append(DetectionCOCOEvaluator(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.TEST.PANOPTIC_ON:
                evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
        # COCO
        if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.TEST.INSTANCE_ON:
            evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
        if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.TEST.SEMANTIC_ON:
            evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))
        if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.TEST.DETECTION_ON:
            evaluator_list.append(DetectionCOCOEvaluator(dataset_name, output_dir=output_folder))
        if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.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.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.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.TEST.INSTANCE_ON:
            evaluator_list.append(InstanceSegEvaluator(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):
        # Unified segmentation dataset mapper
        if cfg.INPUT.DATASET_MAPPER_NAME == "oneformer_unified":
            mapper = OneFormerUnifiedDatasetMapper(cfg, True)
            return build_detection_train_loader(cfg, mapper=mapper)
        # coco unified segmentation lsj new baseline
        elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_unified_lsj":
            mapper = COCOUnifiedNewBaselineDatasetMapper(cfg, True)
            return build_detection_train_loader(cfg, mapper=mapper)
        else:
            mapper = None
            return build_detection_train_loader(cfg, mapper=mapper)

    def build_writers(self):
        """
        Build a list of writers to be used. By default it contains
        writers that write metrics to the screen,
        a json file, and a tensorboard event file respectively.


            if cfg.MODEL.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.TEST.INSTANCE_ON:
            evaluator_list.append(InstanceSegEvaluator(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):
        # Unified segmentation dataset mapper
        if cfg.INPUT.DATASET_MAPPER_NAME == "oneformer_unified":
            mapper = OneFormerUnifiedDatasetMapper(cfg, True)
            return build_detection_train_loader(cfg, mapper=mapper)
        # coco unified segmentation lsj new baseline
        elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_unified_lsj":
            mapper = COCOUnifiedNewBaselineDatasetMapper(cfg, True)
            return build_detection_train_loader(cfg, mapper=mapper)
        else:
            mapper = None
            return build_detection_train_loader(cfg, mapper=mapper)

    def build_writers(self):
        """
        Build a list of writers to be used. By default it contains
        writers that write metrics to the screen,
        a json file, and a tensorboard event file respectively.
        If you'd like a different list of writers, you can overwrite it in
        your trainer.
        Returns:
            list[EventWriter]: a list of :class:`EventWriter` objects.
        It is now implemented by:
        ::
            return [
                CommonMetricPrinter(self.max_iter),
                JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, "metrics.json")),
                TensorboardXWriter(self.cfg.OUTPUT_DIR),
            ]
        """
        # Here the default print/log frequency of each writer is used.
        return [


            )
        elif len(evaluator_list) == 1:
            return evaluator_list[0]

        return DatasetEvaluators(evaluator_list)

    @classmethod
    def build_train_loader(cls, cfg):
        # Unified segmentation dataset mapper
        if cfg.INPUT.DATASET_MAPPER_NAME == "oneformer_unified":
            mapper = OneFormerUnifiedDatasetMapper(cfg, True)
            return build_detection_train_loader(cfg, mapper=mapper)
        # coco unified segmentation lsj new baseline
        elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_unified_lsj":
            mapper = COCOUnifiedNewBaselineDatasetMapper(cfg, True)
            return build_detection_train_loader(cfg, mapper=mapper)
        else:
            mapper = None
            return build_detection_train_loader(cfg, mapper=mapper)

    def build_writers(self):
        """
        Build a list of writers to be used. By default it contains
        writers that write metrics to the screen,
        a json file, and a tensorboard event file respectively.
        If you'd like a different list of writers, you can overwrite it in
        your trainer.
        Returns:
            list[EventWriter]: a list of :class:`EventWriter` objects.
        It is now implemented by:
        ::
            return [
                CommonMetricPrinter(self.max_iter),
                JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, "metrics.json")),
                TensorboardXWriter(self.cfg.OUTPUT_DIR),
            ]
        """
        # Here the default print/log frequency of each writer is used.
        return [
            # It may not always print what you want to see, since it prints "common" metrics only.
            CommonMetricPrinter(self.max_iter),
            JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, "metrics.json")),
            WandbWriter(),
        ]

    @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,


        your trainer.
        Returns:
            list[EventWriter]: a list of :class:`EventWriter` objects.
        It is now implemented by:
        ::
            return [
                CommonMetricPrinter(self.max_iter),
                JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, "metrics.json")),
                TensorboardXWriter(self.cfg.OUTPUT_DIR),
            ]
        """
        # Here the default print/log frequency of each writer is used.
        return [
            # It may not always print what you want to see, since it prints "common" metrics only.
            CommonMetricPrinter(self.max_iter),
            JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, "metrics.json")),
            WandbWriter(),
        ]

    @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,


                TensorboardXWriter(self.cfg.OUTPUT_DIR),
            ]
        """
        # Here the default print/log frequency of each writer is used.
        return [
            # It may not always print what you want to see, since it prints "common" metrics only.
            CommonMetricPrinter(self.max_iter),
            JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, "metrics.json")),
            WandbWriter(),
        ]

    @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])
                    for p in all_params:
                        torch.nan_to_num(p.grad, nan=0.0, posinf=1e5, neginf=-1e5, out=p.grad)
                    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_SEMANTIC
        ]
        res = cls.test(cfg, model, evaluators)
        res = OrderedDict({k + "_TTA": v for k, v in res.items()})
        return res

    @classmethod
    def build_test_loader(cls, cfg, dataset_name):
        """


                    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_SEMANTIC
        ]
        res = cls.test(cfg, model, evaluators)
        res = OrderedDict({k + "_TTA": v for k, v in res.items()})
        return res

