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552 lines
21 KiB
Python
552 lines
21 KiB
Python
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
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import glob
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import json
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import logging
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import os
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import sys
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from pathlib import Path
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logger = logging.getLogger(__name__)
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[3] # YOLOv5 root directory
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if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT)) # add ROOT to PATH
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try:
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import comet_ml
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# Project Configuration
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config = comet_ml.config.get_config()
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COMET_PROJECT_NAME = config.get_string(os.getenv("COMET_PROJECT_NAME"), "comet.project_name", default="yolov5")
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except ImportError:
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comet_ml = None
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COMET_PROJECT_NAME = None
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import PIL
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import torch
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import torchvision.transforms as T
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import yaml
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from utils.dataloaders import img2label_paths
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from utils.general import check_dataset, scale_boxes, xywh2xyxy
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from utils.metrics import box_iou
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COMET_PREFIX = "comet://"
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COMET_MODE = os.getenv("COMET_MODE", "online")
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# Model Saving Settings
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COMET_MODEL_NAME = os.getenv("COMET_MODEL_NAME", "yolov5")
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# Dataset Artifact Settings
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COMET_UPLOAD_DATASET = os.getenv("COMET_UPLOAD_DATASET", "false").lower() == "true"
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# Evaluation Settings
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COMET_LOG_CONFUSION_MATRIX = os.getenv("COMET_LOG_CONFUSION_MATRIX", "true").lower() == "true"
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COMET_LOG_PREDICTIONS = os.getenv("COMET_LOG_PREDICTIONS", "true").lower() == "true"
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COMET_MAX_IMAGE_UPLOADS = int(os.getenv("COMET_MAX_IMAGE_UPLOADS", 100))
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# Confusion Matrix Settings
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CONF_THRES = float(os.getenv("CONF_THRES", 0.001))
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IOU_THRES = float(os.getenv("IOU_THRES", 0.6))
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# Batch Logging Settings
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COMET_LOG_BATCH_METRICS = os.getenv("COMET_LOG_BATCH_METRICS", "false").lower() == "true"
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COMET_BATCH_LOGGING_INTERVAL = os.getenv("COMET_BATCH_LOGGING_INTERVAL", 1)
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COMET_PREDICTION_LOGGING_INTERVAL = os.getenv("COMET_PREDICTION_LOGGING_INTERVAL", 1)
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COMET_LOG_PER_CLASS_METRICS = os.getenv("COMET_LOG_PER_CLASS_METRICS", "false").lower() == "true"
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RANK = int(os.getenv("RANK", -1))
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to_pil = T.ToPILImage()
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class CometLogger:
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"""Log metrics, parameters, source code, models and much more with Comet."""
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def __init__(self, opt, hyp, run_id=None, job_type="Training", **experiment_kwargs) -> None:
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"""Initializes CometLogger with given options, hyperparameters, run ID, job type, and additional experiment
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arguments.
