823 lines
33 KiB
Python
823 lines
33 KiB
Python
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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General utils
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"""
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import contextlib
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import glob
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import logging
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import math
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import os
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import platform
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import random
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import re
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import signal
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import time
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import urllib
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from itertools import repeat
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from multiprocessing.pool import ThreadPool
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from pathlib import Path
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from subprocess import check_output
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from zipfile import ZipFile
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import cv2
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import numpy as np
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import pandas as pd
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import pkg_resources as pkg
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import torch
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import torchvision
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import yaml
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from utils.downloads import gsutil_getsize
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from utils.metrics import box_iou, fitness
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# Settings
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torch.set_printoptions(linewidth=320, precision=5, profile='long')
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np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5
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pd.options.display.max_columns = 10
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cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
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os.environ['NUMEXPR_MAX_THREADS'] = str(min(os.cpu_count(), 8)) # NumExpr max threads
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[1] # YOLOv5 root directory
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class Profile(contextlib.ContextDecorator):
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# Usage: @Profile() decorator or 'with Profile():' context manager
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def __enter__(self):
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self.start = time.time()
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def __exit__(self, type, value, traceback):
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print(f'Profile results: {time.time() - self.start:.5f}s')
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class Timeout(contextlib.ContextDecorator):
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# Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager
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def __init__(self, seconds, *, timeout_msg='', suppress_timeout_errors=True):
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self.seconds = int(seconds)
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self.timeout_message = timeout_msg
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self.suppress = bool(suppress_timeout_errors)
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def _timeout_handler(self, signum, frame):
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raise TimeoutError(self.timeout_message)
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def __enter__(self):
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signal.signal(signal.SIGALRM, self._timeout_handler) # Set handler for SIGALRM
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signal.alarm(self.seconds) # start countdown for SIGALRM to be raised
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def __exit__(self, exc_type, exc_val, exc_tb):
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signal.alarm(0) # Cancel SIGALRM if it's scheduled
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if self.suppress and exc_type is TimeoutError: # Suppress TimeoutError
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return True
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def try_except(func):
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# try-except function. Usage: @try_except decorator
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def handler(*args, **kwargs):
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try:
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func(*args, **kwargs)
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except Exception as e:
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print(e)
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return handler
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def methods(instance):
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# Get class/instance methods
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return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith("__")]
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def set_logging(rank=-1, verbose=True):
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logging.basicConfig(
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format="%(message)s",
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level=logging.INFO if (verbose and rank in [-1, 0]) else logging.WARN)
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def print_args(name, opt):
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# Print argparser arguments
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print(colorstr(f'{name}: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
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def init_seeds(seed=0):
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# Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html
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# cudnn seed 0 settings are slower and more reproducible, else faster and less reproducible
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import torch.backends.cudnn as cudnn
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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cudnn.benchmark, cudnn.deterministic = (False, True) if seed == 0 else (True, False)
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def get_latest_run(search_dir='.'):
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# Return path to most recent 'last.pt' in /runs (i.e. to --resume from)
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last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
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return max(last_list, key=os.path.getctime) if last_list else ''
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def user_config_dir(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR'):
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# Return path of user configuration directory. Prefer environment variable if exists. Make dir if required.
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env = os.getenv(env_var)
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if env:
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path = Path(env) # use environment variable
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else:
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cfg = {'Windows': 'AppData/Roaming', 'Linux': '.config', 'Darwin': 'Library/Application Support'} # 3 OS dirs
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path = Path.home() / cfg.get(platform.system(), '') # OS-specific config dir
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path = (path if is_writeable(path) else Path('/tmp')) / dir # GCP and AWS lambda fix, only /tmp is writeable
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path.mkdir(exist_ok=True) # make if required
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return path
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def is_writeable(dir, test=False):
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# Return True if directory has write permissions, test opening a file with write permissions if test=True
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if test: # method 1
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file = Path(dir) / 'tmp.txt'
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try:
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with open(file, 'w'): # open file with write permissions
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pass
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file.unlink() # remove file
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return True
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except IOError:
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return False
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else: # method 2
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return os.access(dir, os.R_OK) # possible issues on Windows
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def is_docker():
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# Is environment a Docker container?
