xdobro23/YOLOv8-16bit
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

New ASSETS and trackers GMC cleanup (#4425)

Browse Source 
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-08-17 18:19:05 +02:00
committed by GitHub
parent aaba14e6b2
commit 9d27e7ada4
32 changed files with 222 additions and 201 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
ultralytics/cfg/__init__.py
View File

@ -9,7 +9,7 @@ from pathlib import Path
from types import SimpleNamespace
from typing import Dict, List, Union
from ultralytics.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_PATH, LOGGER, ROOT, SETTINGS, SETTINGS_YAML,
from ultralytics.utils import (ASSETS, DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_PATH, LOGGER, SETTINGS, SETTINGS_YAML,
IterableSimpleNamespace, __version__, checks, colorstr, deprecation_warn, yaml_load,
yaml_print)
@ -415,8 +415,7 @@ def entrypoint(debug=''):
# Mode
if mode in ('predict', 'track') and 'source' not in overrides:
overrides['source'] = DEFAULT_CFG.source or ROOT / 'assets' if (ROOT / 'assets').exists() \
else 'https://ultralytics.com/images/bus.jpg'
overrides['source'] = DEFAULT_CFG.source or ASSETS
LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using default 'source={overrides['source']}'.")
elif mode in ('train', 'val'):
if 'data' not in overrides:

2
ultralytics/cfg/trackers/botsort.yaml
View File

@ -11,7 +11,7 @@ match_thresh: 0.8 # threshold for matching tracks
# mot20: False # for tracker evaluation(not used for now)
# BoT-SORT settings
cmc_method: sparseOptFlow # method of global motion compensation
gmc_method: sparseOptFlow # method of global motion compensation
# ReID model related thresh (not supported yet)
proximity_thresh: 0.5
appearance_thresh: 0.25

32
ultralytics/data/converter.py
View File

@ -1,6 +1,7 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
import json
import shutil
from collections import defaultdict
from pathlib import Path
@ -9,7 +10,6 @@ import numpy as np
from tqdm import tqdm
from ultralytics.utils.checks import check_requirements
from ultralytics.utils.files import make_dirs
def coco91_to_coco80_class():
@ -27,6 +27,27 @@ def coco91_to_coco80_class():
None, 73, 74, 75, 76, 77, 78, 79, None]
def coco80_to_coco91_class(): #
"""
Converts 80-index (val2014) to 91-index (paper).
For details see https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/.
Example:
```python
import numpy as np
a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco
x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet
```
"""
return [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
def convert_coco(labels_dir='../coco/annotations/', use_segments=False, use_keypoints=False, cls91to80=True):
"""Converts COCO dataset annotations to a format suitable for training YOLOv5 models.
@ -47,7 +68,14 @@ def convert_coco(labels_dir='../coco/annotations/', use_segments=False, use_keyp
Generates output files in the specified output directory.
"""
save_dir = make_dirs('yolo_labels') # output directory
# Create dataset directory
save_dir = Path('yolo_labels')
if save_dir.exists():
shutil.rmtree(save_dir) # delete dir
for p in save_dir / 'labels', save_dir / 'images':
p.mkdir(parents=True, exist_ok=True) # make dir
# Convert classes
coco80 = coco91_to_coco80_class()
# Import json

4
ultralytics/data/loaders.py
View File

@ -16,7 +16,7 @@ import torch
from PIL import Image
from ultralytics.data.utils import IMG_FORMATS, VID_FORMATS
from ultralytics.utils import LOGGER, ROOT, is_colab, is_kaggle, ops
from ultralytics.utils import ASSETS, LOGGER, is_colab, is_kaggle, ops
from ultralytics.utils.checks import check_requirements
@ -403,7 +403,7 @@ def get_best_youtube_url(url, use_pafy=False):
if __name__ == '__main__':
img = cv2.imread(str(ROOT / 'assets/bus.jpg'))
img = cv2.imread(str(ASSETS / 'bus.jpg'))
dataset = LoadPilAndNumpy(im0=img)
for d in dataset:
print(d[0])