    @classmethod
    def build_test_loader(cls, cfg, dataset_name):
        """
        Returns:
            iterable
        It now calls :func:`detectron2.data.build_detection_test_loader`.
        Overwrite it if you'd like a different data loader.
        """
        mapper = DatasetMapper(cfg, False)
        return build_detection_test_loader(cfg, dataset_name, mapper=mapper)

    @classmethod
    def test(cls, cfg, model, evaluators=None):
        """
        Evaluate the given model. The given model is expected to already contain
        weights to evaluate.
        Args:
            cfg (CfgNode):
            model (nn.Module):


            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_SEMANTIC
        ]
        res = cls.test(cfg, model, evaluators)
        res = OrderedDict({k + "_TTA": v for k, v in res.items()})
        return res

    @classmethod
    def build_test_loader(cls, cfg, dataset_name):
        """
        Returns:
            iterable
        It now calls :func:`detectron2.data.build_detection_test_loader`.
        Overwrite it if you'd like a different data loader.
        """
        mapper = DatasetMapper(cfg, False)
        return build_detection_test_loader(cfg, dataset_name, mapper=mapper)

    @classmethod
    def test(cls, cfg, model, evaluators=None):
        """
        Evaluate the given model. The given model is expected to already contain
        weights to evaluate.
        Args:
            cfg (CfgNode):
            model (nn.Module):
            evaluators (list[DatasetEvaluator] or None): if None, will call
                :meth:`build_evaluator`. Otherwise, must have the same length as
                ``cfg.DATASETS.TEST_{TASK}``.
        Returns:
            dict: a dict of result metrics
        """
        logger = logging.getLogger(__name__)
        if isinstance(evaluators, DatasetEvaluator):
            evaluators = [evaluators]

        if cfg.MODEL.TEST.TASK == "panoptic":


test

                cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, "inference_TTA")
            )
            for name in cfg.DATASETS.TEST_SEMANTIC
        ]
        res = cls.test(cfg, model, evaluators)
        res = OrderedDict({k + "_TTA": v for k, v in res.items()})
        return res

    @classmethod
    def build_test_loader(cls, cfg, dataset_name):
        """
        Returns:
            iterable
        It now calls :func:`detectron2.data.build_detection_test_loader`.
        Overwrite it if you'd like a different data loader.
        """
        mapper = DatasetMapper(cfg, False)
        return build_detection_test_loader(cfg, dataset_name, mapper=mapper)

    @classmethod
    def test(cls, cfg, model, evaluators=None):
        """
        Evaluate the given model. The given model is expected to already contain
        weights to evaluate.
        Args:
            cfg (CfgNode):
            model (nn.Module):
            evaluators (list[DatasetEvaluator] or None): if None, will call
                :meth:`build_evaluator`. Otherwise, must have the same length as
                ``cfg.DATASETS.TEST_{TASK}``.
        Returns:
            dict: a dict of result metrics
        """
        logger = logging.getLogger(__name__)
        if isinstance(evaluators, DatasetEvaluator):
            evaluators = [evaluators]

        if cfg.MODEL.TEST.TASK == "panoptic":
            test_dataset = cfg.DATASETS.TEST_PANOPTIC
        elif cfg.MODEL.TEST.TASK == "instance":
            test_dataset = cfg.DATASETS.TEST_INSTANCE
        elif cfg.MODEL.TEST.TASK == "semantic":
            test_dataset = cfg.DATASETS.TEST_SEMANTIC
        else:
            warnings.warn(f"WARNING: No task provided! Setting task to default value: 'panoptic'")
            test_dataset = cfg.DATASETS.TEST_PANOPTIC

        if evaluators is not None:
            assert len(test_dataset) == len(evaluators), "{} != {}".format(
                len(test_dataset), len(evaluators)
            )

        results = OrderedDict

        results = OrderedDict()
        for idx, dataset_name in enumerate(test_dataset):
            data_loader = cls.build_test_loader(cfg, dataset_name)
            # When evaluators are passed in as arguments,
            # implicitly assume that evaluators can be created before data_loader.
            if evaluators is not None:
                evaluator = evaluators[idx]
            else:
                try:
                    evaluator = cls.build_evaluator(cfg, dataset_name)
                except NotImplementedError:
                    logger.warn(
                        "No evaluator found. Use `DefaultTrainer.test(evaluators=)`, "
                        "or implement its `build_evaluator` method."
                    )
                    results[dataset_name] = {}
                    continue
            results_i = inference_on_dataset(model, data_loader, evaluator)

            results[dataset_name] = results_i
            if comm.is_main_process():
                assert isinstance(
                    results_i, dict
                ), "Evaluator must return a dict on the main process. Got {} instead.".format(
                    results_i
                )
                logger.info("Evaluation results for {} in csv format:".format(dataset_name))
                print_csv_format(results_i)

        if len(results) == 1:
            results = list(results.values())[0]
        return results

def setup(args):
    """
    Create configs and perform basic setups.
    """
    cfg = get_cfg()
    # for poly lr schedule
    add_deeplab_config(cfg)
    add_common_config(cfg)
    add_swin_config(cfg)
    add_dinat_config(cfg)
    add_convnext_config(cfg)
    add_oneformer_config(cfg)
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()
    default_setup(cfg, args)
    if not args.eval_only:
        setup_wandb(cfg, args)


                # 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])
                    for p in all_params:
                        torch.nan_to_num(p.grad, nan=0.0, posinf=1e5, neginf=-1e5, out=p.grad)
                    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.


