Improvements (#142)

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Glenn Jocher 2 years ago committed by GitHub
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@ -73,7 +73,7 @@ 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.checks import check_imgsz, check_requirements, check_version, check_yaml
from ultralytics.yolo.utils.files import file_size, increment_path
from ultralytics.yolo.utils.files import file_size
from ultralytics.yolo.utils.ops import Profile
from ultralytics.yolo.utils.torch_utils import guess_task_from_head, select_device, smart_inference_mode
@ -138,10 +138,6 @@ class Exporter:
if overrides is None:
overrides = {}
self.args = get_config(config, overrides)
project = self.args.project or f"runs/{self.args.task}"
name = self.args.name or "exp" # hardcode mode as export doesn't require it
self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok)
self.save_dir.mkdir(parents=True, exist_ok=True)
self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()}) # add callbacks
callbacks.add_integration_callbacks(self)

@ -35,7 +35,7 @@ from ultralytics.nn.autobackend import AutoBackend
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages, LoadScreenshots, LoadStreams
from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, callbacks, colorstr, ops
from ultralytics.yolo.utils import DEFAULT_CONFIG, 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
@ -73,7 +73,7 @@ class BasePredictor:
if overrides is None:
overrides = {}
self.args = get_config(config, overrides)
project = self.args.project or f"runs/{self.args.task}"
project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task
name = self.args.name or f"{self.args.mode}"
self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok)
(self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)

@ -25,7 +25,8 @@ import ultralytics.yolo.utils as utils
from ultralytics import __version__
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, TQDM_BAR_FORMAT, callbacks, colorstr, yaml_save
from ultralytics.yolo.utils import (DEFAULT_CONFIG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr,
yaml_save)
from ultralytics.yolo.utils.checks import check_file, print_args
from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_command
from ultralytics.yolo.utils.files import get_latest_run, increment_path
@ -88,7 +89,7 @@ class BaseTrainer:
init_seeds(self.args.seed + 1 + RANK, deterministic=self.args.deterministic)
# Dirs
project = self.args.project or f"runs/{self.args.task}"
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(

@ -8,7 +8,7 @@ from tqdm import tqdm
from ultralytics.nn.autobackend import AutoBackend
from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, RANK, TQDM_BAR_FORMAT, callbacks
from ultralytics.yolo.utils import DEFAULT_CONFIG, 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
@ -59,7 +59,7 @@ class BaseValidator:
self.speed = None
self.jdict = None
project = self.args.project or f"runs/{self.args.task}"
project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task
name = self.args.name or f"{self.args.mode}"
self.save_dir = save_dir or increment_path(Path(project) / name,
exist_ok=self.args.exist_ok if RANK in {-1, 0} else True)

@ -18,7 +18,6 @@ FILE = Path(__file__).resolve()
ROOT = FILE.parents[2] # YOLO
DEFAULT_CONFIG = ROOT / "yolo/configs/default.yaml"
RANK = int(os.getenv('RANK', -1))
DATASETS_DIR = Path(os.getenv('YOLOv5_DATASETS_DIR', ROOT.parent / 'datasets')) # global datasets directory
NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads
AUTOINSTALL = str(os.getenv('YOLOv5_AUTOINSTALL', True)).lower() == 'true' # global auto-install mode
FONT = 'Arial.ttf' # https://ultralytics.com/assets/Arial.ttf
@ -119,6 +118,41 @@ def is_docker() -> bool:
return 'docker' in f.read()
def is_git_directory() -> bool:
"""
Check if the current working directory is inside a git repository.
Returns:
bool: True if the current working directory is inside a git repository, False otherwise.
"""
from git import Repo
try:
# Check if the current working directory is a git repository
Repo(search_parent_directories=True)
return True
except Exception:
return False
def is_pip_package(filepath: str = __name__) -> bool:
"""
Determines if the file at the given filepath is part of a pip package.
Args:
filepath (str): The filepath to check.
Returns:
bool: True if the file is part of a pip package, False otherwise.
"""
import importlib.util
# Get the spec for the module
spec = importlib.util.find_spec(filepath)
# Return whether the spec is not None and the origin is not None (indicating it is a package)
return spec is not None and spec.origin is not None
def is_dir_writeable(dir_path: str) -> bool:
"""
Check if a directory is writeable.
@ -305,10 +339,11 @@ def get_settings(file=USER_CONFIG_DIR / 'settings.yaml'):
"""
from ultralytics.yolo.utils.torch_utils import torch_distributed_zero_first
git_install = not is_pip_package()
defaults = {
'datasets_dir': None, # default datasets directory. If None, current working directory is used.
'weights_dir': None, # default weights directory. If None, current working directory is used.
'runs_dir': None, # default runs directory. If None, current working directory is used.
'datasets_dir': str(ROOT / 'datasets') if git_install else 'datasets', # default datasets directory.
'weights_dir': str(ROOT / 'weights') if git_install else 'weights', # default weights directory.
'runs_dir': str(ROOT / 'runs') if git_install else 'runs', # default runs directory.
'sync': True, # sync analytics to help with YOLO development
'uuid': uuid.getnode(), # device UUID to align analytics
'yaml_file': str(file)} # setting YAML file path
@ -336,6 +371,7 @@ if platform.system() == 'Windows':
# Check first-install steps
SETTINGS = get_settings()
DATASETS_DIR = Path(SETTINGS['datasets_dir']) # global datasets directory
def set_settings(kwargs, file=USER_CONFIG_DIR / 'settings.yaml'):