7
ultralytics/engine/model.py
View File

@ -9,8 +9,8 @@ from ultralytics.cfg import get_cfg
from ultralytics.engine.exporter import Exporter
from ultralytics.hub.utils import HUB_WEB_ROOT
from ultralytics.nn.tasks import attempt_load_one_weight, guess_model_task, nn, yaml_model_load
from ultralytics.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, ROOT, callbacks, emojis,
is_git_dir, yaml_load)
from ultralytics.utils import (ASSETS, DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, callbacks, emojis,
yaml_load)
from ultralytics.utils.checks import check_file, check_imgsz, check_pip_update_available, check_yaml
from ultralytics.utils.downloads import GITHUB_ASSET_STEMS
from ultralytics.utils.torch_utils import smart_inference_mode
@ -218,7 +218,7 @@ class Model:
(List[ultralytics.engine.results.Results]): The prediction results.
"""
if source is None:
source = ROOT / 'assets' if is_git_dir() else 'https://ultralytics.com/images/bus.jpg'
source = ASSETS
LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using 'source={source}'.")
is_cli = (sys.argv[0].endswith('yolo') or sys.argv[0].endswith('ultralytics')) and any(
x in sys.argv for x in ('predict', 'track', 'mode=predict', 'mode=track'))
@ -390,6 +390,7 @@ class Model:
"""
self._check_is_pytorch_model()
self.model.to(device)
return self
def tune(self, *args, **kwargs):
"""

2
ultralytics/engine/predictor.py
View File

@ -47,7 +47,7 @@ STREAM_WARNING = """
WARNING ⚠️ stream/video/webcam/dir predict source will accumulate results in RAM unless `stream=True` is passed,
causing potential out-of-memory errors for large sources or long-running streams/videos.
Usage:
Example:
results = model(source=..., stream=True) # generator of Results objects
for r in results:
boxes = r.boxes # Boxes object for bbox outputs

2
ultralytics/models/rtdetr/train.py
View File

@ -59,7 +59,7 @@ class RTDETRTrainer(DetectionTrainer):
def train(cfg=DEFAULT_CFG, use_python=False):
"""Train and optimize RTDETR model given training data and device."""
model = 'rtdetr-l.yaml'
data = cfg.data or 'coco128.yaml' # or yolo.ClassificationDataset("mnist")
data = cfg.data or 'coco8.yaml' # or yolo.ClassificationDataset("mnist")
device = cfg.device if cfg.device is not None else ''
# NOTE: F.grid_sample which is in rt-detr does not support deterministic=True

5
ultralytics/models/yolo/classify/predict.py
View File

@ -4,7 +4,7 @@ import torch
from ultralytics.engine.predictor import BasePredictor
from ultralytics.engine.results import Results
from ultralytics.utils import DEFAULT_CFG, ROOT
from ultralytics.utils import ASSETS, DEFAULT_CFG
class ClassificationPredictor(BasePredictor):
@ -35,8 +35,7 @@ class ClassificationPredictor(BasePredictor):
def predict(cfg=DEFAULT_CFG, use_python=False):
"""Run YOLO model predictions on input images/videos."""
model = cfg.model or 'yolov8n-cls.pt' # or "resnet18"
source = cfg.source if cfg.source is not None else ROOT / 'assets' if (ROOT / 'assets').exists() \
else 'https://ultralytics.com/images/bus.jpg'
source = cfg.source or ASSETS
args = dict(model=model, source=source)
if use_python:

5
ultralytics/models/yolo/detect/predict.py
View File

@ -4,7 +4,7 @@ import torch
from ultralytics.engine.predictor import BasePredictor
from ultralytics.engine.results import Results
from ultralytics.utils import DEFAULT_CFG, ROOT, ops
from ultralytics.utils import ASSETS, DEFAULT_CFG, ops
class DetectionPredictor(BasePredictor):
@ -32,8 +32,7 @@ class DetectionPredictor(BasePredictor):
def predict(cfg=DEFAULT_CFG, use_python=False):
"""Runs YOLO model inference on input image(s)."""
model = cfg.model or 'yolov8n.pt'
source = cfg.source if cfg.source is not None else ROOT / 'assets' if (ROOT / 'assets').exists() \
else 'https://ultralytics.com/images/bus.jpg'
source = cfg.source or ASSETS
args = dict(model=model, source=source)
if use_python:

2
ultralytics/models/yolo/detect/train.py
View File

@ -107,7 +107,7 @@ class DetectionTrainer(BaseTrainer):
def train(cfg=DEFAULT_CFG, use_python=False):
"""Train and optimize YOLO model given training data and device."""
model = cfg.model or 'yolov8n.pt'
data = cfg.data or 'coco128.yaml' # or yolo.ClassificationDataset("mnist")
data = cfg.data or 'coco8.yaml' # or yolo.ClassificationDataset("mnist")
device = cfg.device if cfg.device is not None else ''
args = dict(model=model, data=data, device=device)

6
ultralytics/models/yolo/detect/val.py
View File

@ -6,7 +6,7 @@ from pathlib import Path
import numpy as np
import torch
from ultralytics.data import build_dataloader, build_yolo_dataset
from ultralytics.data import build_dataloader, build_yolo_dataset, converter
from ultralytics.engine.validator import BaseValidator
from ultralytics.utils import DEFAULT_CFG, LOGGER, ops
from ultralytics.utils.checks import check_requirements
@ -50,7 +50,7 @@ class DetectionValidator(BaseValidator):
"""Initialize evaluation metrics for YOLO."""
val = self.data.get(self.args.split, '') # validation path
self.is_coco = isinstance(val, str) and 'coco' in val and val.endswith(f'{os.sep}val2017.txt') # is COCO
self.class_map = ops.coco80_to_coco91_class() if self.is_coco else list(range(1000))
self.class_map = converter.coco80_to_coco91_class() if self.is_coco else list(range(1000))
self.args.save_json |= self.is_coco and not self.training # run on final val if training COCO
self.names = model.names
self.nc = len(model.names)
@ -259,7 +259,7 @@ class DetectionValidator(BaseValidator):
def val(cfg=DEFAULT_CFG, use_python=False):
"""Validate trained YOLO model on validation dataset."""
model = cfg.model or 'yolov8n.pt'
data = cfg.data or 'coco128.yaml'
data = cfg.data or 'coco8.yaml'
args = dict(model=model, data=data)
if use_python:

5
ultralytics/models/yolo/pose/predict.py
View File

@ -2,7 +2,7 @@
from ultralytics.engine.results import Results
from ultralytics.models.yolo.detect.predict import DetectionPredictor
from ultralytics.utils import DEFAULT_CFG, LOGGER, ROOT, ops
from ultralytics.utils import ASSETS, DEFAULT_CFG, LOGGER, ops
class PosePredictor(DetectionPredictor):
@ -45,8 +45,7 @@ class PosePredictor(DetectionPredictor):
def predict(cfg=DEFAULT_CFG, use_python=False):
"""Runs YOLO to predict objects in an image or video."""
model = cfg.model or 'yolov8n-pose.pt'
source = cfg.source if cfg.source is not None else ROOT / 'assets' if (ROOT / 'assets').exists() \
else 'https://ultralytics.com/images/bus.jpg'
source = cfg.source or ASSETS
args = dict(model=model, source=source)
if use_python:

5
ultralytics/models/yolo/segment/predict.py
View File

@ -4,7 +4,7 @@ import torch
from ultralytics.engine.results import Results
from ultralytics.models.yolo.detect.predict import DetectionPredictor
from ultralytics.utils import DEFAULT_CFG, ROOT, ops
from ultralytics.utils import ASSETS, DEFAULT_CFG, ops
class SegmentationPredictor(DetectionPredictor):
@ -47,8 +47,7 @@ class SegmentationPredictor(DetectionPredictor):
def predict(cfg=DEFAULT_CFG, use_python=False):
"""Runs YOLO object detection on an image or video source."""
model = cfg.model or 'yolov8n-seg.pt'
source = cfg.source if cfg.source is not None else ROOT / 'assets' if (ROOT / 'assets').exists() \
else 'https://ultralytics.com/images/bus.jpg'
source = cfg.source or ASSETS
args = dict(model=model, source=source)
if use_python:

2
ultralytics/models/yolo/segment/train.py
View File

@ -49,7 +49,7 @@ class SegmentationTrainer(yolo.detect.DetectionTrainer):
def train(cfg=DEFAULT_CFG, use_python=False):
"""Train a YOLO segmentation model based on passed arguments."""
model = cfg.model or 'yolov8n-seg.pt'
data = cfg.data or 'coco128-seg.yaml' # or yolo.ClassificationDataset("mnist")
data = cfg.data or 'coco8-seg.yaml'
device = cfg.device if cfg.device is not None else ''
args = dict(model=model, data=data, device=device)

2
ultralytics/models/yolo/segment/val.py
View File

@ -236,7 +236,7 @@ class SegmentationValidator(DetectionValidator):
def val(cfg=DEFAULT_CFG, use_python=False):
"""Validate trained YOLO model on validation data."""
model = cfg.model or 'yolov8n-seg.pt'
data = cfg.data or 'coco128-seg.yaml'
data = cfg.data or 'coco8-seg.yaml'
args = dict(model=model, data=data)
if use_python:

14
ultralytics/nn/tasks.py
View File

@ -303,13 +303,6 @@ class SegmentationModel(DetectionModel):
def init_criterion(self):
return v8SegmentationLoss(self)
def _predict_augment(self, x):
"""Perform augmentations on input image x and return augmented inference."""
LOGGER.warning(
f'WARNING ⚠️ {self.__class__.__name__} has not supported augment inference yet! Now using single-scale inference instead.'
)
return self._predict_once(x)
class PoseModel(DetectionModel):
"""YOLOv8 pose model."""
@ -326,13 +319,6 @@ class PoseModel(DetectionModel):
def init_criterion(self):
return v8PoseLoss(self)
def _predict_augment(self, x):
"""Perform augmentations on input image x and return augmented inference."""
LOGGER.warning(
f'WARNING ⚠️ {self.__class__.__name__} has not supported augment inference yet! Now using single-scale inference instead.'
)
return self._predict_once(x)
class ClassificationModel(BaseModel):
"""YOLOv8 classification model."""

3
ultralytics/trackers/bot_sort.py
View File

@ -110,8 +110,7 @@ class BOTSORT(BYTETracker):
if args.with_reid:
# Haven't supported BoT-SORT(reid) yet
self.encoder = None
# self.gmc = GMC(method=args.cmc_method, verbose=[args.name, args.ablation])
self.gmc = GMC(method=args.cmc_method)
self.gmc = GMC(method=args.gmc_method)
def get_kalmanfilter(self):
"""Returns an instance of KalmanFilterXYWH for object tracking."""

