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回传数据解析，兼容v5和v10

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jiajie555
2025-04-18 14:41:53 +08:00
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# Ultralytics YOLO 🚀, AGPL-3.0 license
from ultralytics.utils import LOGGER, RANK, SETTINGS, TESTS_RUNNING, ops
try:
assert not TESTS_RUNNING # do not log pytest
assert SETTINGS["comet"] is True # verify integration is enabled
import comet_ml
assert hasattr(comet_ml, "__version__") # verify package is not directory
import os
from pathlib import Path
# Ensures certain logging functions only run for supported tasks
COMET_SUPPORTED_TASKS = ["detect"]
# Names of plots created by YOLOv8 that are logged to Comet
EVALUATION_PLOT_NAMES = "F1_curve", "P_curve", "R_curve", "PR_curve", "confusion_matrix"
LABEL_PLOT_NAMES = "labels", "labels_correlogram"
_comet_image_prediction_count = 0
except (ImportError, AssertionError):
comet_ml = None
def _get_comet_mode():
"""Returns the mode of comet set in the environment variables, defaults to 'online' if not set."""
return os.getenv("COMET_MODE", "online")
def _get_comet_model_name():
"""Returns the model name for Comet from the environment variable 'COMET_MODEL_NAME' or defaults to 'YOLOv8'."""
return os.getenv("COMET_MODEL_NAME", "YOLOv8")
def _get_eval_batch_logging_interval():
"""Get the evaluation batch logging interval from environment variable or use default value 1."""
return int(os.getenv("COMET_EVAL_BATCH_LOGGING_INTERVAL", 1))
def _get_max_image_predictions_to_log():
"""Get the maximum number of image predictions to log from the environment variables."""
return int(os.getenv("COMET_MAX_IMAGE_PREDICTIONS", 100))
def _scale_confidence_score(score):
"""Scales the given confidence score by a factor specified in an environment variable."""
scale = float(os.getenv("COMET_MAX_CONFIDENCE_SCORE", 100.0))
return score * scale
def _should_log_confusion_matrix():
"""Determines if the confusion matrix should be logged based on the environment variable settings."""
return os.getenv("COMET_EVAL_LOG_CONFUSION_MATRIX", "false").lower() == "true"
def _should_log_image_predictions():
"""Determines whether to log image predictions based on a specified environment variable."""
return os.getenv("COMET_EVAL_LOG_IMAGE_PREDICTIONS", "true").lower() == "true"
def _get_experiment_type(mode, project_name):
"""Return an experiment based on mode and project name."""
if mode == "offline":
return comet_ml.OfflineExperiment(project_name=project_name)
return comet_ml.Experiment(project_name=project_name)
def _create_experiment(args):
"""Ensures that the experiment object is only created in a single process during distributed training."""
if RANK not in (-1, 0):
return
try:
comet_mode = _get_comet_mode()
_project_name = os.getenv("COMET_PROJECT_NAME", args.project)
experiment = _get_experiment_type(comet_mode, _project_name)
experiment.log_parameters(vars(args))
experiment.log_others(
{
"eval_batch_logging_interval": _get_eval_batch_logging_interval(),
"log_confusion_matrix_on_eval": _should_log_confusion_matrix(),
"log_image_predictions": _should_log_image_predictions(),
"max_image_predictions": _get_max_image_predictions_to_log(),
}
)
experiment.log_other("Created from", "yolov8")
except Exception as e:
LOGGER.warning(f"WARNING ⚠️ Comet installed but not initialized correctly, not logging this run. {e}")
def _fetch_trainer_metadata(trainer):
"""Returns metadata for YOLO training including epoch and asset saving status."""
curr_epoch = trainer.epoch + 1
train_num_steps_per_epoch = len(trainer.train_loader.dataset) // trainer.batch_size
curr_step = curr_epoch * train_num_steps_per_epoch
final_epoch = curr_epoch == trainer.epochs
save = trainer.args.save
save_period = trainer.args.save_period
save_interval = curr_epoch % save_period == 0
save_assets = save and save_period > 0 and save_interval and not final_epoch
return dict(curr_epoch=curr_epoch, curr_step=curr_step, save_assets=save_assets, final_epoch=final_epoch)
def _scale_bounding_box_to_original_image_shape(box, resized_image_shape, original_image_shape, ratio_pad):
"""
YOLOv8 resizes images during training and the label values are normalized based on this resized shape.
