zjc
/
yolov5


			
							"""Utilities and tools for tracking runs with Weights & Biases."""

import logging
import os
import sys
from contextlib import contextmanager
from pathlib import Path
from typing import Dict

import yaml
from tqdm import tqdm

FILE = Path(__file__).resolve()
ROOT = FILE.parents[3]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH

from utils.datasets import LoadImagesAndLabels, img2label_paths
from utils.general import LOGGER, check_dataset, check_file

try:
    import wandb

    assert hasattr(wandb, '__version__')  # verify package import not local dir
except (ImportError, AssertionError):
    wandb = None

RANK = int(os.getenv('RANK', -1))
WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'


def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX):
    return from_string[len(prefix):]


def check_wandb_config_file(data_config_file):
    wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1))  # updated data.yaml path
    if Path(wandb_config).is_file():
        return wandb_config
    return data_config_file


def check_wandb_dataset(data_file):
    is_trainset_wandb_artifact = False
    is_valset_wandb_artifact = False
    if check_file(data_file) and data_file.endswith('.yaml'):
        with open(data_file, errors='ignore') as f:
            data_dict = yaml.safe_load(f)
        is_trainset_wandb_artifact = (isinstance(data_dict['train'], str) and
                                      data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX))
        is_valset_wandb_artifact = (isinstance(data_dict['val'], str) and
                                    data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX))
    if is_trainset_wandb_artifact or is_valset_wandb_artifact:
        return data_dict
    else:
        return check_dataset(data_file)


def get_run_info(run_path):
    run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX))
    run_id = run_path.stem
    project = run_path.parent.stem
    entity = run_path.parent.parent.stem
    model_artifact_name = 'run_' + run_id + '_model'
    return entity, project, run_id, model_artifact_name


def check_wandb_resume(opt):
    process_wandb_config_ddp_mode(opt) if RANK not in [-1, 0] else None
    if isinstance(opt.resume, str):
        if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
            if RANK not in [-1, 0]:  # For resuming DDP runs
                entity, project, run_id, model_artifact_name = get_run_info(opt.resume)
                api = wandb.Api()
                artifact = api.artifact(entity + '/' + project + '/' + model_artifact_name + ':latest')
                modeldir = artifact.download()
                opt.weights = str(Path(modeldir) / "last.pt")
            return True
    return None


def process_wandb_config_ddp_mode(opt):
    with open(check_file(opt.data), errors='ignore') as f:
        data_dict = yaml.safe_load(f)  # data dict
    train_dir, val_dir = None, None
    if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX):
        api = wandb.Api()
        train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias)
        train_dir = train_artifact.download()
        train_path = Path(train_dir) / 'data/images/'
        data_dict['train'] = str(train_path)

    if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX):
        api = wandb.Api()
        val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias)
        val_dir = val_artifact.download()
        val_path = Path(val_dir) / 'data/images/'
        data_dict['val'] = str(val_path)
    if train_dir or val_dir:
        ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml')
        with open(ddp_data_path, 'w') as f:
            yaml.safe_dump(data_dict, f)
        opt.data = ddp_data_path


class WandbLogger():
    """Log training runs, datasets, models, and predictions to Weights & Biases.

    This logger sends information to W&B at wandb.ai. By default, this information
    includes hyperparameters, system configuration and metrics, model metrics,
    and basic data metrics and analyses.

    By providing additional command line arguments to train.py, datasets,
    models and predictions can also be logged.

