# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license """ General utils """ import contextlib import glob import inspect import logging import logging.config import math import os import platform import random import re import signal import subprocess import sys import time import urllib from copy import deepcopy from datetime import datetime from itertools import repeat from multiprocessing.pool import ThreadPool from pathlib import Path from subprocess import check_output from tarfile import is_tarfile from typing import Optional from zipfile import ZipFile, is_zipfile import cv2 import numpy as np import pandas as pd import pkg_resources as pkg import torch import torchvision import yaml # Import 'ultralytics' package or install if if missing try: import ultralytics assert hasattr(ultralytics, '__version__') # verify package is not directory except (ImportError, AssertionError): os.system('pip install -U ultralytics') import ultralytics from ultralytics.utils.checks import check_requirements from utils import TryExcept, emojis from utils.downloads_torchscript import curl_download, gsutil_getsize from utils.metrics import box_iou, fitness FILE = Path(__file__).resolve() ROOT = FILE.parents[1] # YOLOv5 root directory RANK = int(os.getenv('RANK', -1)) # Settings NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads DATASETS_DIR = Path(os.getenv('YOLOv5_DATASETS_DIR', ROOT.parent / 'datasets')) # global datasets directory AUTOINSTALL = str(os.getenv('YOLOv5_AUTOINSTALL', True)).lower() == 'true' # global auto-install mode VERBOSE = str(os.getenv('YOLOv5_VERBOSE', True)).lower() == 'true' # global verbose mode TQDM_BAR_FORMAT = '{l_bar}{bar:10}{r_bar}' # tqdm bar format FONT = 'Arial.ttf' # https://ultralytics.com/assets/Arial.ttf torch.set_printoptions(linewidth=320, precision=5, profile='long') np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5 pd.options.display.max_columns = 10 cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) os.environ['NUMEXPR_MAX_THREADS'] = str(NUM_THREADS) # NumExpr max threads os.environ['OMP_NUM_THREADS'] = '1' if platform.system() == 'darwin' else str(NUM_THREADS) # OpenMP (PyTorch and SciPy) os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # suppress verbose TF compiler warnings in Colab 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_chinese(s='人工智能'): # Is string composed of any Chinese characters? return bool(re.search('[\u4e00-\u9fff]', str(s))) def is_colab(): # Is environment a Google Colab instance? return 'google.colab' in sys.modules def is_jupyter(): """ Check if the current script is running inside a Jupyter Notebook. Verified on Colab, Jupyterlab, Kaggle, Paperspace. Returns: bool: True if running inside a Jupyter Notebook, False otherwise. """ with contextlib.suppress(Exception): from IPython import get_ipython return get_ipython() is not None return False def is_kaggle(): # Is environment a Kaggle Notebook? return os.environ.get('PWD') == '/kaggle/working' and os.environ.get('KAGGLE_URL_BASE') == 'https://www.kaggle.com' def is_docker() -> bool: """Check if the process runs inside a docker container.""" if Path('/.dockerenv').exists(): return True try: # check if docker is in control groups with open('/proc/self/cgroup') as file: return any('docker' in line for line in file) except OSError: return False def is_writeable(dir, test=False): # Return True if directory has write permissions, test opening a file with write permissions if test=True if not test: return os.access(dir, os.W_OK) # possible issues on Windows file = Path(dir) / 'tmp.txt' try: with open(file, 'w'): # open file with write permissions pass file.unlink() # remove file return True except OSError: return False LOGGING_NAME = 'yolov5' def set_logging(name=LOGGING_NAME, verbose=True): # sets up logging for the given name rank = int(os.getenv('RANK', -1)) # rank in world for Multi-GPU trainings level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR logging.config.dictConfig({ 'version': 1, 'disable_existing_loggers': False, 'formatters': { name: { 'format': '%(message)s'}}, 'handlers': { name: { 'class': 'logging.StreamHandler', 'formatter': name, 'level': level, }}, 'loggers': { name: { 'level': level, 'handlers': [name], 'propagate': False, }}}) set_logging(LOGGING_NAME) # run before defining LOGGER LOGGER = logging.getLogger(LOGGING_NAME) # define globally (used in train.py, val.py, detect.py, etc.) if platform.system() == 'Windows': for fn in LOGGER.info, LOGGER.warning: setattr(LOGGER, fn.