# YOLOv5 🚀 by Ultralytics, GPL-3.0 license """ Validate a trained YOLOv5 detection model on a detection dataset Usage: $ python val.py --weights yolov5s.pt --data coco128.yaml --img 640 Usage - formats: $ python val.py --weights yolov5s.pt # PyTorch yolov5s.torchscript # TorchScript yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn yolov5s.xml # OpenVINO yolov5s.engine # TensorRT yolov5s.mlmodel # CoreML (macOS-only) yolov5s_saved_model # TensorFlow SavedModel yolov5s.pb # TensorFlow GraphDef yolov5s.tflite # TensorFlow Lite yolov5s_edgetpu.tflite # TensorFlow Edge TPU """ import argparse import json import os import sys import ast import yaml import shutil from pathlib import Path import numpy as np import torch from tqdm import tqdm FILE = Path(__file__).resolve() ROOT = FILE.parents[0] # YOLOv5 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative from app.yolov5.models.common import DetectMultiBackend from app.yolov5.utils.callbacks import Callbacks from app.yolov5.utils.dataloaders import create_dataloader from app.yolov5.utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_yaml, coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args, scale_coords, xywh2xyxy, xyxy2xywh) from app.yolov5.utils.metrics import ConfusionMatrix, ap_per_class, box_iou from app.yolov5.utils.plots import output_to_target, plot_images, plot_val_study from app.yolov5.utils.torch_utils import select_device, smart_inference_mode def save_one_txt(predn, save_conf, shape, file): # Save one txt result gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh for *xyxy, conf, cls in predn.tolist(): xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format with open(file, 'a') as f: f.write(('%g ' * len(line)).rstrip() % line + '\n') def save_one_json(predn, jdict, path, class_map): # Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236} image_id = int(path.stem) if path.stem.isnumeric() else path.stem box = xyxy2xywh(predn[:, :4]) # xywh box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner for p, b in zip(predn.tolist(), box.tolist()): jdict.append({ 'image_id': image_id, 'category_id': class_map[int(p[5])], 'bbox': [round(x, 3) for x in b], 'score': round(p[4], 5)}) def process_batch(detections, labels, iouv): """ Return correct predictions matrix. Both sets of boxes are in (x1, y1, x2, y2) format. Arguments: detections (Array[N, 6]), x1, y1, x2, y2, conf, class labels (Array[M, 5]), class, x1, y1, x2, y2 Returns: correct (Array[N, 10]), for 10 IoU levels """ correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool) iou = box_iou(labels[:, 1:], detections[:, :4]) correct_class = labels[:, 0:1] == detections[:, 5] for i in range(len(iouv)): x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match if x[0].shape[0]: matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou] if x[0].shape[0] > 1: matches = matches[matches[:, 2].argsort()[::-1]] matches = matches[np.unique(matches[:, 1], return_index=True)[1]] # matches = matches[matches[:, 2].argsort()[::-1]] matches = matches[np.unique(matches[:, 0], return_index=True)[1]] correct[matches[:, 1].astype(int), i] = True return torch.tensor(correct, dtype=torch.bool, device=iouv.device) @smart_inference_mode() def run(id, data, weights=None, # model.pt path(s) version=1, output='', batch_size=32, # batch size imgsz=640, # inference size (pixels) conf_thres=0.001, # confidence threshold iou_thres=0.6, # NMS IoU threshold task='val', # train, val, test, speed or study device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu workers=8, # max dataloader workers (per RANK in DDP mode) single_cls=False, # treat as single-class dataset augment=False, # augmented inference verbose=False, # verbose output save_txt=False, # save results to *.txt save_hybrid=False, # save label+prediction hybrid results to *.txt save_conf=False, # save confidences in --save-txt labels save_json=False, # save a COCO-JSON results file project=ROOT / 'runs/val', # save to project/name name='exp', # save to project/name exist_ok=False, # existing project/name ok, do not increment half=True, # use FP16 half-precision inference dnn=False, # use OpenCV DNN for ONNX inference model=None, dataloader=None, save_dir=Path(''), plots=True, callbacks=Callbacks(), compute_loss=None, ): # Initialize/load model and set device training = model is not None if training: # called by train.py device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model half &= device.type != 'cpu' # half precision only supported on CUDA model.half() if half else model.float() else: # called directly device = select_device(device, batch_size=batch_size) # Directories with open(data, errors='ignore') as f: coco_dict = yaml.safe_load(f) out_path = coco_dict['val'].split('trained') #out_dir = out_path[0] + 'val_results/' + 'v' + str(version) + '/' out_dir = output if os.path.exists(out_dir): shutil.rmtree(out_dir) # delete output folder os.makedirs(out_dir) # make new output folder save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir # Load model model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine imgsz = check_img_size(imgsz, s=stride) # check image size half = model.fp16 # FP16 supported on limited backends with CUDA if engine: batch_size = model.batch_size else: device = model.device if not (pt or jit): batch_size = 1 # export.py models default to batch-size 1 LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models') # Data data = check_dataset(data) # check # Configure model.eval() cuda = device.type != 'cpu' is_coco = isinstance(data.get('val'), str) and data['val'].endswith(f'coco{os.sep}val2017.txt') # COCO dataset nc = 1 if single_cls else int(data['nc']) # number of classes iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95 niou = iouv.numel() # Dataloader if not training: if pt and not single_cls: # check --weights are trained on --data ncm = model.model.nc assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \ f'classes). Pass correct combination of --weights and --data that are trained together.' model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup pad = 0.0 if task in ('speed', 'benchmark') else 0.5 rect = False if task == 'benchmark' else pt # square inference for benchmarks task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images #data[task]:验证数据路径 dataloader = create_dataloader(data[task], imgsz, batch_size, stride, single_cls, pad=pad, rect=rect, workers=workers, prefix=colorstr(f'{task}: '))[0] seen = 0 confusion_matrix = ConfusionMatrix(nc=nc) names = model.names if hasattr(model, 'names') else model.module.names # get class names if isinstance(names, (list, tuple)): # old format names = dict(enumerate(names)) class_map = coco80_to_coco91_class() if is_coco else list(range(1000)) s = ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'P', 'R', 'mAP@.5', 'mAP@.5:.95') dt, p, r, f1, mp, mr,mf,map50, map = (Profile(), Profile(), Profile()), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 loss = torch.zeros(3, device=device) jdict, stats, ap, ap_class = [], [], [], [] callbacks.run('on_val_start') pbar = tqdm(dataloader, desc=s, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar val_results_list = [] for batch_i, (im, targets, paths, shapes) in enumerate(pbar): callbacks.run('on_val_batch_start') img_name = paths[0].split('val') result_path = out_dir + img_name[1][1:] with dt[0]: if cuda: im = im.to(device, non_blocking=True) targets = targets.to(device) im = im.half() if half else im.float() # uint8 to fp16/32 im /= 255 # 0 - 255 to 0.0 - 1.0 nb, _, height, width = im.shape # batch size, channels, height, width # Inference with dt[1]: out, train_out = model(im) if compute_loss else (model(im, augment=augment), None) # Loss if compute_loss: loss += compute_loss(train_out, targets)[1] # box, obj, cls # NMS targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling with dt[2]: out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls) # Metrics for si, pred in enumerate(out): labels = targets[targets[:, 0] == si, 1:] nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions path, shape = Path(paths[si]), shapes[si][0] correct = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init seen += 1 if npr == 0: if nl: stats.append((correct, *torch.zeros((2, 0), device=device), labels[:, 0])) if plots: confusion_matrix.process_batch(detections=None, labels=labels[:, 0]) continue # Predictions if single_cls: pred[:, 5] = 0 predn = pred.clone() scale_coords(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred # Evaluate if nl: tbox = xywh2xyxy(labels[:, 1:5]) # target boxes scale_coords(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels correct = process_batch(predn, labelsn, iouv) if plots: confusion_matrix.process_batch(predn, labelsn) stats.append((correct, pred[:, 4], pred[:, 5], labels[:, 0])) # (correct, conf, pcls, tcls) # Save/log if save_txt: save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt') if save_json: save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary callbacks.run('on_val_image_end', pred, predn, path, names, im[si]) # Plot images if plots: # and batch_i < 3 #plot_images(im, targets, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names) # labels plot_images(im, output_to_target(out), paths, result_path, names) # pred val_results_list.append(result_path) #保存结果图片路径列表 #print(names) #cv2.imwrite callbacks.run('on_val_batch_end') # Compute metrics stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy test_result_all = [] if len(stats) and stats[0].any(): tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names) ap50, ap = ap[:, 0], ap.mean(1) # AP@0.5, AP@0.5:0.95 mp, mr, mf,map50, map = p.mean(), r.mean(), f1.mean(), ap50.mean(), ap.mean() nt = np.bincount(stats[3].astype(int), minlength=nc) # number of targets per class test_result_all.append(mp) test_result_all.append(mr) test_result_all.append(mf) print(test_result_all) else: test_result_all = [0.0,0.0,0.0] nt = torch.zeros(1) # Print results pf = '%22s' + '%11i' * 2 + '%11.3g' * 4 # print format LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map)) if nt.sum() == 0: LOGGER.warning(f'WARNING: no labels found in {task} set, can not compute metrics without labels ⚠️') # Print results per class test_result_per_class = [] #每一类的指标 if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats): for i, c in enumerate(ap_class): LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i])) per_class_info = {'name': 'NO', 'target_num': 0, 'P': 0.0, 'R': 0.0, 'F1': 0.0} # per_class_info = {'name': 'T1', 'target_num': 