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general.py

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+import contextlib
+import glob
+import inspect
+import logging
+import logging.config
+import math
+import os
+import platform
+import random
+import re
+import signal
+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 IPython
+import numpy as np
+import pandas as pd
+import pkg_resources as pkg
+import torch
+import torchvision
+import yaml
+
+from utils import TryExcept, emojis
+from utils.downloads import gsutil_getsize
+from utils.metrics import box_iou, fitness
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLO 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}| {n_fmt}/{total_fmt} {elapsed}'  # 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)
+
+
+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_notebook():
+    # Is environment a Jupyter notebook? Verified on Colab, Jupyterlab, Kaggle, Paperspace
+    ipython_type = str(type(IPython.get_ipython()))
+    return 'colab' in ipython_type or 'zmqshell' in ipython_type
+
+
+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):
+    # YOLO 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):
+    # YOLO 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='WongKinYiu/yolov9', branch='main'):
+    # YOLO 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"⚠️ YOLO 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='.'):
+    # YOLO 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/WongKinYiu/yolov9'
+        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='3.7.0'):
+    # 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 YOLO, 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
+
+
+@TryExcept()
+def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), install=True, cmds=''):
+    # Check installed dependencies meet YOLO requirements (pass *.txt file or list of packages or single package str)
+    prefix = colorstr('red', 'bold', 'requirements:')
+    check_python()  # check python version
+    if isinstance(requirements, Path):  # requirements.txt file
+        file = requirements.resolve()
+        assert file.exists(), f"{prefix} {file} not found, check failed."
+        with file.open() as f:
+            requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(f) if x.name not in exclude]
+    elif isinstance(requirements, str):
+        requirements = [requirements]
+
+    s = ''
+    n = 0
+    for r in requirements:
+        try:
+            pkg.require(r)
+        except (pkg.VersionConflict, pkg.DistributionNotFound):  # exception if requirements not met
+            s += f'"{r}" '
+            n += 1
+
+    if s and install and AUTOINSTALL:  # check environment variable
+        LOGGER.info(f"{prefix} YOLO requirement{'s' * (n > 1)} {s}not found, attempting AutoUpdate...")
+        try:
+            # assert check_online(), "AutoUpdate skipped (offline)"
+            LOGGER.info(check_output(f'pip install {s} {cmds}', shell=True).decode())
+            source = file if 'file' in locals() else requirements
+            s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \
+                f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n"
+            LOGGER.info(s)
+        except Exception as e:
+            LOGGER.warning(f'{prefix} ❌ {e}')
+
+
+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_notebook()
+        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='yolo.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 = os.system(s)
+            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('yolo.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:
+                    s = 'sS' if threads > 1 else ''  # silent
+                    r = os.system(
+                        f'curl -# -{s}L "{url}" -o "{f}" --retry 9 -C -')  # curl download with retry, continue
+                    success = r == 0
+                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):
+                os.system(f'tar xf {f} --directory {f.parent}')  # unzip
+            elif f.suffix == '.gz':
+                os.system(f'tar xfz {f} --directory {f.parent}')  # 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 one_flat_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
+    return lambda x: ((1 - math.cos((x - (steps // 2)) * math.pi / (steps // 2))) / 2) * (y2 - y1) + y1 if (x > (steps // 2)) else 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]
+    """
+
+    if isinstance(prediction, (list, tuple)):  # YOLO 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[1] - nm - 4  # number of classes
+    mi = 4 + nc  # mask start index
+    xc = prediction[:, 4:mi].amax(1) > conf_thres  # candidates
+
+    # 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'
+
+    # 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 = 2.5 + 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()
+    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.T[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[range(len(lb)), lb[:, 0].long() + 4] = 1.0  # cls
+            x = torch.cat((x, v), 0)
+
+        # If none remain process next image
+        if not x.shape[0]:
+            continue
+
+        # Detections matrix nx6 (xyxy, conf, cls)
+        box, cls, mask = x.split((4, nc, nm), 1)
+        box = xywh2xyxy(box)  # center_x, center_y, width, height) to (x1, y1, x2, y2)
+        if multi_label:
+            i, j = (cls > conf_thres).nonzero(as_tuple=False).T
+            x = torch.cat((box[i], x[i, 4 + j, None], j[:, None].float(), mask[i]), 1)
+        else:  # best class only
+            conf, j = cls.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
+        elif n > max_nms:  # excess boxes
+            x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence
+        else:
+            x = x[x[:, 4].argsort(descending=True)]  # sort by confidence
+
+        # 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
+        if i.shape[0] > max_det:  # limit detections
+            i = i[:max_det]
+        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):
+            os.system(f'gsutil cp {url} {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)
+        data = data.rename(columns=lambda x: x.strip())  # strip keys
+        i = np.argmax(fitness(data.values[:, :4]))  #
+        generations = len(data)
+        f.write('# YOLO 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:
+        os.system(f'gsutil cp {evolve_csv} {evolve_yaml} 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 Chinese-friendly functions ------------------------------------------------------------------------------------
+imshow_ = cv2.imshow  # copy to avoid recursion errors
+
+
+def imread(path, flags=cv2.IMREAD_COLOR):
+    return cv2.imdecode(np.fromfile(path, np.uint8), flags)
+
+
+def imwrite(path, im):
+    try:
+        cv2.imencode(Path(path).suffix, im)[1].tofile(path)
+        return True
+    except Exception:
+        return False
+
+