__name__, lambda x: fn(emojis(x))) # emoji safe logging def user_config_dir(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR'): # Return path of user configuration directory. Prefer environment variable if exists. Make dir if required. env = os.getenv(env_var) if env: path = Path(env) # use environment variable else: cfg = {'Windows': 'AppData/Roaming', 'Linux': '.config', 'Darwin': 'Library/Application Support'} # 3 OS dirs path = Path.home() / cfg.get(platform.system(), '') # OS-specific config dir path = (path if is_writeable(path) else Path('/tmp')) / dir # GCP and AWS lambda fix, only /tmp is writeable path.mkdir(exist_ok=True) # make if required return path CONFIG_DIR = user_config_dir() # Ultralytics settings dir class Profile(contextlib.ContextDecorator): # YOLOv5 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager def __init__(self, t=0.0): self.t = t self.cuda = torch.cuda.is_available() def __enter__(self): self.start = self.time() return self def __exit__(self, type, value, traceback): self.dt = self.time() - self.start # delta-time self.t += self.dt # accumulate dt def time(self): if self.cuda: torch.cuda.synchronize() return time.time() class Timeout(contextlib.ContextDecorator): # YOLOv5 Timeout class. Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager def __init__(self, seconds, *, timeout_msg='', suppress_timeout_errors=True): self.seconds = int(seconds) self.timeout_message = timeout_msg self.suppress = bool(suppress_timeout_errors) def _timeout_handler(self, signum, frame): raise TimeoutError(self.timeout_message) def __enter__(self): if platform.system() != 'Windows': # not supported on Windows signal.signal(signal.SIGALRM, self._timeout_handler) # Set handler for SIGALRM signal.alarm(self.seconds) # start countdown for SIGALRM to be raised def __exit__(self, exc_type, exc_val, exc_tb): if platform.system() != 'Windows': signal.alarm(0) # Cancel SIGALRM if it's scheduled if self.suppress and exc_type is TimeoutError: # Suppress TimeoutError return True class WorkingDirectory(contextlib.ContextDecorator): # Usage: @WorkingDirectory(dir) decorator or 'with WorkingDirectory(dir):' context manager def __init__(self, new_dir): self.dir = new_dir # new dir self.cwd = Path.cwd().resolve() # current dir def __enter__(self): os.chdir(self.dir) def __exit__(self, exc_type, exc_val, exc_tb): os.chdir(self.cwd) def methods(instance): # Get class/instance methods return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith('__')] def print_args(args: Optional[dict] = None, show_file=True, show_func=False): # Print function arguments (optional args dict) x = inspect.currentframe().f_back # previous frame file, _, func, _, _ = inspect.getframeinfo(x) if args is None: # get args automatically args, _, _, frm = inspect.getargvalues(x) args = {k: v for k, v in frm.items() if k in args} try: file = Path(file).resolve().relative_to(ROOT).with_suffix('') except ValueError: file = Path(file).stem s = (f'{file}: ' if show_file else '') + (f'{func}: ' if show_func else '') LOGGER.info(colorstr(s) + ', '.join(f'{k}={v}' for k, v in args.items())) def init_seeds(seed=0, deterministic=False): # Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) # for Multi-GPU, exception safe # torch.backends.cudnn.benchmark = True # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287 if deterministic and check_version(torch.__version__, '1.12.0'): # https://github.com/ultralytics/yolov5/pull/8213 torch.use_deterministic_algorithms(True) torch.backends.cudnn.deterministic = True os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8' os.environ['PYTHONHASHSEED'] = str(seed) def intersect_dicts(da, db, exclude=()): # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values return {k: v for k, v in da.items() if k in db and all(x not in k for x in exclude) and v.shape == db[k].shape} def get_default_args(func): # Get func() default arguments signature = inspect.signature(func) return {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty} 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 file_age(path=__file__): # Return days since last file update dt = (datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime)) # delta return dt.days # + dt.seconds / 86400 # fractional days def file_date(path=__file__): # Return human-readable file modification date, i.e. '2021-3-26' t = datetime.fromtimestamp(Path(path).stat().st_mtime) return f'{t.year}-{t.month}-{t.day}' def file_size(path): # Return file/dir size (MB) mb = 1 << 20 # bytes to MiB (1024 ** 2) path = Path(path) if path.is_file(): return path.stat().st_size / mb elif path.is_dir(): return sum(f.stat().st_size for f in path.glob('**/*') if f.is_file()) / mb else: return 0.0 def check_online(): # Check internet connectivity import socket def run_once(): # Check once try: socket.create_connection(('1.1.1.1', 443), 5) # check host accessibility return True except OSError: return False return run_once() or run_once() # check twice to increase robustness to intermittent connectivity issues 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() return check_output(f'git -C {path} describe --tags --long --always', shell=True).decode()[:-1] except Exception: return '' @TryExcept() @WorkingDirectory(ROOT) def check_git_status(repo='ultralytics/yolov5', branch='master'): # YOLOv5 status check, recommend 'git pull' if code is out of date url = f'https://github.com/{repo}' msg = f', for updates see {url}' s = colorstr('github: ') # string assert Path('.git').exists(), s + 'skipping check (not a git repository)' + msg assert check_online(), s + 'skipping check (offline)' + msg splits = re.split(pattern=r'\s', string=check_output('git remote -v', shell=True).decode()) matches = [repo in s for s in splits] if any(matches): remote = splits[matches.index(True) - 1] else: remote = 'ultralytics' check_output(f'git remote add {remote} {url}', shell=True) check_output(f'git fetch {remote}', shell=True, timeout=5) # git fetch local_branch = check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out n = int(check_output(f'git rev-list {local_branch}..{remote}/{branch} --count', shell=True)) # commits behind if n > 0: pull = 'git pull' if remote == 'origin' else f'git pull {remote} {branch}' s += f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use '{pull}' or 'git clone {url}' to update." else: s += f'up to date with {url} ✅' LOGGER.info(s) @WorkingDirectory(ROOT) def check_git_info(path='.'): # YOLOv5 git info check, return {remote, branch, commit} check_requirements('gitpython') import git try: repo = git.Repo(path) remote = repo.remotes.origin.url.replace('.git', '') # i.e. 'https://github.com/ultralytics/yolov5' commit = repo.head.commit.hexsha # i.e. '3134699c73af83aac2a481435550b968d5792c0d' try: branch = repo.active_branch.name # i.e. 'main' except TypeError: # not on any branch branch = None # i.e. 'detached HEAD' state return {'remote': remote, 'branch': branch, 'commit': commit} except git.exc.InvalidGitRepositoryError: # path is not a git dir return {'remote': None, 'branch': None, 'commit': None} def check_python(minimum='3.8.0'): # Check current python version vs. required python version check_version(platform.python_version(), minimum, name='Python ', hard=True) 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)) 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 if hard: assert result, emojis(s) # assert min requirements met if verbose and not result: LOGGER.warning(s) return result def check_img_size(imgsz, s=32, floor=0): # Verify image size is a multiple of stride s in each dimension if isinstance(imgsz, int): # integer i.e. img_size=640 new_size = max(make_divisible(imgsz, int(s)), floor) else: # list i.e. img_size=[640, 480] imgsz = list(imgsz) # convert to list if tuple new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz] if new_size != imgsz: LOGGER.warning(f'WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}') return new_size def check_imshow(warn=False): # Check if environment supports image displays try: assert not is_jupyter() assert not is_docker() cv2.imshow('test', np.zeros((1, 1, 3))) cv2.waitKey(1) cv2.destroyAllWindows() cv2.waitKey(1) return True except Exception as e: if warn: LOGGER.warning(f'WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n{e}') return False def check_suffix(file='yolov5s.pt', suffix=('.pt', ), msg=''): # Check file(s) for acceptable suffix if file and suffix: if isinstance(suffix, str): suffix = [suffix] for f in file if isinstance(file, (list, tuple)) else [file]: s = Path(f).suffix.lower() # file suffix if len(s): assert s in suffix, f'{msg}{f} acceptable suffix is {suffix}' def check_yaml(file, suffix=('.yaml', '.yml')): # Search/download YAML file (if necessary) and return path, checking suffix return check_file(file, suffix) def check_file(file, suffix=''): # Search/download file (if necessary) and return path check_suffix(file, suffix) # optional file = str(file) # convert to str() if os.path.isfile(file) or not file: # exists return file elif file.startswith(('http:/', 'https:/')): # download url = file # warning: Pathlib turns :// -> :/ file = Path(urllib.parse.unquote(file).split('?')