FullModelGradientClippingOptimizer

                    "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])
                    for p in all_params:
                        torch.nan_to_num(p.grad, nan=0.0, posinf=1e5, neginf=-1e5, out=p.grad)
                    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):


step

                    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])
                    for p in all_params:
                        torch.nan_to_num(p.grad, nan=0.0, posinf=1e5, neginf=-1e5, out=p.grad)
                    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):


# ------------------------------------------------------------------------------
# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/test_time_augmentation.py
# ------------------------------------------------------------------------------

import copy
import logging
from itertools import count

import numpy as np
import torch
from fvcore.transforms import HFlipTransform
from torch import nn
from torch.nn.parallel import DistributedDataParallel

from detectron2.data.detection_utils import read_image
from .datasetmapper_tta import DatasetMapperTTA
import torch.nn.functional as F

__all__ = [
    "SemanticSegmentorWithTTA",
]

class SemanticSegmentorWithTTA(nn.Module):
    """
    A SemanticSegmentor with test-time augmentation enabled.
    Its :meth:`__call__` method has the same interface as :meth:`SemanticSegmentor.forward`.
    """

    def __init__(self, cfg, model, tta_mapper=None, batch_size=1):
        """
        Args:
            cfg (CfgNode):
            model (SemanticSegmentor): a SemanticSegmentor to apply TTA on.
            tta_mapper (callable): takes a dataset dict and returns a list of
                augmented versions of the dataset dict. Defaults to
                `DatasetMapperTTA(cfg)`.
            batch_size (int): batch the augmented images into this batch size for inference.
        """
        super().__init__()
        if isinstance(model, DistributedDataParallel):
            model = model.module
        self.cfg = cfg.clone()
        self.num_classes = self.cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES

        self.model = model

        if tta_mapper is None:
            tta_mapper = DatasetMapperTTA(cfg)
        self.tta_mapper = tta_mapper
        self.batch_size = batch_size

    def __call__(self, batched_inputs):
        """
        Same input/output format as :meth:`SemanticSegmentor.forward`
        """

        def _maybe_read_image(dataset_dict):
            ret = copy.copy(dataset_dict)
            if "image" not in ret:
                image = read_image(ret.pop("file_name"), self.model.input_format)
                image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1)))  # CHW
                ret["image"] = image
            if "height" not in ret and "width" not in ret:
                ret["height"] = image.shape[1]
                ret["width"] = image.shape[2]
            return ret

        processed_results = []
        for x in batched_inputs:
            result = self._inference_one_image(_maybe_read_image(x))
            processed_results.append(result)
        return processed_results

    def _inference_one_image(self, input):
        """
        Args:
            input (dict): one dataset dict with "image" field being a CHW tensor
        Returns:
            dict: one output dict
        """
        orig_shape = (input["height"], input["width"])
        augmented_inputs, tfms = self._get_augmented_inputs(input)

        final_predictions = None
        count_predictions = 0
        for input, tfm in zip(augmented_inputs, tfms):
            count_predictions += 1
            with torch.no_grad():
                if final_predictions is None:
                    if any(isinstance(t, HFlipTransform) for t in tfm.transforms):
                        final_predictions = self.model([input])[0].pop("sem_seg").flip(dims=[2])
                    else:
                        final_predictions = self.model([input])[0].pop("sem_seg")
                else:
                    if any(isinstance(t, HFlipTransform) for t in tfm.transforms):
                        final_predictions += self.model([input])[0].pop("sem_seg").flip(dims=[2])
                    else:
                        final_predictions += self.model([input])[0].pop("sem_seg")

        final_predictions = final_predictions / count_predictions
        return {"sem_seg": final_predictions}

    def _get_augmented_inputs(self, input):
        augmented_inputs = self.tta_mapper(input)
        tfms = [x.pop("transforms") for x in augmented_inputs]
        return augmented_inputs, tfms


SemanticSegmentorWithTTA

import copy
import logging
from itertools import count

import numpy as np
import torch
from fvcore.transforms import HFlipTransform
from torch import nn
from torch.nn.parallel import DistributedDataParallel

from detectron2.data.detection_utils import read_image
from .datasetmapper_tta import DatasetMapperTTA
import torch.nn.functional as F

__all__ = [
    "SemanticSegmentorWithTTA",
]

class SemanticSegmentorWithTTA(nn.Module):
    """
    A SemanticSegmentor with test-time augmentation enabled.
    Its :meth:`__call__` method has the same interface as :meth:`SemanticSegmentor.forward`.
    """

    def __init__(self, cfg, model, tta_mapper=None, batch_size=1):
        """
        Args:
            cfg (CfgNode):
            model (SemanticSegmentor): a SemanticSegmentor to apply TTA on.
            tta_mapper (callable): takes a dataset dict and returns a list of
                augmented versions of the dataset dict. Defaults to
                `DatasetMapperTTA(cfg)`.
            batch_size (int): batch the augmented images into this batch size for inference.
        """
        super().__init__()
        if isinstance(model, DistributedDataParallel):
            model = model.module
        self.cfg = cfg.clone()
        self.num_classes = self.cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES

        self.model = model

        if tta_mapper is None:
            tta_mapper = DatasetMapperTTA(cfg)
        self.tta_mapper = tta_mapper
        self.batch_size = batch_size

    def __call__(self, batched_inputs):
        """
        Same input/output format as :meth:`SemanticSegmentor.forward`
        """

        def _maybe_read_image(dataset_dict):
            ret = copy.copy(dataset_dict)
            if "image" not in ret:
                image = read_image(ret.pop("file_name"), self.model.input_format)
                image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1)))  # CHW
                ret["image"] = image
            if "height" not in ret and "width" not in ret:
                ret["height"] = image.shape[1]
                ret["width"] = image.shape[2]
            return ret

        processed_results = []
        for x in batched_inputs:
            result = self._inference_one_image(_maybe_read_image(x))
            processed_results.append(result)
        return processed_results