+def imshow(path, im):
+    imshow_(path.encode('unicode_escape').decode(), im)
+
+
+cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow  # redefine
+
+# Variables ------------------------------------------------------------------------------------------------------------

plots.py

@@ -0,0 +1,570 @@
+import contextlib
+import math
+import os
+from copy import copy
+from pathlib import Path
+from urllib.error import URLError
+
+import cv2
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sn
+import torch
+from PIL import Image, ImageDraw, ImageFont
+
+from utils import TryExcept, threaded
+from utils.general import (CONFIG_DIR, FONT, LOGGER, check_font, check_requirements, clip_boxes, increment_path,
+                           is_ascii, xywh2xyxy, xyxy2xywh)
+from utils.metrics import fitness
+from utils.segment.general import scale_image
+
+# Settings
+RANK = int(os.getenv('RANK', -1))
+matplotlib.rc('font', **{'size': 11})
+matplotlib.use('Agg')  # for writing to files only
+
+
+class Colors:
+    # Ultralytics color palette https://ultralytics.com/
+    def __init__(self):
+        # hex = matplotlib.colors.TABLEAU_COLORS.values()
+        hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
+                '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
+        self.palette = [self.hex2rgb(f'#{c}') for c in hexs]
+        self.n = len(self.palette)
+
+    def __call__(self, i, bgr=False):
+        c = self.palette[int(i) % self.n]
+        return (c[2], c[1], c[0]) if bgr else c
+
+    @staticmethod
+    def hex2rgb(h):  # rgb order (PIL)
+        return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
+
+
+colors = Colors()  # create instance for 'from utils.plots import colors'
+
+
+def check_pil_font(font=FONT, size=10):
+    # Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary
+    font = Path(font)
+    font = font if font.exists() else (CONFIG_DIR / font.name)
+    try:
+        return ImageFont.truetype(str(font) if font.exists() else font.name, size)
+    except Exception:  # download if missing
+        try:
+            check_font(font)
+            return ImageFont.truetype(str(font), size)
+        except TypeError:
+            check_requirements('Pillow>=8.4.0')  # known issue https://github.com/ultralytics/yolov5/issues/5374
+        except URLError:  # not online
+            return ImageFont.load_default()
+
+
+class Annotator:
+    # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
+    def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
+        assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
+        non_ascii = not is_ascii(example)  # non-latin labels, i.e. asian, arabic, cyrillic
+        self.pil = pil or non_ascii
+        if self.pil:  # use PIL
+            self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
+            self.draw = ImageDraw.Draw(self.im)
+            self.font = check_pil_font(font='Arial.Unicode.ttf' if non_ascii else font,
+                                       size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
+        else:  # use cv2
+            self.im = im
+        self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2)  # line width
+
+    def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
+        # Add one xyxy box to image with label
+        if self.pil or not is_ascii(label):
+            self.draw.rectangle(box, width=self.lw, outline=color)  # box
+            if label:
+                w, h = self.font.getsize(label)  # text width, height
+                outside = box[1] - h >= 0  # label fits outside box
+                self.draw.rectangle(
+                    (box[0], box[1] - h if outside else box[1], box[0] + w + 1,
+                     box[1] + 1 if outside else box[1] + h + 1),
+                    fill=color,
+                )
+                # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls')  # for PIL>8.0
+                self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
+        else:  # cv2
+            p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
+            cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
+            if label:
+                tf = max(self.lw - 1, 1)  # font thickness
+                w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0]  # text width, height
+                outside = p1[1] - h >= 3
+                p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
+                cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA)  # filled
+                cv2.putText(self.im,
+                            label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
+                            0,
+                            self.lw / 3,
+                            txt_color,
+                            thickness=tf,
+                            lineType=cv2.LINE_AA)
+
+    def masks(self, masks, colors, im_gpu=None, alpha=0.5):
+        """Plot masks at once.
+        Args:
+            masks (tensor): predicted masks on cuda, shape: [n, h, w]
+            colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n]
+            im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1]
+            alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque
+        """
+        if self.pil:
+            # convert to numpy first
+            self.im = np.asarray(self.im).copy()
+        if im_gpu is None:
+            # Add multiple masks of shape(h,w,n) with colors list([r,g,b], [r,g,b], ...)
+            if len(masks) == 0:
+                return
+            if isinstance(masks, torch.Tensor):
+                masks = torch.as_tensor(masks, dtype=torch.uint8)
+                masks = masks.permute(1, 2, 0).contiguous()
+                masks = masks.cpu().numpy()
+            # masks = np.ascontiguousarray(masks.transpose(1, 2, 0))
+            masks = scale_image(masks.shape[:2], masks, self.im.shape)
+            masks = np.asarray(masks, dtype=np.float32)
+            colors = np.asarray(colors, dtype=np.float32)  # shape(n,3)
+            s = masks.sum(2, keepdims=True).clip(0, 1)  # add all masks together
+            masks = (masks @ colors).clip(0, 255)  # (h,w,n) @ (n,3) = (h,w,3)
+            self.im[:] = masks * alpha + self.im * (1 - s * alpha)
+        else:
+            if len(masks) == 0:
+                self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
+            colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0
+            colors = colors[:, None, None]  # shape(n,1,1,3)
+            masks = masks.unsqueeze(3)  # shape(n,h,w,1)
+            masks_color = masks * (colors * alpha)  # shape(n,h,w,3)
+
+            inv_alph_masks = (1 - masks * alpha).cumprod(0)  # shape(n,h,w,1)
+            mcs = (masks_color * inv_alph_masks).sum(0) * 2  # mask color summand shape(n,h,w,3)
+
+            im_gpu = im_gpu.flip(dims=[0])  # flip channel
+            im_gpu = im_gpu.permute(1, 2, 0).contiguous()  # shape(h,w,3)
+            im_gpu = im_gpu * inv_alph_masks[-1] + mcs
