610 lines
26 KiB
Python
610 lines
26 KiB
Python
# YOLOv5 general utils
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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 subprocess
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import time
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from pathlib import Path
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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 torch
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import torchvision
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import yaml
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from utils.google_utils import gsutil_getsize
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from utils.metrics import fitness
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from utils.torch_utils import init_torch_seeds
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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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def set_logging(rank=-1):
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logging.basicConfig( # Python 内置模块 logging 的一个方法，用于配置日志系统的基本设置。
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format="%(message)s", #设置日志的输出格式。 "%(message)s" 表示只打印日志的消息内容，而不包含额外信息（如时间、日志级别等）。
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level=logging.INFO if rank in [-1, 0] else logging.WARN)#如果 rank 是 -1 或 0，设置日志级别为 logging.INFO，即显示所有信息级别及更高的日志（包括 INFO、WARNING、ERROR）。
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#如果 rank 是其他值，则设置为 logging.WARN，即只显示警告及更高的日志（包括 WARNING、ERROR）。
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# 这样设计的原因：
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# 在分布式训练中，只有主进程（通常 rank=0 或 rank=-1）需要详细的日志输出。
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# 其他工作进程只需要关注重要信息（如警告或错误），以减少冗余。
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def init_seeds(seed=0):
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# Initialize random number generator (RNG) seeds
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random.seed(seed)
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np.random.seed(seed)
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init_torch_seeds(seed)
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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 isdocker():
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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 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 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 accesability
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return True
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except OSError:
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return False
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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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print(colorstr('github: '), end='') #end='' 确保后续输出不会换行。
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try:
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assert Path('.git').exists(), 'skipping check (not a git repository)'
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assert not isdocker(), 'skipping check (Docker image)'
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assert check_online(), 'skipping check (offline)'
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cmd = 'git fetch && git config --get remote.origin.url' #git fetch：获取远程仓库的最新信息。 git config --get remote.origin.url：获取 Git 仓库的远程 URL（如 GitHub 地址）。
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url = subprocess.check_output(cmd, shell=True).decode().strip().rstrip('.git') # github repo url strip() 和 rstrip('.git')：清理字符串，移除多余字符。
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branch = subprocess.check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out 获取当前 Git 分支名称。 git rev-parse --abbrev-ref HEAD：返回当前分支的名字。
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n = int(subprocess.check_output(f'git rev-list {branch}..origin/master --count', shell=True)) # commits behind 获取当前分支落后于远程主分支（origin/master）的提交数量。 git rev-list：列出两个分支之间的提交差异，并计数。
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if n > 0: #如果有落后提交，提示用户执行 git pull 更新代码。
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s = f"⚠️ WARNING: code is out of date by {n} commit{'s' * (n > 1)}. " #\ # 如果代码最新，提示用户仓库与远程同步。
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f"Use 'git pull' to update or 'git clone {url}' to download latest."
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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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except Exception as e:
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print(e)
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def check_requirements(requirements='requirements.txt', exclude=()):
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# Check installed dependencies meet requirements (pass *.txt file or list of packages)
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import pkg_resources as pkg
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prefix = colorstr('red', 'bold', 'requirements:')
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if isinstance(requirements, (str, Path)): # requirements.txt file
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file = Path(requirements)
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if not file.exists():
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print(f"{prefix} {file.resolve()} not found, check failed.")
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return
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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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n += 1
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print(f"{prefix} {e.req} not found and is required by YOLOv5, attempting auto-update...")
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print(subprocess.check_output(f"pip install '{e.req}'", shell=True).decode())
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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)) # emoji-safe
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def check_img_size(img_size, s=32):
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# Verify img_size is a multiple of stride s
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new_size = make_divisible(img_size, int(s)) # ceil gs-multiple
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if new_size != img_size:
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print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, 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 isdocker(), 'cv2.imshow() is disabled in Docker 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_file(file):
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# Search for file if not found
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if os.path.isfile(file) or file == '':
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return file
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else:
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files = glob.glob('./**/' + file, recursive=True) # find file
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assert len(files), 'File Not Found: %s' % file # assert file was found
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assert len(files) == 1, "Multiple files match '%s', specify exact path: %s" % (file, files) # assert unique
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return files[0] # return file
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def check_dataset(dict):
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# Download dataset if not found locally
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val, s = dict.get('val'), dict.get('download')
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if val and len(val):
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val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path
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if not all(x.exists() for x in val):
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print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()])
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if s and len(s): # download script
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print('Downloading %s ...' % s)
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if s.startswith('http') and s.endswith('.zip'): # URL
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f = Path(s).name # filename
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torch.hub.download_url_to_file(s, f)
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r = os.system('unzip -q %s -d ../ && rm %s' % (f, f)) # unzip
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else: # bash script
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r = os.system(s)
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print('Dataset autodownload %s\n' % ('success' if r == 0 else 'failure')) # analyze return value
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else:
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raise Exception('Dataset not found.')
