OpenSeeD/train_net.py

# ------------------------------------------------------------------------
# Copyright (c) 2023 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# by Feng Li and Hao Zhang.
# ------------------------------------------------------------------------
"""
OpenSeed Training Script based on MaskDINO.
"""
try:
    from shapely.errors import ShapelyDeprecationWarning
    import warnings
    warnings.filterwarnings('ignore', category=ShapelyDeprecationWarning)
except:
    pass

import sys
import copy
import itertools
import logging
import os
import time

from collections import OrderedDict
from typing import Any, Dict, List, Set
from fvcore.nn.precise_bn import get_bn_modules

import torch

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

from detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler
from detectron2.solver.build import maybe_add_gradient_clipping
from detectron2.utils.logger import setup_logger
from detectron2.config import LazyConfig, instantiate

from utils.arguments import load_opt_command
from detectron2.utils.comm import get_world_size, is_main_process

# MaskDINO

from datasets import (
    build_train_dataloader,
    build_evaluator,
    build_eval_dataloader,
)
import random
from detectron2.engine import (
    DefaultTrainer,
    default_argument_parser,
    default_setup,
    hooks,
    launch,
    create_ddp_model,
    AMPTrainer,
    SimpleTrainer
)
import weakref

from openseed import build_model
from openseed.BaseModel import BaseModel

logger = logging.getLogger(__name__)
logging.basicConfig(level = logging.INFO)


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

    def build_hooks(self):
        """
        Build a list of default hooks, including timing, evaluation,
        checkpointing, lr scheduling, precise BN, writing events.

        Returns:
            list[HookBase]:
        """
        cfg = copy.deepcopy(self.cfg)
        # cfg.defrost()
        cfg.DATALOADER.NUM_WORKERS = 0  # save some memory and time for PreciseBN
        ret = [
            hooks.IterationTimer(),
            hooks.LRScheduler(),
            None,
        ]

        # Do PreciseBN before checkpointer, because it updates the model and need to
        # be saved by checkpointer.
        # This is not always the best: if checkpointing has a different frequency,
        # some checkpoints may have more precise statistics than others.
        if comm.is_main_process():
            ret.append(hooks.PeriodicCheckpointer(self.checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD))

        def test_and_save_results():
            self._last_eval_results = self.test(self.cfg, self.model)
            return self._last_eval_results

        # Do evaluation after checkpointer, because then if it fails,
        # we can use the saved checkpoint to debug.
        ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))

        if comm.is_main_process():
            # Here the default print/log frequency of each writer is used.
            # run writers in the end, so that evaluation metrics are written
            ret.append(hooks.PeriodicWriter(self.build_writers(), period=20))
        return ret

    @classmethod
    def build_model(cls, cfg):
        """
        Returns:
            torch.nn.Module:

        It now calls :func:`detectron2.modeling.build_model`.
        Overwrite it if you'd like a different model.
        """
        model = BaseModel(cfg, build_model(cfg)).cuda()
        logger = logging.getLogger(__name__)
        logger.info("Model:\n{}".format(model))
        return model

    @classmethod
    def build_evaluator(cls, cfg, dataset_name, output_folder=None):
        return build_evaluator(cfg, dataset_name, output_folder=output_folder)

    @classmethod
    def build_train_loader(cls, cfg):
        return build_train_dataloader(cfg, )

    @classmethod
    def build_test_loader(cls, cfg, dataset_name):
        loader = build_eval_dataloader(cfg, )
        return loader

    @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):
        cfg_solver = cfg['SOLVER']
        weight_decay_norm = cfg_solver['WEIGHT_DECAY_NORM']
        weight_decay_embed = cfg_solver['WEIGHT_DECAY_EMBED']
        weight_decay_bias = cfg_solver.get('WEIGHT_DECAY_BIAS', 0.0)

        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,
        )

        lr_multiplier = cfg['SOLVER']['LR_MULTIPLIER']