[0]).name # '%2F' to '/', split https://url.com/file.txt?auth if os.path.isfile(file): LOGGER.info(f'Found {url} locally at {file}') # file already exists else: LOGGER.info(f'Downloading {url} to {file}...') torch.hub.download_url_to_file(url, file) assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}' # check return file elif file.startswith('clearml://'): # ClearML Dataset ID assert 'clearml' in sys.modules, "ClearML is not installed, so cannot use ClearML dataset. Try running 'pip install clearml'." return file else: # search files = [] for d in 'data', 'models', 'utils': # search directories files.extend(glob.glob(str(ROOT / d / '**' / file), recursive=True)) # find file assert len(files), f'File not found: {file}' # assert file was found assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique return files[0] # return file def check_font(font=FONT, progress=False): # Download font to CONFIG_DIR if necessary font = Path(font) file = CONFIG_DIR / font.name if not font.exists() and not file.exists(): 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_dataset(data, autodownload=True): # Download, check and/or unzip dataset if not found locally # Download (optional) extract_dir = '' if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)): download(data, dir=f'{DATASETS_DIR}/{Path(data).stem}', unzip=True, delete=False, curl=False, threads=1) data = next((DATASETS_DIR / Path(data).stem).rglob('*.yaml')) extract_dir, autodownload = data.parent, False # Read yaml (optional) if isinstance(data, (str, Path)): data = yaml_load(data) # dictionary # Checks for k in 'train', 'val', 'names': assert k in data, emojis(f"data.yaml '{k}:' field missing ❌") if isinstance(data['names'], (list, tuple)): # old array format data['names'] = dict(enumerate(data['names'])) # convert to dict assert all(isinstance(k, int) for k in data['names'].keys()), 'data.yaml names keys must be integers, i.e. 2: car' data['nc'] = len(data['names']) # Resolve paths path = Path(extract_dir or data.get('path') or '') # optional 'path' default to '.' if not path.is_absolute(): path = (ROOT / path).resolve() data['path'] = path # download scripts for k in 'train', 'val', 'test': if data.get(k): # prepend path if isinstance(data[k], str): x = (path / data[k]).resolve() if not x.exists() and data[k].startswith('../'): x = (path / data[k][3:]).resolve() data[k] = str(x) else: data[k] = [str((path / x).resolve()) for x in data[k]] # Parse yaml train, val, test, s = (data.get(x) for x in ('train', 'val', 'test', 'download')) if val: val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path if not all(x.exists() for x in val): LOGGER.info('\nDataset not found ⚠️, missing paths %s' % [str(x) for x in val if not x.exists()]) if not s or not autodownload: raise Exception('Dataset not found ❌') t = time.time() if s.startswith('http') and s.endswith('.zip'): # URL f = Path(s).name # filename LOGGER.info(f'Downloading {s} to {f}...') torch.hub.download_url_to_file(s, f) Path(DATASETS_DIR).mkdir(parents=True, exist_ok=True) # create root unzip_file(f, path=DATASETS_DIR) # unzip Path(f).unlink() # remove zip r = None # success elif s.startswith('bash '): # bash script LOGGER.info(f'Running {s} ...') r = subprocess.run(s, shell=True) else: # python script r = exec(s, {'yaml': data}) # return None dt = f'({round(time.time() - t, 1)}s)' s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f'failure {dt} ❌' LOGGER.info(f'Dataset download {s}') check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf', progress=True) # download fonts return data # dictionary def check_amp(model): # Check PyTorch Automatic Mixed Precision (AMP) functionality. Return True on correct operation from models.common import AutoShape, DetectMultiBackend def amp_allclose(model, im): # All