You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
224 lines
8.3 KiB
224 lines
8.3 KiB
import torch
|
|
import yaml
|
|
|
|
from ultralytics import yolo
|
|
from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
|
|
from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
|
|
from ultralytics.yolo.utils import LOGGER
|
|
from ultralytics.yolo.utils.checks import check_yaml
|
|
from ultralytics.yolo.utils.configs import get_config
|
|
from ultralytics.yolo.utils.files import yaml_load
|
|
from ultralytics.yolo.utils.modeling import attempt_load_weights
|
|
from ultralytics.yolo.utils.modeling.tasks import ClassificationModel, DetectionModel, SegmentationModel
|
|
from ultralytics.yolo.utils.torch_utils import smart_inference_mode
|
|
|
|
# map head: [model, trainer, validator, predictor]
|
|
MODEL_MAP = {
|
|
"classify": [
|
|
ClassificationModel, 'yolo.TYPE.classify.ClassificationTrainer', 'yolo.TYPE.classify.ClassificationValidator',
|
|
'yolo.TYPE.classify.ClassificationPredictor'],
|
|
"detect": [
|
|
DetectionModel, 'yolo.TYPE.detect.DetectionTrainer', 'yolo.TYPE.detect.DetectionValidator',
|
|
'yolo.TYPE.detect.DetectionPredictor'],
|
|
"segment": [
|
|
SegmentationModel, 'yolo.TYPE.segment.SegmentationTrainer', 'yolo.TYPE.segment.SegmentationValidator',
|
|
'yolo.TYPE.segment.SegmentationPredictor']}
|
|
|
|
|
|
class YOLO:
|
|
"""
|
|
Python interface which emulates a model-like behaviour by wrapping trainers.
|
|
"""
|
|
|
|
def __init__(self, type="v8") -> None:
|
|
"""
|
|
Args:
|
|
type (str): Type/version of models to use
|
|
"""
|
|
self.type = type
|
|
self.ModelClass = None
|
|
self.TrainerClass = None
|
|
self.ValidatorClass = None
|
|
self.PredictorClass = None
|
|
self.model = None
|
|
self.trainer = None
|
|
self.task = None
|
|
self.ckpt = None
|
|
|
|
def new(self, cfg: str):
|
|
"""
|
|
Initializes a new model and infers the task type from the model definitions
|
|
|
|
Args:
|
|
cfg (str): model configuration file
|
|
"""
|
|
cfg = check_yaml(cfg) # check YAML
|
|
with open(cfg, encoding='ascii', errors='ignore') as f:
|
|
cfg = yaml.safe_load(f) # model dict
|
|
self.task = self._guess_task_from_head(cfg["head"][-1][-2])
|
|
self.ModelClass, self.TrainerClass, self.ValidatorClass, self.PredictorClass = self._guess_ops_from_task(
|
|
self.task)
|
|
self.model = self.ModelClass(cfg) # initialize
|
|
|
|
def load(self, weights: str):
|
|
"""
|
|
Initializes a new model and infers the task type from the model head
|
|
|
|
Args:
|
|
weights (str): model checkpoint to be loaded
|
|
|
|
"""
|
|
self.ckpt = torch.load(weights, map_location="cpu")
|
|
self.task = self.ckpt["train_args"]["task"]
|
|
self.ModelClass, self.TrainerClass, self.ValidatorClass, self.PredictorClass = self._guess_ops_from_task(
|
|
task=self.task)
|
|
self.model = attempt_load_weights(weights)
|
|
|
|
def reset(self):
|
|
"""
|
|
Resets the model modules .
|
|
"""
|
|
for m in self.model.modules():
|
|
if hasattr(m, 'reset_parameters'):
|
|
m.reset_parameters()
|
|
for p in self.model.parameters():
|
|
p.requires_grad = True
|
|
|
|
def info(self, verbose=False):
|
|
"""
|
|
Logs model info
|
|
|
|
Args:
|
|
verbose (bool): Controls verbosity.
|
|
"""
|
|
if not self.model:
|
|
LOGGER.info("model not initialized!")
|
|
self.model.info(verbose=verbose)
|
|
|
|
def fuse(self):
|
|
if not self.model:
|
|
LOGGER.info("model not initialized!")
|
|
self.model.fuse()
|
|
|
|
def predict(self, source, **kwargs):
|
|
"""
|
|
Visualize prection.
