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ultralytics 8.0.67 Pose speeds, Comet and ClearML updates (#1871)

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Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Victor Sonck <victor.sonck@gmail.com>
Co-authored-by: Danny Kim <dh031200@gmail.com>
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Glenn Jocher 2023-04-06 19:07:10 +02:00 • committed by GitHub
parent 1cb92d7f42
commit 2725545090
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28 changed files with 547 additions and 146 deletions
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20
ultralytics/yolo/engine/model.py
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@ -78,7 +78,7 @@ class YOLO:
task (Any, optional): Task type for the YOLO model. Defaults to None.
"""
self._reset_callbacks()
self.callbacks = callbacks.get_default_callbacks()
self.predictor = None # reuse predictor
self.model = None # model object
self.trainer = None # trainer object
@ -238,7 +238,7 @@ class YOLO:
overrides['save'] = kwargs.get('save', False) # not save files by default
if not self.predictor:
self.task = overrides.get('task') or self.task
self.predictor = TASK_MAP[self.task][3](overrides=overrides)
self.predictor = TASK_MAP[self.task][3](overrides=overrides, _callbacks=self.callbacks)
self.predictor.setup_model(model=self.model, verbose=is_cli)
else: # only update args if predictor is already setup
self.predictor.args = get_cfg(self.predictor.args, overrides)
@ -277,7 +277,7 @@ class YOLO:
args.imgsz = self.model.args['imgsz'] # use trained imgsz unless custom value is passed
args.imgsz = check_imgsz(args.imgsz, max_dim=1)
validator = TASK_MAP[self.task][2](args=args)
validator = TASK_MAP[self.task][2](args=args, _callbacks=self.callbacks)
validator(model=self.model)
self.metrics = validator.metrics
@ -316,7 +316,7 @@ class YOLO:
args.imgsz = self.model.args['imgsz'] # use trained imgsz unless custom value is passed
if args.batch == DEFAULT_CFG.batch:
args.batch = 1 # default to 1 if not modified
return Exporter(overrides=args)(model=self.model)
return Exporter(overrides=args, _callbacks=self.callbacks)(model=self.model)
def train(self, **kwargs):
"""
@ -344,7 +344,7 @@ class YOLO:
overrides['resume'] = self.ckpt_path
self.task = overrides.get('task') or self.task
self.trainer = TASK_MAP[self.task][1](overrides=overrides)
self.trainer = TASK_MAP[self.task][1](overrides=overrides, _callbacks=self.callbacks)
if not overrides.get('resume'): # manually set model only if not resuming
self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml)
self.model = self.trainer.model
@ -387,19 +387,17 @@ class YOLO:
"""
return self.model.transforms if hasattr(self.model, 'transforms') else None
@staticmethod
def add_callback(event: str, func):
def add_callback(self, event: str, func):
"""
Add callback
"""
callbacks.default_callbacks[event].append(func)
self.callbacks[event].append(func)
@staticmethod
def _reset_ckpt_args(args):
include = {'imgsz', 'data', 'task', 'single_cls'} # only remember these arguments when loading a PyTorch model
return {k: v for k, v in args.items() if k in include}
@staticmethod
def _reset_callbacks():
def _reset_callbacks(self):
for event in callbacks.default_callbacks.keys():
callbacks.default_callbacks[event] = [callbacks.default_callbacks[event][0]]
self.callbacks[event] = [callbacks.default_callbacks[event][0]]