close FP32 vs AMP results m = AutoShape(model, verbose=False) # model a = m(im).xywhn[0] # FP32 inference m.amp = True b = m(im).xywhn[0] # AMP inference return a.shape == b.shape and torch.allclose(a, b, atol=0.1) # close to 10% absolute tolerance prefix = colorstr('AMP: ') device = next(model.parameters()).device # get model device if device.type in ('cpu', 'mps'): return False # AMP only used on CUDA devices f = ROOT / 'data' / 'images' / 'bus.jpg' # image to check im = f if f.exists() else 'https://ultralytics.com/images/bus.jpg' if check_online() else np.ones((640, 640, 3)) try: assert amp_allclose(deepcopy(model), im) or amp_allclose(DetectMultiBackend('yolov5n.pt', device), im) LOGGER.info(f'{prefix}checks passed ✅') return True except Exception: help_url = 'https://github.com/ultralytics/yolov5/issues/7908' LOGGER.warning(f'{prefix}checks failed ❌, disabling Automatic Mixed Precision. See {help_url}') return False def yaml_load(file='data.yaml'): # Single-line safe yaml loading with open(file, errors='ignore') as f: return yaml.safe_load(f) def yaml_save(file='data.yaml', data={}): # Single-line safe yaml saving with open(file, 'w') as f: yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False) def unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX')): # Unzip a *.zip file to path/, excluding files containing strings in exclude list if path is None: path = Path(file).parent # default path with ZipFile(file) as zipObj: for f in zipObj.namelist(): # list all archived filenames in the zip if all(x not in f for x in exclude): zipObj.extract(f, path=path) def url2file(url): # Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt url = str(Path(url)).replace(':/', '://') # Pathlib turns :// -> :/ return Path(urllib.parse.unquote(url)).name.split('?')[0] # '%2F' to '/', split https://url.com/file.txt?auth def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1, retry=3): # Multithreaded file download and unzip function, used in data.yaml for autodownload def download_one(url, dir): # Download 1 file success = True if os.path.isfile(url): f = Path(url) # filename else: # does not exist f = dir / Path(url).name LOGGER.info(f'Downloading {url} to {f}...') for i in range(retry + 1): if curl: success = curl_download(url, f, silent=(threads > 1)) else: torch.hub.download_url_to_file(url, f, progress=threads == 1) # torch download success = f.is_file() if success: break elif i < retry: LOGGER.warning(f'⚠️ Download failure, retrying {i + 1}/{retry} {url}...') else: LOGGER.warning(f'❌ Failed to download {url}...') if unzip and success and (f.suffix == '.gz' or is_zipfile(f) or is_tarfile(f)): LOGGER.info(f'Unzipping {f}...') if is_zipfile(f): unzip_file(f, dir) # unzip elif is_tarfile(f): subprocess.run(['tar', 'xf', f, '--directory', f.parent], check=True) # unzip elif f.suffix == '.gz': subprocess.run(['tar', 'xfz', f, '--directory', f.parent], check=True) # unzip if delete: f.unlink() # remove zip dir = Path(dir) dir.mkdir(parents=True, exist_ok=True) # make directory if threads > 1: pool = ThreadPool(threads) pool.imap(lambda x: download_one(*x), zip(url, repeat(dir))) # multithreaded pool.close() pool.join() else: for u in [url] if isinstance(url, (str, Path)) else url: download_one(u, dir) def make_divisible(x, divisor): # Returns nearest x divisible by divisor if isinstance(divisor, torch.Tensor): divisor = int(divisor.max()) # to int return math.ceil(x / divisor) * divisor def clean_str(s): # Cleans a string by replacing special characters with underscore _ return re.sub(pattern='[|@#!¡·$€%&()=?¿^*;:,¨´><+]', repl='_', string=s) def one_cycle(y1=0.0, y2=1.0, steps=100): # lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1 def colorstr(*input): # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world') *args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string colors = { 'black': '\033[30m', # basic colors 'red': '\033[31m', 'green': '\033[32m', 'yellow': '\033[33m', 'blue': '\033[34m', 'magenta': '\033[35m', 'cyan': '\033[36m', 'white': '\033[37m', 'bright_black': '\033[90m', # bright colors 'bright_red': '\033[91m', 'bright_green': '\033[92m', 'bright_yellow': '\033[93m', 'bright_blue': '\033[94m', 'bright_magenta': '\033[95m', 'bright_cyan': '\033[96m', 'bright_white': '\033[97m', 'end': '\033[0m', # misc 'bold': '\033[1m', 'underline': '\033[4m'} return ''.join(colors[x] for x in args) + f'{string}' + colors['end'] def labels_to_class_weights(labels, nc=80): # Get class weights (inverse frequency) from training labels if labels[0] is None: # no labels loaded return torch.Tensor() labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO classes = labels[:, 0].astype(int) # labels = [class xywh] weights = np.bincount(classes, minlength=nc) # occurrences per class # Prepend gridpoint count (for uCE training) # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image # weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start weights[weights == 0] = 1 # replace empty bins with 1 weights = 1 / weights # number of targets per class weights /= weights.sum() # normalize return torch.from_numpy(weights).float() def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)): # Produces image weights based on class_weights and image contents # Usage: index = random.choices(range(n), weights=image_weights, k=1) # weighted image sample class_counts = np.array([np.bincount(x[:, 0].astype(int), minlength=nc) for x in labels]) return (class_weights.reshape(1, nc) * class_counts).sum(1) def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper) # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ # 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 xyxy2xywh(x): # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) y[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center y[..., 2] = x[..., 2] - x[..., 0] # width y[..., 3] = x[..., 3] - x[..., 1] # height return y def xywh2xyxy(x): # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y return y def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0): # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw # top left x y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh # top left y y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw # bottom right x y[..., 3] = h * (x[..., 1] + x[..., 3] / 2) + padh # bottom right y return y def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0): # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right if clip: clip_boxes(x, (h - eps, w - eps)) # warning: inplace clip y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w # x center y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h # y center y[..., 2] = (x[..., 2] - x[..., 0]) / w # width y[..., 3] = (x[..., 3] - x[..., 1]) / h # height return y def xyn2xy(x, w=640, h=640, padw=0, padh=0): # Convert normalized segments into pixel segments, shape (n,2) y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) y[..., 0] = w * x[..., 0] + padw # top left x y[..., 1] = h * x[..., 1] + padh # top left y return y 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) x, y = segment.T # segment xy inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height) x, y, = x[inside], y[inside] return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy def segments2boxes(segments): # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh) boxes = [] for s in segments: x, y = s.T # segment xy boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy return xyxy2xywh(np.array(boxes)) # cls, xywh def resample_segments(segments, n=1000): # Up-sample an (n,2) segment for i, s in enumerate(segments): s = np.concatenate((s, s[0:1, :]), axis=0) x = np.linspace(0, len(s) - 1, n) xp = np.arange(len(s)) segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy return segments def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None): # Rescale boxes (xyxy) from img1_shape to img0_shape if ratio_pad is None: # calculate from img0_shape gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding else: gain = ratio_pad[0][0] pad = ratio_pad[1] boxes[..., [0, 2]] -= pad[0] # x padding boxes[..., [1, 3]] -= pad[1] # y padding boxes[..., :4] /= gain clip_boxes(boxes, img0_shape) return boxes def scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, normalize=False): # Rescale coords (xyxy) from img1_shape to img0_shape if ratio_pad is None: # calculate from img0_shape gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding else: gain = ratio_pad[0][0] pad = ratio_pad[1] segments[:, 0] -= pad[0] # x padding segments[:, 1] -= pad[1] # y padding segments /= gain clip_segments(segments, img0_shape) if normalize: segments[:, 0] /= img0_shape[1] # width segments[:, 1] /= img0_shape[0] # height return segments def