# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
"""
General utils
"""
import contextlib
import glob
import inspect
import logging
import logging.config
import math
import os
import platform
import random
import re
import signal
import subprocess
import sys
import time
import urllib
from copy import deepcopy
from datetime import datetime
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from subprocess import check_output
from tarfile import is_tarfile
from typing import Optional
from zipfile import ZipFile, is_zipfile
import cv2
import numpy as np
import pandas as pd
import pkg_resources as pkg
import torch
import torchvision
import yaml
import torch.nn as nn
from torchvision.ops import roi_align
# Import 'ultralytics' package or install if if missing
try:
import ultralytics
assert hasattr(ultralytics, '__version__') # verify package is not directory
except (ImportError, AssertionError):
os.system('pip install -U ultralytics')
import ultralytics
from ultralytics.utils.checks import check_requirements
from utils import TryExcept, emojis
from utils.downloads import curl_download, gsutil_getsize
from utils.metrics import box_iou, fitness
FILE = Path(__file__).resolve()
ROOT = FILE.parents[1] # YOLOv5 root directory
RANK = int(os.getenv('RANK', -1))
# Settings
NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads
DATASETS_DIR = Path(os.getenv('YOLOv5_DATASETS_DIR', ROOT.parent / 'datasets')) # global datasets directory
AUTOINSTALL = str(os.getenv('YOLOv5_AUTOINSTALL', True)).lower() == 'true' # global auto-install mode
VERBOSE = str(os.getenv('YOLOv5_VERBOSE', True)).lower() == 'true' # global verbose mode
TQDM_BAR_FORMAT = '{l_bar}{bar:10}{r_bar}' # tqdm bar format
FONT = 'Arial.ttf' # https://ultralytics.com/assets/Arial.ttf
torch.set_printoptions(linewidth=320, precision=5, profile='long')
np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5
pd.options.display.max_columns = 10
cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
os.environ['NUMEXPR_MAX_THREADS'] = str(NUM_THREADS) # NumExpr max threads
os.environ['OMP_NUM_THREADS'] = '1' if platform.system() == 'darwin' else str(NUM_THREADS) # OpenMP (PyTorch and SciPy)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # suppress verbose TF compiler warnings in Colab
def is_ascii(s=''):
# Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7)
s = str(s) # convert list, tuple, None, etc. to str
return len(s.encode().decode('ascii', 'ignore')) == len(s)
def is_chinese(s='人工智能'):
# Is string composed of any Chinese characters?
return bool(re.search('[\u4e00-\u9fff]', str(s)))
def is_colab():
# Is environment a Google Colab instance?
return 'google.colab' in sys.modules
def is_jupyter():
"""
Check if the current script is running inside a Jupyter Notebook.
Verified on Colab, Jupyterlab, Kaggle, Paperspace.
Returns:
bool: True if running inside a Jupyter Notebook, False otherwise.
"""
with contextlib.suppress(Exception):
from IPython import get_ipython
return get_ipython() is not None
return False
def is_kaggle():
# Is environment a Kaggle Notebook?
return os.environ.get('PWD') == '/kaggle/working' and os.environ.get('KAGGLE_URL_BASE') == 'https://www.kaggle.com'
def is_docker() -> bool:
"""Check if the process runs inside a docker container."""
if Path('/.dockerenv').exists():
return True
try: # check if docker is in control groups
with open('/proc/self/cgroup') as file:
return any('docker' in line for line in file)
except OSError:
return False
def is_writeable(dir, test=False):
# Return True if directory has write permissions, test opening a file with write permissions if test=True
if not test:
return os.access(dir, os.W_OK) # possible issues on Windows
file = Path(dir) / 'tmp.txt'
try:
with open(file, 'w'): # open file with write permissions
pass
file.unlink() # remove file
return True
except OSError:
return False
LOGGING_NAME = 'yolov5'
def set_logging(name=LOGGING_NAME, verbose=True):
# sets up logging for the given name
rank = int(os.getenv('RANK', -1)) # rank in world for Multi-GPU trainings
level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR
logging.config.dictConfig({
'version': 1,
'disable_existing_loggers': False,
'formatters': {
name: {
'format': '%(message)s'}},
'handlers': {
name: {
'class': 'logging.StreamHandler',
'formatter': name,
'level': level, }},
'loggers': {
name: {
'level': level,
'handlers': [name],
'propagate': False, }}})
set_logging(LOGGING_NAME) # run before defining LOGGER
LOGGER = logging.getLogger(LOGGING_NAME) # define globally (used in train.py, val.py, detect.py, etc.)