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return Path('/workspace').exists() # or Path('/.dockerenv').exists()
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def is_colab():
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# Is environment a Google Colab instance?
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try:
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import google.colab
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return True
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except ImportError:
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return False
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def is_pip():
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# Is file in a pip package?
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return 'site-packages' in Path(__file__).resolve().parts
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def is_ascii(s=''):
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# Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7)
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s = str(s) # convert list, tuple, None, etc. to str
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return len(s.encode().decode('ascii', 'ignore')) == len(s)
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def is_chinese(s='人工智能'):
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# Is string composed of any Chinese characters?
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return re.search('[\u4e00-\u9fff]', s)
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def emojis(str=''):
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# Return platform-dependent emoji-safe version of string
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return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str
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def file_size(path):
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# Return file/dir size (MB)
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path = Path(path)
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if path.is_file():
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return path.stat().st_size / 1E6
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elif path.is_dir():
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return sum(f.stat().st_size for f in path.glob('**/*') if f.is_file()) / 1E6
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else:
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return 0.0
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def check_online():
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# Check internet connectivity
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import socket
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try:
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socket.create_connection(("1.1.1.1", 443), 5) # check host accessibility
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return True
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except OSError:
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return False
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@try_except
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def check_git_status():
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# Recommend 'git pull' if code is out of date
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msg = ', for updates see https://github.com/ultralytics/yolov5'
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print(colorstr('github: '), end='')
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assert Path('.git').exists(), 'skipping check (not a git repository)' + msg
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assert not is_docker(), 'skipping check (Docker image)' + msg
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assert check_online(), 'skipping check (offline)' + msg
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cmd = 'git fetch && git config --get remote.origin.url'
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url = check_output(cmd, shell=True, timeout=5).decode().strip().rstrip('.git') # git fetch
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branch = check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out
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n = int(check_output(f'git rev-list {branch}..origin/master --count', shell=True)) # commits behind
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if n > 0:
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s = f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use `git pull` or `git clone {url}` to update."
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else:
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s = f'up to date with {url} ✅'
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print(emojis(s)) # emoji-safe
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def check_python(minimum='3.6.2'):
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# Check current python version vs. required python version
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check_version(platform.python_version(), minimum, name='Python ')
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def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=False):
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# Check version vs. required version
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current, minimum = (pkg.parse_version(x) for x in (current, minimum))
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result = (current == minimum) if pinned else (current >= minimum)
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assert result, f'{name}{minimum} required by YOLOv5, but {name}{current} is currently installed'
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@try_except
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def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), install=True):
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# Check installed dependencies meet requirements (pass *.txt file or list of packages)
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prefix = colorstr('red', 'bold', 'requirements:')
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check_python() # check python version
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if isinstance(requirements, (str, Path)): # requirements.txt file
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file = Path(requirements)
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assert file.exists(), f"{prefix} {file.resolve()} not found, check failed."
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requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(file.open()) if x.name not in exclude]
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else: # list or tuple of packages
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requirements = [x for x in requirements if x not in exclude]
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n = 0 # number of packages updates
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for r in requirements:
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try:
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pkg.require(r)
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except Exception as e: # DistributionNotFound or VersionConflict if requirements not met
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s = f"{prefix} {r} not found and is required by YOLOv5"
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if install:
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print(f"{s}, attempting auto-update...")
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try:
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assert check_online(), f"'pip install {r}' skipped (offline)"
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print(check_output(f"pip install '{r}'", shell=True).decode())
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n += 1
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except Exception as e:
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print(f'{prefix} {e}')
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else:
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print(f'{s}. Please install and rerun your command.')
