648 lines
26 KiB
Python
648 lines
26 KiB
Python
# 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 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 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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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(
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format="%(message)s",
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level=logging.INFO if rank in [-1, 0] else logging.WARN)
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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 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", 53)) # 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='')
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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 Path('/workspace').exists(), 'skipping check (Docker image)' # not Path('/.dockerenv').exists()
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assert check_online(), 'skipping check (offline)'
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cmd = 'git fetch && git config --get remote.origin.url' # github repo url
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url = subprocess.check_output(cmd, shell=True).decode()[:-1]
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cmd = 'git rev-list $(git rev-parse --abbrev-ref HEAD)..origin/master --count' # commits behind
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n = int(subprocess.check_output(cmd, shell=True))
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if n > 0:
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print(f"⚠️ WARNING: code is out of date by {n} {'commits' if n > 1 else 'commmit'}. "
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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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print(f'up to date with {url} ✅')
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except Exception as e:
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print(e)
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def check_requirements(file='requirements.txt'):
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# Check installed dependencies meet requirements
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import pkg_resources
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requirements = pkg_resources.parse_requirements(Path(file).open())
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requirements = [x.name + ''.join(*x.specs) if len(x.specs) else x.name for x in requirements]
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pkg_resources.require(requirements) # DistributionNotFound or VersionConflict exception if requirements not met
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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_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
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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=32, padh=32):
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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 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-9):
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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
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union = w1 * h1 + w2 * h2 - inter + eps
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iou = inter / union
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if GIoU or DIoU or CIoU:
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# convex (smallest enclosing box) width
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cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1)
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ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height
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if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
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c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared
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rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 +
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(b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center distance squared
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if DIoU:
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return iou - rho2 / c2 # DIoU
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elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
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v = (4 / math.pi ** 2) * \
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torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
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with torch.no_grad():
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alpha = v / ((1 + eps) - iou + v)
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return iou - (rho2 / c2 + v * alpha) # CIoU
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else: # GIoU https://arxiv.org/pdf/1902.09630.pdf
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c_area = cw * ch + eps # convex area
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return iou - (c_area - union) / c_area # GIoU
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else:
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return iou # IoU
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def box_iou(box1, box2):
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# https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
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"""
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Return intersection-over-union (Jaccard index) of boxes.
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Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
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Arguments:
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box1 (Tensor[N, 4])
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box2 (Tensor[M, 4])
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Returns:
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iou (Tensor[N, M]): the NxM matrix containing the pairwise
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IoU values for every element in boxes1 and boxes2
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"""
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def box_area(box):
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# box = 4xn
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return (box[2] - box[0]) * (box[3] - box[1])
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area1 = box_area(box1.T)
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area2 = box_area(box2.T)
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# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
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inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) -
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torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
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# iou = inter / (area1 + area2 - inter)
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return inter / (area1[:, None] + area2 - inter)
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def wh_iou(wh1, wh2):
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# Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2
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wh1 = wh1[:, None] # [N,1,2]
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wh2 = wh2[None] # [1,M,2]
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inter = torch.min(wh1, wh2).prod(2) # [N,M]
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# iou = inter / (area1 + area2 - inter)
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return inter / (wh1.prod(2) + wh2.prod(2) - inter)
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def jaccard_diou(box_a, box_b, iscrowd:bool=False):
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use_batch = True
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if box_a.dim() == 2:
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use_batch = False
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box_a = box_a[None, ...]
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box_b = box_b[None, ...]