if platform.system() == 'Windows':
for fn in LOGGER.info, LOGGER.warning:
setattr(LOGGER, fn.__name__, lambda x: fn(emojis(x))) # emoji safe logging
def user_config_dir(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR'):
# Return path of user configuration directory. Prefer environment variable if exists. Make dir if required.
env = os.getenv(env_var)
if env:
path = Path(env) # use environment variable
else:
cfg = {'Windows': 'AppData/Roaming', 'Linux': '.config', 'Darwin': 'Library/Application Support'} # 3 OS dirs
path = Path.home() / cfg.get(platform.system(), '') # OS-specific config dir
path = (path if is_writeable(path) else Path('/tmp')) / dir # GCP and AWS lambda fix, only /tmp is writeable
path.mkdir(exist_ok=True) # make if required
return path
CONFIG_DIR = user_config_dir() # Ultralytics settings dir
class Profile(contextlib.ContextDecorator):
# YOLOv5 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager
def __init__(self, t=0.0, device: torch.device = None):
self.t = t
self.device = device
self.cuda = True if (device and str(device)[:4] == 'cuda') else False
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(self.device)
return time.time()
class Timeout(contextlib.ContextDecorator):
# YOLOv5 Timeout class. Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager
def __init__(self, seconds, *, timeout_msg='', suppress_timeout_errors=True):
self.seconds = int(seconds)
self.timeout_message = timeout_msg
self.suppress = bool(suppress_timeout_errors)
def _timeout_handler(self, signum, frame):
raise TimeoutError(self.timeout_message)
def __enter__(self):
if platform.system() != 'Windows': # not supported on Windows
signal.signal(signal.SIGALRM, self._timeout_handler) # Set handler for SIGALRM
signal.alarm(self.seconds) # start countdown for SIGALRM to be raised
def __exit__(self, exc_type, exc_val, exc_tb):
if platform.system() != 'Windows':
signal.alarm(0) # Cancel SIGALRM if it's scheduled
if self.suppress and exc_type is TimeoutError: # Suppress TimeoutError
return True
class WorkingDirectory(contextlib.ContextDecorator):
# Usage: @WorkingDirectory(dir) decorator or 'with WorkingDirectory(dir):' context manager
def __init__(self, new_dir):
self.dir = new_dir # new dir
self.cwd = Path.cwd().resolve() # current dir
def __enter__(self):
os.chdir(self.dir)
def __exit__(self, exc_type, exc_val, exc_tb):
os.chdir(self.cwd)
def methods(instance):
# Get class/instance methods
return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith('__')]
def print_args(args: Optional[dict] = None, show_file=True, show_func=False):
# Print function arguments (optional args dict)
x = inspect.currentframe().f_back # previous frame
file, _, func, _, _ = inspect.getframeinfo(x)
if args is None: # get args automatically
args, _, _, frm = inspect.getargvalues(x)
args = {k: v for k, v in frm.items() if k in args}
try:
file = Path(file).resolve().relative_to(ROOT).with_suffix('')
except ValueError:
file = Path(file).stem
s = (f'{file}: ' if show_file else '') + (f'{func}: ' if show_func else '')
LOGGER.info(colorstr(s) + ', '.join(f'{k}={v}' for k, v in args.items()))
def init_seeds(seed=0, deterministic=False):
# Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # for Multi-GPU, exception safe
# torch.backends.cudnn.benchmark = True # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287
if deterministic and check_version(torch.__version__, '1.12.0'): # https://github.com/ultralytics/yolov5/pull/8213
torch.use_deterministic_algorithms(True)
torch.backends.cudnn.deterministic = True
os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'
os.environ['PYTHONHASHSEED'] = str(seed)
def intersect_dicts(da, db, exclude=()):
# Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values
return {k: v for k, v in da.items() if k in db and all(x not in k for x in exclude) and v.shape == db[k].shape}
def get_default_args(func):
# Get func() default arguments
signature = inspect.signature(func)
return {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty}
def get_latest_run(search_dir='.'):
# Return path to most recent 'last.pt' in /runs (i.e. to --resume from)
last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
return max(last_list, key=os.path.getctime) if last_list else ''
def file_age(path=__file__):
# Return days since last file update
dt = (datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime)) # delta
return dt.days # + dt.seconds / 86400 # fractional days
def file_date(path=__file__):
# Return human-readable file modification date, i.e. '2021-3-26'
t = datetime.fromtimestamp(Path(path).stat().st_mtime)
return f'{t.year}-{t.month}-{t.day}'
def file_size(path):
# Return file/dir size (MB)
mb = 1 << 20 # bytes to MiB (1024 ** 2)
path = Path(path)
if path.is_file():
return path.stat().st_size / mb
elif path.is_dir():
return sum(f.stat().st_size for f in path.glob('**/*') if f.is_file()) / mb
else:
return 0.0
def check_online():
# Check internet connectivity
import socket
def run_once():
# Check once
try:
socket.create_connection(('1.1.1.1', 443), 5) # check host accessibility
return True
except OSError:
return False
return run_once() or run_once() # check twice to increase robustness to intermittent connectivity issues
def git_describe(path=ROOT): # path must be a directory
# Return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe
try:
assert (Path(path) / '.git').is_dir()
return check_output(f'git -C {path} describe --tags --long --always', shell=True).decode()[:-1]
except Exception:
return ''
@TryExcept()
@WorkingDirectory(ROOT)
def check_git_status(repo='ultralytics/yolov5', branch='master'):
# YOLOv5 status check, recommend 'git pull' if code is out of date
url = f'https://github.com/{repo}'
msg = f', for updates see {url}'
s = colorstr('github: ') # string
assert Path('.git').exists(), s + 'skipping check (not a git repository)' + msg
assert check_online(), s + 'skipping check (offline)' + msg
splits = re.split(pattern=r'\s', string=check_output('git remote -v', shell=True).decode())
matches = [repo in s for s in splits]
if any(matches):
remote = splits[matches.index(True) - 1]
else:
remote = 'ultralytics'
check_output(f'git remote add {remote} {url}', shell=True)
check_output(f'git fetch {remote}', shell=True, timeout=5) # git fetch
local_branch = check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out
n = int(check_output(f'git rev-list {local_branch}..{remote}/{branch} --count', shell=True)) # commits behind
if n > 0:
pull = 'git pull' if remote == 'origin' else f'git pull {remote} {branch}'
s += f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use '{pull}' or 'git clone {url}' to update."