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if n: # if packages updated
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source = file.resolve() if 'file' in locals() else requirements
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s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \
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f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n"
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print(emojis(s))
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def check_img_size(imgsz, s=32, floor=0):
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# Verify image size is a multiple of stride s in each dimension
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if isinstance(imgsz, int): # integer i.e. img_size=640
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new_size = max(make_divisible(imgsz, int(s)), floor)
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else: # list i.e. img_size=[640, 480]
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new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]
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if new_size != imgsz:
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print(f'WARNING: --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}')
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return new_size
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def check_imshow():
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# Check if environment supports image displays
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try:
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assert not is_docker(), 'cv2.imshow() is disabled in Docker environments'
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assert not is_colab(), 'cv2.imshow() is disabled in Google Colab environments'
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cv2.imshow('test', np.zeros((1, 1, 3)))
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cv2.waitKey(1)
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cv2.destroyAllWindows()
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cv2.waitKey(1)
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return True
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except Exception as e:
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print(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}')
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return False
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def check_suffix(file='yolov5s.pt', suffix=('.pt',), msg=''):
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# Check file(s) for acceptable suffix
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if file and suffix:
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if isinstance(suffix, str):
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suffix = [suffix]
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for f in file if isinstance(file, (list, tuple)) else [file]:
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s = Path(f).suffix.lower() # file suffix
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if len(s):
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assert s in suffix, f"{msg}{f} acceptable suffix is {suffix}"
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def check_yaml(file, suffix=('.yaml', '.yml')):
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# Search/download YAML file (if necessary) and return path, checking suffix
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return check_file(file, suffix)
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def check_file(file, suffix=''):
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# Search/download file (if necessary) and return path
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check_suffix(file, suffix) # optional
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file = str(file) # convert to str()
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if Path(file).is_file() or file == '': # exists
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return file
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elif file.startswith(('http:/', 'https:/')): # download
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url = str(Path(file)).replace(':/', '://') # Pathlib turns :// -> :/
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file = Path(urllib.parse.unquote(file).split('?')[0]).name # '%2F' to '/', split https://url.com/file.txt?auth
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print(f'Downloading {url} to {file}...')
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torch.hub.download_url_to_file(url, file)
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assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}' # check
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return file
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else: # search
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files = []
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for d in 'data', 'models', 'utils': # search directories
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files.extend(glob.glob(str(ROOT / d / '**' / file), recursive=True)) # find file
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assert len(files), f'File not found: {file}' # assert file was found
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assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique
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return files[0] # return file
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def check_dataset(data, autodownload=True):
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# Download and/or unzip dataset if not found locally
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# Usage: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128_with_yaml.zip
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# Download (optional)
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extract_dir = ''
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if isinstance(data, (str, Path)) and str(data).endswith('.zip'): # i.e. gs://bucket/dir/coco128.zip
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download(data, dir='../datasets', unzip=True, delete=False, curl=False, threads=1)
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data = next((Path('../datasets') / Path(data).stem).rglob('*.yaml'))
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extract_dir, autodownload = data.parent, False
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# Read yaml (optional)
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if isinstance(data, (str, Path)):
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with open(data, errors='ignore') as f:
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|
|
data = yaml.safe_load(f) # dictionary
|
||
|
|
|
||
|
|
# Parse yaml
|
||
|
|
path = extract_dir or Path(data.get('path') or '') # optional 'path' default to '.'
|
||
|
|
for k in 'train', 'val', 'test':
|
||
|
|
if data.get(k): # prepend path
|
||
|
|
data[k] = str(path / data[k]) if isinstance(data[k], str) else [str(path / x) for x in data[k]]
|
||
|
|
|
||
|
|
assert 'nc' in data, "Dataset 'nc' key missing."
|
||
|
|
if 'names' not in data:
|
||
|
|
data['names'] = [f'class{i}' for i in range(data['nc'])] # assign class names if missing
|
||
|
|
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):
|
||
|
|
print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()])
|
||
|
|
if s and autodownload: # download script
|
||
|
|
root = path.parent if 'path' in data else '..' # unzip directory i.e. '../'
|
||
|
|
if s.startswith('http') and s.endswith('.zip'): # URL
|
||
|
|
f = Path(s).name # filename
|
||
|
|
print(f'Downloading {s} to {f}...')
