train_net1.py

# ------------------------------------------------------------------------
# Copyright (c) 2022 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# by Feng Li and Hao Zhang.
# ------------------------------------------------------------------------
"""
MaskDINO Training Script based on Mask2Former.
"""
try:
    from shapely.errors import ShapelyDeprecationWarning
    import warnings
    warnings.filterwarnings('ignore', category=ShapelyDeprecationWarning)
except:
    pass

import copy
import itertools
import logging
import os

from collections import OrderedDict
from typing import Any, Dict, List, Set

import torch

import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from config.add_cfg import add_ssl_config
# from detectron2.data import MetadataCatalog , build_detection_train_loader
from data import MetadataCatalog, build_detection_train_loader

from detectron2.engine import (
    # DefaultTrainer,
    default_argument_parser,
    default_setup,
    # hooks,
    launch,
    # create_ddp_model,
    # AMPTrainer,
    # SimpleTrainer
)

from modules.defaults import DefaultTrainer

from detectron2.evaluation import (
    CityscapesInstanceEvaluator,
    CityscapesSemSegEvaluator,
    COCOEvaluator,
    COCOPanopticEvaluator,
    DatasetEvaluators,
    LVISEvaluator,
    SemSegEvaluator,
    verify_results,
)
from detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler
from detectron2.solver.build import maybe_add_gradient_clipping
from detectron2.utils.logger import setup_logger

# MaskDINO
from maskdino import (
    COCOInstanceNewBaselineDatasetMapper,
    COCOPanopticNewBaselineDatasetMapper,
    InstanceSegEvaluator,
    MaskFormerSemanticDatasetMapper,
    SemanticSegmentorWithTTA,
    ImageDatasetMapper,
    add_maskdino_config,
    DetrDatasetMapper,
)
import random

import weakref
from maskdino.modeling import ema

class Trainer(DefaultTrainer):
    """
    Extension of the Trainer class adapted to MaskFormer.
    """
    # def __init__(self, cfg):
    #     super(DefaultTrainer, self).__init__()
    #     logger = logging.getLogger("detectron2")
    #     if not logger.isEnabledFor(logging.INFO):  # setup_logger is not called for d2
    #         setup_logger()
    #     cfg = DefaultTrainer.auto_scale_workers(cfg, comm.get_world_size())

    #     # Assume these objects must be constructed in this order.
    #     model = self.build_model(cfg)
    #     optimizer = self.build_optimizer(cfg, model)
    #     data_loader = self.build_train_loader(cfg)
        
    #     model = create_ddp_model(model, broadcast_buffers=False)
    #     self._trainer = (AMPTrainer if cfg.SOLVER.AMP.ENABLED else SimpleTrainer)(
    #         model, data_loader, optimizer
    #     )

    #     self.scheduler = self.build_lr_scheduler(cfg, optimizer)

    #     # add model EMA
    #     # kwargs = {
    #     #     'trainer': weakref.proxy(self),
    #     # }
    #     ema.may_build_model_ema(cfg, model)
    #     # kwargs.update(model_ema.may_get_ema_checkpointer(cfg, model)) TODO: release ema training for large models
    #     self.checkpointer = DetectionCheckpointer(
    #         # Assume you want to save checkpoints together with logs/statistics
    #         model,
    #         cfg.OUTPUT_DIR,
    #         # **kwargs,
    #         **ema.may_get_ema_checkpointer(cfg, model)
    #     )
    #     self.start_iter = 0
    #     self.max_iter = cfg.SOLVER.MAX_ITER
    #     self.cfg = cfg

    #     # self.register_hooks(self.build_hooks())
    #     self.register_hooks(self.build_hooks())

    #     self._trainer.register_hooks([ema.EMAHook(cfg, model)])

    #     # TODO: release model conversion checkpointer from DINO to MaskDINO
    #     self.checkpointer = DetectionCheckpointer(
    #         # Assume you want to save checkpoints together with logs/statistics
    #         model,
    #         cfg.OUTPUT_DIR,
    #         # **kwargs,
    #         **ema.may_get_ema_checkpointer(cfg, model)
    #     )
    #     # TODO: release GPU cluster submit scripts based on submitit for multi-node training

    @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.
        """
        Max_det_per_image = cfg.TEST.DETECTIONS_PER_IMAGE
        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,max_dets_per_image=Max_det_per_image))
        # panoptic segmentation
        if evaluator_type in [
            "coco_panoptic_seg",
            "ade20k_panoptic_seg",
            "cityscapes_panoptic_seg",
            "mapillary_vistas_panoptic_seg",
        ]:
            if cfg.MODEL.MaskDINO.TEST.PANOPTIC_ON:
                evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
        # COCO
        if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MaskDINO.TEST.INSTANCE_ON:
            evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
        if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MaskDINO.TEST.SEMANTIC_ON:
            evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))
        # Mapillary Vistas
        if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.MaskDINO.TEST.INSTANCE_ON:
            evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))
        if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.MaskDINO.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.MaskDINO.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.MaskDINO.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.MaskDINO.TEST.INSTANCE_ON:
            evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))
        # LVIS
        if evaluator_type == "lvis":
            return LVISEvaluator(dataset_name, output_dir=output_folder)
        if len(evaluator_list) == 0:
            raise NotImplementedError(
                "no Evaluator for the dataset {} with the type {}".format(
                    dataset_name, evaluator_type
                )
            )
        elif len(evaluator_list) == 1:
            return evaluator_list[0]
        return DatasetEvaluators(evaluator_list)