+            im_mask = (im_gpu * 255).byte().cpu().numpy()
+            self.im[:] = scale_image(im_gpu.shape, im_mask, self.im.shape)
+        if self.pil:
+            # convert im back to PIL and update draw
+            self.fromarray(self.im)
+
+    def rectangle(self, xy, fill=None, outline=None, width=1):
+        # Add rectangle to image (PIL-only)
+        self.draw.rectangle(xy, fill, outline, width)
+
+    def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'):
+        # Add text to image (PIL-only)
+        if anchor == 'bottom':  # start y from font bottom
+            w, h = self.font.getsize(text)  # text width, height
+            xy[1] += 1 - h
+        self.draw.text(xy, text, fill=txt_color, font=self.font)
+
+    def fromarray(self, im):
+        # Update self.im from a numpy array
+        self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
+        self.draw = ImageDraw.Draw(self.im)
+
+    def result(self):
+        # Return annotated image as array
+        return np.asarray(self.im)
+
+
+def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):
+    """
+    x:              Features to be visualized
+    module_type:    Module type
+    stage:          Module stage within model
+    n:              Maximum number of feature maps to plot
+    save_dir:       Directory to save results
+    """
+    if 'Detect' not in module_type:
+        batch, channels, height, width = x.shape  # batch, channels, height, width
+        if height > 1 and width > 1:
+            f = save_dir / f"stage{stage}_{module_type.split('.')[-1]}_features.png"  # filename
+
+            blocks = torch.chunk(x[0].cpu(), channels, dim=0)  # select batch index 0, block by channels
+            n = min(n, channels)  # number of plots
+            fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True)  # 8 rows x n/8 cols
+            ax = ax.ravel()
+            plt.subplots_adjust(wspace=0.05, hspace=0.05)
+            for i in range(n):
+                ax[i].imshow(blocks[i].squeeze())  # cmap='gray'
+                ax[i].axis('off')
+
+            LOGGER.info(f'Saving {f}... ({n}/{channels})')
+            plt.savefig(f, dpi=300, bbox_inches='tight')
+            plt.close()
+            np.save(str(f.with_suffix('.npy')), x[0].cpu().numpy())  # npy save
+
+
+def hist2d(x, y, n=100):
+    # 2d histogram used in labels.png and evolve.png
+    xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)
+    hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))
+    xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)
+    yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)
+    return np.log(hist[xidx, yidx])
+
+
+def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
+    from scipy.signal import butter, filtfilt
+
+    # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy
+    def butter_lowpass(cutoff, fs, order):
+        nyq = 0.5 * fs
+        normal_cutoff = cutoff / nyq
+        return butter(order, normal_cutoff, btype='low', analog=False)
+
+    b, a = butter_lowpass(cutoff, fs, order=order)
+    return filtfilt(b, a, data)  # forward-backward filter
+
+
+def output_to_target(output, max_det=300):
+    # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting
+    targets = []
+    for i, o in enumerate(output):
+        box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1)
+        j = torch.full((conf.shape[0], 1), i)
+        targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1))
+    return torch.cat(targets, 0).numpy()
+
+
+@threaded
+def plot_images(images, targets, paths=None, fname='images.jpg', names=None):
+    # Plot image grid with labels
+    if isinstance(images, torch.Tensor):
+        images = images.cpu().float().numpy()
+    if isinstance(targets, torch.Tensor):
+        targets = targets.cpu().numpy()
+
+    max_size = 1920  # max image size
+    max_subplots = 16  # max image subplots, i.e. 4x4
+    bs, _, h, w = images.shape  # batch size, _, height, width
+    bs = min(bs, max_subplots)  # limit plot images
+    ns = np.ceil(bs ** 0.5)  # number of subplots (square)
+    if np.max(images[0]) <= 1:
+        images *= 255  # de-normalise (optional)
+
+    # Build Image
+    mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8)  # init
+    for i, im in enumerate(images):
+        if i == max_subplots:  # if last batch has fewer images than we expect
+            break
+        x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin
+        im = im.transpose(1, 2, 0)
+        mosaic[y:y + h, x:x + w, :] = im
+
+    # Resize (optional)
+    scale = max_size / ns / max(h, w)
+    if scale < 1:
+        h = math.ceil(scale * h)
+        w = math.ceil(scale * w)
+        mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))
+
+    # Annotate
+    fs = int((h + w) * ns * 0.01)  # font size
+    annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)
+    for i in range(i + 1):
+        x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin
+        annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2)  # borders
+        if paths:
+            annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames
+        if len(targets) > 0:
+            ti = targets[targets[:, 0] == i]  # image targets
+            boxes = xywh2xyxy(ti[:, 2:6]).T
+            classes = ti[:, 1].astype('int')
+            labels = ti.shape[1] == 6  # labels if no conf column
+            conf = None if labels else ti[:, 6]  # check for confidence presence (label vs pred)
+
+            if boxes.shape[1]:
+                if boxes.max() <= 1.01:  # if normalized with tolerance 0.01
+                    boxes[[0, 2]] *= w  # scale to pixels
+                    boxes[[1, 3]] *= h
+                elif scale < 1:  # absolute coords need scale if image scales
+                    boxes *= scale
+            boxes[[0, 2]] += x
+            boxes[[1, 3]] += y
+            for j, box in enumerate(boxes.T.tolist()):
+                cls = classes[j]
+                color = colors(cls)
+                cls = names[cls] if names else cls
+                if labels or conf[j] > 0.25:  # 0.25 conf thresh
+                    label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}'
+                    annotator.box_label(box, label, color=color)
+    annotator.im.save(fname)  # save
+
+
+def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
+    # Plot LR simulating training for full epochs
+    optimizer, scheduler = copy(optimizer), copy(scheduler)  # do not modify originals
+    y = []
+    for _ in range(epochs):
+        scheduler.step()
+        y.append(optimizer.param_groups[0]['lr'])
+    plt.plot(y, '.-', label='LR')
+    plt.xlabel('epoch')
+    plt.ylabel('LR')
+    plt.grid()
+    plt.xlim(0, epochs)
+    plt.ylim(0)
+    plt.savefig(Path(save_dir) / 'LR.png', dpi=200)
+    plt.close()
+
+
+def plot_val_txt():  # from utils.plots import *; plot_val()
+    # Plot val.txt histograms
+    x = np.loadtxt('val.txt', dtype=np.float32)