|
||
|
|
torch.hub.download_url_to_file(s, f)
|
||
|
|
Path(root).mkdir(parents=True, exist_ok=True) # create root
|
||
|
|
ZipFile(f).extractall(path=root) # unzip
|
||
|
|
Path(f).unlink() # remove zip
|
||
|
|
r = None # success
|
||
|
|
elif s.startswith('bash '): # bash script
|
||
|
|
print(f'Running {s} ...')
|
||
|
|
r = os.system(s)
|
||
|
|
else: # python script
|
||
|
|
r = exec(s, {'yaml': data}) # return None
|
||
|
|
print(f"Dataset autodownload {f'success, saved to {root}' if r in (0, None) else 'failure'}\n")
|
||
|
|
else:
|
||
|
|
raise Exception('Dataset not found.')
|
||
|
|
|
||
|
|
return data # dictionary
|
||
|
|
|
||
|
|
|
||
|
|
def url2file(url):
|
||
|
|
# Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt
|
||
|
|
url = str(Path(url)).replace(':/', '://') # Pathlib turns :// -> :/
|
||
|
|
file = Path(urllib.parse.unquote(url)).name.split('?')[0] # '%2F' to '/', split https://url.com/file.txt?auth
|
||
|
|
return file
|
||
|
|
|
||
|
|
|
||
|
|
def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1):
|
||
|
|
# Multi-threaded file download and unzip function, used in data.yaml for autodownload
|
||
|
|
def download_one(url, dir):
|
||
|
|
# Download 1 file
|
||
|
|
f = dir / Path(url).name # filename
|
||
|
|
if Path(url).is_file(): # exists in current path
|
||
|
|
Path(url).rename(f) # move to dir
|
||
|
|
elif not f.exists():
|
||
|
|
print(f'Downloading {url} to {f}...')
|
||
|
|
if curl:
|
||
|
|
os.system(f"curl -L '{url}' -o '{f}' --retry 9 -C -") # curl download, retry and resume on fail
|
||
|
|
else:
|
||
|
|
torch.hub.download_url_to_file(url, f, progress=True) # torch download
|
||
|
|
if unzip and f.suffix in ('.zip', '.gz'):
|
||
|
|
print(f'Unzipping {f}...')
|
||
|
|
if f.suffix == '.zip':
|
||
|
|
ZipFile(f).extractall(path=dir) # 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))) # multi-threaded
|
||
|
|
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 x evenly divisible by divisor
|
||
|
|
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(np.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)
|
||
|
|
|
||
|
|
|
||
|
|
def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
|
||
|
|
# Produces image weights based on class_weights and image contents
|
||
|
|
class_counts = np.array([np.bincount(x[:, 0].astype(np.int), minlength=nc) for x in labels])
|
||
|
|
image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1)
|
||
|
|
# index = random.choices(range(n), weights=image_weights, k=1) # weight image sample
|
||
|
|
return image_weights
|
||
|
|
|
||
|
|
|
||
|
|
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
|
||
|
|
x = [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]
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
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_coords(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):
|
||
|
|
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_coords(img1_shape, coords, img0_shape, ratio_pad=None):
|
||
|
|
# 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]
|
||
|
|
|
||
|
|
coords[:, [0, 2]] -= pad[0] # x padding
|
||
|
|
coords[:, [1, 3]] -= pad[1] # y padding
|
||
|
|
coords[:, :4] /= gain
|
||
|
|
clip_coords(coords, img0_shape)
|
||
|
|
return coords
|
||
|
|
|
||
|
|
|
||
|
|
def clip_coords(boxes, shape):
|
||
|
|
# Clip bounding xyxy bounding boxes 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 non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
|
||
|
|
labels=(), max_det=300):
|
||
|
|
"""Runs Non-Maximum Suppression (NMS) on inference results
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
list of detections, on (n,6) tensor per image [xyxy, conf, cls]
|
||
|
|
"""
|
||
|
|
|
||
|
|
nc = prediction.shape[2] - 5 # number of classes
|
||
|
|