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"""
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self.job_type = job_type
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self.opt = opt
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self.hyp = hyp
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# Comet Flags
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self.comet_mode = COMET_MODE
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self.save_model = opt.save_period > -1
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self.model_name = COMET_MODEL_NAME
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# Batch Logging Settings
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self.log_batch_metrics = COMET_LOG_BATCH_METRICS
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self.comet_log_batch_interval = COMET_BATCH_LOGGING_INTERVAL
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# Dataset Artifact Settings
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self.upload_dataset = self.opt.upload_dataset or COMET_UPLOAD_DATASET
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self.resume = self.opt.resume
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# Default parameters to pass to Experiment objects
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self.default_experiment_kwargs = {
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"log_code": False,
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"log_env_gpu": True,
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"log_env_cpu": True,
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"project_name": COMET_PROJECT_NAME,
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}
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self.default_experiment_kwargs.update(experiment_kwargs)
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self.experiment = self._get_experiment(self.comet_mode, run_id)
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self.experiment.set_name(self.opt.name)
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self.data_dict = self.check_dataset(self.opt.data)
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self.class_names = self.data_dict["names"]
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self.num_classes = self.data_dict["nc"]
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self.logged_images_count = 0
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self.max_images = COMET_MAX_IMAGE_UPLOADS
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if run_id is None:
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self.experiment.log_other("Created from", "YOLOv5")
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if not isinstance(self.experiment, comet_ml.OfflineExperiment):
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workspace, project_name, experiment_id = self.experiment.url.split("/")[-3:]
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self.experiment.log_other(
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"Run Path",
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f"{workspace}/{project_name}/{experiment_id}",
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)
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self.log_parameters(vars(opt))
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self.log_parameters(self.opt.hyp)
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self.log_asset_data(
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self.opt.hyp,
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name="hyperparameters.json",
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metadata={"type": "hyp-config-file"},
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)
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self.log_asset(
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f"{self.opt.save_dir}/opt.yaml",
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metadata={"type": "opt-config-file"},
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)
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self.comet_log_confusion_matrix = COMET_LOG_CONFUSION_MATRIX
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if hasattr(self.opt, "conf_thres"):
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self.conf_thres = self.opt.conf_thres
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else:
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self.conf_thres = CONF_THRES
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if hasattr(self.opt, "iou_thres"):
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self.iou_thres = self.opt.iou_thres
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else:
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self.iou_thres = IOU_THRES
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self.log_parameters({"val_iou_threshold": self.iou_thres, "val_conf_threshold": self.conf_thres})
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self.comet_log_predictions = COMET_LOG_PREDICTIONS
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if self.opt.bbox_interval == -1:
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self.comet_log_prediction_interval = 1 if self.opt.epochs < 10 else self.opt.epochs // 10
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else:
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self.comet_log_prediction_interval = self.opt.bbox_interval
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if self.comet_log_predictions:
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self.metadata_dict = {}
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self.logged_image_names = []
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self.comet_log_per_class_metrics = COMET_LOG_PER_CLASS_METRICS
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self.experiment.log_others(
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{
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"comet_mode": COMET_MODE,
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"comet_max_image_uploads": COMET_MAX_IMAGE_UPLOADS,
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"comet_log_per_class_metrics": COMET_LOG_PER_CLASS_METRICS,
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"comet_log_batch_metrics": COMET_LOG_BATCH_METRICS,
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"comet_log_confusion_matrix": COMET_LOG_CONFUSION_MATRIX,
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"comet_model_name": COMET_MODEL_NAME,
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}
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)
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# Check if running the Experiment with the Comet Optimizer
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if hasattr(self.opt, "comet_optimizer_id"):
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self.experiment.log_other("optimizer_id", self.opt.comet_optimizer_id)
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self.experiment.log_other("optimizer_objective", self.opt.comet_optimizer_objective)
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self.experiment.log_other("optimizer_metric", self.opt.comet_optimizer_metric)
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self.experiment.log_other("optimizer_parameters", json.dumps(self.hyp))
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def _get_experiment(self, mode, experiment_id=None):
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"""Returns a new or existing Comet.ml experiment based on mode and optional experiment_id."""
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if mode == "offline":
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return (
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comet_ml.ExistingOfflineExperiment(
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previous_experiment=experiment_id,
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**self.default_experiment_kwargs,
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)
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if experiment_id is not None
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else comet_ml.OfflineExperiment(
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**self.default_experiment_kwargs,
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)
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)
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try:
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if experiment_id is not None:
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return comet_ml.ExistingExperiment(
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previous_experiment=experiment_id,
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**self.default_experiment_kwargs,
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)
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return comet_ml.Experiment(**self.default_experiment_kwargs)
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except ValueError:
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logger.warning(
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"COMET WARNING: "
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"Comet credentials have not been set. "
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"Comet will default to offline logging. "
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"Please set your credentials to enable online logging."
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)
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return self._get_experiment("offline", experiment_id)
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return
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def log_metrics(self, log_dict, **kwargs):
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"""Logs metrics to the current experiment, accepting a dictionary of metric names and values."""
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self.experiment.log_metrics(log_dict, **kwargs)
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def log_parameters(self, log_dict, **kwargs):
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"""Logs parameters to the current experiment, accepting a dictionary of parameter names and values."""
