# YOLOR general utils import glob import logging import math import os import platform import random import re import subprocess import time from pathlib import Path import cv2 import numpy as np import pandas as pd import torch import torchvision import yaml from utils.google_utils import gsutil_getsize from utils.metrics import fitness from utils.torch_utils import init_torch_seeds # Settings torch.set_printoptions(linewidth=320, precision=5, profile='long') np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5 pd.options.display.max_columns = 10 cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) os.environ['NUMEXPR_MAX_THREADS'] = str(min(os.cpu_count(), 8)) # NumExpr max threads def set_logging(rank=-1): logging.basicConfig( format="%(message)s", level=logging.INFO if rank in [-1, 0] else logging.WARN) def init_seeds(seed=0): # Initialize random number generator (RNG) seeds random.seed(seed) np.random.seed(seed) init_torch_seeds(seed) def get_latest_run(search_dir='.'): # Return path to most recent 'last.pt' in /runs (i.e. to --resume from) last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True) return max(last_list, key=os.path.getctime) if last_list else '' def isdocker(): # Is environment a Docker container return Path('/workspace').exists() # or Path('/.dockerenv').exists() def emojis(str=''): # Return platform-dependent emoji-safe version of string return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str def check_online(): # Check internet connectivity import socket try: socket.create_connection(("1.1.1.1", 443), 5) # check host accesability return True except OSError: return False def check_git_status(): # Recommend 'git pull' if code is out of date print(colorstr('github: '), end='') try: assert Path('.git').exists(), 'skipping check (not a git repository)' assert not isdocker(), 'skipping check (Docker image)' assert check_online(), 'skipping check (offline)' cmd = 'git fetch && git config --get remote.origin.url' url = subprocess.check_output(cmd, shell=True).decode().strip().rstrip('.git') # github repo url branch = subprocess.check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out n = int(subprocess.check_output(f'git rev-list {branch}..origin/master --count', shell=True)) # commits behind if n > 0: s = f"⚠️ WARNING: code is out of date by {n} commit{'s' * (n > 1)}. " \ f"Use 'git pull' to update or 'git clone {url}' to download latest." else: s = f'up to date with {url} ✅' print(emojis(s)) # emoji-safe except Exception as e: print(e) def check_requirements(requirements='requirements.txt', exclude=()): # Check installed dependencies meet requirements (pass *.txt file or list of packages) import pkg_resources as pkg prefix = colorstr('red', 'bold', 'requirements:') if isinstance(requirements, (str, Path)): # requirements.txt file file = Path(requirements) if not file.exists(): print(f"{prefix} {file.resolve()} not found, check failed.") return requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(file.open()) if x.name not in exclude] else: # list or tuple of packages requirements = [x for x in requirements if x not in exclude] n = 0 # number of packages updates for r in requirements: try: pkg.require(r) except Exception as e: # DistributionNotFound or VersionConflict if requirements not met n += 1 print(f"{prefix} {e.req} not found and is required by YOLOR, attempting auto-update...") print(subprocess.check_output(f"pip install '{e.req}'", shell=True).decode()) if n: # if packages updated source = file.resolve() if 'file' in locals() else requirements s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \ f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n" print(emojis(s)) # emoji-safe def check_img_size(img_size, s=32): # Verify img_size is a multiple of stride s new_size = make_divisible(img_size, int(s)) # ceil gs-multiple if new_size != img_size: print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, new_size)) return new_size def check_imshow(): # Check if environment supports image displays try: assert not isdocker(), 'cv2.imshow() is disabled in Docker environments' cv2.imshow('test', np.zeros((1, 1, 3))) cv2.waitKey(1) cv2.destroyAllWindows() cv2.waitKey(1) return True except Exception as e: print(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}') return False def check_file(file): # Search for file if not found if Path(file).is_file() or file == '': return file else: files = glob.glob('./**/' + file, recursive=True) # find file assert len(files), f'File Not Found: {file}' # assert file was found assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique return files[0] # return file def check_dataset(dict): # Download dataset if not found locally val, s = dict.get('val'), dict.get('download') if val and len(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 len(s): # download script print('Downloading %s ...' % s) if s.startswith('http') and s.endswith('.zip'): # URL f = Path(s).name # filename torch.hub.download_url_to_file(s, f) r = os.system('unzip -q %s -d ../ && rm %s' % (f, f)) # unzip else: # bash script r = os.system(s) print('Dataset autodownload %s\n' % ('success' if r == 0 else 'failure')) # analyze return value else: raise Exception('Dataset not found.') 