48
ultralytics/trackers/utils/gmc.py
View File

@ -10,7 +10,7 @@ from ultralytics.utils import LOGGER
class GMC:
def __init__(self, method='sparseOptFlow', downscale=2, verbose=None):
def __init__(self, method='sparseOptFlow', downscale=2):
"""Initialize a video tracker with specified parameters."""
super().__init__()
@ -40,28 +40,11 @@ class GMC:
blockSize=3,
useHarrisDetector=False,
k=0.04)
# self.gmc_file = open('GMC_results.txt', 'w')
elif self.method in ['file', 'files']:
seqName = verbose[0]
ablation = verbose[1]
if ablation:
filePath = r'tracker/GMC_files/MOT17_ablation'
else:
filePath = r'tracker/GMC_files/MOTChallenge'
if '-FRCNN' in seqName:
seqName = seqName[:-6]
elif '-DPM' in seqName or '-SDP' in seqName:
seqName = seqName[:-4]
self.gmcFile = open(f'{filePath}/GMC-{seqName}.txt')
if self.gmcFile is None:
raise ValueError(f'Error: Unable to open GMC file in directory:{filePath}')
elif self.method in ['none', 'None']:
self.method = 'none'
elif self.method in ['none', 'None', None]:
self.method = None
else:
raise ValueError(f'Error: Unknown CMC method:{method}')
raise ValueError(f'Error: Unknown GMC method:{method}')
self.prevFrame = None
self.prevKeyPoints = None
@ -77,10 +60,6 @@ class GMC:
return self.applyEcc(raw_frame, detections)
elif self.method == 'sparseOptFlow':
return self.applySparseOptFlow(raw_frame, detections)
elif self.method == 'file':
return self.applyFile(raw_frame, detections)
elif self.method == 'none':
return np.eye(2, 3)
else:
return np.eye(2, 3)
@ -244,7 +223,6 @@ class GMC:
def applySparseOptFlow(self, raw_frame, detections=None):
"""Initialize."""
# t0 = time.time()
height, width, _ = raw_frame.shape
frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
H = np.eye(2, 3)
@ -298,22 +276,4 @@ class GMC:
self.prevFrame = frame.copy()
self.prevKeyPoints = copy.copy(keypoints)
# gmc_line = str(1000 * (time.time() - t0)) + "\t" + str(H[0, 0]) + "\t" + str(H[0, 1]) + "\t" + str(
# H[0, 2]) + "\t" + str(H[1, 0]) + "\t" + str(H[1, 1]) + "\t" + str(H[1, 2]) + "\n"
# self.gmc_file.write(gmc_line)
return H
def applyFile(self, raw_frame, detections=None):
"""Return the homography matrix based on the GCPs in the next line of the input GMC file."""
line = self.gmcFile.readline()
tokens = line.split('\t')
H = np.eye(2, 3, dtype=np.float_)
H[0, 0] = float(tokens[1])
H[0, 1] = float(tokens[2])
H[0, 2] = float(tokens[3])
H[1, 0] = float(tokens[4])
H[1, 1] = float(tokens[5])
H[1, 2] = float(tokens[6])
return H

15
ultralytics/utils/__init__.py
View File

@ -30,6 +30,7 @@ LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable
# Other Constants
FILE = Path(__file__).resolve()
ROOT = FILE.parents[1] # YOLO
ASSETS = ROOT / 'assets' # default images
DEFAULT_CFG_PATH = ROOT / 'cfg/default.yaml'
NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads
AUTOINSTALL = str(os.getenv('YOLO_AUTOINSTALL', True)).lower() == 'true' # global auto-install mode
@ -260,11 +261,15 @@ class ThreadingLocked:
Attributes:
lock (threading.Lock): A lock object used to manage access to the decorated function.
Usage:
Example:
```python
from ultralytics.utils import ThreadingLocked
@ThreadingLocked()
def my_function():
# Your code here
pass
```
"""
def __init__(self):
@ -518,7 +523,6 @@ def get_git_dir():
for d in Path(__file__).parents:
if (d / '.git').is_dir():
return d
return None # no .git dir found
def get_git_origin_url():
@ -526,13 +530,12 @@ def get_git_origin_url():
Retrieves the origin URL of a git repository.
Returns:
(str | None): The origin URL of the git repository.
(str | None): The origin URL of the git repository or None if not git directory.
"""
if is_git_dir():
with contextlib.suppress(subprocess.CalledProcessError):
origin = subprocess.check_output(['git', 'config', '--get', 'remote.origin.url'])
return origin.decode().strip()
return None # if not git dir or on error
def get_git_branch():
@ -540,13 +543,12 @@ def get_git_branch():
Returns the current git branch name. If not in a git repository, returns None.
Returns:
(str | None): The current git branch name.
(str | None): The current git branch name or None if not a git directory.
"""
if is_git_dir():
with contextlib.suppress(subprocess.CalledProcessError):
origin = subprocess.check_output(['git', 'rev-parse', '--abbrev-ref', 'HEAD'])
return origin.decode().strip()
return None # if not git dir or on error
def get_default_args(func):
@ -572,7 +574,6 @@ def get_ubuntu_version():
with contextlib.suppress(FileNotFoundError, AttributeError):
with open('/etc/os-release') as f:
return re.search(r'VERSION_ID="(\d+\.\d+)"', f.read())[1]
return None
def get_user_config_dir(sub_dir='Ultralytics'):