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self.experiment.log_parameters(log_dict, **kwargs)
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def log_asset(self, asset_path, **kwargs):
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"""Logs a file or directory as an asset to the current experiment."""
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self.experiment.log_asset(asset_path, **kwargs)
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def log_asset_data(self, asset, **kwargs):
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"""Logs in-memory data as an asset to the current experiment, with optional kwargs."""
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self.experiment.log_asset_data(asset, **kwargs)
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def log_image(self, img, **kwargs):
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"""Logs an image to the current experiment with optional kwargs."""
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self.experiment.log_image(img, **kwargs)
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def log_model(self, path, opt, epoch, fitness_score, best_model=False):
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"""Logs model checkpoint to experiment with path, options, epoch, fitness, and best model flag."""
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if not self.save_model:
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return
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model_metadata = {
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"fitness_score": fitness_score[-1],
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"epochs_trained": epoch + 1,
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"save_period": opt.save_period,
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"total_epochs": opt.epochs,
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}
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model_files = glob.glob(f"{path}/*.pt")
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for model_path in model_files:
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name = Path(model_path).name
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self.experiment.log_model(
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self.model_name,
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file_or_folder=model_path,
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file_name=name,
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metadata=model_metadata,
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overwrite=True,
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)
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def check_dataset(self, data_file):
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"""Validates the dataset configuration by loading the YAML file specified in `data_file`."""
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with open(data_file) as f:
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data_config = yaml.safe_load(f)
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path = data_config.get("path")
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if path and path.startswith(COMET_PREFIX):
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path = data_config["path"].replace(COMET_PREFIX, "")
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return self.download_dataset_artifact(path)
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self.log_asset(self.opt.data, metadata={"type": "data-config-file"})
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return check_dataset(data_file)
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def log_predictions(self, image, labelsn, path, shape, predn):
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"""Logs predictions with IOU filtering, given image, labels, path, shape, and predictions."""
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if self.logged_images_count >= self.max_images:
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return
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detections = predn[predn[:, 4] > self.conf_thres]
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iou = box_iou(labelsn[:, 1:], detections[:, :4])
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mask, _ = torch.where(iou > self.iou_thres)
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if len(mask) == 0:
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return
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filtered_detections = detections[mask]
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filtered_labels = labelsn[mask]
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image_id = path.split("/")[-1].split(".")[0]
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image_name = f"{image_id}_curr_epoch_{self.experiment.curr_epoch}"
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if image_name not in self.logged_image_names:
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native_scale_image = PIL.Image.open(path)
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self.log_image(native_scale_image, name=image_name)
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self.logged_image_names.append(image_name)
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metadata = [
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{
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"label": f"{self.class_names[int(cls)]}-gt",
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"score": 100,
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"box": {"x": xyxy[0], "y": xyxy[1], "x2": xyxy[2], "y2": xyxy[3]},
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}
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for cls, *xyxy in filtered_labels.tolist()
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]
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metadata.extend(
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{
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"label": f"{self.class_names[int(cls)]}",
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"score": conf * 100,
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"box": {"x": xyxy[0], "y": xyxy[1], "x2": xyxy[2], "y2": xyxy[3]},
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}
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for *xyxy, conf, cls in filtered_detections.tolist()
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)
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self.metadata_dict[image_name] = metadata
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self.logged_images_count += 1
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return
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def preprocess_prediction(self, image, labels, shape, pred):
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"""Processes prediction data, resizing labels and adding dataset metadata."""
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nl, _ = labels.shape[0], pred.shape[0]
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# Predictions
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if self.opt.single_cls:
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pred[:, 5] = 0
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predn = pred.clone()
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scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1])
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labelsn = None
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if nl:
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tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
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scale_boxes(image.shape[1:], tbox, shape[0], shape[1]) # native-space labels
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labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
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scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1]) # native-space pred
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return predn, labelsn
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def add_assets_to_artifact(self, artifact, path, asset_path, split):
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"""Adds image and label assets to a wandb artifact given dataset split and paths."""