This function rescales the bounding box labels to the original image shape.
"""
resized_image_height, resized_image_width = resized_image_shape
# Convert normalized xywh format predictions to xyxy in resized scale format
box = ops.xywhn2xyxy(box, h=resized_image_height, w=resized_image_width)
# Scale box predictions from resized image scale back to original image scale
box = ops.scale_boxes(resized_image_shape, box, original_image_shape, ratio_pad)
# Convert bounding box format from xyxy to xywh for Comet logging
box = ops.xyxy2xywh(box)
# Adjust xy center to correspond top-left corner
box[:2] -= box[2:] / 2
box = box.tolist()
return box
def _format_ground_truth_annotations_for_detection(img_idx, image_path, batch, class_name_map=None):
"""Format ground truth annotations for detection."""
indices = batch["batch_idx"] == img_idx
bboxes = batch["bboxes"][indices]
if len(bboxes) == 0:
LOGGER.debug(f"COMET WARNING: Image: {image_path} has no bounding boxes labels")
return None
cls_labels = batch["cls"][indices].squeeze(1).tolist()
if class_name_map:
cls_labels = [str(class_name_map[label]) for label in cls_labels]
original_image_shape = batch["ori_shape"][img_idx]
resized_image_shape = batch["resized_shape"][img_idx]
ratio_pad = batch["ratio_pad"][img_idx]
data = []
for box, label in zip(bboxes, cls_labels):
box = _scale_bounding_box_to_original_image_shape(box, resized_image_shape, original_image_shape, ratio_pad)
data.append(
{
"boxes": [box],
"label": f"gt_{label}",
"score": _scale_confidence_score(1.0),
}
)
return {"name": "ground_truth", "data": data}
def _format_prediction_annotations_for_detection(image_path, metadata, class_label_map=None):
"""Format YOLO predictions for object detection visualization."""
stem = image_path.stem
image_id = int(stem) if stem.isnumeric() else stem
predictions = metadata.get(image_id)
if not predictions:
LOGGER.debug(f"COMET WARNING: Image: {image_path} has no bounding boxes predictions")
return None
data = []
for prediction in predictions:
boxes = prediction["bbox"]
score = _scale_confidence_score(prediction["score"])
cls_label = prediction["category_id"]
if class_label_map:
cls_label = str(class_label_map[cls_label])
data.append({"boxes": [boxes], "label": cls_label, "score": score})
return {"name": "prediction", "data": data}
def _fetch_annotations(img_idx, image_path, batch, prediction_metadata_map, class_label_map):
"""Join the ground truth and prediction annotations if they exist."""
ground_truth_annotations = _format_ground_truth_annotations_for_detection(
img_idx, image_path, batch, class_label_map
)
prediction_annotations = _format_prediction_annotations_for_detection(
image_path, prediction_metadata_map, class_label_map
)
annotations = [
annotation for annotation in [ground_truth_annotations, prediction_annotations] if annotation is not None
]
return [annotations] if annotations else None
def _create_prediction_metadata_map(model_predictions):
"""Create metadata map for model predictions by groupings them based on image ID."""
pred_metadata_map = {}
for prediction in model_predictions:
pred_metadata_map.setdefault(prediction["image_id"], [])
pred_metadata_map[prediction["image_id"]].append(prediction)
return pred_metadata_map
def _log_confusion_matrix(experiment, trainer, curr_step, curr_epoch):
"""Log the confusion matrix to Comet experiment."""
conf_mat = trainer.validator.confusion_matrix.matrix
names = list(trainer.data["names"].values()) + ["background"]
experiment.log_confusion_matrix(
matrix=conf_mat, labels=names, max_categories=len(names), epoch=curr_epoch, step=curr_step
)
def _log_images(experiment, image_paths, curr_step, annotations=None):
"""Logs images to the experiment with optional annotations."""
if annotations:
for image_path, annotation in zip(image_paths, annotations):
experiment.log_image(image_path, name=image_path.stem, step=curr_step, annotations=annotation)
else:
for image_path in image_paths:
experiment.log_image(image_path, name=image_path.stem, step=curr_step)
def _log_image_predictions(experiment, validator, curr_step):
"""Logs predicted boxes for a single image during training."""