xc = prediction[..., 4] > 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, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height
|
||
|
|
max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()
|
||
|
|
time_limit = 10.0 # 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), device=prediction.device)] * prediction.shape[0]
|
||
|
|
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]):
|
||
|
|
l = labels[xi]
|
||
|
|
v = torch.zeros((len(l), nc + 5), device=x.device)
|
||
|
|
v[:, :4] = l[:, 1:5] # box
|
||
|
|
v[:, 4] = 1.0 # conf
|
||
|
|
v[range(len(l)), l[:, 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 (center x, center y, width, height) to (x1, y1, x2, y2)
|
||
|
|
box = xywh2xyxy(x[:, :4])
|
||
|
|
|
||
|
|
# Detections matrix nx6 (xyxy, conf, cls)
|
||
|
|
if multi_label:
|
||
|
|
i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
|
||
|
|
x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
|
||
|
|
else: # best class only
|
||
|
|
conf, j = x[:, 5:].max(1, keepdim=True)
|
||
|
|
x = torch.cat((box, conf, j.float()), 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
|
||
|
|
|
||
|
|
# 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 (time.time() - t) > time_limit:
|
||
|
|
print(f'WARNING: NMS time limit {time_limit}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', 'training_results', 'wandb_id', '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
|
||
|
|
print(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB")
|
||
|
|
|
||
|
|
|
||
|
|
def print_mutation(results, hyp, save_dir, bucket):
|
||
|
|
evolve_csv, results_csv, evolve_yaml = save_dir / 'evolve.csv', save_dir / 'results.csv', save_dir / 'hyp_evolve.yaml'
|
||
|
|
keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
|
||
|
|
'val/box_loss', 'val/obj_loss', 'val/cls_loss') + 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) > (os.path.getsize(evolve_csv) if os.path.exists(evolve_csv) 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')
|
||
|
|
|
||
|
|
# Print to screen
|
||
|
|
print(colorstr('evolve: ') + ', '.join(f'{x.strip():>20s}' for x in keys))
|
||
|
|
print(colorstr('evolve: ') + ', '.join(f'{x:20.5g}' for x in vals), end='\n\n\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[:, :7])) #
|
||
|
|
f.write('# YOLOv5 Hyperparameter Evolution Results\n' +
|
||
|
|
f'# Best generation: {i}\n' +
|
||
|
|
f'# Last generation: {len(data)}\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(hyp, f, sort_keys=False)
|
||
|
|
|
||
|
|
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
|
||
|
|
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_coords(img.shape[2:], d[:, :4], im0[i].shape)
|
||
|
|
|
||
|
|
# Classes
|
||
|
|
pred_cls1 = d[:, 5].long()
|
||
|
|
ims = []
|
||
|
|
for j, a in enumerate(d): # per item
|
||
|
|
cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])]
|
||
|
|
im = cv2.resize(cutout, (224, 224)) # BGR
|
||
|
|
# cv2.imwrite('example%i.jpg' % j, cutout)
|
||
|
|
|
||
|
|
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 # 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 save_one_box(xyxy, im, file='image.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_coords(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:
|
||
|
|
cv2.imwrite(str(increment_path(file, mkdir=True).with_suffix('.jpg')), crop)
|
||
|
|
return crop
|
||
|
|
|
||
|
|
|
||
|
|
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:
|
||
|
|
suffix = path.suffix
|
||
|
|
path = path.with_suffix('')
|
||
|
|
dirs = glob.glob(f"{path}{sep}*") # similar paths
|
||
|
|
matches = [re.search(rf"%s{sep}(\d+)" % path.stem, 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}") # update path
|
||
|
|
dir = path if path.suffix == '' else path.parent # directory
|
||
|
|
if not dir.exists() and mkdir:
|
||
|
|
dir.mkdir(parents=True, exist_ok=True) # make directory
|
||
|
|
return path
|