else:
s += f'up to date with {url} ✅'
LOGGER.info(s)
@WorkingDirectory(ROOT)
def check_git_info(path='.'):
# YOLOv5 git info check, return {remote, branch, commit}
check_requirements('gitpython')
import git
try:
repo = git.Repo(path)
remote = repo.remotes.origin.url.replace('.git', '') # i.e. 'https://github.com/ultralytics/yolov5'
commit = repo.head.commit.hexsha # i.e. '3134699c73af83aac2a481435550b968d5792c0d'
try:
branch = repo.active_branch.name # i.e. 'main'
except TypeError: # not on any branch
branch = None # i.e. 'detached HEAD' state
return {'remote': remote, 'branch': branch, 'commit': commit}
except git.exc.InvalidGitRepositoryError: # path is not a git dir
return {'remote': None, 'branch': None, 'commit': None}
def check_python(minimum='3.8.0'):
# Check current python version vs. required python version
check_version(platform.python_version(), minimum, name='Python ', hard=True)
def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=False, hard=False, verbose=False):
# Check version vs. required version
current, minimum = (pkg.parse_version(x) for x in (current, minimum))
result = (current == minimum) if pinned else (current >= minimum) # bool
s = f'WARNING ⚠️ {name}{minimum} is required by YOLOv5, but {name}{current} is currently installed' # string
if hard:
assert result, emojis(s) # assert min requirements met
if verbose and not result:
LOGGER.warning(s)
return result
def check_img_size(imgsz, s=32, floor=0):
# Verify image size is a multiple of stride s in each dimension
if isinstance(imgsz, int): # integer i.e. img_size=640
new_size = max(make_divisible(imgsz, int(s)), floor)
else: # list i.e. img_size=[640, 480]
imgsz = list(imgsz) # convert to list if tuple
new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]
if new_size != imgsz:
LOGGER.warning(f'WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}')
return new_size
def check_imshow(warn=False):
# Check if environment supports image displays
try:
assert not is_jupyter()
assert not is_docker()
cv2.imshow('test', np.zeros((1, 1, 3)))
cv2.waitKey(1)
cv2.destroyAllWindows()
cv2.waitKey(1)
return True
except Exception as e:
if warn:
LOGGER.warning(f'WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n{e}')
return False
def check_suffix(file='yolov5s.pt', suffix=('.pt', ), msg=''):
# Check file(s) for acceptable suffix
if file and suffix:
if isinstance(suffix, str):
suffix = [suffix]
for f in file if isinstance(file, (list, tuple)) else [file]:
s = Path(f).suffix.lower() # file suffix
if len(s):
assert s in suffix, f'{msg}{f} acceptable suffix is {suffix}'
def check_yaml(file, suffix=('.yaml', '.yml')):
# Search/download YAML file (if necessary) and return path, checking suffix
return check_file(file, suffix)
def check_file(file, suffix=''):
# Search/download file (if necessary) and return path
check_suffix(file, suffix) # optional
file = str(file) # convert to str()
if os.path.isfile(file) or not file: # exists
return file
elif file.startswith(('http:/', 'https:/')): # download
url = file # warning: Pathlib turns :// -> :/
file = Path(urllib.parse.unquote(file).split('?')[0]).name # '%2F' to '/', split https://url.com/file.txt?auth
if os.path.isfile(file):
LOGGER.info(f'Found {url} locally at {file}') # file already exists
else:
LOGGER.info(f'Downloading {url} to {file}...')
torch.hub.download_url_to_file(url, file)
assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}' # check
return file
elif file.startswith('clearml://'): # ClearML Dataset ID
assert 'clearml' in sys.modules, "ClearML is not installed, so cannot use ClearML dataset. Try running 'pip install clearml'."
return file
else: # search
files = []
for d in 'data', 'models', 'utils': # search directories
files.extend(glob.glob(str(ROOT / d / '**' / file), recursive=True)) # find file
assert len(files), f'File not found: {file}' # assert file was found
assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique
return files[0] # return file
def check_font(font=FONT, progress=False):
# Download font to CONFIG_DIR if necessary
font = Path(font)
file = CONFIG_DIR / font.name
if not font.exists() and not file.exists():
url = f'https://ultralytics.com/assets/{font.name}'
LOGGER.info(f'Downloading {url} to {file}...')