    For more on how this logger is used, see the Weights & Biases documentation:
    https://docs.wandb.com/guides/integrations/yolov5
    """

    def __init__(self, opt, run_id=None, job_type='Training'):
        """
        - Initialize WandbLogger instance
        - Upload dataset if opt.upload_dataset is True
        - Setup trainig processes if job_type is 'Training'

        arguments:
        opt (namespace) -- Commandline arguments for this run
        run_id (str) -- Run ID of W&B run to be resumed
        job_type (str) -- To set the job_type for this run

       """
        # Pre-training routine --
        self.job_type = job_type
        self.wandb, self.wandb_run = wandb, None if not wandb else wandb.run
        self.val_artifact, self.train_artifact = None, None
        self.train_artifact_path, self.val_artifact_path = None, None
        self.result_artifact = None
        self.val_table, self.result_table = None, None
        self.bbox_media_panel_images = []
        self.val_table_path_map = None
        self.max_imgs_to_log = 16
        self.wandb_artifact_data_dict = None
        self.data_dict = None
        # It's more elegant to stick to 1 wandb.init call,
        #  but useful config data is overwritten in the WandbLogger's wandb.init call
        if isinstance(opt.resume, str):  # checks resume from artifact
            if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
                entity, project, run_id, model_artifact_name = get_run_info(opt.resume)
                model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name
                assert wandb, 'install wandb to resume wandb runs'
                # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config
                self.wandb_run = wandb.init(id=run_id,
                                            project=project,
                                            entity=entity,
                                            resume='allow',
                                            allow_val_change=True)
                opt.resume = model_artifact_name
        elif self.wandb:
            self.wandb_run = wandb.init(config=opt,
                                        resume="allow",
                                        project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
                                        entity=opt.entity,
                                        name=opt.name if opt.name != 'exp' else None,
                                        job_type=job_type,
                                        id=run_id,
                                        allow_val_change=True) if not wandb.run else wandb.run
        if self.wandb_run:
            if self.job_type == 'Training':
                if opt.upload_dataset:
                    if not opt.resume:
                        self.wandb_artifact_data_dict = self.check_and_upload_dataset(opt)

                if opt.resume:
                    # resume from artifact
                    if isinstance(opt.resume, str) and opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
                        self.data_dict = dict(self.wandb_run.config.data_dict)
                    else:  # local resume
                        self.data_dict = check_wandb_dataset(opt.data)
                else:
                    self.data_dict = check_wandb_dataset(opt.data)
                    self.wandb_artifact_data_dict = self.wandb_artifact_data_dict or self.data_dict

                    # write data_dict to config. useful for resuming from artifacts. Do this only when not resuming.
                    self.wandb_run.config.update({'data_dict': self.wandb_artifact_data_dict},
                                                 allow_val_change=True)
                self.setup_training(opt)

            if self.job_type == 'Dataset Creation':
                self.wandb_run.config.update({"upload_dataset": True})
                self.data_dict = self.check_and_upload_dataset(opt)

    def check_and_upload_dataset(self, opt):
        """
        Check if the dataset format is compatible and upload it as W&B artifact

        arguments:
        opt (namespace)-- Commandline arguments for current run

        returns:
        Updated dataset info dictionary where local dataset paths are replaced by WAND_ARFACT_PREFIX links.
        """
        assert wandb, 'Install wandb to upload dataset'
        config_path = self.log_dataset_artifact(opt.data,
                                                opt.single_cls,
                                                'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem)
        with open(config_path, errors='ignore') as f:
            wandb_data_dict = yaml.safe_load(f)
        return wandb_data_dict

    def setup_training(self, opt):
        """
        Setup the necessary processes for training YOLO models:
          - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX
          - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded
          - Setup log_dict, initialize bbox_interval

        arguments:
        opt (namespace) -- commandline arguments for this run

        """
        self.log_dict, self.current_epoch = {}, 0
        self.bbox_interval = opt.bbox_interval
        if isinstance(opt.resume, str):
            modeldir, _ = self.download_model_artifact(opt)
            if modeldir:
                self.weights = Path(modeldir) / "last.pt"
                config = self.wandb_run.config
                opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = str(
                    self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs,\
                    config.hyp, config.imgsz
        data_dict = self.data_dict
        if self.val_artifact is None:  # If --upload_dataset is set, use the existing artifact, don't download
            self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'),
                                                                                           opt.artifact_alias)
            self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'),
                                                                                       opt.artifact_alias)