+    box = xyxy2xywh(x[:, :4])
+    cx, cy = box[:, 0], box[:, 1]
+
+    fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True)
+    ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
+    ax.set_aspect('equal')
+    plt.savefig('hist2d.png', dpi=300)
+
+    fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True)
+    ax[0].hist(cx, bins=600)
+    ax[1].hist(cy, bins=600)
+    plt.savefig('hist1d.png', dpi=200)
+
+
+def plot_targets_txt():  # from utils.plots import *; plot_targets_txt()
+    # Plot targets.txt histograms
+    x = np.loadtxt('targets.txt', dtype=np.float32).T
+    s = ['x targets', 'y targets', 'width targets', 'height targets']
+    fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
+    ax = ax.ravel()
+    for i in range(4):
+        ax[i].hist(x[i], bins=100, label=f'{x[i].mean():.3g} +/- {x[i].std():.3g}')
+        ax[i].legend()
+        ax[i].set_title(s[i])
+    plt.savefig('targets.jpg', dpi=200)
+
+
+def plot_val_study(file='', dir='', x=None):  # from utils.plots import *; plot_val_study()
+    # Plot file=study.txt generated by val.py (or plot all study*.txt in dir)
+    save_dir = Path(file).parent if file else Path(dir)
+    plot2 = False  # plot additional results
+    if plot2:
+        ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel()
+
+    fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
+    # for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]:
+    for f in sorted(save_dir.glob('study*.txt')):
+        y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
+        x = np.arange(y.shape[1]) if x is None else np.array(x)
+        if plot2:
+            s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_preprocess (ms/img)', 't_inference (ms/img)', 't_NMS (ms/img)']
+            for i in range(7):
+                ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)
+                ax[i].set_title(s[i])
+
+        j = y[3].argmax() + 1
+        ax2.plot(y[5, 1:j],
+                 y[3, 1:j] * 1E2,
+                 '.-',
+                 linewidth=2,
+                 markersize=8,
+                 label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO'))
+
+    ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],
+             'k.-',
+             linewidth=2,
+             markersize=8,
+             alpha=.25,
+             label='EfficientDet')
+
+    ax2.grid(alpha=0.2)
+    ax2.set_yticks(np.arange(20, 60, 5))
+    ax2.set_xlim(0, 57)
+    ax2.set_ylim(25, 55)
+    ax2.set_xlabel('GPU Speed (ms/img)')
+    ax2.set_ylabel('COCO AP val')
+    ax2.legend(loc='lower right')
+    f = save_dir / 'study.png'
+    print(f'Saving {f}...')
+    plt.savefig(f, dpi=300)
+
+
+@TryExcept()  # known issue https://github.com/ultralytics/yolov5/issues/5395
+def plot_labels(labels, names=(), save_dir=Path('')):
+    # plot dataset labels
+    LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")
+    c, b = labels[:, 0], labels[:, 1:].transpose()  # classes, boxes
+    nc = int(c.max() + 1)  # number of classes
+    x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
+
+    # seaborn correlogram
+    sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
+    plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)
+    plt.close()
+
+    # matplotlib labels
+    matplotlib.use('svg')  # faster
+    ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
+    y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
+    with contextlib.suppress(Exception):  # color histogram bars by class
+        [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)]  # known issue #3195
+    ax[0].set_ylabel('instances')
+    if 0 < len(names) < 30:
+        ax[0].set_xticks(range(len(names)))
+        ax[0].set_xticklabels(list(names.values()), rotation=90, fontsize=10)
+    else:
+        ax[0].set_xlabel('classes')
+    sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
+    sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)
+
+    # rectangles
+    labels[:, 1:3] = 0.5  # center
+    labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000
+    img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)
+    for cls, *box in labels[:1000]:
+        ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls))  # plot
+    ax[1].imshow(img)
+    ax[1].axis('off')
+
+    for a in [0, 1, 2, 3]:
+        for s in ['top', 'right', 'left', 'bottom']:
+            ax[a].spines[s].set_visible(False)
+
+    plt.savefig(save_dir / 'labels.jpg', dpi=200)
+    matplotlib.use('Agg')
+    plt.close()
+
+
+def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path('images.jpg')):
+    # Show classification image grid with labels (optional) and predictions (optional)
+    from utils.augmentations import denormalize
+
+    names = names or [f'class{i}' for i in range(1000)]
+    blocks = torch.chunk(denormalize(im.clone()).cpu().float(), len(im),
+                         dim=0)  # select batch index 0, block by channels
+    n = min(len(blocks), nmax)  # number of plots
+    m = min(8, round(n ** 0.5))  # 8 x 8 default
+    fig, ax = plt.subplots(math.ceil(n / m), m)  # 8 rows x n/8 cols
+    ax = ax.ravel() if m > 1 else [ax]
+    # plt.subplots_adjust(wspace=0.05, hspace=0.05)
+    for i in range(n):
+        ax[i].imshow(blocks[i].squeeze().permute((1, 2, 0)).numpy().clip(0.0, 1.0))
+        ax[i].axis('off')
+        if labels is not None:
+            s = names[labels[i]] + (f'—{names[pred[i]]}' if pred is not None else '')
+            ax[i].set_title(s, fontsize=8, verticalalignment='top')
+    plt.savefig(f, dpi=300, bbox_inches='tight')
+    plt.close()
+    if verbose:
+        LOGGER.info(f"Saving {f}")
+        if labels is not None:
+            LOGGER.info('True:     ' + ' '.join(f'{names[i]:3s}' for i in labels[:nmax]))
+        if pred is not None:
+            LOGGER.info('Predicted:' + ' '.join(f'{names[i]:3s}' for i in pred[:nmax]))
+    return f
+
+
+def plot_evolve(evolve_csv='path/to/evolve.csv'):  # from utils.plots import *; plot_evolve()
+    # Plot evolve.csv hyp evolution results
+    evolve_csv = Path(evolve_csv)
+    data = pd.read_csv(evolve_csv)
+    keys = [x.strip() for x in data.columns]
+    x = data.values
+    f = fitness(x)
+    j = np.argmax(f)  # max fitness index
+    plt.figure(figsize=(10, 12), tight_layout=True)
+    matplotlib.rc('font', **{'size': 8})
+    print(f'Best results from row {j} of {evolve_csv}:')
+    for i, k in enumerate(keys[7:]):
+        v = x[:, 7 + i]
+        mu = v[j]  # best single result
+        plt.subplot(6, 5, i + 1)
+        plt.scatter(v, f, c=hist2d(v, f, 20), cmap='viridis', alpha=.8, edgecolors='none')
+        plt.plot(mu, f.max(), 'k+', markersize=15)
+        plt.title(f'{k} = {mu:.3g}', fontdict={'size': 9})  # limit to 40 characters
+        if i % 5 != 0:
+            plt.yticks([])
+        print(f'{k:>15}: {mu:.3g}')
+    f = evolve_csv.with_suffix('.png')  # filename
+    plt.savefig(f, dpi=200)
+    plt.close()
+    print(f'Saved {f}')
+