torch.hub.download_url_to_file(url, str(file), progress=progress)
def check_dataset(data, autodownload=True):
# Download, check and/or unzip dataset if not found locally
# Download (optional)
extract_dir = ''
if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)):
download(data, dir=f'{DATASETS_DIR}/{Path(data).stem}', unzip=True, delete=False, curl=False, threads=1)
data = next((DATASETS_DIR / Path(data).stem).rglob('*.yaml'))
extract_dir, autodownload = data.parent, False
# Read yaml (optional)
if isinstance(data, (str, Path)):
data = yaml_load(data) # dictionary
# Checks
for k in 'train', 'val', 'names':
assert k in data, emojis(f"data.yaml '{k}:' field missing ❌")
if isinstance(data['names'], (list, tuple)): # old array format
data['names'] = dict(enumerate(data['names'])) # convert to dict
assert all(isinstance(k, int) for k in data['names'].keys()), 'data.yaml names keys must be integers, i.e. 2: car'
data['nc'] = len(data['names'])
# Resolve paths
path = Path(extract_dir or data.get('path') or '') # optional 'path' default to '.'
if not path.is_absolute():
path = (ROOT / path).resolve()
data['path'] = path # download scripts
for k in 'train', 'val', 'test':
if data.get(k): # prepend path
if isinstance(data[k], str):
x = (path / data[k]).resolve()
if not x.exists() and data[k].startswith('../'):
x = (path / data[k][3:]).resolve()
data[k] = str(x)
else:
data[k] = [str((path / x).resolve()) for x in data[k]]
# Parse yaml
train, val, test, s = (data.get(x) for x in ('train', 'val', 'test', 'download'))
if val:
val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path
if not all(x.exists() for x in val):
LOGGER.info('\nDataset not found ⚠️, missing paths %s' % [str(x) for x in val if not x.exists()])
if not s or not autodownload:
raise Exception('Dataset not found ❌')
t = time.time()
if s.startswith('http') and s.endswith('.zip'): # URL
f = Path(s).name # filename
LOGGER.info(f'Downloading {s} to {f}...')
torch.hub.download_url_to_file(s, f)
Path(DATASETS_DIR).mkdir(parents=True, exist_ok=True) # create root
unzip_file(f, path=DATASETS_DIR) # unzip
Path(f).unlink() # remove zip
r = None # success
elif s.startswith('bash '): # bash script
LOGGER.info(f'Running {s} ...')
r = subprocess.run(s, shell=True)
else: # python script
r = exec(s, {'yaml': data}) # return None
dt = f'({round(time.time() - t, 1)}s)'
s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f'failure {dt} ❌'
LOGGER.info(f'Dataset download {s}')
check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf', progress=True) # download fonts
return data # dictionary
def check_amp(model):
# Check PyTorch Automatic Mixed Precision (AMP) functionality. Return True on correct operation
from models.common import AutoShape, DetectMultiBackend
def amp_allclose(model, im):
# All close FP32 vs AMP results
m = AutoShape(model, verbose=False) # model
a = m(im).xywhn[0] # FP32 inference
m.amp = True
b = m(im).xywhn[0] # AMP inference
return a.shape == b.shape and torch.allclose(a, b, atol=0.1) # close to 10% absolute tolerance
prefix = colorstr('AMP: ')
device = next(model.parameters()).device # get model device
if device.type in ('cpu', 'mps'):
return False # AMP only used on CUDA devices
f = ROOT / 'data' / 'images' / 'bus.jpg' # image to check
im = f if f.exists() else 'https://ultralytics.com/images/bus.jpg' if check_online() else np.ones((640, 640, 3))
try:
assert amp_allclose(deepcopy(model), im) or amp_allclose(DetectMultiBackend('yolov5n.pt', device), im)
LOGGER.info(f'{prefix}checks passed ✅')
return True
except Exception:
help_url = 'https://github.com/ultralytics/yolov5/issues/7908'
LOGGER.warning(f'{prefix}checks failed ❌, disabling Automatic Mixed Precision. See {help_url}')
return False
def yaml_load(file='data.yaml'):
# Single-line safe yaml loading
with open(file, errors='ignore') as f:
return yaml.safe_load(f)
def yaml_save(file='data.yaml', data={}):
# Single-line safe yaml saving
with open(file, 'w') as f:
yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False)
def unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX')):
# Unzip a *.zip file to path/, excluding files containing strings in exclude list
if path is None:
path = Path(file).parent # default path
with ZipFile(file) as zipObj:
for f in zipObj.namelist(): # list all archived filenames in the zip
if all(x not in f for x in exclude):
zipObj.extract(f, path=path)
def url2file(url):
# Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt
url = str(Path(url)).replace(':/', '://') # Pathlib turns :// -> :/
return Path(urllib.parse.unquote(url)).name.split('?')[0] # '%2F' to '/', split https://url.com/file.txt?auth
def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1, retry=3):
# Multithreaded file download and unzip function, used in data.yaml for autodownload
def download_one(url, dir):
# Download 1 file
success = True
if os.path.isfile(url):
f = Path(url) # filename
else: # does not exist
f = dir / Path(url).name
LOGGER.info(f'Downloading {url} to {f}...')