10, 'P': 0.5, 'R': 0.5, 'F1': 0.5} # print(pf2 % (names[c], seen, nt[c], p[i], r[i], f1[i], ap50[i], ap[i])) ####每一类的精度指标 per_class_info['name'] = names[c] per_class_info['target_num'] = nt[c] #标签中的目标数量 per_class_info['P'] = p[i] per_class_info['R'] = r[i] per_class_info['F1'] = f1[i] test_result_per_class.append(per_class_info) # 传参 print(test_result_per_class) #save_test_report_result(pro, seen, test_result_all, test_result_per_class, val_results_list) # Print speeds t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image if not training: shape = (batch_size, 3, imgsz, imgsz) LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t) # Plots if plots: confusion_matrix.plot(save_dir=save_dir, names=list(names.values())) callbacks.run('on_val_end') # Save JSON if save_json and len(jdict): w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json') # annotations json pred_json = str(save_dir / f"{w}_predictions.json") # predictions json LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...') with open(pred_json, 'w') as f: json.dump(jdict, f) try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb check_requirements(['pycocotools']) from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval anno = COCO(anno_json) # init annotations api pred = anno.loadRes(pred_json) # init predictions api eval = COCOeval(anno, pred, 'bbox') if is_coco: eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # image IDs to evaluate eval.evaluate() eval.accumulate() eval.summarize() map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5) except Exception as e: LOGGER.info(f'pycocotools unable to run: {e}') # Return results model.float() # for training # if not training: # s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' # LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") maps = np.zeros(nc) + map for i, c in enumerate(ap_class): maps[c] = ap[i] return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t def parse_opt(weights,img_size,batch_size,device,output,id): parser = argparse.ArgumentParser() parser.add_argument('--id', nargs='+', type=str, default=id, help='model path(s)') parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') parser.add_argument('--weights', nargs='+', type=str, default=weights, help='model.pt path(s)') parser.add_argument('--output', type=str, default=output, help='file/dir/URL/glob, 0 for webcam') parser.add_argument('--version', type=int, default='1', help='*.data path') parser.add_argument('--batch-size', type=int, default=batch_size, help='batch size') parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=img_size, help='inference size (pixels)') parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.6, help='NMS IoU threshold') parser.add_argument('--task', default='val', help='train, val, test, speed or study') parser.add_argument('--device', default=device, help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset') parser.add_argument('--augment', action='store_true', help='augmented inference') parser.add_argument('--verbose', action='store_true', help='report mAP by class') parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt') parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file') parser.add_argument('--project', default=ROOT / 'runs/val', help='save to project/name') parser.add_argument('--name', default='exp', help='save to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') opt = parser.parse_args() opt.data = check_yaml(opt.data) # check YAML opt.save_json |= opt.data.endswith('coco.yaml') opt.save_txt |= opt.save_hybrid print_args(vars(opt)) return opt def main(opt,id): #check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop')) if opt.task in ('train', 'val', 'test'): # run normally if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466 LOGGER.info(f'WARNING: confidence threshold {opt.conf_thres} > 0.001 produces invalid results ⚠️') if opt.save_hybrid: LOGGER.info('WARNING: --save-hybrid will return high mAP from hybrid labels, not from predictions alone ⚠️') run(**vars(opt)) else: weights = opt.weights if isinstance(opt.weights, list) else [opt.weights] opt.half = True # FP16 for fastest results if opt.task == 'speed': # speed benchmarks # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt... opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False for opt.weights in weights: run(**vars(opt), plots=False) elif opt.task == 'study': # speed vs mAP benchmarks # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt... for opt.weights in weights: f = f'study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt' # filename to save to x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis for opt.imgsz in x: # img-size LOGGER.info(f'\nRunning {f} --imgsz {opt.imgsz}...') r, _, t = run(**vars(opt), plots=False) y.append(r + t) # results and times np.savetxt(f, y, fmt='%10.4g') # save os.system('zip -r study.zip study_*.txt') plot_val_study(x=x) # plot #启动验证算法，参数：pro:项目编号，path:模型路径,v_num:版本号 def validate_start(weights,img_size,batch_size,device,output,id): opt = parse_opt(weights,img_size,batch_size,device,output,id) main(opt,id) if __name__ == "__main__": # opt = parse_opt() # main(opt) v_num = 2 path = 'E:/aicheck/weights/标识贴检测_R-ODXv33.pt' pro ='1234' opt = parse_opt(v_num, path) main(opt, pro)