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def make_divisible(x, divisor):
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# Returns x evenly divisible by divisor
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return math.ceil(x / divisor) * divisor #result = math.ceil(2.0) # 结果是 2
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#math.ceil 是一个向上取整的函数，确保某些数值（如图像尺寸）是某个倍数的整数。
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def clean_str(s):
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# Cleans a string by replacing special characters with underscore _
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return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s)
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def one_cycle(y1=0.0, y2=1.0, steps=100):
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# lambda function for sinusoidal ramp from y1 to y2
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return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
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def colorstr(*input):
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# Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world')
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*args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string
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colors = {'black': '\033[30m', # basic colors
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'red': '\033[31m',
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'green': '\033[32m',
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'yellow': '\033[33m',
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'blue': '\033[34m',
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'magenta': '\033[35m',
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'cyan': '\033[36m',
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'white': '\033[37m',
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'bright_black': '\033[90m', # bright colors
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'bright_red': '\033[91m',
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'bright_green': '\033[92m',
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'bright_yellow': '\033[93m',
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'bright_blue': '\033[94m',
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'bright_magenta': '\033[95m',
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'bright_cyan': '\033[96m',
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'bright_white': '\033[97m',
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'end': '\033[0m', # misc
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'bold': '\033[1m',
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'underline': '\033[4m'}
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return ''.join(colors[x] for x in args) + f'{string}' + colors['end']
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def labels_to_class_weights(labels, nc=80):
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# Get class weights (inverse frequency) from training labels
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if labels[0] is None: # no labels loaded
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return torch.Tensor()
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labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO
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classes = labels[:, 0].astype(np.int) # labels = [class xywh]
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weights = np.bincount(classes, minlength=nc) # occurrences per class
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# Prepend gridpoint count (for uCE training)
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# gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image
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# weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start
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weights[weights == 0] = 1 # replace empty bins with 1
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weights = 1 / weights # number of targets per class
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weights /= weights.sum() # normalize
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return torch.from_numpy(weights)
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def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
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# Produces image weights based on class_weights and image contents
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class_counts = np.array([np.bincount(x[:, 0].astype(np.int), minlength=nc) for x in labels])
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image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1)
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# index = random.choices(range(n), weights=image_weights, k=1) # weight image sample
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return image_weights
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def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper)
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# https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
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# a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
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# b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
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# x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco
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# x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet
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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,
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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,
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64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
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return x
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def xyxy2xywh(x):
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# Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center
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y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center
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y[:, 2] = x[:, 2] - x[:, 0] # width
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y[:, 3] = x[:, 3] - x[:, 1] # height
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return y
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def xywh2xyxy(x):
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# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
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y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
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y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
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y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
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return y
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def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
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# Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x
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y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh # top left y
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y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw # bottom right x
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y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh # bottom right y
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return y
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def xyn2xy(x, w=640, h=640, padw=0, padh=0):
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# Convert normalized segments into pixel segments, shape (n,2)
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y[:, 0] = w * x[:, 0] + padw # top left x
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y[:, 1] = h * x[:, 1] + padh # top left y
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return y
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def segment2box(segment, width=640, height=640):
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# Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
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x, y = segment.T # segment xy
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inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
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x, y, = x[inside], y[inside]
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return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy
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def segments2boxes(segments):
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# Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
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boxes = []
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for s in segments:
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x, y = s.T # segment xy
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boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy
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return xyxy2xywh(np.array(boxes)) # cls, xywh
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def resample_segments(segments, n=1000):
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# Up-sample an (n,2) segment
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for i, s in enumerate(segments):
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x = np.linspace(0, len(s) - 1, n)
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xp = np.arange(len(s))
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segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy
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return segments
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def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
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# Rescale coords (xyxy) from img1_shape to img0_shape
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if ratio_pad is None: # calculate from img0_shape
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gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
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pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
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else:
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gain = ratio_pad[0][0]
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pad = ratio_pad[1]
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coords[:, [0, 2]] -= pad[0] # x padding
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coords[:, [1, 3]] -= pad[1] # y padding
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coords[:, :4] /= gain
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clip_coords(coords, img0_shape)
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return coords
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def clip_coords(boxes, img_shape):
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# Clip bounding xyxy bounding boxes to image shape (height, width)
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boxes[:, 0].clamp_(0, img_shape[1]) # x1
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boxes[:, 1].clamp_(0, img_shape[0]) # y1
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boxes[:, 2].clamp_(0, img_shape[1]) # x2
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boxes[:, 3].clamp_(0, img_shape[0]) # y2
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def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
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# Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
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box2 = box2.T
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# Get the coordinates of bounding boxes
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if x1y1x2y2: # x1, y1, x2, y2 = box1
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b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
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b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
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else: # transform from xywh to xyxy
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b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2
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b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2
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b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2
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b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2
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# Intersection area
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inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
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(torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
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# Union Area
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w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
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w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
|
||
union = w1 * h1 + w2 * h2 - inter + eps
|
||
|
||
iou = inter / union
|
||
if GIoU or DIoU or CIoU:
|
||
cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width
|
||
ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height
|
||
if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
|
||
c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared
|
||
rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 +
|
||
(b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center distance squared
|
||
if DIoU:
|
||
return iou - rho2 / c2 # DIoU
|
||
elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
|
||
v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
|
||
with torch.no_grad():
|
||
alpha = v / (v - iou + (1 + eps))
|
||
return iou - (rho2 / c2 + v * alpha) # CIoU
|
||
else: # GIoU https://arxiv.org/pdf/1902.09630.pdf
|
||
c_area = cw * ch + eps # convex area
|
||
return iou - (c_area - union) / c_area # GIoU
|
||
else:
|
||
return iou # IoU
|
||
|
||
|
||
def box_iou(box1, box2):
|
||
# https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
|
||
"""
|
||
Return intersection-over-union (Jaccard index) of boxes.