for i in range(retry + 1):
if curl:
success = curl_download(url, f, silent=(threads > 1))
else:
torch.hub.download_url_to_file(url, f, progress=threads == 1) # torch download
success = f.is_file()
if success:
break
elif i < retry:
LOGGER.warning(f'⚠️ Download failure, retrying {i + 1}/{retry} {url}...')
else:
LOGGER.warning(f'❌ Failed to download {url}...')
if unzip and success and (f.suffix == '.gz' or is_zipfile(f) or is_tarfile(f)):
LOGGER.info(f'Unzipping {f}...')
if is_zipfile(f):
unzip_file(f, dir) # unzip
elif is_tarfile(f):
subprocess.run(['tar', 'xf', f, '--directory', f.parent], check=True) # unzip
elif f.suffix == '.gz':
subprocess.run(['tar', 'xfz', f, '--directory', f.parent], check=True) # unzip
if delete:
f.unlink() # remove zip
dir = Path(dir)
dir.mkdir(parents=True, exist_ok=True) # make directory
if threads > 1:
pool = ThreadPool(threads)
pool.imap(lambda x: download_one(*x), zip(url, repeat(dir))) # multithreaded
pool.close()
pool.join()
else:
for u in [url] if isinstance(url, (str, Path)) else url:
download_one(u, dir)
def make_divisible(x, divisor):
# Returns nearest x divisible by divisor
if isinstance(divisor, torch.Tensor):
divisor = int(divisor.max()) # to int
return math.ceil(x / divisor) * divisor
def clean_str(s):
# Cleans a string by replacing special characters with underscore _
return re.sub(pattern='[|@#!¡·$€%&()=?¿^*;:,¨´><+]', repl='_', string=s)
def one_cycle(y1=0.0, y2=1.0, steps=100):
# lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf
return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
def colorstr(*input):
# Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world')
*args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string
colors = {
'black': '\033[30m', # basic colors
'red': '\033[31m',
'green': '\033[32m',
'yellow': '\033[33m',
'blue': '\033[34m',
'magenta': '\033[35m',
'cyan': '\033[36m',
'white': '\033[37m',
'bright_black': '\033[90m', # bright colors
'bright_red': '\033[91m',
'bright_green': '\033[92m',
'bright_yellow': '\033[93m',
'bright_blue': '\033[94m',
'bright_magenta': '\033[95m',
'bright_cyan': '\033[96m',
'bright_white': '\033[97m',
'end': '\033[0m', # misc
'bold': '\033[1m',
'underline': '\033[4m'}
return ''.join(colors[x] for x in args) + f'{string}' + colors['end']
def labels_to_class_weights(labels, nc=80):
# Get class weights (inverse frequency) from training labels
if labels[0] is None: # no labels loaded
return torch.Tensor()
labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO
classes = labels[:, 0].astype(int) # labels = [class xywh]
weights = np.bincount(classes, minlength=nc) # occurrences per class
# Prepend gridpoint count (for uCE training)
# gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image
# weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start
weights[weights == 0] = 1 # replace empty bins with 1
weights = 1 / weights # number of targets per class
weights /= weights.sum() # normalize
return torch.from_numpy(weights).float()
def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
# Produces image weights based on class_weights and image contents
# Usage: index = random.choices(range(n), weights=image_weights, k=1) # weighted image sample
class_counts = np.array([np.bincount(x[:, 0].astype(int), minlength=nc) for x in labels])
return (class_weights.reshape(1, nc) * class_counts).sum(1)
def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper)
# https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
# a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
# b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
# x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco
# x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet
return [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
def xyxy2xywh(x):
# Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center
y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center
y[..., 2] = x[..., 2] - x[..., 0] # width
y[..., 3] = x[..., 3] - x[..., 1] # height
return y
def xywh2xyxy(x):
# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x
y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y
y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x
y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y
return y
def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
# Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw # top left x
y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh # top left y
y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw # bottom right x
y[..., 3] = h * (x[..., 1] + x[..., 3] / 2) + padh # bottom right y
return y
def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
# Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right
if clip:
clip_boxes(x, (h - eps, w - eps)) # warning: inplace clip
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w # x center
y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h # y center
y[..., 2] = (x[..., 2] - x[..., 0]) / w # width
y[..., 3] = (x[..., 3] - x[..., 1]) / h # height
return y
def xyn2xy(x, w=640, h=640, padw=0, padh=0):
# Convert normalized segments into pixel segments, shape (n,2)
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = w * x[..., 0] + padw # top left x
