123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353 |
- import argparse
- import json
- import os
- from pathlib import Path
- from threading import Thread
- import numpy as np
- import torch
- import yaml
- from tqdm import tqdm
- 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_synchronized, TracedModel
- def test(data,
- weights=None,
- batch_size=32,
- imgsz=640,
- conf_thres=0.001,
- iou_thres=0.6,
- save_json=False,
- single_cls=False,
- augment=False,
- verbose=False,
- model=None,
- dataloader=None,
- save_dir=Path(''),
- save_txt=False,
- save_hybrid=False,
- save_conf=False,
- plots=True,
- wandb_logger=None,
- compute_loss=None,
- half_precision=True,
- trace=False,
- is_coco=False,
- v5_metric=False):
-
- training = model is not None
- if training:
- device = next(model.parameters()).device
- else:
- set_logging()
- device = select_device(opt.device, batch_size=batch_size)
-
- save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))
- (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)
-
- model = attempt_load(weights, map_location=device)
- gs = max(int(model.stride.max()), 32)
- imgsz = check_img_size(imgsz, s=gs)
-
- if trace:
- model = TracedModel(model, device, imgsz)
-
- half = device.type != 'cpu' and half_precision
- if half:
- model.half()
-
- model.eval()
- if isinstance(data, str):
- is_coco = data.endswith('coco.yaml')
- with open(data) as f:
- data = yaml.load(f, Loader=yaml.SafeLoader)
- check_dataset(data)
- nc = 1 if single_cls else int(data['nc'])
- iouv = torch.linspace(0.5, 0.95, 10).to(device)
- niou = iouv.numel()
-
- log_imgs = 0
- if wandb_logger and wandb_logger.wandb:
- log_imgs = min(wandb_logger.log_imgs, 100)
-
- if not training:
- if device.type != 'cpu':
- model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))
- task = opt.task if opt.task in ('train', 'val', 'test') else 'val'
- dataloader = create_dataloader(data[task], imgsz, batch_size, gs, opt, pad=0.5, rect=True,
- prefix=colorstr(f'{task}: '))[0]
- if v5_metric:
- print("Testing with YOLOv5 AP metric...")
-
- 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)}
- coco91class = coco80_to_coco91_class()
- s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
- p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
- loss = torch.zeros(3, device=device)
- jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []
- for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
- img = img.to(device, non_blocking=True)
- img = img.half() if half else img.float()
- img /= 255.0
- targets = targets.to(device)
- nb, _, height, width = img.shape
- with torch.no_grad():
-
- t = time_synchronized()
- out, train_out = model(img, augment=augment)
- t0 += time_synchronized() - t
-
- if compute_loss:
- loss += compute_loss([x.float() for x in train_out], targets)[1][:3]
-
- targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device)
- lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else []
- t = time_synchronized()
- out = non_max_suppression(out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb, multi_label=True)
- t1 += time_synchronized() - t
-
- for si, pred in enumerate(out):
- labels = targets[targets[:, 0] == si, 1:]
- nl = len(labels)
- tcls = labels[:, 0].tolist() if nl else []
- path = Path(paths[si])
- seen += 1
- if len(pred) == 0:
- if nl:
- stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
- continue
-
- predn = pred.clone()
- scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1])
-
- if save_txt:
- gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]]
- for *xyxy, conf, cls in predn.tolist():
- xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()
- line = (cls, *xywh, conf) if save_conf else (cls, *xywh)
- with open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f:
- f.write(('%g ' * len(line)).rstrip() % line + '\n')
-
- if len(wandb_images) < log_imgs and wandb_logger.current_epoch > 0:
- if wandb_logger.current_epoch % wandb_logger.bbox_interval == 0:
- box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
- "class_id": int(cls),
- "box_caption": "%s %.3f" % (names[cls], conf),
- "scores": {"class_score": conf},
- "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
- boxes = {"predictions": {"box_data": box_data, "class_labels": names}}
- wandb_images.append(wandb_logger.wandb.Image(img[si], boxes=boxes, caption=path.name))
- wandb_logger.log_training_progress(predn, path, names) if wandb_logger and wandb_logger.wandb_run else None
-
- if save_json:
-
- image_id = int(path.stem) if path.stem.isnumeric() else path.stem
- box = xyxy2xywh(predn[:, :4])
- box[:, :2] -= box[:, 2:] / 2
- for p, b in zip(pred.tolist(), box.tolist()):
- jdict.append({'image_id': image_id,
- 'category_id': coco91class[int(p[5])] if is_coco else int(p[5]),
- 'bbox': [round(x, 3) for x in b],
- 'score': round(p[4], 5)})
-
- correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device)
- if nl:
- detected = []