        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)

                for key, lr_mul in lr_multiplier.items():
                    if key in "{}.{}".format(module_name, module_param_name):
                        hyperparams["lr"] = hyperparams["lr"] * lr_mul
                        if is_main_process():
                            logger.info("Modify Learning rate of {}: {}".format(
                                "{}.{}".format(module_name, module_param_name), lr_mul))

                if (
                        "relative_position_bias_table" in module_param_name
                        or "absolute_pos_embed" in 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
                if "bias" in module_name:
                    hyperparams["weight_decay"] = weight_decay_bias
                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}")
        return optimizer

    @staticmethod
    def auto_scale_workers(cfg, num_workers: int):
        """
        Returns:
            CfgNode: a new config. Same as original if ``cfg.SOLVER.REFERENCE_WORLD_SIZE==0``.
        """
        old_world_size = cfg.SOLVER.REFERENCE_WORLD_SIZE
        if old_world_size == 0 or old_world_size == num_workers:
            return cfg
        cfg = copy.deepcopy(cfg)
        # frozen = cfg.is_frozen()
        # cfg.defrost()

        assert (
                cfg.SOLVER.IMS_PER_BATCH % old_world_size == 0
        ), "Invalid REFERENCE_WORLD_SIZE in config!"
        scale = num_workers / old_world_size
        bs = cfg.SOLVER.IMS_PER_BATCH = int(round(cfg.SOLVER.IMS_PER_BATCH * scale))
        lr = cfg.SOLVER.BASE_LR = cfg.SOLVER.BASE_LR * scale
        max_iter = cfg.SOLVER.MAX_ITER = int(round(cfg.SOLVER.MAX_ITER / scale))
        warmup_iter = cfg.SOLVER.WARMUP_ITERS = int(round(cfg.SOLVER.WARMUP_ITERS / scale))
        cfg.SOLVER.STEPS = tuple(int(round(s / scale)) for s in cfg.SOLVER.STEPS)
        cfg.TEST.EVAL_PERIOD = int(round(cfg.TEST.EVAL_PERIOD / scale))
        cfg.SOLVER.CHECKPOINT_PERIOD = int(round(cfg.SOLVER.CHECKPOINT_PERIOD / scale))
        cfg.SOLVER.REFERENCE_WORLD_SIZE = num_workers  # maintain invariant
        logger = logging.getLogger(__name__)
        logger.info(
            f"Auto-scaling the config to batch_size={bs}, learning_rate={lr}, "
            f"max_iter={max_iter}, warmup={warmup_iter}."
        )
        return cfg

    @classmethod
    def test(cls, cfg, model, evaluators=None):
        from utils.misc import hook_metadata, hook_switcher, hook_opt
        from openseed.utils import get_class_names
        from detectron2.utils.logger import log_every_n_seconds
        import datetime
        # build dataloade
        dataloaders = cls.build_test_loader(cfg, dataset_name=None)
        dataset_names = cfg['DATASETS']['TEST']
        model = model.eval().cuda()
        model_without_ddp = model
        if not type(model) == BaseModel:
            model_without_ddp = model.module

        for dataloader, dataset_name in zip(dataloaders, dataset_names):
            # build evaluator
            evaluator = build_evaluator(cfg, dataset_name, cfg['OUTPUT_DIR'])
            evaluator.reset()
            with torch.no_grad():
                # setup model
                names = get_class_names(dataset_name, cfg['MODEL'].get('BACKGROUND', True))
                # names = get_class_names(dataset_name)
                model_without_ddp.model.metadata = MetadataCatalog.get(dataset_name)
                eval_type = model_without_ddp.model.metadata.evaluator_type
                if 'background' in names:
                    model_without_ddp.model.sem_seg_head.num_classes = len(names) - 1
                else:
                    model_without_ddp.model.sem_seg_head.num_classes = len(names)
                model_without_ddp.model.sem_seg_head.predictor.lang_encoder.get_text_embeddings(names, is_eval=True)
                hook_switcher(model_without_ddp, dataset_name)
                # hook_opt(model, dataset_name)

