train_net.py

# ------------------------------------------------------------------------------
# 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,),
    )