train_net.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

import sys
sys.path.append("/usr/lib/python3.8/site-packages/MultiScaleDeformableAttention-1.0-py3.8-linux-x86_64.egg")
sys.path.append("../detectron2")

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

import torch

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

from detectron2.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,
    add_maskdino_config,
    DetrDatasetMapper,
)
import random
from detectron2.engine import (
    DefaultTrainer,
    default_argument_parser,
    default_setup,
    hooks,
    launch,
    create_ddp_model,
    AMPTrainer,
    SimpleTrainer
)
import weakref


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),
        }
        # 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,
        )
        self.start_iter = 0
        self.max_iter = cfg.SOLVER.MAX_ITER
        self.cfg = cfg

        self.register_hooks(self.build_hooks())
        # 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,
        )
        # 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.
        """
        if output_folder is None:
            output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
        evaluator_list = []
        evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
        # semantic segmentation
        if evaluator_type in ["sem_seg", "ade20k_panoptic_seg"]:
            evaluator_list.append(
                SemSegEvaluator(
                    dataset_name,
                    distributed=True,
                    output_dir=output_folder,
                )
            )
        # instance segmentation
        if evaluator_type == "coco":
            evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
        # panoptic segmentation
        if evaluator_type in [
            "coco_panoptic_seg",
            "ade20k_panoptic_seg",
            "cityscapes_panoptic_seg",
            "mapillary_vistas_panoptic_seg",
        ]:
            if cfg.MODEL.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)
            return build_detection_train_loader(cfg, mapper=mapper)
        # 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_maskdino_config(cfg)
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()
    default_setup(cfg, args)
    setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="maskdino")
    return cfg


def main(args):
    cfg = setup(args)
    print("Command cfg:", cfg)
    if args.eval_only:
        model = Trainer.build_model(cfg)
        DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
            cfg.MODEL.WEIGHTS, resume=args.resume
        )
        checkpointer = DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR)
        checkpointer.resume_or_load(
            cfg.MODEL.WEIGHTS, resume=args.resume
        )
        res = Trainer.test(cfg, model)
        if cfg.TEST.AUG.ENABLED:
            res.update(Trainer.test_with_TTA(cfg, model))
        if comm.is_main_process():
            verify_results(cfg, res)
        return res

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


if __name__ == "__main__":
    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,),
    )