clip_boxes(boxes, shape): # Clip boxes (xyxy) to image shape (height, width) if isinstance(boxes, torch.Tensor): # faster individually boxes[..., 0].clamp_(0, shape[1]) # x1 boxes[..., 1].clamp_(0, shape[0]) # y1 boxes[..., 2].clamp_(0, shape[1]) # x2 boxes[..., 3].clamp_(0, shape[0]) # y2 else: # np.array (faster grouped) boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2 boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2 def clip_segments(segments, shape): # Clip segments (xy1,xy2,...) to image shape (height, width) if isinstance(segments, torch.Tensor): # faster individually segments[:, 0].clamp_(0, shape[1]) # x segments[:, 1].clamp_(0, shape[0]) # y else: # np.array (faster grouped) segments[:, 0] = segments[:, 0].clip(0, shape[1]) # x segments[:, 1] = segments[:, 1].clip(0, shape[0]) # y def non_max_suppression( prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, labels=(), max_det=300, nm=0, # number of masks ): """Non-Maximum Suppression (NMS) on inference results to reject overlapping detections Returns: list of detections, on (n,6) tensor per image [xyxy, conf, cls] """ # Checks assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0' assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0' if isinstance(prediction, (list, tuple)): # YOLOv5 model in validation model, output = (inference_out, loss_out) prediction = prediction[0] # select only inference output device = prediction.device mps = 'mps' in device.type # Apple MPS if mps: # MPS not fully supported yet, convert tensors to CPU before NMS prediction = prediction.cpu() bs = prediction.shape[0] # batch size nc = prediction.shape[2] - nm - 5 # number of classes xc = prediction[..., 4] > conf_thres # candidates # Settings # min_wh = 2 # (pixels) minimum box width and height max_wh = 7680 # (pixels) maximum box width and height max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() time_limit = 0.5 + 0.05 * bs # seconds to quit after redundant = True # require redundant detections multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) merge = False # use merge-NMS t = time.time() mi = 5 + nc # mask start index output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs for xi, x in enumerate(prediction): # image index, image inference # Apply constraints # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height x = x[xc[xi]] # confidence # Cat apriori labels if autolabelling if labels and len(labels[xi]): lb = labels[xi] v = torch.zeros((len(lb), nc + nm + 5), device=x.device) v[:, :4] = lb[:, 1:5] # box v[:, 4] = 1.0 # conf v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls x = torch.cat((x, v), 0) # If none remain process next image if not x.shape[0]: continue # Compute conf x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf # Box/Mask box = xywh2xyxy(x[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2) mask = x[:, mi:] # zero columns if no masks # Detections matrix nx6 (xyxy, conf, cls) if multi_label: i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1) else: # best class only conf, j = x[:, 5:mi].max(1, keepdim=True) x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres] # Filter by class if classes is not None: x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] # Apply finite constraint # if not torch.isfinite(x).all(): # x = x[torch.isfinite(x).all(1)] # Check shape n = x.shape[0] # number of boxes if not n: # no boxes continue x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence and remove excess boxes # Batched NMS c = x[:, 5:6] * (0 if agnostic else max_wh) # classes boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS i = i[:max_det] # limit detections if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix weights = iou * scores[None] # box weights x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes if redundant: i = i[iou.sum(1) > 1] # require redundancy output[xi] = x[i] if mps: output[xi] = output[xi].to(device) if (time.time() - t) > time_limit: LOGGER.warning(f'WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded') break # time limit exceeded return output def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_optimizer() # Strip optimizer from 'f' to finalize training, optionally save as 's' x = torch.load(f, map_location=torch.device('cpu')) if x.get('ema'): x['model'] = x['ema'] # replace model with ema for k in 'optimizer', 'best_fitness', 'ema', 