+
+def plot_results(file='path/to/results.csv', dir=''):
+    # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
+    save_dir = Path(file).parent if file else Path(dir)
+    fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
+    ax = ax.ravel()
+    files = list(save_dir.glob('results*.csv'))
+    assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'
+    for f in files:
+        try:
+            data = pd.read_csv(f)
+            s = [x.strip() for x in data.columns]
+            x = data.values[:, 0]
+            for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):
+                y = data.values[:, j].astype('float')
+                # y[y == 0] = np.nan  # don't show zero values
+                ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)
+                ax[i].set_title(s[j], fontsize=12)
+                # if j in [8, 9, 10]:  # share train and val loss y axes
+                #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
+        except Exception as e:
+            LOGGER.info(f'Warning: Plotting error for {f}: {e}')
+    ax[1].legend()
+    fig.savefig(save_dir / 'results.png', dpi=200)
+    plt.close()
+
+
+def profile_idetection(start=0, stop=0, labels=(), save_dir=''):
+    # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection()
+    ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel()
+    s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS']
+    files = list(Path(save_dir).glob('frames*.txt'))
+    for fi, f in enumerate(files):
+        try:
+            results = np.loadtxt(f, ndmin=2).T[:, 90:-30]  # clip first and last rows
+            n = results.shape[1]  # number of rows
+            x = np.arange(start, min(stop, n) if stop else n)
+            results = results[:, x]
+            t = (results[0] - results[0].min())  # set t0=0s
+            results[0] = x
+            for i, a in enumerate(ax):
+                if i < len(results):
+                    label = labels[fi] if len(labels) else f.stem.replace('frames_', '')
+                    a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5)
+                    a.set_title(s[i])
+                    a.set_xlabel('time (s)')
+                    # if fi == len(files) - 1:
+                    #     a.set_ylim(bottom=0)
+                    for side in ['top', 'right']:
+                        a.spines[side].set_visible(False)
+                else:
+                    a.remove()
+        except Exception as e:
+            print(f'Warning: Plotting error for {f}; {e}')
+    ax[1].legend()
+    plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200)
+
+
+def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True):
+    # Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop
+    xyxy = torch.tensor(xyxy).view(-1, 4)
+    b = xyxy2xywh(xyxy)  # boxes
+    if square:
+        b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1)  # attempt rectangle to square
+    b[:, 2:] = b[:, 2:] * gain + pad  # box wh * gain + pad
+    xyxy = xywh2xyxy(b).long()
+    clip_boxes(xyxy, im.shape)
+    crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)]
+    if save:
+        file.parent.mkdir(parents=True, exist_ok=True)  # make directory
+        f = str(increment_path(file).with_suffix('.jpg'))
+        # cv2.imwrite(f, crop)  # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue
+        Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0)  # save RGB
+    return crop

torch_utils.py

@@ -0,0 +1,529 @@
+import math
+import os
+import platform
+import subprocess
+import time
+import warnings
+from contextlib import contextmanager
+from copy import deepcopy
+from pathlib import Path
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+from utils.general import LOGGER, check_version, colorstr, file_date, git_describe
+from utils.lion import Lion
+
+LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
+RANK = int(os.getenv('RANK', -1))
+WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
+
+try:
+    import thop  # for FLOPs computation
+except ImportError:
+    thop = None
+
+# Suppress PyTorch warnings
+warnings.filterwarnings('ignore', message='User provided device_type of \'cuda\', but CUDA is not available. Disabling')
+warnings.filterwarnings('ignore', category=UserWarning)
+
+
+def smart_inference_mode(torch_1_9=check_version(torch.__version__, '1.9.0')):
+    # Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator
+    def decorate(fn):
+        return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn)
+
+    return decorate
+
+
+def smartCrossEntropyLoss(label_smoothing=0.0):
+    # Returns nn.CrossEntropyLoss with label smoothing enabled for torch>=1.10.0
+    if check_version(torch.__version__, '1.10.0'):
+        return nn.CrossEntropyLoss(label_smoothing=label_smoothing)
+    if label_smoothing > 0:
+        LOGGER.warning(f'WARNING ⚠️ label smoothing {label_smoothing} requires torch>=1.10.0')
+    return nn.CrossEntropyLoss()
+
+
+def smart_DDP(model):
+    # Model DDP creation with checks
+    assert not check_version(torch.__version__, '1.12.0', pinned=True), \
+        'torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. ' \
+        'Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395'
+    if check_version(torch.__version__, '1.11.0'):
+        return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True)
+    else:
+        return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
+
+
+def reshape_classifier_output(model, n=1000):
+    # Update a TorchVision classification model to class count 'n' if required
+    from models.common import Classify
+    name, m = list((model.model if hasattr(model, 'model') else model).named_children())[-1]  # last module
+    if isinstance(m, Classify):  # YOLOv5 Classify() head
+        if m.linear.out_features != n:
+            m.linear = nn.Linear(m.linear.in_features, n)
+    elif isinstance(m, nn.Linear):  # ResNet, EfficientNet
+        if m.out_features != n:
+            setattr(model, name, nn.Linear(m.in_features, n))
+    elif isinstance(m, nn.Sequential):
+        types = [type(x) for x in m]
+        if nn.Linear in types:
+            i = types.index(nn.Linear)  # nn.Linear index
+            if m[i].out_features != n:
+                m[i] = nn.Linear(m[i].in_features, n)
+        elif nn.Conv2d in types:
+            i = types.index(nn.Conv2d)  # nn.Conv2d index
+            if m[i].out_channels != n:
+                m[i] = nn.Conv2d(m[i].in_channels, n, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None)
+
+
+@contextmanager
+def torch_distributed_zero_first(local_rank: int):
+    # Decorator to make all processes in distributed training wait for each local_master to do something
+    if local_rank not in [-1, 0]:
+        dist.barrier(device_ids=[local_rank])
+    yield
+    if local_rank == 0:
+        dist.barrier(device_ids=[0])
+
+
+def device_count():
+    # Returns number of CUDA devices available. Safe version of torch.cuda.device_count(). Supports Linux and Windows
