376 lines
13 KiB
Python
376 lines
13 KiB
Python
# 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 {}
|
|
)
|