y[..., 1] = h * x[..., 1] + padh # top left y
return y
def segment2box(segment, width=640, height=640):
# Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
x, y = segment.T # segment xy
inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
x, y, = x[inside], y[inside]
return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy
def segments2boxes(segments):
# Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
boxes = []
for s in segments:
x, y = s.T # segment xy
boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy
return xyxy2xywh(np.array(boxes)) # cls, xywh
def resample_segments(segments, n=1000):
# Up-sample an (n,2) segment
for i, s in enumerate(segments):
s = np.concatenate((s, s[0:1, :]), axis=0)
x = np.linspace(0, len(s) - 1, n)
xp = np.arange(len(s))
segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy
return segments
def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):
# Rescale boxes (xyxy) from img1_shape to img0_shape
if ratio_pad is None: # calculate from img0_shape
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
else:
gain = ratio_pad[0][0]
pad = ratio_pad[1]
boxes[..., [0, 2]] -= pad[0] # x padding
boxes[..., [1, 3]] -= pad[1] # y padding
boxes[..., :4] /= gain
clip_boxes(boxes, img0_shape)
return boxes
def scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, normalize=False):
# Rescale coords (xyxy) from img1_shape to img0_shape
if ratio_pad is None: # calculate from img0_shape
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
else:
gain = ratio_pad[0][0]
pad = ratio_pad[1]
segments[:, 0] -= pad[0] # x padding
segments[:, 1] -= pad[1] # y padding
segments /= gain
clip_segments(segments, img0_shape)
if normalize:
segments[:, 0] /= img0_shape[1] # width
segments[:, 1] /= img0_shape[0] # height
return segments
def clip_boxes(boxes, shape):
# Clip boxes (xyxy) to image shape (height, width)
if isinstance(boxes, torch.Tensor): # faster individually
boxes[..., 0].clamp_(0, shape[1]) # x1
boxes[..., 1].clamp_(0, shape[0]) # y1
boxes[..., 2].clamp_(0, shape[1]) # x2
boxes[..., 3].clamp_(0, shape[0]) # y2
else: # np.array (faster grouped)
boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2
boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2
def clip_segments(segments, shape):
# Clip segments (xy1,xy2,...) to image shape (height, width)
if isinstance(segments, torch.Tensor): # faster individually
segments[:, 0].clamp_(0, shape[1]) # x
segments[:, 1].clamp_(0, shape[0]) # y
else: # np.array (faster grouped)
segments[:, 0] = segments[:, 0].clip(0, shape[1]) # x
segments[:, 1] = segments[:, 1].clip(0, shape[0]) # y
def non_max_suppression(
prediction,
conf_thres=0.25,
iou_thres=0.45,
classes=None,
agnostic=False,
multi_label=False,
labels=(),
max_det=300,
nm=0, # number of masks
):
"""Non-Maximum Suppression (NMS) on inference results to reject overlapping detections
Returns:
list of detections, on (n,6) tensor per image [xyxy, conf, cls]
"""
# Checks
assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
if isinstance(prediction, (list, tuple)): # YOLOv5 model in validation model, output = (inference_out, loss_out)
prediction = prediction[0] # select only inference output
device = prediction.device
mps = 'mps' in device.type # Apple MPS
if mps: # MPS not fully supported yet, convert tensors to CPU before NMS
prediction = prediction.cpu()
bs = prediction.shape[0] # batch size
nc = prediction.shape[2] - nm - 5 # number of classes
xc = prediction[..., 4] > conf_thres # candidates
# Settings
# min_wh = 2 # (pixels) minimum box width and height
max_wh = 7680 # (pixels) maximum box width and height
max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()
time_limit = 0.5 + 0.05 * bs # seconds to quit after
redundant = True # require redundant detections
multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img)
merge = False # use merge-NMS
t = time.time()
mi = 5 + nc # mask start index
output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs
for xi, x in enumerate(prediction): # image index, image inference
# Apply constraints
# x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
x = x[xc[xi]] # confidence
# Cat apriori labels if autolabelling
if labels and len(labels[xi]):
lb = labels[xi]
v = torch.zeros((len(lb), nc + nm + 5), device=x.device)
v[:, :4] = lb[:, 1:5] # box
v[:, 4] = 1.0 # conf
v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls
x = torch.cat((x, v), 0)
# If none remain process next image
if not x.shape[0]:
continue
# Compute conf
x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf
# Box/Mask
box = xywh2xyxy(x[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2)
mask = x[:, mi:] # zero columns if no masks
# Detections matrix nx6 (xyxy, conf, cls)
if multi_label:
i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T
x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1)
else: # best class only
conf, j = x[:, 5:mi].max(1, keepdim=True)
x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres]
# Filter by class
if classes is not None:
x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
# Apply finite constraint
# if not torch.isfinite(x).all():
# x = x[torch.isfinite(x).all(1)]
# Check shape
n = x.shape[0] # number of boxes