29
ultralytics/utils/benchmarks.py
View File

@ -37,9 +37,8 @@ from tqdm import tqdm
from ultralytics import YOLO
from ultralytics.cfg import TASK2DATA, TASK2METRIC
from ultralytics.engine.exporter import export_formats
from ultralytics.utils import LINUX, LOGGER, MACOS, ROOT, SETTINGS
from ultralytics.utils import ASSETS, LINUX, LOGGER, MACOS, SETTINGS
from ultralytics.utils.checks import check_requirements, check_yolo
from ultralytics.utils.downloads import download
from ultralytics.utils.files import file_size
from ultralytics.utils.torch_utils import select_device
@ -68,6 +67,13 @@ def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
Returns:
df (pandas.DataFrame): A pandas DataFrame with benchmark results for each format, including file size,
metric, and inference time.
Example:
```python
from ultralytics.utils.benchmarks import benchmark
benchmark(model='yolov8n.pt', imgsz=640)
```
"""
import pandas as pd
@ -106,9 +112,7 @@ def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
assert model.task != 'pose' or i != 7, 'GraphDef Pose inference is not supported'
assert i not in (9, 10), 'inference not supported' # Edge TPU and TF.js are unsupported
assert i != 5 or platform.system() == 'Darwin', 'inference only supported on macOS>=10.13' # CoreML
if not (ROOT / 'assets/bus.jpg').exists():
download(url='https://ultralytics.com/images/bus.jpg', dir=ROOT / 'assets')
export.predict(ROOT / 'assets/bus.jpg', imgsz=imgsz, device=device, half=half)
export.predict(ASSETS / 'bus.jpg', imgsz=imgsz, device=device, half=half)
# Validate
data = data or TASK2DATA[model.task] # task to dataset, i.e. coco8.yaml for task=detect
@ -163,6 +167,13 @@ class ProfileModels:
Methods:
profile(): Profiles the models and prints the result.
Example:
```python
from ultralytics.utils.benchmarks import ProfileModels
ProfileModels(['yolov8n.yaml', 'yolov8s.yaml'], imgsz=640).profile()
```
"""
def __init__(self,
@ -353,11 +364,3 @@ class ProfileModels:
print(separator)
for row in table_rows:
print(row)
if __name__ == '__main__':
# Benchmark all export formats
benchmark()
# Profiling models on ONNX and TensorRT
ProfileModels(['yolov8n.yaml', 'yolov8s.yaml'])

11
ultralytics/utils/checks.py
View File

@ -20,7 +20,7 @@ import requests
import torch
from matplotlib import font_manager
from ultralytics.utils import (AUTOINSTALL, LOGGER, ONLINE, ROOT, USER_CONFIG_DIR, ThreadingLocked, TryExcept,
from ultralytics.utils import (ASSETS, AUTOINSTALL, LOGGER, ONLINE, ROOT, USER_CONFIG_DIR, ThreadingLocked, TryExcept,
clean_url, colorstr, downloads, emojis, is_colab, is_docker, is_jupyter, is_kaggle,
is_online, is_pip_package, url2file)
@ -460,8 +460,7 @@ def check_amp(model):
del m
return a.shape == b.shape and torch.allclose(a, b.float(), atol=0.5) # close to 0.5 absolute tolerance
f = ROOT / 'assets/bus.jpg' # image to check
im = f if f.exists() else 'https://ultralytics.com/images/bus.jpg' if ONLINE else np.ones((640, 640, 3))
im = ASSETS / 'bus.jpg' # image to check
prefix = colorstr('AMP: ')
LOGGER.info(f'{prefix}running Automatic Mixed Precision (AMP) checks with YOLOv8n...')
warning_msg = "Setting 'amp=True'. If you experience zero-mAP or NaN losses you can disable AMP with amp=False."
@ -484,11 +483,9 @@ def check_amp(model):
def git_describe(path=ROOT): # path must be a directory
"""Return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe."""
try:
assert (Path(path) / '.git').is_dir()
with contextlib.suppress(Exception):
return subprocess.check_output(f'git -C {path} describe --tags --long --always', shell=True).decode()[:-1]
except AssertionError:
return ''
return ''
def print_args(args: Optional[dict] = None, show_file=True, show_func=False):