    def _inference_one_image(self, input):
        """
        Args:
            input (dict): one dataset dict with "image" field being a CHW tensor
        Returns:
            dict: one output dict
        """
        orig_shape = (input["height"], input["width"])
        augmented_inputs, tfms = self._get_augmented_inputs(input)

        final_predictions = None
        count_predictions = 0
        for input, tfm in zip(augmented_inputs, tfms):
            count_predictions += 1
            with torch.no_grad():
                if final_predictions is None:
                    if any(isinstance(t, HFlipTransform) for t in tfm.transforms):
                        final_predictions = self.model([input])[0].pop("sem_seg").flip(dims=[2])
                    else:
                        final_predictions = self.model([input])[0].pop("sem_seg")
                else:
                    if any(isinstance(t, HFlipTransform) for t in tfm.transforms):
                        final_predictions += self.model([input])[0].pop("sem_seg").flip(dims=[2])
                    else:
                        final_predictions += self.model([input])[0].pop("sem_seg")

        final_predictions = final_predictions / count_predictions
        return {"sem_seg": final_predictions}

    def _get_augmented_inputs(self, input):
        augmented_inputs = self.tta_mapper(input)
        tfms = [x.pop("transforms") for x in augmented_inputs]
        return augmented_inputs, tfms


import torch
from fvcore.transforms import HFlipTransform
from torch import nn
from torch.nn.parallel import DistributedDataParallel

from detectron2.data.detection_utils import read_image
from .datasetmapper_tta import DatasetMapperTTA
import torch.nn.functional as F

__all__ = [
    "SemanticSegmentorWithTTA",
]

class SemanticSegmentorWithTTA(nn.Module):
    """
    A SemanticSegmentor with test-time augmentation enabled.
    Its :meth:`__call__` method has the same interface as :meth:`SemanticSegmentor.forward`.
    """

    def __init__(self, cfg, model, tta_mapper=None, batch_size=1):
        """
        Args:
            cfg (CfgNode):
            model (SemanticSegmentor): a SemanticSegmentor to apply TTA on.
            tta_mapper (callable): takes a dataset dict and returns a list of
                augmented versions of the dataset dict. Defaults to
                `DatasetMapperTTA(cfg)`.
            batch_size (int): batch the augmented images into this batch size for inference.
        """
        super().__init__()
        if isinstance(model, DistributedDataParallel):
            model = model.module
        self.cfg = cfg.clone()
        self.num_classes = self.cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES

        self.model = model

        if tta_mapper is None:
            tta_mapper = DatasetMapperTTA(cfg)
        self.tta_mapper = tta_mapper
        self.batch_size = batch_size

    def __call__(self, batched_inputs):
        """
        Same input/output format as :meth:`SemanticSegmentor.forward`
        """

        def _maybe_read_image(dataset_dict):
            ret = copy.copy(dataset_dict)
            if "image" not in ret:
                image = read_image(ret.pop("file_name"), self.model.input_format)
                image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1)))  # CHW
                ret["image"] = image
            if "height" not in ret and "width" not in ret:
                ret["height"] = image.shape[1]
                ret["width"] = image.shape[2]
            return ret

        processed_results = []
        for x in batched_inputs:
            result = self._inference_one_image(_maybe_read_image(x))


            cfg (CfgNode):
            model (SemanticSegmentor): a SemanticSegmentor to apply TTA on.
            tta_mapper (callable): takes a dataset dict and returns a list of
                augmented versions of the dataset dict. Defaults to
                `DatasetMapperTTA(cfg)`.
            batch_size (int): batch the augmented images into this batch size for inference.
        """
        super().__init__()
        if isinstance(model, DistributedDataParallel):
            model = model.module
        self.cfg = cfg.clone()
        self.num_classes = self.cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES

        self.model = model

        if tta_mapper is None:
            tta_mapper = DatasetMapperTTA(cfg)
        self.tta_mapper = tta_mapper
        self.batch_size = batch_size

    def __call__(self, batched_inputs):
        """
        Same input/output format as :meth:`SemanticSegmentor.forward`
        """

        def _maybe_read_image(dataset_dict):
            ret = copy.copy(dataset_dict)
            if "image" not in ret:
                image = read_image(ret.pop("file_name"), self.model.input_format)
                image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1)))  # CHW
                ret["image"] = image
            if "height" not in ret and "width" not in ret:
                ret["height"] = image.shape[1]
                ret["width"] = image.shape[2]
            return ret

        processed_results = []
        for x in batched_inputs:
            result = self._inference_one_image(_maybe_read_image(x))
            processed_results.append(result)
        return processed_results

    def _inference_one_image(self, input):
        """
        Args:
            input (dict): one dataset dict with "image" field being a CHW tensor
        Returns:
            dict: one output dict
        """
        orig_shape = (input["height"], input["width"])
        augmented_inputs, tfms = self._get_augmented_inputs(input)

        final_predictions = None
        count_predictions = 0
        for input, tfm in zip(augmented_inputs, tfms):
            count_predictions += 1
            with torch.no_grad():
                if final_predictions is None:
                    if any(isinstance(t, HFlipTransform) for t in tfm.transforms):
                        final_predictions = self.model([input])[0].pop("sem_seg").flip(dims=[2])
                    else:


        Same input/output format as :meth:`SemanticSegmentor.forward`
        """

        def _maybe_read_image(dataset_dict):
            ret = copy.copy(dataset_dict)
            if "image" not in ret:
                image = read_image(ret.pop("file_name"), self.model.input_format)
                image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1)))  # CHW
                ret["image"] = image
            if "height" not in ret and "width" not in ret:
                ret["height"] = image.shape[1]
                ret["width"] = image.shape[2]
            return ret

        processed_results = []
        for x in batched_inputs:
            result = self._inference_one_image(_maybe_read_image(x))
            processed_results.append(result)
        return processed_results

    def _inference_one_image(self, input):
        """
        Args:
            input (dict): one dataset dict with "image" field being a CHW tensor
        Returns:
            dict: one output dict
        """
        orig_shape = (input["height"], input["width"])
        augmented_inputs, tfms = self._get_augmented_inputs(input)

        final_predictions = None
        count_predictions = 0
        for input, tfm in zip(augmented_inputs, tfms):
            count_predictions += 1
            with torch.no_grad():
                if final_predictions is None:
                    if any(isinstance(t, HFlipTransform) for t in tfm.transforms):
                        final_predictions = self.model([input])[0].pop("sem_seg").flip(dims=[2])
                    else:
                        final_predictions = self.model([input])[0].pop("sem_seg")
                else:
                    if any(isinstance(t, HFlipTransform) for t in tfm.transforms):
                        final_predictions += self.model([input])[0].pop("sem_seg").flip(dims=[2])
                    else:
                        final_predictions += self.model([input])[0].pop("sem_seg")

        final_predictions = final_predictions / count_predictions
        return {"sem_seg": final_predictions}

    def _get_augmented_inputs(self, input):
        augmented_inputs = self.tta_mapper(input)
        tfms = [x.pop("transforms") for x in augmented_inputs]
        return augmented_inputs, tfms


        final_predictions = None
        count_predictions = 0
        for input, tfm in zip(augmented_inputs, tfms):
            count_predictions += 1
            with torch.no_grad():
                if final_predictions is None:
                    if any(isinstance(t, HFlipTransform) for t in tfm.transforms):
                        final_predictions = self.model([input])[0].pop("sem_seg").flip(dims=[2])
                    else:
                        final_predictions = self.model([input])[0].pop("sem_seg")
                else:
                    if any(isinstance(t, HFlipTransform) for t in tfm.transforms):
                        final_predictions += self.model([input])[0].pop("sem_seg").flip(dims=[2])
                    else:
                        final_predictions += self.model([input])[0].pop("sem_seg")

        final_predictions = final_predictions / count_predictions
        return {"sem_seg": final_predictions}

    def _get_augmented_inputs(self, input):
        augmented_inputs = self.tta_mapper(input)
        tfms = [x.pop("transforms") for x in augmented_inputs]
        return augmented_inputs, tfms


            batch_size (int): batch the augmented images into this batch size for inference.
        """
        super().__init__()
        if isinstance(model, DistributedDataParallel):
            model = model.module
        self.cfg = cfg.clone()
        self.num_classes = self.cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES

        self.model = model

        if tta_mapper is None:
            tta_mapper = DatasetMapperTTA(cfg)
        self.tta_mapper = tta_mapper
        self.batch_size = batch_size

    def __call__(self, batched_inputs):
        """
        Same input/output format as :meth:`SemanticSegmentor.forward`
        """

        def _maybe_read_image(dataset_dict):
            ret = copy.copy(dataset_dict)
            if "image" not in ret:
                image = read_image(ret.pop("file_name"), self.model.input_format)
                image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1)))  # CHW
                ret["image"] = image
            if "height" not in ret and "width" not in ret:
                ret["height"] = image.shape[1]
                ret["width"] = image.shape[2]
            return ret

        processed_results = []
        for x in batched_inputs:
            result = self._inference_one_image(_maybe_read_image(x))
            processed_results.append(result)
        return processed_results

    def _inference_one_image(self, input):
        """
        Args:
            input (dict): one dataset dict with "image" field being a CHW tensor
        Returns:
            dict: one output dict
        """
        orig_shape = (input["height"], input["width"])
        augmented_inputs, tfms = self._get_augmented_inputs(input)

        final_predictions = None
        count_predictions = 0
        for input, tfm in zip(augmented_inputs, tfms):


oneformer.config

# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
from detectron2.config import CfgNode as CN

__all__ = ["add_common_config", "add_oneformer_config", "add_swin_config",
            "add_dinat_config", "add_convnext_config"]

def add_common_config(cfg):
    """
    Add config for common configuration
    """

    # data config
    # select the dataset mapper
    cfg.INPUT.DATASET_MAPPER_NAME = "oneformer_unified"
    # Color augmentation
    cfg.INPUT.COLOR_AUG_SSD = False
    # We retry random cropping until no single category in semantic segmentation GT occupies more
    # than `SINGLE_CATEGORY_MAX_AREA` part of the crop.
    cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0
    # Pad image and segmentation GT in dataset mapper.
    cfg.INPUT.SIZE_DIVISIBILITY = -1

    cfg.INPUT.TASK_SEQ_LEN = 77
    cfg.INPUT.MAX_SEQ_LEN = 77

    cfg.INPUT.TASK_PROB = CN()
    cfg.INPUT.TASK_PROB.SEMANTIC = 0.33
    cfg.INPUT.TASK_PROB.INSTANCE = 0.66

    # test dataset
    cfg.DATASETS.TEST_PANOPTIC = ("",)
    cfg.DATASETS.TEST_INSTANCE = ("",)
    cfg.DATASETS.TEST_SEMANTIC = ("",)

    # solver config
    # weight decay on embedding
    cfg.SOLVER.WEIGHT_DECAY_EMBED = 0.0
    # optimizer
    cfg.SOLVER.OPTIMIZER = "ADAMW"
    cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1