- tcls_tensor = labels[:, 0]
-
- tbox = xywh2xyxy(labels[:, 1:5])
- scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1])
- if plots:
- confusion_matrix.process_batch(predn, torch.cat((labels[:, 0:1], tbox), 1))
-
- for cls in torch.unique(tcls_tensor):
- ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1)
- pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1)
-
- if pi.shape[0]:
-
- ious, i = box_iou(predn[pi, :4], tbox[ti]).max(1)
-
- detected_set = set()
- for j in (ious > iouv[0]).nonzero(as_tuple=False):
- d = ti[i[j]]
- if d.item() not in detected_set:
- detected_set.add(d.item())
- detected.append(d)
- correct[pi[j]] = ious[j] > iouv
- if len(detected) == nl:
- break
-
- stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
-
- if plots and batch_i < 3:
- f = save_dir / f'test_batch{batch_i}_labels.jpg'
- Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start()
- f = save_dir / f'test_batch{batch_i}_pred.jpg'
- Thread(target=plot_images, args=(img, output_to_target(out), paths, f, names), daemon=True).start()
-
- stats = [np.concatenate(x, 0) for x in zip(*stats)]
- if len(stats) and stats[0].any():
- p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, v5_metric=v5_metric, save_dir=save_dir, names=names)
- ap50, ap = ap[:, 0], ap.mean(1)
- mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
- nt = np.bincount(stats[3].astype(np.int64), minlength=nc)
- else:
- nt = torch.zeros(1)
-
- pf = '%20s' + '%12i' * 2 + '%12.3g' * 4
- print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
-
- 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]))
-
- t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size)
- if not training:
- print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t)
-
- if plots:
- confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
- if wandb_logger and wandb_logger.wandb:
- val_batches = [wandb_logger.wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]
- wandb_logger.log({"Validation": val_batches})
- if wandb_images:
- wandb_logger.log({"Bounding Box Debugger/Images": wandb_images})
-
- if save_json and len(jdict):
- w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ''
- anno_json = './coco/annotations/instances_val2017.json'
- pred_json = str(save_dir / f"{w}_predictions.json")
- print('\nEvaluating pycocotools mAP... saving %s...' % pred_json)
- with open(pred_json, 'w') as f:
- json.dump(jdict, f)
- try:
- from pycocotools.coco import COCO
- from pycocotools.cocoeval import COCOeval
- anno = COCO(anno_json)
- pred = anno.loadRes(pred_json)
- eval = COCOeval(anno, pred, 'bbox')
- if is_coco:
- eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files]
- eval.evaluate()
- eval.accumulate()
- eval.summarize()
- map, map50 = eval.stats[:2]
- except Exception as e:
- print(f'pycocotools unable to run: {e}')
-
- model.float()
- 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
- if __name__ == '__main__':
- parser = argparse.ArgumentParser(prog='test.py')
- parser.add_argument('--weights', nargs='+', type=str, default='yolov7.pt', help='model.pt path(s)')
- parser.add_argument('--data', type=str, default='data/coco.yaml', help='*.data path')
- parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch')
- parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
- parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
- parser.add_argument('--iou-thres', type=float, default=0.65, help='IOU threshold for NMS')
- 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 cocoapi-compatible JSON results file')
- parser.add_argument('--project', default='runs/test', 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('--no-trace', action='store_true', help='don`t trace model')
- parser.add_argument('--v5-metric', action='store_true', help='assume maximum recall as 1.0 in AP calculation')
- opt = parser.parse_args()
- opt.save_json |= opt.data.endswith('coco.yaml')
- opt.data = check_file(opt.data)
- print(opt)
-
- if opt.task in ('train', 'val', 'test'):
- test(opt.data,
- opt.weights,
- opt.batch_size,
- opt.img_size,
- opt.conf_thres,
- opt.iou_thres,
- opt.save_json,
- opt.single_cls,
- opt.augment,
- opt.verbose,
- save_txt=opt.save_txt | opt.save_hybrid,
- save_hybrid=opt.save_hybrid,
- save_conf=opt.save_conf,
- trace=not opt.no_trace,
- v5_metric=opt.v5_metric
- )
- elif opt.task == 'speed':
- for w in opt.weights:
- test(opt.data, w, opt.batch_size, opt.img_size, 0.25, 0.45, save_json=False, plots=False, v5_metric=opt.v5_metric)
- elif opt.task == 'study':
-
- x = list(range(256, 1536 + 128, 128))
- for w in opt.weights:
- f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt'
- y = []
- for i in x:
- print(f'\nRunning {f} point {i}...')
- r, _, t = test(opt.data, w, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json,
- plots=False, v5_metric=opt.v5_metric)
- y.append(r + t)
- np.savetxt(f, y, fmt='%10.4g')
- os.system('zip -r study.zip study_*.txt')
- plot_study_txt(x=x)
|