+    assert platform.system() in ('Linux', 'Windows'), 'device_count() only supported on Linux or Windows'
+    try:
+        cmd = 'nvidia-smi -L | wc -l' if platform.system() == 'Linux' else 'nvidia-smi -L | find /c /v ""'  # Windows
+        return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1])
+    except Exception:
+        return 0
+
+
+def select_device(device='', batch_size=0, newline=True):
+    # device = None or 'cpu' or 0 or '0' or '0,1,2,3'
+    s = f'YOLOv5 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} '
+    device = str(device).strip().lower().replace('cuda:', '').replace('none', '')  # to string, 'cuda:0' to '0'
+    cpu = device == 'cpu'
+    mps = device == 'mps'  # Apple Metal Performance Shaders (MPS)
+    if cpu or mps:
+        os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # force torch.cuda.is_available() = False
+    elif device:  # non-cpu device requested
+        os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable - must be before assert is_available()
+        assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \
+            f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)"
+
+    if not cpu and not mps and torch.cuda.is_available():  # prefer GPU if available
+        devices = device.split(',') if device else '0'  # range(torch.cuda.device_count())  # i.e. 0,1,6,7
+        n = len(devices)  # device count
+        if n > 1 and batch_size > 0:  # check batch_size is divisible by device_count
+            assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'
+        space = ' ' * (len(s) + 1)
+        for i, d in enumerate(devices):
+            p = torch.cuda.get_device_properties(i)
+            s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n"  # bytes to MB
+        arg = 'cuda:0'
+    elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available():  # prefer MPS if available
+        s += 'MPS\n'
+        arg = 'mps'
+    else:  # revert to CPU
+        s += 'CPU\n'
+        arg = 'cpu'
+
+    if not newline:
+        s = s.rstrip()
+    LOGGER.info(s)
+    return torch.device(arg)
+
+
+def time_sync():
+    # PyTorch-accurate time
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
+    return time.time()
+
+
+def profile(input, ops, n=10, device=None):
+    """ YOLOv5 speed/memory/FLOPs profiler
+    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):
+        device = select_device(device)
+    print(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}"
+          f"{'input':>24s}{'output':>24s}")
+
+    for x in input if isinstance(input, list) else [input]:
+        x = x.to(device)
+        x.requires_grad = True
+        for m in ops if isinstance(ops, list) else [ops]:
+            m = m.to(device) if hasattr(m, 'to') else m  # device
+            m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m
+            tf, tb, t = 0, 0, [0, 0, 0]  # dt forward, backward
+            try:
+                flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # GFLOPs
+            except Exception:
+                flops = 0
+
+            try:
+                for _ in range(n):
+                    t[0] = time_sync()
+                    y = m(x)
+                    t[1] = time_sync()
+                    try:
+                        _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward()
+                        t[2] = time_sync()
+                    except Exception:  # no backward method
+                        # print(e)  # for debug
+                        t[2] = float('nan')
+                    tf += (t[1] - t[0]) * 1000 / n  # ms per op forward
+                    tb += (t[2] - t[1]) * 1000 / n  # ms per op backward
+                mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0  # (GB)
+                s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' for x in (x, y))  # shapes
+                p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0  # parameters
+                print(f'{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}')
+                results.append([p, flops, mem, tf, tb, s_in, s_out])
+            except Exception as e:
+                print(e)
+                results.append(None)
+            torch.cuda.empty_cache()
+    return results
+
+
+def is_parallel(model):
+    # Returns True if model is of type DP or DDP
+    return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
+
+
+def de_parallel(model):
+    # De-parallelize a model: returns single-GPU model if model is of type DP or DDP
+    return model.module if is_parallel(model) else model
+
+
+def initialize_weights(model):
+    for m in model.modules():
+        t = type(m)
+        if t is nn.Conv2d:
+            pass  # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+        elif t is nn.BatchNorm2d:
+            m.eps = 1e-3
+            m.momentum = 0.03
+        elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
+            m.inplace = True
+
+
+def find_modules(model, mclass=nn.Conv2d):
+    # Finds layer indices matching module class 'mclass'
+    return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]
+
+
+def sparsity(model):
+    # Return global model sparsity
+    a, b = 0, 0
+    for p in model.parameters():
+        a += p.numel()
+        b += (p == 0).sum()
+    return b / a
+
+
+def prune(model, amount=0.3):
+    # Prune model to requested global sparsity
+    import torch.nn.utils.prune as prune
+    for name, m in model.named_modules():
+        if isinstance(m, nn.Conv2d):
+            prune.l1_unstructured(m, name='weight', amount=amount)  # prune
+            prune.remove(m, 'weight')  # make permanent
+    LOGGER.info(f'Model pruned to {sparsity(model):.3g} global sparsity')
+
+
+def fuse_conv_and_bn(conv, bn):
+    # Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
+    fusedconv = nn.Conv2d(conv.in_channels,
+                          conv.out_channels,
+                          kernel_size=conv.kernel_size,
+                          stride=conv.stride,
+                          padding=conv.padding,
+                          dilation=conv.dilation,
+                          groups=conv.groups,
+                          bias=True).requires_grad_(False).to(conv.weight.device)
+
+    # Prepare filters
+    w_conv = conv.weight.clone().view(conv.out_channels, -1)
+    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
+    fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))
+
+    # Prepare spatial bias
+    b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
+    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
+    fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
+
+    return fusedconv
+
+
+def model_info(model, verbose=False, imgsz=640):
+    # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320]
+    n_p = sum(x.numel() for x in model.parameters())  # number parameters
+    n_g = sum(x.numel() for x in model.parameters() if x.requires_grad)  # number gradients