    # wandb
    cfg.WANDB = CN()
    cfg.WANDB.PROJECT = "OneFormer"
    cfg.WANDB.NAME = None

    cfg.MODEL.IS_TRAIN = True
    cfg.MODEL.IS_DEMO = False

    # text encoder config
    cfg.MODEL.TEXT_ENCODER = CN()

    cfg.MODEL.TEXT_ENCODER.WIDTH = 256
    cfg.MODEL.TEXT_ENCODER.CONTEXT_LENGTH = 77
    cfg.MODEL.TEXT_ENCODER.NUM_LAYERS = 12
    cfg.MODEL.TEXT_ENCODER.VOCAB_SIZE = 49408
    cfg.MODEL.TEXT_ENCODER.PROJ_NUM_LAYERS = 2
    cfg.MODEL.TEXT_ENCODER.N_CTX = 16

    # oneformer inference config
    cfg.MODEL.TEST = CN()
    cfg.MODEL.TEST.SEMANTIC_ON = True
    cfg.MODEL.TEST.INSTANCE_ON = False
    cfg.MODEL.TEST.PANOPTIC_ON = False
    cfg.MODEL.TEST.DETECTION_ON = False
    cfg.MODEL.TEST.OBJECT_MASK_THRESHOLD = 0.0
    cfg.MODEL.TEST.OVERLAP_THRESHOLD = 0.0
    cfg.MODEL.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE = False
    cfg.MODEL.TEST.TASK = "panoptic"

    # TEST AUG Slide
    cfg.TEST.AUG.IS_SLIDE = False
    cfg.TEST.AUG.CROP_SIZE = (640, 640)
    cfg.TEST.AUG.STRIDE = (426, 426)
    cfg.TEST.AUG.SCALE = (2048, 640)
    cfg.TEST.AUG.SETR_MULTI_SCALE = True
    cfg.TEST.AUG.KEEP_RATIO = True
    cfg.TEST.AUG.SIZE_DIVISOR = 32

    # pixel decoder config
    cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256
    # adding transformer in pixel decoder
    cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 0
    # pixel decoder
    cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME = "BasePixelDecoder"
    cfg.MODEL.SEM_SEG_HEAD.SEM_EMBED_DIM = 256
    cfg.MODEL.SEM_SEG_HEAD.INST_EMBED_DIM = 256

    # LSJ aug
    cfg.INPUT.IMAGE_SIZE = 1024
    cfg.INPUT.MIN_SCALE = 0.1
    cfg.INPUT.MAX_SCALE = 2.0

    # MSDeformAttn encoder configs
    cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = ["res3", "res4", "res5"]
    cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_POINTS = 4
    cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_HEADS = 8

def add_oneformer_config(cfg):
    """
    Add config for ONE_FORMER.
    """

    # oneformer model config
    cfg.MODEL.ONE_FORMER = CN()

    # loss
    cfg.MODEL.ONE_FORMER.DEEP_SUPERVISION = True
    cfg.MODEL.ONE_FORMER.NO_OBJECT_WEIGHT = 0.1
    cfg.MODEL.ONE_FORMER.CLASS_WEIGHT = 1.0
    cfg.MODEL.ONE_FORMER.DICE_WEIGHT = 1.0
    cfg.MODEL.ONE_FORMER.MASK_WEIGHT = 20.0
    cfg.MODEL.ONE_FORMER.CONTRASTIVE_WEIGHT = 0.5
    cfg.MODEL.ONE_FORMER.CONTRASTIVE_TEMPERATURE = 0.07

    # transformer config
    cfg.MODEL.ONE_FORMER.NHEADS = 8
    cfg.MODEL.ONE_FORMER.DROPOUT = 0.1
    cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD = 2048
    cfg.MODEL.ONE_FORMER.ENC_LAYERS = 0
    cfg.MODEL.ONE_FORMER.CLASS_DEC_LAYERS = 2
    cfg.MODEL.ONE_FORMER.DEC_LAYERS = 6
    cfg.MODEL.ONE_FORMER.PRE_NORM = False

    cfg.MODEL.ONE_FORMER.HIDDEN_DIM = 256
    cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES = 120
    cfg.MODEL.ONE_FORMER.NUM_OBJECT_CTX = 16
    cfg.MODEL.ONE_FORMER.USE_TASK_NORM = True

    cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE = "res5"
    cfg.MODEL.ONE_FORMER.ENFORCE_INPUT_PROJ = False

    # Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet)
    # you can use this config to override
    cfg.MODEL.ONE_FORMER.SIZE_DIVISIBILITY = 32

    # transformer module
    cfg.MODEL.ONE_FORMER.TRANSFORMER_DECODER_NAME = "ContrastiveMultiScaleMaskedTransformerDecoder"

    # point loss configs
    # Number of points sampled during training for a mask point head.
    cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS = 112 * 112
    # Oversampling parameter for PointRend point sampling during training. Parameter `k` in the
    # original paper.
    cfg.MODEL.ONE_FORMER.OVERSAMPLE_RATIO = 3.0
    # Importance sampling parameter for PointRend point sampling during training. Parametr `beta` in
    # the original paper.
    cfg.MODEL.ONE_FORMER.IMPORTANCE_SAMPLE_RATIO = 0.75

def add_swin_config(cfg):
    """
    Add config forSWIN Backbone.
    """

    # swin transformer backbone
    cfg.MODEL.SWIN = CN()
    cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224
    cfg.MODEL.SWIN.PATCH_SIZE = 4
    cfg.MODEL.SWIN.EMBED_DIM = 96
    cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]
    cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]
    cfg.MODEL.SWIN.WINDOW_SIZE = 7
    cfg.MODEL.SWIN.MLP_RATIO = 4.0
    cfg.MODEL.SWIN.QKV_BIAS = True
    cfg.MODEL.SWIN.QK_SCALE = None
    cfg.MODEL.SWIN.DROP_RATE = 0.0
    cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0
    cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3
    cfg.MODEL.SWIN.APE = False
    cfg.MODEL.SWIN.PATCH_NORM = True
    cfg.MODEL.SWIN.OUT_FEATURES = ["res2", "res3", "res4", "res5"]
    cfg.MODEL.SWIN.USE_CHECKPOINT = False

def add_dinat_config(cfg):
    """
    Add config for NAT Backbone.
    """

