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ultralytics 8.0.226 Validator Path and Tuner space (#6901)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Muhammad Rizwan Munawar <chr043416@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: DennisJ <106725464+DennisJcy@users.noreply.github.com>
Co-authored-by: Kirill Ionkin <56236621+kirill-ionkin@users.noreply.github.com>
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Glenn Jocher 2023-12-10 18:15:59 +01:00 • committed by GitHub
parent 6e660dfaaf
commit 412eb57fca
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12 changed files with 60 additions and 22 deletions
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4
ultralytics/engine/exporter.py
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@ -64,7 +64,7 @@ import torch
from ultralytics.cfg import get_cfg
from ultralytics.data.dataset import YOLODataset
from ultralytics.data.utils import check_det_dataset
from ultralytics.nn.autobackend import check_class_names
from ultralytics.nn.autobackend import check_class_names, default_class_names
from ultralytics.nn.modules import C2f, Detect, RTDETRDecoder
from ultralytics.nn.tasks import DetectionModel, SegmentationModel
from ultralytics.utils import (ARM64, DEFAULT_CFG, LINUX, LOGGER, MACOS, ROOT, WINDOWS, __version__, callbacks,
@ -172,6 +172,8 @@ class Exporter:
self.device = select_device('cpu' if self.args.device is None else self.args.device)
# Checks
if not hasattr(model, 'names'):
model.names = default_class_names()
model.names = check_class_names(model.names)
if self.args.half and onnx and self.device.type == 'cpu':
LOGGER.warning('WARNING ⚠️ half=True only compatible with GPU export, i.e. use device=0')

14
ultralytics/engine/tuner.py
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@ -56,6 +56,14 @@ class Tuner:
model = YOLO('yolov8n.pt')
model.tune(data='coco8.yaml', epochs=10, iterations=300, optimizer='AdamW', plots=False, save=False, val=False)
```
Tune with custom search space.
```python
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
model.tune(space={key1: val1, key2: val2}) # custom search space dictionary
```
"""
def __init__(self, args=DEFAULT_CFG, _callbacks=None):
@ -65,10 +73,9 @@ class Tuner:
Args:
args (dict, optional): Configuration for hyperparameter evolution.
"""
self.args = get_cfg(overrides=args)
self.space = { # key: (min, max, gain(optional))
self.space = args.pop('space', None) or { # key: (min, max, gain(optional))
# 'optimizer': tune.choice(['SGD', 'Adam', 'AdamW', 'NAdam', 'RAdam', 'RMSProp']),
'lr0': (1e-5, 1e-1),
'lr0': (1e-5, 1e-1), # initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
'lrf': (0.0001, 0.1), # final OneCycleLR learning rate (lr0 * lrf)
'momentum': (0.7, 0.98, 0.3), # SGD momentum/Adam beta1
'weight_decay': (0.0, 0.001), # optimizer weight decay 5e-4
@ -90,6 +97,7 @@ class Tuner:
'mosaic': (0.0, 1.0), # image mixup (probability)
'mixup': (0.0, 1.0), # image mixup (probability)
'copy_paste': (0.0, 1.0)} # segment copy-paste (probability)
self.args = get_cfg(overrides=args)
self.tune_dir = get_save_dir(self.args, name='tune')
self.tune_csv = self.tune_dir / 'tune_results.csv'
self.callbacks = _callbacks or callbacks.get_default_callbacks()

2
ultralytics/engine/validator.py
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@ -135,7 +135,7 @@ class BaseValidator:
self.args.batch = 1 # export.py models default to batch-size 1
LOGGER.info(f'Forcing batch=1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
if isinstance(self.args.data, str) and self.args.data.split('.')[-1] in ('yaml', 'yml'):
if str(self.args.data).split('.')[-1] in ('yaml', 'yml'):
self.data = check_det_dataset(self.args.data)
elif self.args.task == 'classify':
self.data = check_cls_dataset(self.args.data, split=self.args.split)