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ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
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Glenn Jocher 2024-01-10 03:16:08 +01:00 • committed by GitHub
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commit fe27db2f6e
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147
ultralytics/utils/callbacks/comet.py
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@ -4,20 +4,20 @@ 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
assert SETTINGS["comet"] is True # verify integration is enabled
import comet_ml
assert hasattr(comet_ml, '__version__') # verify package is not directory
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']
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'
EVALUATION_PLOT_NAMES = "F1_curve", "P_curve", "R_curve", "PR_curve", "confusion_matrix"
LABEL_PLOT_NAMES = "labels", "labels_correlogram"
_comet_image_prediction_count = 0
@ -27,43 +27,43 @@ except (ImportError, AssertionError):
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')
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')
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))
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))
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))
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'
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'
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':
if mode == "offline":
return comet_ml.OfflineExperiment(project_name=project_name)
return comet_ml.Experiment(project_name=project_name)
@ -75,18 +75,21 @@ def _create_experiment(args):
return
try:
comet_mode = _get_comet_mode()
_project_name = os.getenv('COMET_PROJECT_NAME', args.project)
_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')
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}')
LOGGER.warning(f"WARNING ⚠️ Comet installed but not initialized correctly, not logging this run. {e}")
def _fetch_trainer_metadata(trainer):
@ -134,29 +137,32 @@ def _scale_bounding_box_to_original_image_shape(box, resized_image_shape, origin
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]
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')
LOGGER.debug(f"COMET WARNING: Image: {image_path} has no bounding boxes labels")
return None
cls_labels = batch['cls'][indices].squeeze(1).tolist()
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]
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), })
data.append(
{
"boxes": [box],
"label": f"gt_{label}",
"score": _scale_confidence_score(1.0),
}
)
return {'name': 'ground_truth', 'data': data}
return {"name": "ground_truth", "data": data}
def _format_prediction_annotations_for_detection(image_path, metadata, class_label_map=None):
@ -166,31 +172,34 @@ def _format_prediction_annotations_for_detection(image_path, metadata, class_lab
predictions = metadata.get(image_id)
if not predictions:
LOGGER.debug(f'COMET WARNING: Image: {image_path} has no bounding boxes 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']
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})
data.append({"boxes": [boxes], "label": cls_label, "score": score})
return {'name': 'prediction', 'data': data}
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)
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]
annotation for annotation in [ground_truth_annotations, prediction_annotations] if annotation is not None
]
return [annotations] if annotations else None
@ -198,8 +207,8 @@ 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)
pred_metadata_map.setdefault(prediction["image_id"], [])
pred_metadata_map[prediction["image_id"]].append(prediction)
return pred_metadata_map
@ -207,7 +216,7 @@ def _create_prediction_metadata_map(model_predictions):
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']
names = list(trainer.data["names"].values()) + ["background"]
experiment.log_confusion_matrix(
matrix=conf_mat,
labels=names,
@ -251,7 +260,7 @@ def _log_image_predictions(experiment, validator, curr_step):
if (batch_idx + 1) % batch_logging_interval != 0:
continue
image_paths = batch['im_file']
image_paths = batch["im_file"]
for img_idx, image_path in enumerate(image_paths):
if _comet_image_prediction_count >= max_image_predictions:
return
@ -275,10 +284,10 @@ def _log_image_predictions(experiment, validator, curr_step):
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]
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]
label_plot_filenames = [trainer.save_dir / f"{labels}.jpg" for labels in LABEL_PLOT_NAMES]
_log_images(experiment, label_plot_filenames, None)
@ -288,7 +297,7 @@ def _log_model(experiment, trainer):
experiment.log_model(
model_name,
file_or_folder=str(trainer.best),
file_name='best.pt',
file_name="best.pt",
overwrite=True,
)
@ -296,7 +305,7 @@ def _log_model(experiment, trainer):
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)
is_alive = getattr(experiment, "alive", False)
if not experiment or not is_alive:
_create_experiment(trainer.args)
@ -308,17 +317,17 @@ def on_train_epoch_end(trainer):
return
metadata = _fetch_trainer_metadata(trainer)
curr_epoch = metadata['curr_epoch']
curr_step = metadata['curr_step']
curr_epoch = metadata["curr_epoch"]
curr_step = metadata["curr_step"]
experiment.log_metrics(
trainer.label_loss_items(trainer.tloss, prefix='train'),
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)
_log_images(experiment, trainer.save_dir.glob("train_batch*.jpg"), curr_step)
def on_fit_epoch_end(trainer):
@ -328,14 +337,15 @@ def on_fit_epoch_end(trainer):
return
metadata = _fetch_trainer_metadata(trainer)
curr_epoch = metadata['curr_epoch']
curr_step = metadata['curr_step']
save_assets = metadata['save_assets']
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:
@ -355,8 +365,8 @@ def on_train_end(trainer):
return
metadata = _fetch_trainer_metadata(trainer)
curr_epoch = metadata['curr_epoch']
curr_step = metadata['curr_step']
curr_epoch = metadata["curr_epoch"]
curr_step = metadata["curr_step"]
plots = trainer.args.plots
_log_model(experiment, trainer)
@ -371,8 +381,13 @@ def on_train_end(trainer):
_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 {}
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 {}
)