if not n: # no boxes
continue
x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence and remove excess boxes
# Batched NMS
c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
i = i[:max_det] # limit detections
if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)
# update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
weights = iou * scores[None] # box weights
x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
if redundant:
i = i[iou.sum(1) > 1] # require redundancy
output[xi] = x[i]
if mps:
output[xi] = output[xi].to(device)
if (time.time() - t) > time_limit:
LOGGER.warning(f'WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded')
break # time limit exceeded
return output
def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_optimizer()
# Strip optimizer from 'f' to finalize training, optionally save as 's'
x = torch.load(f, map_location=torch.device('cpu'))
if x.get('ema'):
x['model'] = x['ema'] # replace model with ema
for k in 'optimizer', 'best_fitness', 'ema', 'updates': # keys
x[k] = None
x['epoch'] = -1
x['model'].half() # to FP16
for p in x['model'].parameters():
p.requires_grad = False
torch.save(x, s or f)
mb = os.path.getsize(s or f) / 1E6 # filesize
LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB")
def print_mutation(keys, results, hyp, save_dir, bucket, prefix=colorstr('evolve: ')):
evolve_csv = save_dir / 'evolve.csv'
evolve_yaml = save_dir / 'hyp_evolve.yaml'
keys = tuple(keys) + tuple(hyp.keys()) # [results + hyps]
keys = tuple(x.strip() for x in keys)
vals = results + tuple(hyp.values())
n = len(keys)
# Download (optional)
if bucket:
url = f'gs://{bucket}/evolve.csv'
if gsutil_getsize(url) > (evolve_csv.stat().st_size if evolve_csv.exists() else 0):
subprocess.run(['gsutil', 'cp', f'{url}', f'{save_dir}']) # download evolve.csv if larger than local
# Log to evolve.csv
s = '' if evolve_csv.exists() else (('%20s,' * n % keys).rstrip(',') + '\n') # add header
with open(evolve_csv, 'a') as f:
f.write(s + ('%20.5g,' * n % vals).rstrip(',') + '\n')
# Save yaml
with open(evolve_yaml, 'w') as f:
data = pd.read_csv(evolve_csv, skipinitialspace=True)
data = data.rename(columns=lambda x: x.strip()) # strip keys
i = np.argmax(fitness(data.values[:, :4])) #
generations = len(data)
f.write('# YOLOv5 Hyperparameter Evolution Results\n' + f'# Best generation: {i}\n' +
f'# Last generation: {generations - 1}\n' + '# ' + ', '.join(f'{x.strip():>20s}' for x in keys[:7]) +
'\n' + '# ' + ', '.join(f'{x:>20.5g}' for x in data.values[i, :7]) + '\n\n')
yaml.safe_dump(data.loc[i][7:].to_dict(), f, sort_keys=False)
# Print to screen
LOGGER.info(prefix + f'{generations} generations finished, current result:\n' + prefix +
', '.join(f'{x.strip():>20s}' for x in keys) + '\n' + prefix + ', '.join(f'{x:20.5g}'
for x in vals) + '\n\n')
if bucket:
subprocess.run(['gsutil', 'cp', f'{evolve_csv}', f'{evolve_yaml}', f'gs://{bucket}']) # upload
def apply_classifier(x, model, img, im0):
# Apply a second stage classifier to YOLO outputs
# Example model = torchvision.models.__dict__['efficientnet_b0'](pretrained=True).to(device).eval()
im0 = [im0] if isinstance(im0, np.ndarray) else im0
for i, d in enumerate(x): # per image
if d is not None and len(d):
d = d.clone()
# Reshape and pad cutouts
b = xyxy2xywh(d[:, :4]) # boxes
b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square
b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad
d[:, :4] = xywh2xyxy(b).long()
# Rescale boxes from img_size to im0 size
scale_boxes(img.shape[2:], d[:, :4], im0[i].shape)
# Classes
pred_cls1 = d[:, 5].long()
ims = []
for a in d:
cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])]
im = cv2.resize(cutout, (224, 224)) # BGR
im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32
im /= 255 # 0 - 255 to 0.0 - 1.0
ims.append(im)
pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction
x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections
return x
def increment_path(path, exist_ok=False, sep='', mkdir=False):
# Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc.
path = Path(path) # os-agnostic
if path.exists() and not exist_ok:
path, suffix = (path.with_suffix(''), path.suffix) if path.is_file() else (path, '')
# Method 1
for n in range(2, 9999):
p = f'{path}{sep}{n}{suffix}' # increment path
if not os.path.exists(p): #
break
path = Path(p)
# Method 2 (deprecated)
# dirs = glob.glob(f"{path}{sep}*") # similar paths
# matches = [re.search(rf"{path.stem}{sep}(\d+)", d) for d in dirs]
# i = [int(m.groups()[0]) for m in matches if m] # indices
# n = max(i) + 1 if i else 2 # increment number
# path = Path(f"{path}{sep}{n}{suffix}") # increment path
if mkdir:
path.mkdir(parents=True, exist_ok=True) # make directory
return path
# OpenCV Multilanguage-friendly functions ------------------------------------------------------------------------------------
imshow_ = cv2.imshow # copy to avoid recursion errors
def imread(filename, flags=cv2.IMREAD_COLOR):
return cv2.imdecode(np.fromfile(filename, np.uint8), flags)
def imwrite(filename, img):
try:
cv2.imencode(Path(filename).suffix, img)[1].tofile(filename)
return True
except Exception:
return False