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img_paths = sorted(glob.glob(f"{asset_path}/*"))
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label_paths = img2label_paths(img_paths)
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for image_file, label_file in zip(img_paths, label_paths):
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image_logical_path, label_logical_path = map(lambda x: os.path.relpath(x, path), [image_file, label_file])
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try:
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artifact.add(
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image_file,
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logical_path=image_logical_path,
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metadata={"split": split},
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)
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artifact.add(
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label_file,
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logical_path=label_logical_path,
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metadata={"split": split},
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)
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except ValueError as e:
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logger.error("COMET ERROR: Error adding file to Artifact. Skipping file.")
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logger.error(f"COMET ERROR: {e}")
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continue
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return artifact
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def upload_dataset_artifact(self):
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"""Uploads a YOLOv5 dataset as an artifact to the Comet.ml platform."""
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dataset_name = self.data_dict.get("dataset_name", "yolov5-dataset")
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path = str((ROOT / Path(self.data_dict["path"])).resolve())
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metadata = self.data_dict.copy()
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for key in ["train", "val", "test"]:
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split_path = metadata.get(key)
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if split_path is not None:
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metadata[key] = split_path.replace(path, "")
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artifact = comet_ml.Artifact(name=dataset_name, artifact_type="dataset", metadata=metadata)
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for key in metadata.keys():
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if key in ["train", "val", "test"]:
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if isinstance(self.upload_dataset, str) and (key != self.upload_dataset):
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continue
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asset_path = self.data_dict.get(key)
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if asset_path is not None:
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artifact = self.add_assets_to_artifact(artifact, path, asset_path, key)
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self.experiment.log_artifact(artifact)
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return
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def download_dataset_artifact(self, artifact_path):
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"""Downloads a dataset artifact to a specified directory using the experiment's logged artifact."""
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logged_artifact = self.experiment.get_artifact(artifact_path)
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artifact_save_dir = str(Path(self.opt.save_dir) / logged_artifact.name)
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logged_artifact.download(artifact_save_dir)
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metadata = logged_artifact.metadata
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data_dict = metadata.copy()
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data_dict["path"] = artifact_save_dir
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metadata_names = metadata.get("names")
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if isinstance(metadata_names, dict):
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data_dict["names"] = {int(k): v for k, v in metadata.get("names").items()}
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elif isinstance(metadata_names, list):
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data_dict["names"] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)}
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else:
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raise "Invalid 'names' field in dataset yaml file. Please use a list or dictionary"
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return self.update_data_paths(data_dict)
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def update_data_paths(self, data_dict):
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"""Updates data paths in the dataset dictionary, defaulting 'path' to an empty string if not present."""
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path = data_dict.get("path", "")
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for split in ["train", "val", "test"]:
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if data_dict.get(split):
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split_path = data_dict.get(split)
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data_dict[split] = (
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f"{path}/{split_path}" if isinstance(split, str) else [f"{path}/{x}" for x in split_path]
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)
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return data_dict
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def on_pretrain_routine_end(self, paths):
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"""Called at the end of pretraining routine to handle paths if training is not being resumed."""
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if self.opt.resume:
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return
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for path in paths:
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self.log_asset(str(path))
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if self.upload_dataset and not self.resume:
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self.upload_dataset_artifact()
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return
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def on_train_start(self):
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"""Logs hyperparameters at the start of training."""
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self.log_parameters(self.hyp)
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def on_train_epoch_start(self):
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"""Called at the start of each training epoch."""
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return
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def on_train_epoch_end(self, epoch):
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"""Updates the current epoch in the experiment tracking at the end of each epoch."""
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self.experiment.curr_epoch = epoch
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return
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def on_train_batch_start(self):
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"""Called at the start of each training batch."""
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return
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def on_train_batch_end(self, log_dict, step):
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"""Callback function that updates and logs metrics at the end of each training batch if conditions are met."""
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self.experiment.curr_step = step
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if self.log_batch_metrics and (step % self.comet_log_batch_interval == 0):
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self.log_metrics(log_dict, step=step)
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return
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def on_train_end(self, files, save_dir, last, best, epoch, results):
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"""Logs metadata and optionally saves model files at the end of training."""
