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New ASSETS and trackers GMC cleanup (#4425)

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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
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15
ultralytics/utils/__init__.py
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@ -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
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@ -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
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@ -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
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@ -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):