def imshow(path, im):
imshow_(path.encode('unicode_escape').decode(), im)
if Path(inspect.stack()[0].filename).parent.parent.as_posix() in inspect.stack()[-1].filename:
cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow # redefine
def get_object_level_feature_maps(feature_map, targets):
feature_map_shape = feature_map.shape[2:]
# Assuming targets contain batch, class, x_center, y_center, width, height
x_center = targets[:, 2] * feature_map_shape[1]
y_center = targets[:, 3] * feature_map_shape[0]
width = targets[:, 4] * feature_map_shape[1]
height = targets[:, 5] * feature_map_shape[0]
# Calculate pixel coordinates for the bounding boxes
x_min = torch.clamp((x_center - width / 2).int(), 0, feature_map_shape[1] - 1)
y_min = torch.clamp((y_center - height / 2).int(), 0, feature_map_shape[0] - 1)
x_max = torch.clamp((x_center + width / 2).int(), 0, feature_map_shape[1] - 1)
y_max = torch.clamp((y_center + height / 2).int(), 0, feature_map_shape[0] - 1)
# Extract regions from the feature_map based on the bounding boxes
extracted_regions = [feature_map[:, :, y_min[i]:y_max[i] + 1, x_min[i]:x_max[i] + 1] for i in range(targets.shape[0])]
return extracted_regions
def get_object_level_feature_maps2(feature_map, targets):
feature_map_shape = feature_map.shape[2:]
# Assuming targets contain batch, class, x_center, y_center, width, height
x_center = targets[:, 1] * feature_map_shape[1]
y_center = targets[:, 2] * feature_map_shape[0]
width = targets[:, 3] * feature_map_shape[1]
height = targets[:, 4] * feature_map_shape[0]
# Calculate pixel coordinates for the bounding boxes
x_min = torch.clamp((x_center - width / 2).int(), 0, feature_map_shape[1] - 1)
y_min = torch.clamp((y_center - height / 2).int(), 0, feature_map_shape[0] - 1)
x_max = torch.clamp((x_center + width / 2).int(), 0, feature_map_shape[1] - 1)
y_max = torch.clamp((y_center + height / 2).int(), 0, feature_map_shape[0] - 1)
# Extract regions from the feature_map based on the bounding boxes
extracted_regions = [feature_map[:, :, y_min[i]:y_max[i] + 1, x_min[i]:x_max[i] + 1] for i in range(targets.shape[0])]
return extracted_regions
def extract_roi_features(concatenated_features, resize_boxes):
"""
Extracts regions of interest (ROIs) from the concatenated_features based on resize_boxes.
Args:
concatenated_features (torch.Tensor): Feature map with shape [batch, channels, height, width].
resize_boxes (torch.Tensor): Boxes with shape [num_boxes, 5], where each row is [batch, x1, y1, x2, y2].
Returns:
torch.Tensor: Tensor containing the ROI features with shape [num_boxes, channels, roi_height, roi_width].
"""
# Initialize a list to store ROI features for each box
roi_features_list = []
for box_idx in range(resize_boxes.size(0)):
# Extract box coordinates
box_coords = resize_boxes[box_idx, 1:]
# Calculate the spatial coordinates of the box
box_x1, box_y1, box_x2, box_y2 = box_coords
roi_x1 = (box_x1 / concatenated_features.size(3)) * concatenated_features.size(3)
roi_y1 = (box_y1 / concatenated_features.size(2)) * concatenated_features.size(2)
roi_x2 = (box_x2 / concatenated_features.size(3)) * concatenated_features.size(3)
roi_y2 = (box_y2 / concatenated_features.size(2)) * concatenated_features.size(2)
# Convert to integer indices
roi_x1, roi_y1, roi_x2, roi_y2 = map(int, [roi_x1, roi_y1, roi_x2, roi_y2])
# Extract ROI from the feature map
roi_features = concatenated_features[:, :, roi_y1:roi_y2, roi_x1:roi_x2]
# Append the ROI features to the list
roi_features_list.append(roi_features)
return roi_features_list
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import torch
def plot_multi_channel_feature_map_with_boxes(feature_map, boxes, channels, title, save_path=None):
fig, axs = plt.subplots(1, len(channels) + 1, figsize=(12, 4))
for i, channel in enumerate(channels):
axs[i].imshow(feature_map[0, channel].cpu().detach().numpy(), cmap='viridis', aspect='auto')
axs[i].set_title(f'Channel {channel}')
# Plot bounding boxes on the last axis
axs[-1].imshow(feature_map[0, channels[-1]].cpu().detach().numpy(), cmap='viridis', aspect='auto')
axs[-1].set_title('Bounding Boxes')
for box in range(len(boxes.shape)):
xmin, ymin, xmax, ymax = boxes.cpu().detach().numpy()
rect = patches.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, linewidth=1, edgecolor='r', facecolor='none')
axs[-1].add_patch(rect)
fig.suptitle(title)
# Save the image if save_path is provided
if save_path:
plt.savefig(save_path)
print(f"Image saved at: {save_path}")
else:
plt.show()
# Denormalize the box
def xywh_to_xyxy(xywh):
x_center, y_center, width, height = xywh
x_min = x_center - width / 2
y_min = y_center - height / 2
x_max = x_center + width / 2
y_max = y_center + height / 2
return torch.tensor([x_min, y_min, x_max, y_max])
def get_fixed_xyxy(normalized_xyxy,int_feat):
x_min, y_min, x_max, y_max = normalized_xyxy.int()
if x_min == x_max:
x_max += 1
if y_min == y_max:
y_max += 1
if x_min == x_max and x_max == int_feat.size(2):
x_min -= 1
if y_min == y_max and y_max == int_feat.size(1):
y_min -= 1
return x_min, y_min, x_max, y_max
# Variables ------------------------------------------------------------------------------------------------------------
Datasets
Models