        if self.train_artifact_path is not None:
            train_path = Path(self.train_artifact_path) / 'data/images/'
            data_dict['train'] = str(train_path)
        if self.val_artifact_path is not None:
            val_path = Path(self.val_artifact_path) / 'data/images/'
            data_dict['val'] = str(val_path)

        if self.val_artifact is not None:
            self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
            columns = ["epoch", "id", "ground truth", "prediction"]
            columns.extend(self.data_dict['names'])
            self.result_table = wandb.Table(columns)
            self.val_table = self.val_artifact.get("val")
            if self.val_table_path_map is None:
                self.map_val_table_path()
        if opt.bbox_interval == -1:
            self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
            if opt.evolve:
                self.bbox_interval = opt.bbox_interval = opt.epochs + 1
        train_from_artifact = self.train_artifact_path is not None and self.val_artifact_path is not None
        # Update the the data_dict to point to local artifacts dir
        if train_from_artifact:
            self.data_dict = data_dict

    def download_dataset_artifact(self, path, alias):
        """
        download the model checkpoint artifact if the path starts with WANDB_ARTIFACT_PREFIX

        arguments:
        path -- path of the dataset to be used for training
        alias (str)-- alias of the artifact to be download/used for training

        returns:
        (str, wandb.Artifact) -- path of the downladed dataset and it's corresponding artifact object if dataset
        is found otherwise returns (None, None)
        """
        if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX):
            artifact_path = Path(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias)
            dataset_artifact = wandb.use_artifact(artifact_path.as_posix().replace("\\", "/"))
            assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'"
            datadir = dataset_artifact.download()
            return datadir, dataset_artifact
        return None, None

    def download_model_artifact(self, opt):
        """
        download the model checkpoint artifact if the resume path starts with WANDB_ARTIFACT_PREFIX

        arguments:
        opt (namespace) -- Commandline arguments for this run
        """
        if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
            model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest")
            assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist'
            modeldir = model_artifact.download()
            # epochs_trained = model_artifact.metadata.get('epochs_trained')
            total_epochs = model_artifact.metadata.get('total_epochs')
            is_finished = total_epochs is None
            assert not is_finished, 'training is finished, can only resume incomplete runs.'
            return modeldir, model_artifact
        return None, None

    def log_model(self, path, opt, epoch, fitness_score, best_model=False):
        """
        Log the model checkpoint as W&B artifact

        arguments:
        path (Path)   -- Path of directory containing the checkpoints
        opt (namespace) -- Command line arguments for this run
        epoch (int)  -- Current epoch number
        fitness_score (float) -- fitness score for current epoch
        best_model (boolean) -- Boolean representing if the current checkpoint is the best yet.
        """
        model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={
            'original_url': str(path),
            'epochs_trained': epoch + 1,
            'save period': opt.save_period,
            'project': opt.project,
            'total_epochs': opt.epochs,
            'fitness_score': fitness_score
        })
        model_artifact.add_file(str(path / 'last.pt'), name='last.pt')
        wandb.log_artifact(model_artifact,
                           aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else ''])
        LOGGER.info(f"Saving model artifact on epoch {epoch + 1}")

    def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False):
        """
        Log the dataset as W&B artifact and return the new data file with W&B links

        arguments:
        data_file (str) -- the .yaml file with information about the dataset like - path, classes etc.
        single_class (boolean)  -- train multi-class data as single-class
        project (str) -- project name. Used to construct the artifact path
        overwrite_config (boolean) -- overwrites the data.yaml file if set to true otherwise creates a new
        file with _wandb postfix. Eg -> data_wandb.yaml

        returns:
        the new .yaml file with artifact links. it can be used to start training directly from artifacts
        """
        upload_dataset = self.wandb_run.config.upload_dataset
        log_val_only = isinstance(upload_dataset, str) and upload_dataset == 'val'
        self.data_dict = check_dataset(data_file)  # parse and check
        data = dict(self.data_dict)
        nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names'])
        names = {k: v for k, v in enumerate(names)}  # to index dictionary

