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ultralytics 8.0.12 - Hydra removal (#506)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Pronoy Mandal <lukex9442@gmail.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
This commit is contained in:
Glenn Jocher
2023-01-21 00:45:19 +01:00
committed by GitHub
parent 6eec39162a
commit c5fccc3fc4
37 changed files with 395 additions and 469 deletions
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14
ultralytics/yolo/engine/exporter.py
View File

@ -60,7 +60,6 @@ from collections import defaultdict
from copy import deepcopy
from pathlib import Path
import hydra
import numpy as np
import pandas as pd
import torch
@ -71,7 +70,7 @@ from ultralytics.nn.tasks import ClassificationModel, DetectionModel, Segmentati
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages
from ultralytics.yolo.data.utils import check_dataset
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, callbacks, colorstr, get_default_args, yaml_save
from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, callbacks, colorstr, get_default_args, yaml_save
from ultralytics.yolo.utils.checks import check_imgsz, check_requirements, check_version, check_yaml
from ultralytics.yolo.utils.files import file_size
from ultralytics.yolo.utils.ops import Profile
@ -123,11 +122,11 @@ class Exporter:
A class for exporting a model.
Attributes:
args (OmegaConf): Configuration for the exporter.
args (SimpleNamespace): Configuration for the exporter.
save_dir (Path): Directory to save results.
"""
def __init__(self, config=DEFAULT_CONFIG, overrides=None):
def __init__(self, config=DEFAULT_CFG, overrides=None):
"""
Initializes the Exporter class.
@ -135,8 +134,6 @@ class Exporter:
config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
overrides (dict, optional): Configuration overrides. Defaults to None.
"""
if overrides is None:
overrides = {}
self.args = get_config(config, overrides)
self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()}) # add callbacks
callbacks.add_integration_callbacks(self)
@ -799,8 +796,7 @@ class Exporter:
callback(self)
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def export(cfg):
def export(cfg=DEFAULT_CFG):
cfg.model = cfg.model or "yolov8n.yaml"
cfg.format = cfg.format or "torchscript"
@ -818,7 +814,7 @@ def export(cfg):
from ultralytics import YOLO
model = YOLO(cfg.model)
model.export(**cfg)
model.export(**vars(cfg))
if __name__ == "__main__":

6
ultralytics/yolo/engine/model.py
View File

@ -6,7 +6,7 @@ from ultralytics import yolo # noqa
from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel, attempt_load_one_weight
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.engine.exporter import Exporter
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, yaml_load
from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, yaml_load
from ultralytics.yolo.utils.checks import check_yaml
from ultralytics.yolo.utils.torch_utils import guess_task_from_head, smart_inference_mode
@ -151,7 +151,7 @@ class YOLO:
overrides = self.overrides.copy()
overrides.update(kwargs)
overrides["mode"] = "val"
args = get_config(config=DEFAULT_CONFIG, overrides=overrides)
args = get_config(config=DEFAULT_CFG_PATH, overrides=overrides)
args.data = data or args.data
args.task = self.task
@ -169,7 +169,7 @@ class YOLO:
overrides = self.overrides.copy()
overrides.update(kwargs)
args = get_config(config=DEFAULT_CONFIG, overrides=overrides)
args = get_config(config=DEFAULT_CFG_PATH, overrides=overrides)
args.task = self.task
print(args)

12
ultralytics/yolo/engine/predictor.py
View File

@ -36,7 +36,7 @@ from ultralytics.nn.autobackend import AutoBackend
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages, LoadPilAndNumpy, LoadScreenshots, LoadStreams
from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, SETTINGS, callbacks, colorstr, ops
from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, SETTINGS, callbacks, colorstr, ops
from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_imshow
from ultralytics.yolo.utils.files import increment_path
from ultralytics.yolo.utils.torch_utils import select_device, smart_inference_mode
@ -49,7 +49,7 @@ class BasePredictor:
A base class for creating predictors.
Attributes:
args (OmegaConf): Configuration for the predictor.
args (SimpleNamespace): Configuration for the predictor.
save_dir (Path): Directory to save results.
done_setup (bool): Whether the predictor has finished setup.
model (nn.Module): Model used for prediction.
@ -62,7 +62,7 @@ class BasePredictor:
data_path (str): Path to data.
"""
def __init__(self, config=DEFAULT_CONFIG, overrides=None):
def __init__(self, config=DEFAULT_CFG_PATH, overrides=None):
"""
Initializes the BasePredictor class.
@ -70,8 +70,6 @@ class BasePredictor:
config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
overrides (dict, optional): Configuration overrides. Defaults to None.
"""
if overrides is None:
overrides = {}
self.args = get_config(config, overrides)
project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task
name = self.args.name or f"{self.args.mode}"
@ -157,7 +155,7 @@ class BasePredictor:
if stream:
return self.stream_inference(source, model, verbose)
else:
return list(chain(*list(self.stream_inference(source, model, verbose)))) # merge list of Result into one
return list(self.stream_inference(source, model, verbose)) # merge list of Result into one
def predict_cli(self):
# Method used for CLI prediction. It uses always generator as outputs as not required by CLI mode
@ -211,7 +209,7 @@ class BasePredictor:
if self.args.save:
self.save_preds(vid_cap, i, str(self.save_dir / p.name))
yield results
yield from results
# Print time (inference-only)
if verbose:

28
ultralytics/yolo/engine/trainer.py
View File

@ -15,8 +15,6 @@ import numpy as np
import torch
import torch.distributed as dist
import torch.nn as nn
from omegaconf import OmegaConf # noqa
from omegaconf import open_dict
from torch.cuda import amp
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.optim import lr_scheduler
@ -27,7 +25,7 @@ from ultralytics import __version__
from ultralytics.nn.tasks import attempt_load_one_weight
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
from ultralytics.yolo.utils import (DEFAULT_CONFIG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr,
from ultralytics.yolo.utils import (DEFAULT_CFG_PATH, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr,
yaml_save)
from ultralytics.yolo.utils.autobatch import check_train_batch_size
from ultralytics.yolo.utils.checks import check_file, check_imgsz, print_args
@ -43,7 +41,7 @@ class BaseTrainer:
A base class for creating trainers.
Attributes:
args (OmegaConf): Configuration for the trainer.
args (SimpleNamespace): Configuration for the trainer.
check_resume (method): Method to check if training should be resumed from a saved checkpoint.
console (logging.Logger): Logger instance.
validator (BaseValidator): Validator instance.
@ -73,7 +71,7 @@ class BaseTrainer:
csv (Path): Path to results CSV file.
"""
def __init__(self, config=DEFAULT_CONFIG, overrides=None):
def __init__(self, config=DEFAULT_CFG_PATH, overrides=None):
"""
Initializes the BaseTrainer class.
@ -81,8 +79,6 @@ class BaseTrainer:
config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
overrides (dict, optional): Configuration overrides. Defaults to None.
"""
if overrides is None:
overrides = {}
self.args = get_config(config, overrides)
self.device = utils.torch_utils.select_device(self.args.device, self.args.batch)
self.check_resume()
@ -95,23 +91,23 @@ class BaseTrainer:
# Dirs
project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task
name = self.args.name or f"{self.args.mode}"
self.save_dir = Path(
self.args.get(
"save_dir",
increment_path(Path(project) / name, exist_ok=self.args.exist_ok if RANK in {-1, 0} else True)))
if hasattr(self.args, 'save_dir'):
self.save_dir = Path(self.args.save_dir)
else:
self.save_dir = Path(
increment_path(Path(project) / name, exist_ok=self.args.exist_ok if RANK in {-1, 0} else True))
self.wdir = self.save_dir / 'weights' # weights dir
if RANK in {-1, 0}:
self.wdir.mkdir(parents=True, exist_ok=True) # make dir
with open_dict(self.args):
self.args.save_dir = str(self.save_dir)
yaml_save(self.save_dir / 'args.yaml', OmegaConf.to_container(self.args, resolve=True)) # save run args
self.args.save_dir = str(self.save_dir)
yaml_save(self.save_dir / 'args.yaml', vars(self.args)) # save run args
self.last, self.best = self.wdir / 'last.pt', self.wdir / 'best.pt' # checkpoint paths
self.batch_size = self.args.batch
self.epochs = self.args.epochs
self.start_epoch = 0
if RANK == -1:
print_args(dict(self.args))
print_args(vars(self.args))
# Device
self.amp = self.device.type != 'cpu'
@ -373,7 +369,7 @@ class BaseTrainer:
'ema': deepcopy(self.ema.ema).half(),
'updates': self.ema.updates,
'optimizer': self.optimizer.state_dict(),
'train_args': self.args,
'train_args': vars(self.args), # save as dict
'date': datetime.now().isoformat(),
'version': __version__}

10
ultralytics/yolo/engine/validator.py
View File

@ -5,12 +5,12 @@ from collections import defaultdict
from pathlib import Path
import torch
from omegaconf import OmegaConf # noqa
from tqdm import tqdm
from ultralytics.nn.autobackend import AutoBackend
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks
from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks
from ultralytics.yolo.utils.checks import check_imgsz
from ultralytics.yolo.utils.files import increment_path
from ultralytics.yolo.utils.ops import Profile
@ -27,7 +27,7 @@ class BaseValidator:
dataloader (DataLoader): Dataloader to use for validation.
pbar (tqdm): Progress bar to update during validation.
logger (logging.Logger): Logger to use for validation.
args (OmegaConf): Configuration for the validator.
args (SimpleNamespace): Configuration for the validator.
model (nn.Module): Model to validate.
data (dict): Data dictionary.
device (torch.device): Device to use for validation.
@ -47,12 +47,12 @@ class BaseValidator:
save_dir (Path): Directory to save results.
pbar (tqdm.tqdm): Progress bar for displaying progress.
logger (logging.Logger): Logger to log messages.
args (OmegaConf): Configuration for the validator.
args (SimpleNamespace): Configuration for the validator.
"""
self.dataloader = dataloader
self.pbar = pbar
self.logger = logger or LOGGER
self.args = args or OmegaConf.load(DEFAULT_CONFIG)
self.args = args or get_config(DEFAULT_CFG_PATH)
self.model = None
self.data = None
self.device = None