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 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 xyn2xy(x, w=640, h=640, padw=0, padh=0): # Convert normalized segments into pixel segments, shape (n,2) y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) y[:, 0] = w * x[:, 0] + padw # top left x y[:, 1] = h * x[:, 1] + padh # top left y return y def segment2box(segment, width=640, height=640): # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy) x, y = segment.T # segment xy inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height) x, y, = x[inside], y[inside] return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy def segments2boxes(segments): # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh) boxes = [] for s in segments: x, y = s.T # segment xy boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy return xyxy2xywh(np.array(boxes)) # cls, xywh def resample_segments(segments, n=1000): # Up-sample an (n,2) segment for i, s in enumerate(segments): s = np.concatenate((s, s[0:1, :]), axis=0) x = np.linspace(0, len(s) - 1, n) xp = np.arange(len(s)) segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy return segments def scale_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, img_shape): # Clip bounding xyxy bounding boxes to image shape (height, width) boxes[:, 0].clamp_(0, img_shape[1]) # x1 boxes[:, 1].clamp_(0, img_shape[0]) # y1 boxes[:, 2].clamp_(0, img_shape[1]) # x2 boxes[:, 3].clamp_(0, img_shape[0]) # y2 def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4 box2 = box2.T # Get the coordinates of bounding boxes if x1y1x2y2: # x1, y1, x2, y2 = box1 b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] else: # transform from xywh to xyxy b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2 b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2 b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2 b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2 # Intersection area inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \ (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0) # Union Area w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps 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 + eps)) - torch.atan(w1 / (h1 + eps)), 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 bbox_alpha_iou(box1, box2, x1y1x2y2=False, GIoU=False, DIoU=False, CIoU=False, alpha=2, eps=1e-9): # Returns tsqrt_he IoU of box1 to box2. box1 is 4, box2 is nx4 box2 = box2.T # Get the coordinates of bounding boxes if x1y1x2y2: # x1, y1, x2, y2 = box1 b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] else: # transform from xywh to xyxy b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2 b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2 b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2 b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2 # Intersection area inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \ (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0) # Union Area w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps union = w1 * h1 + w2 * h2 - inter + eps # change iou into pow(iou+eps) # iou = inter / union iou = torch.pow(inter/union + eps, alpha) # beta = 2 * alpha 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) ** alpha + eps # convex diagonal rho_x = torch.abs(b2_x1 + b2_x2 - b1_x1 - b1_x2) rho_y = torch.abs(b2_y1 + b2_y2 - b1_y1 - b1_y2) rho2 = ((rho_x ** 2 + rho_y ** 2) / 4) ** alpha # center distance 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_ciou = v / ((1 + eps) - inter / union + v) # return iou - (rho2 / c2 + v * alpha_ciou) # CIoU return iou - (rho2 / c2 + torch.pow(v * alpha_ciou + eps, 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 c_area = torch.max(cw * ch + eps, union) # convex area return iou - torch.pow((c_area - union) / c_area + eps, alpha) # GIoU else: return iou # torch.log(iou+eps) or 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 box_giou(box1, box2): """ Return generalized intersection-over-union (Jaccard index) between two sets of boxes. Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with ``0 <= x1 < x2`` and ``0 <= y1 < y2``. Args: boxes1 (Tensor[N, 4]): first set of boxes boxes2 (Tensor[M, 4]): second set of boxes Returns: Tensor[N, M]: the NxM matrix containing the pairwise generalized 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 = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2) union = (area1[:, None] + area2 - inter) iou = inter / union lti = torch.min(box1[:, None, :2], box2[:, :2]) rbi = torch.max(box1[:, None, 2:], box2[:, 2:]) whi = (rbi - lti).clamp(min=0) # [N,M,2] areai = whi[:, :, 0] * whi[:, :, 1] return iou - (areai - union) / areai def box_ciou(box1, box2, eps: float = 1e-7): """ Return complete intersection-over-union (Jaccard index) between two sets of boxes. Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with ``0 <= x1 < x2`` and ``0 <= y1 < y2``. Args: boxes1 (Tensor[N, 4]): first set of boxes boxes2 (Tensor[M, 4]): second set of boxes eps (float, optional): small number to prevent division by zero. Default: 1e-7 Returns: Tensor[N, M]: the NxM matrix containing the pairwise complete 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 = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2) union = (area1[:, None] + area2 - inter) iou = inter / union lti = torch.min(box1[:, None, :2], box2[:, :2]) rbi = torch.max(box1[:, None, 2:], box2[:, 2:]) whi = (rbi - lti).clamp(min=0) # [N,M,2] diagonal_distance_squared = (whi[:, :, 0] ** 2) + (whi[:, :, 1] ** 2) + eps # centers of boxes x_p = (box1[:, None, 0] + box1[:, None, 2]) / 2 y_p = (box1[:, None, 1] + box1[:, None, 3]) / 2 x_g = (box2[:, 0] + box2[:, 2]) / 2 y_g = (box2[:, 1] + box2[:, 3]) / 2 # The distance between boxes' centers squared. centers_distance_squared = (x_p - x_g) ** 2 + (y_p - y_g) ** 2 w_pred = box1[:, None, 2] - box1[:, None, 0] h_pred = box1[:, None, 3] - box1[:, None, 1] w_gt = box2[:, 2] - box2[:, 0] h_gt = box2[:, 3] - box2[:, 1] v = (4 / (torch.pi ** 2)) * torch.pow((torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2) with torch.no_grad(): alpha = v / (1 - iou + v + eps) return iou - (centers_distance_squared / diagonal_distance_squared) - alpha * v def box_diou(box1, box2, eps: float = 1e-7): """ Return distance intersection-over-union (Jaccard index) between two sets of boxes. Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with ``0 <= x1 < x2`` and ``0 <= y1 < y2``. Args: boxes1 (Tensor[N, 4]): first set of boxes boxes2 (Tensor[M, 4]): second set of boxes eps (float, optional): small number to prevent division by zero. Default: 1e-7 Returns: Tensor[N, M]: the NxM matrix containing the pairwise distance 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 = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2) union = (area1[:, None] + area2 - inter) iou = inter / union lti = torch.min(box1[:, None, :2], box2[:, :2]) rbi = torch.max(box1[:, None, 2:], box2[:, 2:]) whi = (rbi - lti).clamp(min=0) # [N,M,2] diagonal_distance_squared = (whi[:, :, 0] ** 2) + (whi[:, :, 1] ** 2) + eps # centers of boxes x_p = (box1[:, None, 0] + box1[:, None, 2]) / 2 y_p = (box1[:, None, 1] + box1[:, None, 3]) / 2 x_g = (box2[:, 0] + box2[:, 2]) / 2 y_g = (box2[:, 1] + box2[:, 3]) / 2 # The distance between boxes' centers squared. centers_distance_squared = (x_p - x_g) ** 2 + (y_p - y_g) ** 2 # The distance IoU is the IoU penalized by a normalized # distance between boxes' centers squared. return iou - (centers_distance_squared / diagonal_distance_squared) 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 if nc == 1: x[:, 5:] = x[:, 4:5] # for models with one class, cls_loss is 0 and cls_conf is always 0.5, # so there is no need to multiplicate. else: 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 non_max_suppression_kpt(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, labels=(), kpt_label=False, nc=None, nkpt=None): """Runs Non-Maximum Suppression (NMS) on inference results Returns: list of detections, on (n,6) tensor per image [xyxy, conf, cls] """ if nc is None: nc = prediction.shape[2] - 5 if not kpt_label else prediction.shape[2] - 56 # 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[:, 5:5+nc] *= 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 if not kpt_label: conf, j = x[:, 5:].max(1, keepdim=True) x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] else: kpts = x[:, 6:] conf, j = x[:, 5:6].max(1, keepdim=True) x = torch.cat((box, conf, j.float(), kpts), 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