+    if verbose:
+        print(f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}")
+        for i, (name, p) in enumerate(model.named_parameters()):
+            name = name.replace('module_list.', '')
+            print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
+                  (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
+
+    try:  # FLOPs
+        p = next(model.parameters())
+        stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32  # max stride
+        im = torch.empty((1, p.shape[1], stride, stride), device=p.device)  # input image in BCHW format
+        flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1E9 * 2  # stride GFLOPs
+        imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz]  # expand if int/float
+        fs = f', {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs'  # 640x640 GFLOPs
+    except Exception:
+        fs = ''
+
+    name = Path(model.yaml_file).stem.replace('yolov5', 'YOLOv5') if hasattr(model, 'yaml_file') else 'Model'
+    LOGGER.info(f"{name} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
+
+
+def scale_img(img, ratio=1.0, same_shape=False, gs=32):  # img(16,3,256,416)
+    # Scales img(bs,3,y,x) by ratio constrained to gs-multiple
+    if ratio == 1.0:
+        return img
+    h, w = img.shape[2:]
+    s = (int(h * ratio), int(w * ratio))  # new size
+    img = F.interpolate(img, size=s, mode='bilinear', align_corners=False)  # resize
+    if not same_shape:  # pad/crop img
+        h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w))
+    return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447)  # value = imagenet mean
+
+
+def copy_attr(a, b, include=(), exclude=()):
+    # Copy attributes from b to a, options to only include [...] and to exclude [...]
+    for k, v in b.__dict__.items():
+        if (len(include) and k not in include) or k.startswith('_') or k in exclude:
+            continue
+        else:
+            setattr(a, k, v)
+
+
+def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):
+    # YOLOv5 3-param group optimizer: 0) weights with decay, 1) weights no decay, 2) biases no decay
+    g = [], [], []  # optimizer parameter groups
+    bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k)  # normalization layers, i.e. BatchNorm2d()
+    #for v in model.modules():
+    #    for p_name, p in v.named_parameters(recurse=0):
+    #        if p_name == 'bias':  # bias (no decay)
+    #            g[2].append(p)
+    #        elif p_name == 'weight' and isinstance(v, bn):  # weight (no decay)
+    #            g[1].append(p)
+    #        else:
+    #            g[0].append(p)  # weight (with decay)
+                
+    for v in model.modules():
+        if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):  # bias (no decay)
+            g[2].append(v.bias)
+        if isinstance(v, bn):  # weight (no decay)
+            g[1].append(v.weight)
+        elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):  # weight (with decay)
+            g[0].append(v.weight)
+            
+        if hasattr(v, 'im'):
+            if hasattr(v.im, 'implicit'):           
+                g[1].append(v.im.implicit)
+            else:
+                for iv in v.im:
+                    g[1].append(iv.implicit)
+        if hasattr(v, 'ia'):
+            if hasattr(v.ia, 'implicit'):           
+                g[1].append(v.ia.implicit)
+            else:
+                for iv in v.ia:
+                    g[1].append(iv.implicit)
+                    
+        if hasattr(v, 'im2'):
+            if hasattr(v.im2, 'implicit'):           
+                g[1].append(v.im2.implicit)
+            else:
+                for iv in v.im2:
+                    g[1].append(iv.implicit)
+        if hasattr(v, 'ia2'):
+            if hasattr(v.ia2, 'implicit'):           
+                g[1].append(v.ia2.implicit)
+            else:
+                for iv in v.ia2:
+                    g[1].append(iv.implicit)
+                    
+        if hasattr(v, 'im3'):
+            if hasattr(v.im3, 'implicit'):           
+                g[1].append(v.im3.implicit)
+            else:
+                for iv in v.im3:
+                    g[1].append(iv.implicit)
+        if hasattr(v, 'ia3'):
+            if hasattr(v.ia3, 'implicit'):           
+                g[1].append(v.ia3.implicit)
+            else:
+                for iv in v.ia3:
+                    g[1].append(iv.implicit)
+                    
+        if hasattr(v, 'im4'):
+            if hasattr(v.im4, 'implicit'):           
+                g[1].append(v.im4.implicit)
+            else:
+                for iv in v.im4:
+                    g[1].append(iv.implicit)
+        if hasattr(v, 'ia4'):
+            if hasattr(v.ia4, 'implicit'):           
+                g[1].append(v.ia4.implicit)
+            else:
+                for iv in v.ia4:
+                    g[1].append(iv.implicit)
+                    
+        if hasattr(v, 'im5'):
+            if hasattr(v.im5, 'implicit'):           
+                g[1].append(v.im5.implicit)
+            else:
+                for iv in v.im5:
+                    g[1].append(iv.implicit)
+        if hasattr(v, 'ia5'):
+            if hasattr(v.ia5, 'implicit'):           
+                g[1].append(v.ia5.implicit)
+            else:
+                for iv in v.ia5:
+                    g[1].append(iv.implicit)
+                    
+        if hasattr(v, 'im6'):
+            if hasattr(v.im6, 'implicit'):           
+                g[1].append(v.im6.implicit)
+            else:
+                for iv in v.im6:
+                    g[1].append(iv.implicit)
+        if hasattr(v, 'ia6'):
+            if hasattr(v.ia6, 'implicit'):           
+                g[1].append(v.ia6.implicit)
+            else:
+                for iv in v.ia6:
+                    g[1].append(iv.implicit)
+                    
+        if hasattr(v, 'im7'):
+            if hasattr(v.im7, 'implicit'):           
+                g[1].append(v.im7.implicit)
+            else:
+                for iv in v.im7:
+                    g[1].append(iv.implicit)
+        if hasattr(v, 'ia7'):
+            if hasattr(v.ia7, 'implicit'):           
+                g[1].append(v.ia7.implicit)
+            else:
+                for iv in v.ia7:
+                    g[1].append(iv.implicit)
+
+    if name == 'Adam':
+        optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999))  # adjust beta1 to momentum
+    elif name == 'AdamW':
+        optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0, amsgrad=True)
+    elif name == 'RMSProp':
+        optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum)
+    elif name == 'SGD':
+        optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True)
+    elif name == 'LION':
+        optimizer = Lion(g[2], lr=lr, betas=(momentum, 0.99), weight_decay=0.0)
+    else:
+        raise NotImplementedError(f'Optimizer {name} not implemented.')
