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- """Validate a trained YOLOv5 model accuracy on a custom dataset
- Usage:
- $ python path/to/val.py --data coco128.yaml --weights yolov5s.pt --img 640
- """
- import argparse
- import json
- import os
- import sys
- from pathlib import Path
- from threading import Thread
- import numpy as np
- import torch
- import yaml
- from tqdm import tqdm
- FILE = Path(__file__).absolute()
- sys.path.append(FILE.parents[0].as_posix()) # add yolov5/ to path
- from models.experimental import attempt_load
- from utils.datasets import create_dataloader
- from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, check_requirements, \
- box_iou, non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path, colorstr
- from utils.metrics import ap_per_class, ConfusionMatrix
- from utils.plots import plot_images, output_to_target, plot_study_txt
- from utils.torch_utils import select_device, time_sync
- from utils.loggers import Loggers
- 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(predictions, labels, iouv):
- # Evaluate 1 batch of predictions
- correct = torch.zeros(predictions.shape[0], len(iouv), dtype=torch.bool, device=iouv.device)
- detected = [] # label indices
- tcls, pcls = labels[:, 0], predictions[:, 5]
- nl = labels.shape[0] # number of labels
- for cls in torch.unique(tcls):
- ti = (cls == tcls).nonzero().view(-1) # label indices
- pi = (cls == pcls).nonzero().view(-1) # prediction indices
- if pi.shape[0]: # find detections
- ious, i = box_iou(predictions[pi, 0:4], labels[ti, 1:5]).max(1) # best ious, indices
- detected_set = set()
- for j in (ious > iouv[0]).nonzero():
- d = ti[i[j]] # detected label
- if d.item() not in detected_set:
- detected_set.add(d.item())
- detected.append(d) # append detections
- correct[pi[j]] = ious[j] > iouv # iou_thres is 1xn
- if len(detected) == nl: # all labels already located in image
- break
- return correct
- @torch.no_grad()
- def run(data,
- weights=None, # model.pt path(s)
- 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
- 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='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
- model=None,
- dataloader=None,
- save_dir=Path(''),
- plots=True,
- loggers=Loggers(),
- compute_loss=None,
- ):
- # Initialize/load model and set device
- training = model is not None
- if training: # called by train.py
- device = next(model.parameters()).device # get model device
- else: # called directly
- device = select_device(device, batch_size=batch_size)
- # Directories
- 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 = attempt_load(weights, map_location=device) # load FP32 model
- gs = max(int(model.stride.max()), 32) # grid size (max stride)
- imgsz = check_img_size(imgsz, s=gs) # check image size
- # Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99
- # if device.type != 'cpu' and torch.cuda.device_count() > 1:
- # model = nn.DataParallel(model)
- # Data
- with open(data) as f:
- data = yaml.safe_load(f)
- check_dataset(data) # check
- # Half
- half &= device.type != 'cpu' # half precision only supported on CUDA
- if half:
- model.half()
- # Configure
- model.eval()
- is_coco = type(data['val']) is str and data['val'].endswith('coco/val2017.txt') # COCO dataset
- nc = 1 if single_cls else int(data['nc']) # number of classes
- iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95
- niou = iouv.numel()
- # Dataloader
- if not training:
- if device.type != 'cpu':
- model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
- task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images
- dataloader = create_dataloader(data[task], imgsz, batch_size, gs, single_cls, pad=0.5, rect=True,
- prefix=colorstr(f'{task}: '))[0]
- seen = 0
- confusion_matrix = ConfusionMatrix(nc=nc)
- names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
- class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
- s = ('%20s' + '%11s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
- p, r, f1, mp, mr, map50, map, t0, t1, t2 = 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
- loss = torch.zeros(3, device=device)
- jdict, stats, ap, ap_class = [], [], [], []
- for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
- t_ = time_sync()
- img = img.to(device, non_blocking=True)
- img = img.half() if half else img.float() # uint8 to fp16/32
- img /= 255.0 # 0 - 255 to 0.0 - 1.0
- targets = targets.to(device)
- nb, _, height, width = img.shape # batch size, channels, height, width
- t = time_sync()
- t0 += t - t_
- # Run model
- out, train_out = model(img, augment=augment) # inference and training outputs
- t1 += time_sync() - t
- # Compute loss
- if compute_loss:
- loss += compute_loss([x.float() for x in train_out], targets)[1][:3] # box, obj, cls
- # Run NMS
- targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device) # to pixels
- lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
- t = time_sync()
- out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls)
- t2 += time_sync() - t
- # Statistics per image
- for si, pred in enumerate(out):
- labels = targets[targets[:, 0] == si, 1:]
- nl = len(labels)
- tcls = labels[:, 0].tolist() if nl else [] # target class
- path, shape = Path(paths[si]), shapes[si][0]
- seen += 1
- if len(pred) == 0:
- if nl:
- stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
- continue
- # Predictions
- if single_cls:
- pred[:, 5] = 0
- predn = pred.clone()
- scale_coords(img[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred
- # Evaluate
- if nl:
- tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
- scale_coords(img[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)
- else:
- correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool)
- stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls)) # (correct, conf, pcls, tcls)
- # Save/log
- if save_txt:
- save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / (path.stem + '.txt'))
- if save_json:
- save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary
- loggers.on_val_batch_end(pred, predn, path, names, img[si])
- # Plot images
- if plots and batch_i < 3:
- f = save_dir / f'val_batch{batch_i}_labels.jpg' # labels
- Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start()
- f = save_dir / f'val_batch{batch_i}_pred.jpg' # predictions
- Thread(target=plot_images, args=(img, output_to_target(out), paths, f, names), daemon=True).start()
- # Compute statistics
- stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
- if len(stats) and stats[0].any():
- p, r, ap, f1, 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, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
- nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class
- else:
- nt = torch.zeros(1)
- # Print results
- pf = '%20s' + '%11i' * 2 + '%11.3g' * 4 # print format
- print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
- # Print results per class
- if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
- for i, c in enumerate(ap_class):
- print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
- # Print speeds
- t = tuple(x / seen * 1E3 for x in (t0, t1, t2)) # speeds per image
- if not training:
- shape = (batch_size, 3, imgsz, imgsz)
- print(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()))
- loggers.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
- print(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.img_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:
- print(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 ''
- print(f"Results saved to {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():
- parser = argparse.ArgumentParser(prog='val.py')
- parser.add_argument('--data', type=str, default='data/coco128.yaml', help='dataset.yaml path')
- parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
- parser.add_argument('--batch-size', type=int, default=32, help='batch size')
- parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, 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='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
- 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='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')
- opt = parser.parse_args()
- opt.save_json |= opt.data.endswith('coco.yaml')
- opt.save_txt |= opt.save_hybrid
- opt.data = check_file(opt.data) # check file
- return opt
- def main(opt):
- set_logging()
- print(colorstr('val: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
- check_requirements(exclude=('tensorboard', 'thop'))
- if opt.task in ('train', 'val', 'test'): # run normally
- run(**vars(opt))
- elif opt.task == 'speed': # speed benchmarks
- for w in opt.weights if isinstance(opt.weights, list) else [opt.weights]:
- run(opt.data, weights=w, batch_size=opt.batch_size, imgsz=opt.imgsz, conf_thres=.25, iou_thres=.45,
- save_json=False, plots=False)
- elif opt.task == 'study': # run over a range of settings and save/plot
- # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5s.pt yolov5m.pt yolov5l.pt yolov5x.pt
- x = list(range(256, 1536 + 128, 128)) # x axis (image sizes)
- for w in opt.weights if isinstance(opt.weights, list) else [opt.weights]:
- f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt' # filename to save to
- y = [] # y axis
- for i in x: # img-size
- print(f'\nRunning {f} point {i}...')
- r, _, t = run(opt.data, weights=w, batch_size=opt.batch_size, imgsz=i, conf_thres=opt.conf_thres,
- iou_thres=opt.iou_thres, save_json=opt.save_json, 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_study_txt(x=x) # plot
- if __name__ == "__main__":
- opt = parse_opt()
- main(opt)
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