    # DINAT transformer backbone
    cfg.MODEL.DiNAT = CN()
    cfg.MODEL.DiNAT.DEPTHS = [3, 4, 18, 5]
    cfg.MODEL.DiNAT.OUT_FEATURES = ["res2", "res3", "res4", "res5"]
    cfg.MODEL.DiNAT.EMBED_DIM = 64
    cfg.MODEL.DiNAT.MLP_RATIO = 3.0
    cfg.MODEL.DiNAT.NUM_HEADS = [2, 4, 8, 16]
    cfg.MODEL.DiNAT.DROP_PATH_RATE = 0.2
    cfg.MODEL.DiNAT.KERNEL_SIZE = 7
    cfg.MODEL.DiNAT.DILATIONS = [[1, 16, 1], [1, 4, 1, 8], [1, 2, 1, 3, 1, 4], [1, 2, 1, 2, 1]]
    cfg.MODEL.DiNAT.OUT_INDICES = (0, 1, 2, 3)
    cfg.MODEL.DiNAT.QKV_BIAS = True
    cfg.MODEL.DiNAT.QK_SCALE = None
    cfg.MODEL.DiNAT.DROP_RATE = 0
    cfg.MODEL.DiNAT.ATTN_DROP_RATE = 0.
    cfg.MODEL.DiNAT.IN_PATCH_SIZE = 4

def add_convnext_config(cfg):
    """
    Add config for ConvNeXt Backbone.
    """

    # swin transformer backbone
    cfg.MODEL.CONVNEXT = CN()
    cfg.MODEL.CONVNEXT.IN_CHANNELS = 3
    cfg.MODEL.CONVNEXT.DEPTHS = [3, 3, 27, 3]
    cfg.MODEL.CONVNEXT.DIMS = [192, 384, 768, 1536]
    cfg.MODEL.CONVNEXT.DROP_PATH_RATE = 0.4
    cfg.MODEL.CONVNEXT.LSIT = 1.0
    cfg.MODEL.CONVNEXT.OUT_INDICES = [0, 1, 2, 3]
    cfg.MODEL.CONVNEXT.OUT_FEATURES = ["res2", "res3", "res4", "res5"]


# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
from detectron2.config import CfgNode as CN

__all__ = ["add_common_config", "add_oneformer_config", "add_swin_config",
            "add_dinat_config", "add_convnext_config"]

def add_common_config(cfg):
    """
    Add config for common configuration
    """

    # data config
    # select the dataset mapper
    cfg.INPUT.DATASET_MAPPER_NAME = "oneformer_unified"
    # Color augmentation
    cfg.INPUT.COLOR_AUG_SSD = False
    # We retry random cropping until no single category in semantic segmentation GT occupies more
    # than `SINGLE_CATEGORY_MAX_AREA` part of the crop.
    cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0
    # Pad image and segmentation GT in dataset mapper.
    cfg.INPUT.SIZE_DIVISIBILITY = -1

    cfg.INPUT.TASK_SEQ_LEN = 77
    cfg.INPUT.MAX_SEQ_LEN = 77

    cfg.INPUT.TASK_PROB = CN()
    cfg.INPUT.TASK_PROB.SEMANTIC = 0.33
    cfg.INPUT.TASK_PROB.INSTANCE = 0.66

    # test dataset
    cfg.DATASETS.TEST_PANOPTIC = ("",)
    cfg.DATASETS.TEST_INSTANCE = ("",)
    cfg.DATASETS.TEST_SEMANTIC = ("",)

    # solver config
    # weight decay on embedding
    cfg.SOLVER.WEIGHT_DECAY_EMBED = 0.0
    # optimizer
    cfg.SOLVER.OPTIMIZER = "ADAMW"
    cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1

    # wandb
    cfg.WANDB = CN()
    cfg.WANDB.PROJECT = "OneFormer"
    cfg.WANDB.NAME = None

    cfg.MODEL.IS_TRAIN = True
    cfg.MODEL.IS_DEMO = False

    # text encoder config
    cfg.MODEL.TEXT_ENCODER = CN()

    cfg.MODEL.TEXT_ENCODER.WIDTH = 256
    cfg.MODEL.TEXT_ENCODER.CONTEXT_LENGTH = 77
    cfg.MODEL.TEXT_ENCODER.NUM_LAYERS = 12
    cfg.MODEL.TEXT_ENCODER.VOCAB_SIZE = 49408
    cfg.MODEL.TEXT_ENCODER.PROJ_NUM_LAYERS = 2
    cfg.MODEL.TEXT_ENCODER.N_CTX = 16

    # oneformer inference config
    cfg.MODEL.TEST = CN()
    cfg.MODEL.TEST.SEMANTIC_ON = True
    cfg.MODEL.TEST.INSTANCE_ON = False
    cfg.MODEL.TEST.PANOPTIC_ON = False
    cfg.MODEL.TEST.DETECTION_ON = False
    cfg.MODEL.TEST.OBJECT_MASK_THRESHOLD = 0.0
    cfg.MODEL.TEST.OVERLAP_THRESHOLD = 0.0
    cfg.MODEL.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE = False
    cfg.MODEL.TEST.TASK = "panoptic"

    # TEST AUG Slide
    cfg.TEST.AUG.IS_SLIDE = False
    cfg.TEST.AUG.CROP_SIZE = (640, 640)
    cfg.TEST.AUG.STRIDE = (426, 426)
    cfg.TEST.AUG.SCALE = (2048, 640)
    cfg.TEST.AUG.SETR_MULTI_SCALE = True
    cfg.TEST.AUG.KEEP_RATIO = True
    cfg.TEST.AUG.SIZE_DIVISOR = 32

