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# Ultralytics YOLO 🚀, GPL-3.0 license
from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
try:
import clearml
from clearml import Task
assert hasattr(clearml, '__version__')
except (ImportError, AssertionError):
clearml = None
def _log_images(imgs_dict, group="", step=0):
task = Task.current_task()
if task:
for k, v in imgs_dict.items():
task.get_logger().report_image(group, k, step, v)
def on_pretrain_routine_start(trainer):
# TODO: reuse existing task
task = Task.init(project_name=trainer.args.project or "YOLOv8",
task_name=trainer.args.name,
tags=['YOLOv8'],
output_uri=True,
reuse_last_task_id=False,
auto_connect_frameworks={'pytorch': False})
task.connect(dict(trainer.args), name='General')
def on_train_epoch_end(trainer):
if trainer.epoch == 1:
_log_images({f.stem: str(f) for f in trainer.save_dir.glob('train_batch*.jpg')}, "Mosaic", trainer.epoch)
def on_fit_epoch_end(trainer):
if trainer.epoch == 0:
model_info = {
"Parameters": get_num_params(trainer.model),
"GFLOPs": round(get_flops(trainer.model), 3),
"Inference speed (ms/img)": round(trainer.validator.speed[1], 3)}
Task.current_task().connect(model_info, name='Model')
def on_train_end(trainer):
Task.current_task().update_output_model(model_path=str(trainer.best),
model_name=trainer.args.name,
auto_delete_file=False)
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 clearml else {}
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