12
ultralytics/utils/files.py
View File

@ -42,6 +42,8 @@ def spaces_in_path(path):
Example:
```python
with ultralytics.utils.files import spaces_in_path
with spaces_in_path('/path/with spaces') as new_path:
# your code here
```
@ -143,13 +145,3 @@ def get_latest_run(search_dir='.'):
"""Return path to most recent 'last.pt' in /runs (i.e. to --resume from)."""
last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
return max(last_list, key=os.path.getctime) if last_list else ''
def make_dirs(dir='new_dir/'):
"""Create directories."""
dir = Path(dir)
if dir.exists():
shutil.rmtree(dir) # delete dir
for p in dir, dir / 'labels', dir / 'images':
p.mkdir(parents=True, exist_ok=True) # make dir
return dir

21
ultralytics/utils/ops.py
View File

@ -55,27 +55,6 @@ class Profile(contextlib.ContextDecorator):
return time.time()
def coco80_to_coco91_class(): #
"""
Converts 80-index (val2014) to 91-index (paper).
For details see https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/.
Example:
```python
import numpy as np
a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco
x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet
```
"""
return [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
def segment2box(segment, width=640, height=640):
"""
Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)

37
ultralytics/utils/torch_utils.py
View File

@ -239,16 +239,18 @@ def get_flops(model, imgsz=640):
def get_flops_with_torch_profiler(model, imgsz=640):
"""Compute model FLOPs (thop alternative)."""
model = de_parallel(model)
p = next(model.parameters())
stride = (max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32) * 2 # max stride
im = torch.zeros((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format
with torch.profiler.profile(with_flops=True) as prof:
model(im)
flops = sum(x.flops for x in prof.key_averages()) / 1E9
imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float
flops = flops * imgsz[0] / stride * imgsz[1] / stride # 640x640 GFLOPs
return flops
if TORCH_2_0:
model = de_parallel(model)
p = next(model.parameters())
stride = (max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32) * 2 # max stride
im = torch.zeros((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format
with torch.profiler.profile(with_flops=True) as prof:
model(im)
flops = sum(x.flops for x in prof.key_averages()) / 1E9
imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float
flops = flops * imgsz[0] / stride * imgsz[1] / stride # 640x640 GFLOPs
return flops
return 0
def initialize_weights(model):
@ -384,11 +386,14 @@ def strip_optimizer(f: Union[str, Path] = 'best.pt', s: str = '') -> None:
Returns:
None
Usage:
Example:
```python
from pathlib import Path
from ultralytics.utils.torch_utils import strip_optimizer
for f in Path('/Users/glennjocher/Downloads/weights').rglob('*.pt'):
for f in Path('path/to/weights').rglob('*.pt'):
strip_optimizer(f)
```
"""
# Use dill (if exists) to serialize the lambda functions where pickle does not do this
try:
@ -421,13 +426,17 @@ def strip_optimizer(f: Union[str, Path] = 'best.pt', s: str = '') -> None:
def profile(input, ops, n=10, device=None):
"""
YOLOv8 speed/memory/FLOPs profiler
Ultralytics speed, memory and FLOPs profiler.
Example:
```python
from ultralytics.utils.torch_utils import profile
Usage:
input = torch.randn(16, 3, 640, 640)
m1 = lambda x: x * torch.sigmoid(x)
m2 = nn.SiLU()
profile(input, [m1, m2], n=100) # profile over 100 iterations
```
"""
results = []
if not isinstance(device, torch.device):