        # log train set
        if not log_val_only:
            self.train_artifact = self.create_dataset_table(LoadImagesAndLabels(
                data['train'], rect=True, batch_size=1), names, name='train') if data.get('train') else None
            if data.get('train'):
                data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train')

        self.val_artifact = self.create_dataset_table(LoadImagesAndLabels(
            data['val'], rect=True, batch_size=1), names, name='val') if data.get('val') else None
        if data.get('val'):
            data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val')

        path = Path(data_file)
        # create a _wandb.yaml file with artifacts links if both train and test set are logged
        if not log_val_only:
            path = (path.stem if overwrite_config else path.stem + '_wandb') + '.yaml'  # updated data.yaml path
            path = ROOT / 'data' / path
            data.pop('download', None)
            data.pop('path', None)
            with open(path, 'w') as f:
                yaml.safe_dump(data, f)
                LOGGER.info(f"Created dataset config file {path}")

        if self.job_type == 'Training':  # builds correct artifact pipeline graph
            if not log_val_only:
                self.wandb_run.log_artifact(
                    self.train_artifact)  # calling use_artifact downloads the dataset. NOT NEEDED!
            self.wandb_run.use_artifact(self.val_artifact)
            self.val_artifact.wait()
            self.val_table = self.val_artifact.get('val')
            self.map_val_table_path()
        else:
            self.wandb_run.log_artifact(self.train_artifact)
            self.wandb_run.log_artifact(self.val_artifact)
        return path

    def map_val_table_path(self):
        """
        Map the validation dataset Table like name of file -> it's id in the W&B Table.
        Useful for - referencing artifacts for evaluation.
        """
        self.val_table_path_map = {}
        LOGGER.info("Mapping dataset")
        for i, data in enumerate(tqdm(self.val_table.data)):
            self.val_table_path_map[data[3]] = data[0]

    def create_dataset_table(self, dataset: LoadImagesAndLabels, class_to_id: Dict[int, str], name: str = 'dataset'):
        """
        Create and return W&B artifact containing W&B Table of the dataset.

        arguments:
        dataset -- instance of LoadImagesAndLabels class used to iterate over the data to build Table
        class_to_id -- hash map that maps class ids to labels
        name -- name of the artifact

        returns:
        dataset artifact to be logged or used
        """
        # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging
        artifact = wandb.Artifact(name=name, type="dataset")
        img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None
        img_files = tqdm(dataset.im_files) if not img_files else img_files
        for img_file in img_files:
            if Path(img_file).is_dir():
                artifact.add_dir(img_file, name='data/images')
                labels_path = 'labels'.join(dataset.path.rsplit('images', 1))
                artifact.add_dir(labels_path, name='data/labels')
            else:
                artifact.add_file(img_file, name='data/images/' + Path(img_file).name)
                label_file = Path(img2label_paths([img_file])[0])
                artifact.add_file(str(label_file),
                                  name='data/labels/' + label_file.name) if label_file.exists() else None
        table = wandb.Table(columns=["id", "train_image", "Classes", "name"])
        class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()])
        for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)):
            box_data, img_classes = [], {}
            for cls, *xywh in labels[:, 1:].tolist():
                cls = int(cls)
                box_data.append({"position": {"middle": [xywh[0], xywh[1]], "width": xywh[2], "height": xywh[3]},
                                 "class_id": cls,
                                 "box_caption": "%s" % (class_to_id[cls])})
                img_classes[cls] = class_to_id[cls]
            boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}}  # inference-space
            table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), list(img_classes.values()),
                           Path(paths).name)
        artifact.add(table, name)
        return artifact

    def log_training_progress(self, predn, path, names):
        """
        Build evaluation Table. Uses reference from validation dataset table.

