HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

[Docs]: Add customization tutorial and address feedback (#155)

Browse source 
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Ayush Chaurasia 2023-01-08 18:31:22 +05:30 • committed by GitHub
parent c985eaba0d
commit d387359f74
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
8 changed files with 133 additions and 74 deletions
Download patch file Download diff file Expand all files Collapse all files

22
ultralytics/yolo/engine/model.py
View file

@ -30,7 +30,7 @@ class YOLO:
def __init__(self, model='yolov8n.yaml', type="v8") -> None:
"""
Initializes the YOLO object.
> Initializes the YOLO object.
Args:
model (str, Path): model to load or create
@ -57,7 +57,7 @@ class YOLO:
def _new(self, cfg: str, verbose=True):
"""
Initializes a new model and infers the task type from the model definitions.
> Initializes a new model and infers the task type from the model definitions.
Args:
cfg (str): model configuration file
@ -73,7 +73,7 @@ class YOLO:
def _load(self, weights: str):
"""
Initializes a new model and infers the task type from the model head
> Initializes a new model and infers the task type from the model head.
Args:
weights (str): model checkpoint to be loaded
@ -88,7 +88,7 @@ class YOLO:
def reset(self):
"""
Resets the model modules .
> Resets the model modules.
"""
for m in self.model.modules():
if hasattr(m, 'reset_parameters'):
@ -98,7 +98,7 @@ class YOLO:
def info(self, verbose=False):
"""
Logs model info
> Logs model info.
Args:
verbose (bool): Controls verbosity.
@ -129,7 +129,7 @@ class YOLO:
@smart_inference_mode()
def val(self, data=None, **kwargs):
"""
Validate a model on a given dataset
> Validate a model on a given dataset .
Args:
data (str): The dataset to validate on. Accepts all formats accepted by yolo
@ -148,7 +148,7 @@ class YOLO:
@smart_inference_mode()
def export(self, **kwargs):
"""
Export model.
> Export model.
Args:
**kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in docs
@ -164,7 +164,7 @@ class YOLO:
def train(self, **kwargs):
"""
Trains the model on given dataset.
> Trains the model on a given dataset.
Args:
**kwargs (Any): Any number of arguments representing the training configuration. List of all args can be found in 'config' section.
@ -189,6 +189,12 @@ class YOLO:
self.trainer.train()
def to(self, device):
"""
> Sends the model to the given device.
Args:
device (str): device
"""
self.model.to(device)
def _guess_ops_from_task(self, task):