@ -1,5 +1,6 @@
import glob
import inspect
import math
import platform
import urllib
from pathlib import Path
@ -13,71 +14,141 @@ import torch
from ultralytics.yolo.utils import (AUTOINSTALL, FONT, LOGGER, ROOT, USER_CONFIG_DIR, TryExcept, colorstr, emojis,
is_docker, is_jupyter_notebook)
from ultralytics.yolo.utils.ops import make_divisible
def is_ascii(s=''):
# Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7)
s = str(s) # convert list, tuple, None, etc. to str
return len(s.encode().decode('ascii', 'ignore')) == len(s)
def is_ascii(s) -> bool:
"""
Check if a string is composed of only ASCII characters.
Args:
s (str): String to be checked.
def check_imgsz(imgsz, stride=32, min_dim=1, floor=0):
# Verify image size is a multiple of stride s in each dimension
Returns:
bool: True if the string is composed only of ASCII characters, False otherwise.
"""
# Convert list, tuple, None, etc. to string
s = str(s)
# Check if the string is composed of only ASCII characters
return all(ord(c) < 128 for c in s)
def check_imgsz(imgsz, stride=32, min_dim=1, floor=0):
"""
Verify image size is a multiple of the given stride in each dimension. If the image size is not a multiple of the
stride, update it to the nearest multiple of the stride that is greater than or equal to the given floor value.
Args:
imgsz (int or List[int]): Image size.
stride (int): Stride value.
min_dim (int): Minimum number of dimensions.
floor (int): Minimum allowed value for image size.
Returns:
List[int]: Updated image size.
"""
# Convert stride to integer if it is a tensor
stride = int(stride.max() if isinstance(stride, torch.Tensor) else stride)
if isinstance(imgsz, int): # integer i.e. imgsz=640
sz = max(make_divisible(imgsz, stride), floor)
else: # list i.e. imgsz=[640, 480]
imgsz = list(imgsz) # convert to list if tuple
sz = [max(make_divisible(x, stride), floor) for x in imgsz]
# Convert image size to list if it is an integer
if isinstance(imgsz, int):
imgsz = [imgsz]
# Make image size a multiple of the stride
sz = [max(math.ceil(x / stride) * stride, floor) for x in imgsz]
# Print warning message if image size was updated
if sz != imgsz:
LOGGER.warning(f'WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {stride}, updating to {sz}')
# Check dims
if min_dim == 2:
if isinstance(imgsz, int):
sz = [sz, sz]
elif len(sz) == 1:
sz = [sz[0], sz[0]]
# Add missing dimensions if necessary
if min_dim == 2 and len(sz) == 1:
sz = [sz[0], sz[0]]
return sz
def check_version(current="0.0.0", minimum="0.0.0", name="version ", pinned=False, hard=False, verbose=False):
# Check version vs. required version
current, minimum = (pkg.parse_version(x) for x in (current, minimum))
def check_version(current: str = "0.0.0",
minimum: str = "0.0.0",
name: str = "version ",
pinned: bool = False,
hard: bool = False,
verbose: bool = False) -> bool:
"""
Check current version against the required minimum version.
Args:
current (str): Current version.
minimum (str): Required minimum version.
name (str): Name to be used in warning message.
pinned (bool): If True, versions must match exactly. If False, minimum version must be satisfied.
hard (bool): If True, raise an AssertionError if the minimum version is not met.
verbose (bool): If True, print warning message if minimum version is not met.
Returns:
bool: True if minimum version is met, False otherwise.
"""
from pkg_resources import parse_version
current, minimum = (parse_version(x) for x in (current, minimum))
result = (current == minimum) if pinned else (current >= minimum) # bool
s = f"WARNING ⚠️ {name}{minimum} is required by YOLOv5, but {name}{current} is currently installed" # string
warning_message = f"WARNING ⚠️ {name}{minimum} is required by YOLOv5, but {name}{current} is currently installed"
if hard:
assert result, emojis(s) # assert min requirements met
assert result, emojis(warning_message) # assert min requirements met
if verbose and not result:
LOGGER.warning(s)
LOGGER.warning(warning_message)
return result
def check_font(font=FONT, progress=False):
# Download font to CONFIG_DIR if necessary
def check_font(font: str = FONT, progress: bool = False) -> None:
"""
Download font file to the user's configuration directory if it does not already exist.
Args:
font (str): Path to font file.
progress (bool): If True, display a progress bar during the download.
Returns:
None
"""
font = Path(font)
# Destination path for the font file
file = USER_CONFIG_DIR / font.name
# Check if font file exists at the source or destination path
if not font.exists() and not file.exists():
# Download font file
url = f'https://ultralytics.com/assets/{font.name}'
LOGGER.info(f'Downloading {url} to {file}...')
torch.hub.download_url_to_file(url, str(file), progress=progress)
def check_online():
# Check internet connectivity
def check_online() -> bool:
"""
Check internet connectivity by attempting to connect to a known online host.
Returns:
bool: True if connection is successful, False otherwise.
"""
import socket
try:
socket.create_connection(("1.1.1.1", 443), 5) # check host accessibility
# Check host accessibility by attempting to establish a connection
socket.create_connection(("1.1.1.1", 443), timeout=5)
return True
except OSError:
return False
def check_python(minimum='3.7.0'):
# Check current python version vs. required python version
def check_python(minimum: str = '3.7.0') -> bool:
"""
Check current python version against the required minimum version.
Args:
minimum (str): Required minimum version of python.
Returns:
None
"""
check_version(platform.python_version(), minimum, name='Python ', hard=True)

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