        arguments:
        predn (list): list of predictions in the native space in the format - [xmin, ymin, xmax, ymax, confidence, class]
        path (str): local path of the current evaluation image
        names (dict(int, str)): hash map that maps class ids to labels
        """
        class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])
        box_data = []
        avg_conf_per_class = [0] * len(self.data_dict['names'])
        pred_class_count = {}
        for *xyxy, conf, cls in predn.tolist():
            if conf >= 0.25:
                cls = int(cls)
                box_data.append(
                    {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
                     "class_id": cls,
                     "box_caption": f"{names[cls]} {conf:.3f}",
                     "scores": {"class_score": conf},
                     "domain": "pixel"})
                avg_conf_per_class[cls] += conf

                if cls in pred_class_count:
                    pred_class_count[cls] += 1
                else:
                    pred_class_count[cls] = 1

        for pred_class in pred_class_count.keys():
            avg_conf_per_class[pred_class] = avg_conf_per_class[pred_class] / pred_class_count[pred_class]

        boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
        id = self.val_table_path_map[Path(path).name]
        self.result_table.add_data(self.current_epoch,
                                   id,
                                   self.val_table.data[id][1],
                                   wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set),
                                   *avg_conf_per_class
                                   )

    def val_one_image(self, pred, predn, path, names, im):
        """
        Log validation data for one image. updates the result Table if validation dataset is uploaded and log bbox media panel

        arguments:
        pred (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class]
        predn (list): list of predictions in the native space - [xmin, ymin, xmax, ymax, confidence, class]
        path (str): local path of the current evaluation image
        """
        if self.val_table and self.result_table:  # Log Table if Val dataset is uploaded as artifact
            self.log_training_progress(predn, path, names)

        if len(self.bbox_media_panel_images) < self.max_imgs_to_log and self.current_epoch > 0:
            if self.current_epoch % self.bbox_interval == 0:
                box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
                             "class_id": int(cls),
                             "box_caption": f"{names[int(cls)]} {conf:.3f}",
                             "scores": {"class_score": conf},
                             "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
                boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
                self.bbox_media_panel_images.append(wandb.Image(im, boxes=boxes, caption=path.name))

    def log(self, log_dict):
        """
        save the metrics to the logging dictionary

        arguments:
        log_dict (Dict) -- metrics/media to be logged in current step
        """
        if self.wandb_run:
            for key, value in log_dict.items():
                self.log_dict[key] = value

    def end_epoch(self, best_result=False):
        """
        commit the log_dict, model artifacts and Tables to W&B and flush the log_dict.

        arguments:
        best_result (boolean): Boolean representing if the result of this evaluation is best or not
        """
        if self.wandb_run:
            with all_logging_disabled():
                if self.bbox_media_panel_images:
                    self.log_dict["BoundingBoxDebugger"] = self.bbox_media_panel_images
                try:
                    wandb.log(self.log_dict)
                except BaseException as e:
                    LOGGER.info(
                        f"An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}")
                    self.wandb_run.finish()
                    self.wandb_run = None

                self.log_dict = {}
                self.bbox_media_panel_images = []
            if self.result_artifact:
                self.result_artifact.add(self.result_table, 'result')
                wandb.log_artifact(self.result_artifact, aliases=['latest', 'last', 'epoch ' + str(self.current_epoch),
                                                                  ('best' if best_result else '')])

                wandb.log({"evaluation": self.result_table})
                columns = ["epoch", "id", "ground truth", "prediction"]
                columns.extend(self.data_dict['names'])
                self.result_table = wandb.Table(columns)
                self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")

    def finish_run(self):
        """
        Log metrics if any and finish the current W&B run
        """
        if self.wandb_run:
            if self.log_dict:
                with all_logging_disabled():
                    wandb.log(self.log_dict)
            wandb.run.finish()


@contextmanager
def all_logging_disabled(highest_level=logging.CRITICAL):
    """ source - https://gist.github.com/simon-weber/7853144
    A context manager that will prevent any logging messages triggered during the body from being processed.
    :param highest_level: the maximum logging level in use.
      This would only need to be changed if a custom level greater than CRITICAL is defined.
    """
    previous_level = logging.root.manager.disable
    logging.disable(highest_level)
    try:
        yield
    finally:
        logging.disable(previous_level)