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inter = intersect(box_a, box_b)
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area_a = ((box_a[:, :, 2]-box_a[:, :, 0]) *
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(box_a[:, :, 3]-box_a[:, :, 1])).unsqueeze(2).expand_as(inter) # [A,B]
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area_b = ((box_b[:, :, 2]-box_b[:, :, 0]) *
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(box_b[:, :, 3]-box_b[:, :, 1])).unsqueeze(1).expand_as(inter) # [A,B]
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union = area_a + area_b - inter
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x1 = ((box_a[:, :, 2]+box_a[:, :, 0]) / 2).unsqueeze(2).expand_as(inter)
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y1 = ((box_a[:, :, 3]+box_a[:, :, 1]) / 2).unsqueeze(2).expand_as(inter)
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x2 = ((box_b[:, :, 2]+box_b[:, :, 0]) / 2).unsqueeze(1).expand_as(inter)
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y2 = ((box_b[:, :, 3]+box_b[:, :, 1]) / 2).unsqueeze(1).expand_as(inter)
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t1 = box_a[:, :, 1].unsqueeze(2).expand_as(inter)
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b1 = box_a[:, :, 3].unsqueeze(2).expand_as(inter)
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l1 = box_a[:, :, 0].unsqueeze(2).expand_as(inter)
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r1 = box_a[:, :, 2].unsqueeze(2).expand_as(inter)
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t2 = box_b[:, :, 1].unsqueeze(1).expand_as(inter)
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b2 = box_b[:, :, 3].unsqueeze(1).expand_as(inter)
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l2 = box_b[:, :, 0].unsqueeze(1).expand_as(inter)
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r2 = box_b[:, :, 2].unsqueeze(1).expand_as(inter)
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cr = torch.max(r1, r2)
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cl = torch.min(l1, l2)
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ct = torch.min(t1, t2)
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cb = torch.max(b1, b2)
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D = (((x2 - x1)**2 + (y2 - y1)**2) / ((cr-cl)**2 + (cb-ct)**2 + 1e-7))
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out = inter / area_a if iscrowd else inter / (union + 1e-7) - D ** 0.7
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return out if use_batch else out.squeeze(0)
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|
|
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def non_max_suppression_face(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()):
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|
"""Performs Non-Maximum Suppression (NMS) on inference results
|
|
Returns:
|
|
detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
|
|
"""
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|
|
|
nc = prediction.shape[2] - 13 # number of classes
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xc = prediction[..., 4] > conf_thres # candidates
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|
|
|
# Settings
|
|
min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height
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|
time_limit = 10.0 # seconds to quit after
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|
redundant = True # require redundant detections
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|
multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img)
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|
multi_label=False
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|
merge = False # use merge-NMS
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|
|
|
t = time.time()
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|
output = [torch.zeros((0, 14), device=prediction.device)] * prediction.shape[0]
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|
for xi, x in enumerate(prediction): # image index, image inference
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|
# Apply constraints
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|
# x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
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x = x[xc[xi]] # confidence
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|
|
|
# Cat apriori labels if autolabelling
|
|
if labels and len(labels[xi]):
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|
l = labels[xi]
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|
v = torch.zeros((len(l), nc + 13), device=x.device)
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v[:, :4] = l[:, 1:5] # box
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|
v[:, 4] = 1.0 # conf
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|
v[range(len(l)), l[:, 0].long() + 13] = 1.0 # cls
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|
x = torch.cat((x, v), 0)
|
|
|
|
# If none remain process next image
|
|
if not x.shape[0]:
|
|
continue
|
|
|
|
# Compute conf
|
|
x[:, 13:] *= x[:, 4:5] # conf = obj_conf * cls_conf
|
|
|
|
# Box (center x, center y, width, height) to (x1, y1, x2, y2)
|
|
box = xywh2xyxy(x[:, :4])
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|
|
|
# Detections matrix nx6 (xyxy, conf, landmarks, cls)
|
|
if multi_label:
|
|
i, j = (x[:, 13:] > conf_thres).nonzero(as_tuple=False).T
|
|
x = torch.cat((box[i], x[i, j + 13, None], x[i, 5:13] ,j[:, None].float()), 1)
|
|
else: # best class only
|
|
conf, j = x[:, 13:].max(1, keepdim=True)
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|
x = torch.cat((box, conf, x[:, 5:13], j.float()), 1)[conf.view(-1) > conf_thres]
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|
|
|
# Filter by class
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|
if classes is not None:
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|
x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
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|
|
|
# If none remain process next image
|
|
n = x.shape[0] # number of boxes
|
|
if not n:
|
|
continue
|
|
|
|
# Batched NMS
|
|
c = x[:, 13:14] * (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
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|
#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:
|
|
break # time limit exceeded
|
|
|
|
return output
|
|
|
|
|
|
def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()):
|
|
"""Performs Non-Maximum Suppression (NMS) on inference results
|
|
|
|
Returns:
|
|
detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
|
|
"""
|
|
|
|
nc = prediction.shape[2] - 5 # number of classes
|
|
xc = prediction[..., 4] > conf_thres # candidates
|
|
|
|
# Settings
|
|
# (pixels) minimum and maximum box width and height
|
|
min_wh, max_wh = 2, 4096
|
|
#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
|
|
x = x[x[:, 4].argsort(descending=True)] # sort by confidence
|
|
|
|
# Batched NMS
|
|
c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
|
|
boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
|
|
i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
|
|
#if i.shape[0] > max_det: # limit detections
|
|
# i = i[:max_det]
|
|
if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)
|
|
# update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
|
|
iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
|
|
weights = iou * scores[None] # box weights
|
|
x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
|
|
if redundant:
|
|
i = i[iou.sum(1) > 1] # require redundancy
|
|
|
|
output[xi] = x[i]
|
|
if (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='weights/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'))
|
|
for key in 'optimizer', 'training_results', 'wandb_id':
|
|
x[key] = 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('Optimizer stripped from %s,%s %.1fMB' % (f, (' saved as %s,' % s) if s else '', 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)
|
|
|
|
# BGR to RGB, to 3x416x416
|
|
im = im[:, :, ::-1].transpose(2, 0, 1)
|
|
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
|
|
# retain matching class detections
|
|
x[i] = x[i][pred_cls1 == pred_cls2]
|
|
|
|
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
|