|
||
Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
|
||
Arguments:
|
||
box1 (Tensor[N, 4])
|
||
box2 (Tensor[M, 4])
|
||
Returns:
|
||
iou (Tensor[N, M]): the NxM matrix containing the pairwise
|
||
IoU values for every element in boxes1 and boxes2
|
||
"""
|
||
|
||
def box_area(box):
|
||
# box = 4xn
|
||
return (box[2] - box[0]) * (box[3] - box[1])
|
||
|
||
area1 = box_area(box1.T)
|
||
area2 = box_area(box2.T)
|
||
|
||
# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
|
||
inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
|
||
return inter / (area1[:, None] + area2 - inter) # iou = inter / (area1 + area2 - inter)
|
||
|
||
|
||
def wh_iou(wh1, wh2):
|
||
# Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2
|
||
wh1 = wh1[:, None] # [N,1,2]
|
||
wh2 = wh2[None] # [1,M,2]
|
||
inter = torch.min(wh1, wh2).prod(2) # [N,M]
|
||
return inter / (wh1.prod(2) + wh2.prod(2) - inter) # iou = inter / (area1 + area2 - inter)
|
||
|
||
|
||
def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
|
||
labels=()):
|
||
"""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
|
||
|
||
# Settings
|
||
min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height
|
||
max_det = 300 # maximum number of detections per image
|
||
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(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''):
|
||
# Print mutation results to evolve.txt (for use with train.py --evolve)
|
||
a = '%10s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys
|
||
b = '%10.3g' * len(hyp) % tuple(hyp.values()) # hyperparam values
|
||
c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3)
|
||
print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
|
||
|
||
if bucket:
|
||
url = 'gs://%s/evolve.txt' % bucket
|
||
if gsutil_getsize(url) > (os.path.getsize('evolve.txt') if os.path.exists('evolve.txt') else 0):
|
||
os.system('gsutil cp %s .' % url) # download evolve.txt if larger than local
|
||
|
||
with open('evolve.txt', 'a') as f: # append result
|
||
f.write(c + b + '\n')
|
||
x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows
|
||
x = x[np.argsort(-fitness(x))] # sort
|
||
np.savetxt('evolve.txt', x, '%10.3g') # save sort by fitness
|
||
|
||
# Save yaml
|
||
for i, k in enumerate(hyp.keys()):
|
||
hyp[k] = float(x[0, i + 7])
|
||
with open(yaml_file, 'w') as f:
|
||
results = tuple(x[0, :7])
|
||
c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3)
|
||
f.write('# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: ' % len(x) + c + '\n\n')
|
||
yaml.dump(hyp, f, sort_keys=False)
|
||
|
||
if bucket:
|
||
os.system('gsutil cp evolve.txt %s gs://%s' % (yaml_file, bucket)) # upload
|
||
|
||
|
||
def apply_classifier(x, model, img, im0):
|
||
# applies 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('test%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 increment_path(path, exist_ok=True, sep=''):
|
||
# Increment path, i.e. runs/exp --> runs/exp{sep}0, runs/exp{sep}1 etc.
|
||
path = Path(path) # os-agnostic
|
||
if (path.exists() and exist_ok) or (not path.exists()):
|
||
return str(path)
|
||
else:
|
||
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
|
||
return f"{path}{sep}{n}" # update path
|