global _comet_image_prediction_count
task = validator.args.task
if task not in COMET_SUPPORTED_TASKS:
return
jdict = validator.jdict
if not jdict:
return
predictions_metadata_map = _create_prediction_metadata_map(jdict)
dataloader = validator.dataloader
class_label_map = validator.names
batch_logging_interval = _get_eval_batch_logging_interval()
max_image_predictions = _get_max_image_predictions_to_log()
for batch_idx, batch in enumerate(dataloader):
if (batch_idx + 1) % batch_logging_interval != 0:
continue
image_paths = batch["im_file"]
for img_idx, image_path in enumerate(image_paths):
if _comet_image_prediction_count >= max_image_predictions:
return
image_path = Path(image_path)
annotations = _fetch_annotations(
img_idx,
image_path,
batch,
predictions_metadata_map,
class_label_map,
)
_log_images(
experiment,
[image_path],
curr_step,
annotations=annotations,
)
_comet_image_prediction_count += 1
def _log_plots(experiment, trainer):
"""Logs evaluation plots and label plots for the experiment."""
plot_filenames = [trainer.save_dir / f"{plots}.png" for plots in EVALUATION_PLOT_NAMES]
_log_images(experiment, plot_filenames, None)
label_plot_filenames = [trainer.save_dir / f"{labels}.jpg" for labels in LABEL_PLOT_NAMES]
_log_images(experiment, label_plot_filenames, None)
def _log_model(experiment, trainer):
"""Log the best-trained model to Comet.ml."""
model_name = _get_comet_model_name()
experiment.log_model(model_name, file_or_folder=str(trainer.best), file_name="best.pt", overwrite=True)
def on_pretrain_routine_start(trainer):
"""Creates or resumes a CometML experiment at the start of a YOLO pre-training routine."""
experiment = comet_ml.get_global_experiment()
is_alive = getattr(experiment, "alive", False)
if not experiment or not is_alive:
_create_experiment(trainer.args)
def on_train_epoch_end(trainer):
"""Log metrics and save batch images at the end of training epochs."""
experiment = comet_ml.get_global_experiment()
if not experiment:
return
metadata = _fetch_trainer_metadata(trainer)
curr_epoch = metadata["curr_epoch"]
curr_step = metadata["curr_step"]
experiment.log_metrics(trainer.label_loss_items(trainer.tloss, prefix="train"), step=curr_step, epoch=curr_epoch)
if curr_epoch == 1:
_log_images(experiment, trainer.save_dir.glob("train_batch*.jpg"), curr_step)
def on_fit_epoch_end(trainer):
"""Logs model assets at the end of each epoch."""
experiment = comet_ml.get_global_experiment()
if not experiment:
return
metadata = _fetch_trainer_metadata(trainer)
curr_epoch = metadata["curr_epoch"]
curr_step = metadata["curr_step"]
save_assets = metadata["save_assets"]
experiment.log_metrics(trainer.metrics, step=curr_step, epoch=curr_epoch)
experiment.log_metrics(trainer.lr, step=curr_step, epoch=curr_epoch)
if curr_epoch == 1:
from ultralytics.utils.torch_utils import model_info_for_loggers
experiment.log_metrics(model_info_for_loggers(trainer), step=curr_step, epoch=curr_epoch)
if not save_assets:
return
_log_model(experiment, trainer)
if _should_log_confusion_matrix():
_log_confusion_matrix(experiment, trainer, curr_step, curr_epoch)
if _should_log_image_predictions():
_log_image_predictions(experiment, trainer.validator, curr_step)
def on_train_end(trainer):
"""Perform operations at the end of training."""
experiment = comet_ml.get_global_experiment()
if not experiment:
return
metadata = _fetch_trainer_metadata(trainer)
curr_epoch = metadata["curr_epoch"]
curr_step = metadata["curr_step"]
plots = trainer.args.plots
_log_model(experiment, trainer)
if plots:
_log_plots(experiment, trainer)
_log_confusion_matrix(experiment, trainer, curr_step, curr_epoch)
_log_image_predictions(experiment, trainer.validator, curr_step)
experiment.end()
global _comet_image_prediction_count
_comet_image_prediction_count = 0
callbacks = (
{
"on_pretrain_routine_start": on_pretrain_routine_start,
"on_train_epoch_end": on_train_epoch_end,
"on_fit_epoch_end": on_fit_epoch_end,
"on_train_end": on_train_end,
}
if comet_ml
else {}
)