train_net.py

"""
hierarchialdet Training Script. Adapted from DiffusionDet.
"""

import os
import itertools
import weakref
from typing import Any, Dict, List, Set
import logging
from collections import OrderedDict
import random
import torch
from fvcore.nn.precise_bn import get_bn_modules

import detectron2.utils.comm as comm
from detectron2.utils.logger import setup_logger
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import build_detection_train_loader
from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, launch, create_ddp_model, \
    AMPTrainer, SimpleTrainer, hooks
from detectron2.evaluation import LVISEvaluator, verify_results, print_csv_format
from hierarchialdet.util.coco_3class_eval import COCOEvaluator
from detectron2.solver.build import maybe_add_gradient_clipping
from detectron2.modeling import build_model




from evaluator import DatasetEvaluator, inference_on_dataset
from hierarchialdet import DiffusionDetDatasetMapper, add_diffusiondet_config, DiffusionDetWithTTA
from hierarchialdet.util.model_ema import add_model_ema_configs, may_build_model_ema, may_get_ema_checkpointer, EMAHook, \
    apply_model_ema_and_restore, EMADetectionCheckpointer


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

    def __init__(self, cfg):
        """
        Args:
            cfg (CfgNode):
        """
        super(DefaultTrainer, self).__init__()  # call grandfather's `__init__` while avoid father's `__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)

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

        self.register_hooks(self.build_hooks())

    @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 = build_model(cfg)
        logger = logging.getLogger(__name__)
        logger.info("Model:\n{}".format(model))
        # setup EMA
        may_build_model_ema(cfg, model)
        return model

    @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")
        if 'lvis' in dataset_name:
            return LVISEvaluator(dataset_name, cfg, True, output_folder)
        else:
            #return LVISEvaluator(dataset_name, cfg, True, output_folder)
            return COCOEvaluator(dataset_name, cfg, True, output_folder)

    @classmethod
    def build_train_loader(cls, cfg):
        mapper = DiffusionDetDatasetMapper(cfg, is_train=True)
        
        return build_detection_train_loader(cfg, mapper=mapper)

    @classmethod
    def build_optimizer(cls, cfg, model):
        params: List[Dict[str, Any]] = []
        memo: Set[torch.nn.parameter.Parameter] = set()
        for key, value in model.named_parameters(recurse=True):
            if not value.requires_grad:
                continue
            # Avoid duplicating parameters
            if value in memo:
                continue
            memo.add(value)
            lr = cfg.SOLVER.BASE_LR
            weight_decay = cfg.SOLVER.WEIGHT_DECAY
            if "backbone" in key:
                lr = lr * cfg.SOLVER.BACKBONE_MULTIPLIER
            params += [{"params": [value], "lr": lr, "weight_decay": weight_decay}]

        def maybe_add_full_model_gradient_clipping(optim):  # optim: the optimizer class
            # 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(cls, cfg, model, k=0, 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``.
        Returns:
            dict: a dict of result metrics
        """
        logger = logging.getLogger(__name__)
        if isinstance(evaluators, DatasetEvaluator):
            evaluators = [evaluators]
        if evaluators is not None:
            
            assert len(cfg.DATASETS.TEST) == len(evaluators), "{} != {}".format(
                len(cfg.DATASETS.TEST), len(evaluators)
            )
            
           
        
        
        results_1 = OrderedDict()
        #results_2 = OrderedDict()
        #results_3 = OrderedDict()
        for idx, dataset_name in enumerate(cfg.DATASETS.TEST):
            
            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_1[dataset_name] = {}
                    continue
            
            
            
            
            results_i_1 = inference_on_dataset(model, data_loader, k, evaluator)
            results_1[dataset_name] = results_i_1
            if comm.is_main_process():
                assert isinstance(
                    results_i_1[0], dict
                ), "Evaluator must return a dict on the main process. Got {} instead.".format(
                    results_i_1
                )
                logger.info("Evaluation results for {} in csv format:".format(dataset_name+"for class"+str(k+1)))
                print_csv_format(results_i_1)
        """
        for idx, dataset_name in enumerate(cfg.DATASETS.TEST):
            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_2[dataset_name] = {}
        
                    continue
            results_i_2 = inference_on_dataset(model, data_loader, 1, evaluator)
            results_2[dataset_name] = results_i_2
            if comm.is_main_process():
                assert isinstance(
                    results_i_2, dict
                ), "Evaluator must return a dict on the main process. Got {} instead.".format(
                    results_i_2
                )
                logger.info("Evaluation results for {} in csv format:".format(dataset_name+"for class"+str(2)))
                print_csv_format(results_i_2)
         
        for idx, dataset_name in enumerate(cfg.DATASETS.TEST):
            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_3[dataset_name] = {}
                    continue
            results_i_3 = inference_on_dataset(model, data_loader, 2, evaluator)
            results_3[dataset_name] = results_i_3
            if comm.is_main_process():
                assert isinstance(
                    results_i_3, dict
                ), "Evaluator must return a dict on the main process. Got {} instead.".format(
                    results_i_3
                )
                logger.info("Evaluation results for {} in csv format:".format(dataset_name+"for class"+str(3)))
                print_csv_format(results_i_3)
        