    @classmethod
    def build_train_loader(cls, cfg):
        # coco instance segmentation lsj new baseline
        if cfg.INPUT.DATASET_MAPPER_NAME == "coco_instance_lsj":
            mapper = COCOInstanceNewBaselineDatasetMapper(cfg, True)
            cfg.defrost()
            cfg_gan = cfg.clone()
            cfg_gan.DATASETS.TRAIN = ("chestx_instance_train",)
            cfg_gan.freeze()
            cfg.freeze()
            mapper_unl = ImageDatasetMapper(cfg_gan, True)
            return build_detection_train_loader(cfg, mapper=mapper), build_detection_train_loader(cfg_gan, mapper=mapper_unl)
        # coco instance segmentation lsj new baseline
        elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_instance_detr":
            mapper = DetrDatasetMapper(cfg, True)
            return build_detection_train_loader(cfg, mapper=mapper)
        # coco panoptic segmentation lsj new baseline
        elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_panoptic_lsj":
            mapper = COCOPanopticNewBaselineDatasetMapper(cfg, True)
            return build_detection_train_loader(cfg, mapper=mapper)
        # Semantic segmentation dataset mapper
        elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_semantic":
            mapper = MaskFormerSemanticDatasetMapper(cfg, True)
            return build_detection_train_loader(cfg, mapper=mapper)
        else:
            mapper = None
            return build_detection_train_loader(cfg, mapper=mapper)

    @classmethod
    def build_lr_scheduler(cls, cfg, optimizer):
        """
        It now calls :func:`detectron2.solver.build_lr_scheduler`.
        Overwrite it if you'd like a different scheduler.
        """
        return build_lr_scheduler(cfg, optimizer)

    @classmethod
    def build_optimizer(cls, cfg, model):
        weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM
        weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED

        defaults = {}
        defaults["lr"] = cfg.SOLVER.BASE_LR
        defaults["weight_decay"] = cfg.SOLVER.WEIGHT_DECAY

        norm_module_types = (
            torch.nn.BatchNorm1d,
            torch.nn.BatchNorm2d,
            torch.nn.BatchNorm3d,
            torch.nn.SyncBatchNorm,
            # NaiveSyncBatchNorm inherits from BatchNorm2d
            torch.nn.GroupNorm,
            torch.nn.InstanceNorm1d,
            torch.nn.InstanceNorm2d,
            torch.nn.InstanceNorm3d,
            torch.nn.LayerNorm,
            torch.nn.LocalResponseNorm,
        )

        params: List[Dict[str, Any]] = []
        memo: Set[torch.nn.parameter.Parameter] = set()
        for module_name, module in model.named_modules():
            for module_param_name, value in module.named_parameters(recurse=False):
                if not value.requires_grad:
                    continue
                # Avoid duplicating parameters
                if value in memo:
                    continue
                memo.add(value)

                hyperparams = copy.copy(defaults)
                if "backbone" in module_name:
                    hyperparams["lr"] = hyperparams["lr"] * cfg.SOLVER.BACKBONE_MULTIPLIER
                if (
                    "relative_position_bias_table" in module_param_name
                    or "absolute_pos_embed" in module_param_name
                ):
                    print(module_param_name)
                    hyperparams["weight_decay"] = 0.0
                if isinstance(module, norm_module_types):
                    hyperparams["weight_decay"] = weight_decay_norm
                if isinstance(module, torch.nn.Embedding):
                    hyperparams["weight_decay"] = weight_decay_embed
                params.append({"params": [value], **hyperparams})

        def maybe_add_full_model_gradient_clipping(optim):
            # detectron2 doesn't have full model gradient clipping now
            clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE
            enable = (
                cfg.SOLVER.CLIP_GRADIENTS.ENABLED
                and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model"
                and clip_norm_val > 0.0
            )

            class FullModelGradientClippingOptimizer(optim):
                def step(self, closure=None):
                    all_params = itertools.chain(*[x["params"] for x in self.param_groups])
                    torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)
                    super().step(closure=closure)

            return FullModelGradientClippingOptimizer if enable else optim

        optimizer_type = cfg.SOLVER.OPTIMIZER
        if optimizer_type == "SGD":
            optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(
                params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM
            )
        elif optimizer_type == "ADAMW":
            optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(
                params, cfg.SOLVER.BASE_LR
            )
        else:
            raise NotImplementedError(f"no optimizer type {optimizer_type}")
        if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model":
            optimizer = maybe_add_gradient_clipping(cfg, optimizer)
        return optimizer