    # pixel decoder config
    cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256
    # adding transformer in pixel decoder
    cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 0
    # pixel decoder
    cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME = "BasePixelDecoder"
    cfg.MODEL.SEM_SEG_HEAD.SEM_EMBED_DIM = 256
    cfg.MODEL.SEM_SEG_HEAD.INST_EMBED_DIM = 256

    # LSJ aug
    cfg.INPUT.IMAGE_SIZE = 1024
    cfg.INPUT.MIN_SCALE = 0.1
    cfg.INPUT.MAX_SCALE = 2.0

    # MSDeformAttn encoder configs
    cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = ["res3", "res4", "res5"]
    cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_POINTS = 4
    cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_HEADS = 8

def add_oneformer_config(cfg):
    """
    Add config for ONE_FORMER.
    """

    # oneformer model config
    cfg.MODEL.ONE_FORMER = CN()

    # loss
    cfg.MODEL.ONE_FORMER.DEEP_SUPERVISION = True
    cfg.MODEL.ONE_FORMER.NO_OBJECT_WEIGHT = 0.1
    cfg.MODEL.ONE_FORMER.CLASS_WEIGHT = 1.0
    cfg.MODEL.ONE_FORMER.DICE_WEIGHT = 1.0
    cfg.MODEL.ONE_FORMER.MASK_WEIGHT = 20.0
    cfg.MODEL.ONE_FORMER.CONTRASTIVE_WEIGHT = 0.5
    cfg.MODEL.ONE_FORMER.CONTRASTIVE_TEMPERATURE = 0.07

    # transformer config
    cfg.MODEL.ONE_FORMER.NHEADS = 8


    # pixel decoder config
    cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256
    # adding transformer in pixel decoder
    cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 0
    # pixel decoder
    cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME = "BasePixelDecoder"
    cfg.MODEL.SEM_SEG_HEAD.SEM_EMBED_DIM = 256
    cfg.MODEL.SEM_SEG_HEAD.INST_EMBED_DIM = 256

    # LSJ aug
    cfg.INPUT.IMAGE_SIZE = 1024
    cfg.INPUT.MIN_SCALE = 0.1
    cfg.INPUT.MAX_SCALE = 2.0

    # MSDeformAttn encoder configs
    cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = ["res3", "res4", "res5"]
    cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_POINTS = 4
    cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_HEADS = 8

def add_oneformer_config(cfg):
    """
    Add config for ONE_FORMER.
    """

    # oneformer model config
    cfg.MODEL.ONE_FORMER = CN()

    # loss
    cfg.MODEL.ONE_FORMER.DEEP_SUPERVISION = True
    cfg.MODEL.ONE_FORMER.NO_OBJECT_WEIGHT = 0.1
    cfg.MODEL.ONE_FORMER.CLASS_WEIGHT = 1.0
    cfg.MODEL.ONE_FORMER.DICE_WEIGHT = 1.0
    cfg.MODEL.ONE_FORMER.MASK_WEIGHT = 20.0
    cfg.MODEL.ONE_FORMER.CONTRASTIVE_WEIGHT = 0.5
    cfg.MODEL.ONE_FORMER.CONTRASTIVE_TEMPERATURE = 0.07

    # transformer config
    cfg.MODEL.ONE_FORMER.NHEADS = 8
    cfg.MODEL.ONE_FORMER.DROPOUT = 0.1
    cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD = 2048
    cfg.MODEL.ONE_FORMER.ENC_LAYERS = 0
    cfg.MODEL.ONE_FORMER.CLASS_DEC_LAYERS = 2
    cfg.MODEL.ONE_FORMER.DEC_LAYERS = 6
    cfg.MODEL.ONE_FORMER.PRE_NORM = False

    cfg.MODEL.ONE_FORMER.HIDDEN_DIM = 256
    cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES = 120
    cfg.MODEL.ONE_FORMER.NUM_OBJECT_CTX = 16
    cfg.MODEL.ONE_FORMER.USE_TASK_NORM = True

    cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE = "res5"
    cfg.MODEL.ONE_FORMER.ENFORCE_INPUT_PROJ = False

    # Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet)
    # you can use this config to override
    cfg.MODEL.ONE_FORMER.SIZE_DIVISIBILITY = 32

    # transformer module
    cfg.MODEL.ONE_FORMER.TRANSFORMER_DECODER_NAME = "ContrastiveMultiScaleMaskedTransformerDecoder"

    # point loss configs
    # Number of points sampled during training for a mask point head.
    cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS = 112 * 112
    # Oversampling parameter for PointRend point sampling during training. Parameter `k` in the
    # original paper.
    cfg.MODEL.ONE_FORMER.OVERSAMPLE_RATIO = 3.0
    # Importance sampling parameter for PointRend point sampling during training. Parametr `beta` in
    # the original paper.
    cfg.MODEL.ONE_FORMER.IMPORTANCE_SAMPLE_RATIO = 0.75

def add_swin_config(cfg):
    """
    Add config forSWIN Backbone.
    """

    # swin transformer backbone
    cfg.MODEL.SWIN = CN()
    cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224
    cfg.MODEL.SWIN.PATCH_SIZE = 4
    cfg.MODEL.SWIN.EMBED_DIM = 96
    cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]
    cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]
    cfg.MODEL.SWIN.WINDOW_SIZE = 7
    cfg.MODEL.SWIN.MLP_RATIO = 4.0
    cfg.MODEL.SWIN.QKV_BIAS = True
    cfg.MODEL.SWIN.QK_SCALE = None
    cfg.MODEL.SWIN.DROP_RATE = 0.0
    cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0
    cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3