                # setup task
                task = 'seg'

                # setup timer
                total = len(dataloader)
                num_warmup = min(5, total - 1)
                start_time = time.perf_counter()
                total_data_time = 0
                total_compute_time = 0
                total_eval_time = 0
                start_data_time = time.perf_counter()

                for idx, batch in enumerate(dataloader):
                    total_data_time += time.perf_counter() - start_data_time
                    if idx == num_warmup:
                        start_time = time.perf_counter()
                        total_data_time = 0
                        total_compute_time = 0
                        total_eval_time = 0
                    start_compute_time = time.perf_counter()

                    # forward
                    with torch.autocast(device_type='cuda', dtype=torch.float16):
                        # import ipdb; ipdb.set_trace()
                        outputs = model(batch, inference_task=task)

                    total_compute_time += time.perf_counter() - start_compute_time
                    start_eval_time = time.perf_counter()

                    evaluator.process(batch, outputs)
                    total_eval_time += time.perf_counter() - start_eval_time

                    iters_after_start = idx + 1 - num_warmup * int(idx >= num_warmup)
                    data_seconds_per_iter = total_data_time / iters_after_start
                    compute_seconds_per_iter = total_compute_time / iters_after_start
                    eval_seconds_per_iter = total_eval_time / iters_after_start
                    total_seconds_per_iter = (time.perf_counter() - start_time) / iters_after_start

                    if is_main_process() and (idx >= num_warmup * 2 or compute_seconds_per_iter > 5):
                        eta = datetime.timedelta(seconds=int(total_seconds_per_iter * (total - idx - 1)))
                        log_every_n_seconds(
                            logging.INFO,
                            (
                                f"Inference done {idx + 1}/{total}. "
                                f"Dataloading: {data_seconds_per_iter:.4f} s/iter. "
                                f"Inference: {compute_seconds_per_iter:.4f} s/iter. "
                                f"Eval: {eval_seconds_per_iter:.4f} s/iter. "
                                f"Total: {total_seconds_per_iter:.4f} s/iter. "
                                f"ETA={eta}"
                            ),
                            n=5,
                        )
                    start_data_time = time.perf_counter()

            # evaluate
            results = evaluator.evaluate()

        model = model.train().cuda()


def setup(args):
    """
    Create configs and perform basic setups.
    """
    cfg = get_cfg()
    cfg = LazyConfig.load(args.config_file)
    cfg = LazyConfig.apply_overrides(cfg, args.opts)
    default_setup(cfg, args)
    setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="maskdino")
    return cfg


def main(args=None):
    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
        )
        if args.original_load:
            print("using original loading")
            model = model.from_pretrained(cfg.MODEL.WEIGHTS)
        res = Trainer.test(cfg, model)
        if cfg.TEST.AUG.ENABLED:
            res.update(Trainer.test_with_TTA(cfg, model))

        return res

    trainer = Trainer(cfg)
    if len(args.lang_weight) > 0:
        # load language weight for semantic
        import copy
        weight = copy.deepcopy(trainer.cfg.MODEL.WEIGHTS)
        trainer.cfg.MODEL.WEIGHTS = args.lang_weight
        print("load original language language weight!!!!!!")
        # trainer.resume_or_load(resume=args.resume)
        trainer._trainer.model.module = trainer._trainer.model.module.from_pretrained(cfg.MODEL.WEIGHTS)
        trainer.cfg.MODEL.WEIGHTS = weight
    print("load pretrained model weight!!!!!!")
    trainer.resume_or_load(resume=args.resume)
    if args.original_load:  # loading checkpoints with different name prefix
        print("using original loading")
        try:
            trainer._trainer.model.module = trainer._trainer.model.module.from_pretrained(cfg.MODEL.WEIGHTS)
        except Exception as e: # for debugging
            trainer._trainer.model = trainer._trainer.model.from_pretrained(cfg.MODEL.WEIGHTS)
    return trainer.train()


if __name__ == "__main__":
    parser = default_argument_parser()
    parser.add_argument('--eval_only', action='store_true')
    parser.add_argument('--original_load', action='store_true')
    parser.add_argument('--lang_weight', type=str, default='')
    parser.add_argument('--EVAL_FLAG', type=int, default=1)
    args = parser.parse_args()
    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,),
    )