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if self.comet_log_predictions:
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curr_epoch = self.experiment.curr_epoch
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self.experiment.log_asset_data(self.metadata_dict, "image-metadata.json", epoch=curr_epoch)
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for f in files:
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self.log_asset(f, metadata={"epoch": epoch})
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self.log_asset(f"{save_dir}/results.csv", metadata={"epoch": epoch})
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if not self.opt.evolve:
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model_path = str(best if best.exists() else last)
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name = Path(model_path).name
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if self.save_model:
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self.experiment.log_model(
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self.model_name,
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file_or_folder=model_path,
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|
file_name=name,
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|
overwrite=True,
|
|
)
|
|
|
|
# Check if running Experiment with Comet Optimizer
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|
if hasattr(self.opt, "comet_optimizer_id"):
|
|
metric = results.get(self.opt.comet_optimizer_metric)
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|
self.experiment.log_other("optimizer_metric_value", metric)
|
|
|
|
self.finish_run()
|
|
|
|
def on_val_start(self):
|
|
"""Called at the start of validation, currently a placeholder with no functionality."""
|
|
return
|
|
|
|
def on_val_batch_start(self):
|
|
"""Placeholder called at the start of a validation batch with no current functionality."""
|
|
return
|
|
|
|
def on_val_batch_end(self, batch_i, images, targets, paths, shapes, outputs):
|
|
"""Callback executed at the end of a validation batch, conditionally logs predictions to Comet ML."""
|
|
if not (self.comet_log_predictions and ((batch_i + 1) % self.comet_log_prediction_interval == 0)):
|
|
return
|
|
|
|
for si, pred in enumerate(outputs):
|
|
if len(pred) == 0:
|
|
continue
|
|
|
|
image = images[si]
|
|
labels = targets[targets[:, 0] == si, 1:]
|
|
shape = shapes[si]
|
|
path = paths[si]
|
|
predn, labelsn = self.preprocess_prediction(image, labels, shape, pred)
|
|
if labelsn is not None:
|
|
self.log_predictions(image, labelsn, path, shape, predn)
|
|
|
|
return
|
|
|
|
def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix):
|
|
"""Logs per-class metrics to Comet.ml after validation if enabled and more than one class exists."""
|
|
if self.comet_log_per_class_metrics and self.num_classes > 1:
|
|
for i, c in enumerate(ap_class):
|
|
class_name = self.class_names[c]
|
|
self.experiment.log_metrics(
|
|
{
|
|
"mAP@.5": ap50[i],
|
|
"mAP@.5:.95": ap[i],
|
|
"precision": p[i],
|
|
"recall": r[i],
|
|
"f1": f1[i],
|
|
"true_positives": tp[i],
|
|
"false_positives": fp[i],
|
|
"support": nt[c],
|
|
},
|
|
prefix=class_name,
|
|
)
|
|
|
|
if self.comet_log_confusion_matrix:
|
|
epoch = self.experiment.curr_epoch
|
|
class_names = list(self.class_names.values())
|
|
class_names.append("background")
|
|
num_classes = len(class_names)
|
|
|
|
self.experiment.log_confusion_matrix(
|
|
matrix=confusion_matrix.matrix,
|
|
max_categories=num_classes,
|
|
labels=class_names,
|
|
epoch=epoch,
|
|
column_label="Actual Category",
|
|
row_label="Predicted Category",
|
|
file_name=f"confusion-matrix-epoch-{epoch}.json",
|
|
)
|
|
|
|
def on_fit_epoch_end(self, result, epoch):
|
|
"""Logs metrics at the end of each training epoch."""
|
|
self.log_metrics(result, epoch=epoch)
|
|
|
|
def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
|
|
"""Callback to save model checkpoints periodically if conditions are met."""
|
|
if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1:
|
|
self.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
|
|
|
|
def on_params_update(self, params):
|
|
"""Logs updated parameters during training."""
|
|
self.log_parameters(params)
|
|
|
|
def finish_run(self):
|
|
"""Ends the current experiment and logs its completion."""
|
|
self.experiment.end()
|