    @classmethod
    def test_with_TTA(cls, cfg, model):
        logger = logging.getLogger("detectron2.trainer")
        # In the end of training, run an evaluation with TTA.
        logger.info("Running inference with test-time augmentation ...")
        model = SemanticSegmentorWithTTA(cfg, model)
        evaluators = [
            cls.build_evaluator(
                cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, "inference_TTA")
            )
            for name in cfg.DATASETS.TEST
        ]
        res = cls.test(cfg, model, evaluators)
        res = OrderedDict({k + "_TTA": v for k, v in res.items()})
        return res


def setup(args):
    """
    Create configs and perform basic setups.
    """
    cfg = get_cfg()
    # for poly lr schedule
    add_deeplab_config(cfg)
    add_ssl_config(cfg)
    add_maskdino_config(cfg)
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)

    if cfg.SSL.PERCENTAGE != 100:
        cfg.DATASETS.TRAIN = (cfg.DATASETS.TRAIN[0]+f"_{cfg.SSL.PERCENTAGE}",)
    if cfg.SSL.TRAIN_SSL:
        cfg.DATALOADER.SAMPLER_TRAIN = "RepeatFactorTrainingSampler"
        cfg.DATALOADER.REPEAT_THRESHOLD = 10.0

    cfg.freeze()
    default_setup(cfg, args)
    setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="maskdino")
    return cfg

def parse_ckpt(ckpt_dir):
    if ckpt_dir.startswith('detectron2://'):
        return 'STUDENT'
    ckpt = torch.load(ckpt_dir, map_location='cpu')['model']
    student_keys = False
    teacher_keys = False
    
    # detect which keys are in the ckpt.
    for k in ckpt.keys():
        if k.startswith('modelStudent'):
            student_keys = True
        if k.startswith('modelTeacher'):
            teacher_keys = True
        if student_keys and teacher_keys:
            break

    if student_keys and teacher_keys:
        return 'BOTH'
    elif student_keys:
        return 'STUDENT'
    elif teacher_keys:
        return 'TEACHER'
    else:
        return 'NEITHER'

def main(args):
    cfg = setup(args)
    print("Command cfg:", cfg)
    if args.eval_only:
        model = Trainer.build_model(cfg)
        trainer = Trainer(cfg)
        trainer.resume_or_load(resume=args.resume)
        
        # Load checkpoint to the appropriate model.
        if cfg.SSL.TRAIN_SSL:
            which = parse_ckpt(cfg.MODEL.WEIGHTS)
            if which == 'BOTH':
                checkp = trainer._trainer.ensemble_model
            elif which == 'STUDENT':
                checkp = trainer._trainer.ensemble_model.modelStudent
            elif which == 'TEACHER':
                checkp = trainer._trainer.ensemble_model.modelTeacher
            else:
                checkp = trainer._trainer.ensemble_model.modelStudent
        else:
            checkp = trainer._trainer.ensemble_model #model

        DetectionCheckpointer(checkp, save_dir=cfg.OUTPUT_DIR).resume_or_load(
            cfg.MODEL.WEIGHTS, resume=args.resume
        )
        # checkpointer = DetectionCheckpointer(checkp, save_dir=cfg.OUTPUT_DIR)
        # checkpointer.resume_or_load(
        #     cfg.MODEL.WEIGHTS, resume=args.resume
        # )

        eval_tar = cfg.SSL.EVAL_WHO
        if eval_tar == 'STUDENT':
            model = trainer._trainer.ensemble_model.modelStudent
        else:
            model = trainer._trainer.ensemble_model.modelTeacher

        res = Trainer.test(cfg, model)
        if cfg.TEST.AUG.ENABLED:
            res.update(Trainer.test_with_TTA(cfg, model))
        if comm.is_main_process():
            verify_results(cfg, res)
        return res

    trainer = Trainer(cfg)
    trainer.resume_or_load(resume=args.resume)
    return trainer.train()


if __name__ == "__main__":
    parser = default_argument_parser()
    parser.add_argument('--eval_only', action='store_true')
    parser.add_argument('--EVAL_FLAG', type=int, default=1)
    args = parser.parse_args()
    # random port
    # port = random.randint(1000, 20000)
    # args.dist_url = 'tcp://127.0.0.1:' + str(port)
    # print("Command Line Args:", args)
    # print("pwd:", os.getcwd())
    launch(
        main,
        args.num_gpus,
        num_machines=args.num_machines,
        machine_rank=args.machine_rank,
        dist_url=args.dist_url,
        args=(args,),
    )