|
|
|
|
Args:
|
|
source (str): Accepts all source types accepted by yolo
|
|
**kwargs : Any other args accepted by the predictors. Too see all args check 'configuration' section in the docs
|
|
"""
|
|
predictor = self.PredictorClass(overrides=kwargs)
|
|
|
|
# check size type
|
|
sz = predictor.args.img_size
|
|
if type(sz) != int: # recieved listConfig
|
|
predictor.args.img_size = [sz[0], sz[0]] if len(sz) == 1 else [sz[0], sz[1]] # expand
|
|
else:
|
|
predictor.args.img_size = [sz, sz]
|
|
|
|
predictor.setup(model=self.model, source=source)
|
|
predictor()
|
|
|
|
def val(self, data, **kwargs):
|
|
"""
|
|
Validate a model on a given dataset
|
|
|
|
Args:
|
|
data (str): The dataset to validate on. Accepts all formats accepted by yolo
|
|
kwargs: Any other args accepted by the validators. Too see all args check 'configuration' section in the docs
|
|
"""
|
|
if not self.model:
|
|
raise Exception("model not initialized!")
|
|
|
|
args = get_config(config=DEFAULT_CONFIG, overrides=kwargs)
|
|
args.data = data
|
|
args.task = self.task
|
|
|
|
validator = self.ValidatorClass(args=args)
|
|
validator(model=self.model)
|
|
|
|
def train(self, **kwargs):
|
|
"""
|
|
Trains the model on given dataset.
|
|
|
|
Args:
|
|
**kwargs (Any): Any number of arguments representing the training configuration. List of all args can be found in 'config' section.
|
|
You can pass all arguments as a yaml file in `cfg`. Other args are ignored if `cfg` file is passed
|
|
"""
|
|
if not self.model:
|
|
raise Exception("model not initialized. Use .new() or .load()")
|
|
|
|
overrides = kwargs
|
|
if kwargs.get("cfg"):
|
|
LOGGER.info(f"cfg file passed. Overriding default params with {kwargs['cfg']}.")
|
|
overrides = yaml_load(check_yaml(kwargs["cfg"]))
|
|
overrides["task"] = self.task
|
|
overrides["mode"] = "train"
|
|
if not overrides.get("data"):
|
|
raise Exception("dataset not provided! Please check if you have defined `data` in you configs")
|
|
|
|
self.trainer = self.TrainerClass(overrides=overrides)
|
|
self.trainer.model = self.trainer.load_model(weights=self.ckpt,
|
|
model_cfg=self.model.yaml if self.task != "classify" else None)
|
|
self.model = self.trainer.model # override here to save memory
|
|
|
|
self.trainer.train()
|
|
|
|
def resume(self, task=None, model=None):
|
|
"""
|
|
Resume a training task. Requires either `task` or `model`. `model` takes the higher precederence.
|
|
Args:
|
|
task (str): The task type you want to resume. Automatically finds the last run to resume if `model` is not specified.
|
|
model (str): The model checkpoint to resume from. If not found, the last run of the given task type is resumed.
|
|
If `model` is speficied
|
|
"""
|
|
if task:
|
|
if task.lower() not in MODEL_MAP:
|
|
raise Exception(f"unrecognised task - {task}. Supported tasks are {MODEL_MAP.keys()}")
|
|
else:
|
|
ckpt = torch.load(model, map_location="cpu")
|
|
task = ckpt["train_args"]["task"]
|
|
del ckpt
|
|
self.ModelClass, self.TrainerClass, self.ValidatorClass, self.PredictorClass = self._guess_ops_from_task(
|
|
task=task.lower())
|
|
self.trainer = self.TrainerClass(overrides={"task": task.lower(), "resume": model if model else True})
|
|
self.trainer.train()
|
|
|
|
@staticmethod
|
|
def _guess_task_from_head(head):
|
|
task = None
|
|
if head.lower() in ["classify", "classifier", "cls", "fc"]:
|
|
task = "classify"
|
|
if head.lower() in ["detect"]:
|
|
task = "detect"
|
|
if head.lower() in ["segment"]:
|
|
task = "segment"
|
|
|
|
if not task:
|
|
raise Exception(
|
|
"task or model not recognized! Please refer the docs at : ") # TODO: add gitHub and docs links
|
|
|
|
return task
|
|
|
|
def to(self, device):
|
|
self.model.to(device)
|
|
|
|
def _guess_ops_from_task(self, task):
|
|
model_class, train_lit, val_lit, pred_lit = MODEL_MAP[task]
|
|
# warning: eval is unsafe. Use with caution
|
|
trainer_class = eval(train_lit.replace("TYPE", f"{self.type}"))
|
|
validator_class = eval(val_lit.replace("TYPE", f"{self.type}"))
|
|
predictor_class = eval(pred_lit.replace("TYPE", f"{self.type}"))
|
|
|
|
return model_class, trainer_class, validator_class, predictor_class
|
|
|
|
@smart_inference_mode()
|
|
def __call__(self, imgs):
|
|
if not self.model:
|
|
LOGGER.info("model not initialized!")
|
|
return self.model(imgs)
|
|
|
|
def forward(self, imgs):
|
|
return self.__call__(imgs)
|