        
       """
        if len(results_1) == 1:
            results_1 = list(results_1.values())[0]
        """
        if len(results_2) == 1:
            results_2 = list(results_2.values())[0]
        if len(results_3) == 1:
            results_3 = list(results_3.values())[0]
        """
        #results=[results_1, results_2, results_3]
           
        return results_1
 
    @classmethod
    def ema_test(cls, cfg, model, evaluators=None):
        # model with ema weights
        logger = logging.getLogger("detectron2.trainer")
        
        if cfg.MODEL_EMA.ENABLED:
            logger.info("Run evaluation with EMA.")
            with apply_model_ema_and_restore(model):   
                results = cls.test(cfg, model, evaluators=evaluators,k=2 )
               
        else:
            results= cls.test(cfg, model, evaluators=evaluators,k=2)
            
        return results

    @classmethod
    def test_with_TTA(cls, cfg, model):
        logger = logging.getLogger("detectron2.trainer")
        logger.info("Running inference with test-time augmentation ...")
        model = DiffusionDetWithTTA(cfg, model)
        evaluators = [
            cls.build_evaluator(
                cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, "inference_TTA")
            )
            for name in cfg.DATASETS.TEST
        ]
        if cfg.MODEL_EMA.ENABLED:
            cls.ema_test(cfg, model, evaluators,i)
        else:
            res = cls.test(cfg, model, evaluators,i)
        res = OrderedDict({k + "_TTA": v for k, v in res.items()})
        return res

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

        Returns:
            list[HookBase]:
        """
        cfg = self.cfg.clone()
        cfg.defrost()
        cfg.DATALOADER.NUM_WORKERS = 0  # save some memory and time for PreciseBN

        ret = [
            hooks.IterationTimer(),
            EMAHook(self.cfg, self.model) if cfg.MODEL_EMA.ENABLED else None,  # EMA hook
            hooks.LRScheduler(),
            hooks.PreciseBN(
                # Run at the same freq as (but before) evaluation.
                cfg.TEST.EVAL_PERIOD,
                self.model,
                # Build a new data loader to not affect training
                self.build_train_loader(cfg),
                cfg.TEST.PRECISE_BN.NUM_ITER,
            )
            if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model)
            else 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(k):
            self._last_eval_results = self.test(self.cfg, self.model, k)
            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, 0))
        #if comm.is_main_process():
         #   ret.append(hooks.PeriodicWriter(self.build_writers(), period=20))
            
        ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results, 0))
        #if comm.is_main_process():
         #   ret.append(hooks.PeriodicWriter(self.build_writers(), period=20))
            
        #ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results, 2))
        
        if comm.is_main_process():
            ret.append(hooks.PeriodicWriter(self.build_writers(), period=20))
        return ret


def setup(args):
    """
    Create configs and perform basic setups.
    """
    cfg = get_cfg()
    add_diffusiondet_config(cfg)
    add_model_ema_configs(cfg)
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()
    default_setup(cfg, args)
    return cfg


def main(args):
    cfg = setup(args)
    
    
    if args.eval_only:
        model = Trainer.build_model(cfg)
        kwargs = may_get_ema_checkpointer(cfg, model)
        if cfg.MODEL_EMA.ENABLED:
            EMADetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR, **kwargs).resume_or_load(cfg.MODEL.WEIGHTS,
                                                                                              resume=args.resume)
        else:
            DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR, **kwargs).resume_or_load(cfg.MODEL.WEIGHTS,
                                                                                           resume=args.resume)
        res = Trainer.ema_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__":
    args = default_argument_parser().parse_args()
    print("Command Line Args:", args)
    from detectron2.data.datasets import register_coco_instances
    register_coco_instances('custom_train_class', {}, "../sorted/challenge/train_merged_disease_coco3class_onlyd_fixed.json", "../sorted/challenge/for_coco_disease_train")
    #register_coco_instances('custom_train_class', {}, "datasets/custom_quadrant_enumeration_triple/train.json", "../sorted/tooth_number_w_images")
    #register_coco_instances('custom_train_class2', {}, "datasets/custom_quadrant/train.json", "../sorted/quadrant_w_xrays")
    #register_coco_instances('custom_train_class3', {}, "datasets/custom_quadrant/train.json", "../sorted/quadrant_w_xrays")
    #register_coco_instances('custom_validation_class1', {}, "datasets/custom_quadrant/validation.json", "../sorted/quadrant_w_xrays")
    #register_coco_instances('custom_validation_class2', {}, "datasets/custom_quadrant/validation.json", "../sorted/quadrant_w_xrays")
    #register_coco_instances('custom_validation_class', {}, "datasets/custom_quadrant_enumeration_triple/validation.json", "../sorted/tooth_number_w_images")
    register_coco_instances('custom_validation_class', {}, "../sorted/challenge/test_merged_disease_coco3class.json", "../sorted/challenge/for_coco_disease_test")
    launch(
        main,
        args.num_gpus,
        num_machines=args.num_machines,
        machine_rank=args.machine_rank,
        dist_url=args.dist_url,
        args=(args,),
    )