+
+    optimizer.add_param_group({'params': g[0], 'weight_decay': decay})  # add g0 with weight_decay
+    optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0})  # add g1 (BatchNorm2d weights)
+    LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups "
+                f"{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias")
+    return optimizer
+
+
+def smart_hub_load(repo='ultralytics/yolov5', model='yolov5s', **kwargs):
+    # YOLOv5 torch.hub.load() wrapper with smart error/issue handling
+    if check_version(torch.__version__, '1.9.1'):
+        kwargs['skip_validation'] = True  # validation causes GitHub API rate limit errors
+    if check_version(torch.__version__, '1.12.0'):
+        kwargs['trust_repo'] = True  # argument required starting in torch 0.12
+    try:
+        return torch.hub.load(repo, model, **kwargs)
+    except Exception:
+        return torch.hub.load(repo, model, force_reload=True, **kwargs)
+
+
+def smart_resume(ckpt, optimizer, ema=None, weights='yolov5s.pt', epochs=300, resume=True):
+    # Resume training from a partially trained checkpoint
+    best_fitness = 0.0
+    start_epoch = ckpt['epoch'] + 1
+    if ckpt['optimizer'] is not None:
+        optimizer.load_state_dict(ckpt['optimizer'])  # optimizer
+        best_fitness = ckpt['best_fitness']
+    if ema and ckpt.get('ema'):
+        ema.ema.load_state_dict(ckpt['ema'].float().state_dict())  # EMA
+        ema.updates = ckpt['updates']
+    if resume:
+        assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.\n' \
+                                f"Start a new training without --resume, i.e. 'python train.py --weights {weights}'"
+        LOGGER.info(f'Resuming training from {weights} from epoch {start_epoch} to {epochs} total epochs')
+    if epochs < start_epoch:
+        LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.")
+        epochs += ckpt['epoch']  # finetune additional epochs
+    return best_fitness, start_epoch, epochs
+
+
+class EarlyStopping:
+    # YOLOv5 simple early stopper
+    def __init__(self, patience=30):
+        self.best_fitness = 0.0  # i.e. mAP
+        self.best_epoch = 0
+        self.patience = patience or float('inf')  # epochs to wait after fitness stops improving to stop
+        self.possible_stop = False  # possible stop may occur next epoch
+
+    def __call__(self, epoch, fitness):
+        if fitness >= self.best_fitness:  # >= 0 to allow for early zero-fitness stage of training
+            self.best_epoch = epoch
+            self.best_fitness = fitness
+        delta = epoch - self.best_epoch  # epochs without improvement
+        self.possible_stop = delta >= (self.patience - 1)  # possible stop may occur next epoch
+        stop = delta >= self.patience  # stop training if patience exceeded
+        if stop:
+            LOGGER.info(f'Stopping training early as no improvement observed in last {self.patience} epochs. '
+                        f'Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n'
+                        f'To update EarlyStopping(patience={self.patience}) pass a new patience value, '
+                        f'i.e. `python train.py --patience 300` or use `--patience 0` to disable EarlyStopping.')
+        return stop
+
+
+class ModelEMA:
+    """ Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
+    Keeps a moving average of everything in the model state_dict (parameters and buffers)
+    For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
+    """
+
+    def __init__(self, model, decay=0.9999, tau=2000, updates=0):
+        # Create EMA
+        self.ema = deepcopy(de_parallel(model)).eval()  # FP32 EMA
+        self.updates = updates  # number of EMA updates
+        self.decay = lambda x: decay * (1 - math.exp(-x / tau))  # decay exponential ramp (to help early epochs)
+        for p in self.ema.parameters():
+            p.requires_grad_(False)
+
+    def update(self, model):
+        # Update EMA parameters
+        self.updates += 1
+        d = self.decay(self.updates)
+
+        msd = de_parallel(model).state_dict()  # model state_dict
+        for k, v in self.ema.state_dict().items():
+            if v.dtype.is_floating_point:  # true for FP16 and FP32
+                v *= d
+                v += (1 - d) * msd[k].detach()
+        # assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype} and model {msd[k].dtype} must be FP32'
+
+    def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
+        # Update EMA attributes
+        copy_attr(self.ema, model, include, exclude)