'updates': # keys x[k] = None x['epoch'] = -1 x['model'].half() # to FP16 for p in x['model'].parameters(): p.requires_grad = False torch.save(x, s or f) mb = os.path.getsize(s or f) / 1E6 # filesize LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB") def print_mutation(keys, results, hyp, save_dir, bucket, prefix=colorstr('evolve: ')): evolve_csv = save_dir / 'evolve.csv' evolve_yaml = save_dir / 'hyp_evolve.yaml' keys = tuple(keys) + tuple(hyp.keys()) # [results + hyps] keys = tuple(x.strip() for x in keys) vals = results + tuple(hyp.values()) n = len(keys) # Download (optional) if bucket: url = f'gs://{bucket}/evolve.csv' if gsutil_getsize(url) > (evolve_csv.stat().st_size if evolve_csv.exists() else 0): subprocess.run(['gsutil', 'cp', f'{url}', f'{save_dir}']) # download evolve.csv if larger than local # Log to evolve.csv s = '' if evolve_csv.exists() else (('%20s,' * n % keys).rstrip(',') + '\n') # add header with open(evolve_csv, 'a') as f: f.write(s + ('%20.5g,' * n % vals).rstrip(',') + '\n') # Save yaml with open(evolve_yaml, 'w') as f: data = pd.read_csv(evolve_csv, skipinitialspace=True) data = data.rename(columns=lambda x: x.strip()) # strip keys i = np.argmax(fitness(data.values[:, :4])) # generations = len(data) f.write('# YOLOv5 Hyperparameter Evolution Results\n' + f'# Best generation: {i}\n' + f'# Last generation: {generations - 1}\n' + '# ' + ', '.join(f'{x.strip():>20s}' for x in keys[:7]) + '\n' + '# ' + ', '.join(f'{x:>20.5g}' for x in data.values[i, :7]) + '\n\n') yaml.safe_dump(data.loc[i][7:].to_dict(), f, sort_keys=False) # Print to screen LOGGER.info(prefix + f'{generations} generations finished, current result:\n' + prefix + ', '.join(f'{x.strip():>20s}' for x in keys) + '\n' + prefix + ', '.join(f'{x:20.5g}' for x in vals) + '\n\n') if bucket: subprocess.run(['gsutil', 'cp', f'{evolve_csv}', f'{evolve_yaml}', f'gs://{bucket}']) # upload def apply_classifier(x, model, img, im0): # Apply a second stage classifier to YOLO outputs # Example model = torchvision.models.__dict__['efficientnet_b0'](pretrained=True).to(device).eval() im0 = [im0] if isinstance(im0, np.ndarray) else im0 for i, d in enumerate(x): # per image if d is not None and len(d): d = d.clone() # Reshape and pad cutouts b = xyxy2xywh(d[:, :4]) # boxes b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad d[:, :4] = xywh2xyxy(b).long() # Rescale boxes from img_size to im0 size scale_boxes(img.shape[2:], d[:, :4], im0[i].shape) # Classes pred_cls1 = d[:, 5].long() ims = [] for a in d: cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])] im = cv2.resize(cutout, (224, 224)) # BGR im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32 im /= 255 # 0 - 255 to 0.0 - 1.0 ims.append(im) pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections return x def increment_path(path, exist_ok=False, sep='', mkdir=False): # Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc. path = Path(path) # os-agnostic if path.exists() and not exist_ok: path, suffix = (path.with_suffix(''), path.suffix) if path.is_file() else (path, '') # Method 1 for n in range(2, 9999): p = f'{path}{sep}{n}{suffix}' # increment path if not os.path.exists(p): # break path = Path(p) # Method 2 (deprecated) # dirs = glob.glob(f"{path}{sep}*") # similar paths # matches = [re.search(rf"{path.stem}{sep}(\d+)", d) for d in dirs] # i = [int(m.groups()[0]) for m in matches if m] # indices # n = max(i) + 1 if i else 2 # increment number # path = Path(f"{path}{sep}{n}{suffix}") # increment path if mkdir: path.mkdir(parents=True, exist_ok=True) # make directory return path # OpenCV Multilanguage-friendly functions ------------------------------------------------------------------------------------ imshow_ = cv2.imshow # copy to avoid recursion errors def imread(filename, flags=cv2.IMREAD_COLOR): return cv2.imdecode(np.fromfile(filename, np.uint8), flags) def imwrite(filename, img): try: cv2.imencode(Path(filename).suffix, img)[1].tofile(filename) return True except Exception: return False def imshow(path, im): imshow_(path.encode('unicode_escape').decode(), im) if Path(inspect.stack()[0].filename).parent.parent.as_posix() in inspect.stack()[-1].filename: cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow # redefine # Variables ------------------------------------------------------------------------------------------------------------