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| 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, # for NMS | |
| save_json=False, | |
| single_cls=False, | |
| augment=False, | |
| verbose=False, | |
| model=None, | |
| dataloader=None, | |
| save_dir=Path(''), # for saving images | |
| save_txt=False, # for auto-labelling | |
| save_hybrid=False, # for hybrid auto-labelling | |
| save_conf=False, # save auto-label confidences | |
| plots=True, | |
| wandb_logger=None, | |
| compute_loss=None, | |
| half_precision=True, | |
| trace=False, | |
| is_coco=False): | |
| # 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 | |
| set_logging() | |
| device = select_device(opt.device, batch_size=batch_size) | |
| # Directories | |
| save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.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 img_size | |
| if trace: | |
| model = TracedModel(model, device, opt.img_size) | |
| # Half | |
| half = device.type != 'cpu' and half_precision # half precision only supported on CUDA | |
| if half: | |
| model.half() | |
| # Configure | |
| 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) # check | |
| 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() | |
| # Logging | |
| log_imgs = 0 | |
| if wandb_logger and wandb_logger.wandb: | |
| log_imgs = min(wandb_logger.log_imgs, 100) | |
| # 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 = opt.task if opt.task in ('train', 'val', 'test') else 'val' # path to train/val/test images | |
| dataloader = create_dataloader(data[task], imgsz, batch_size, gs, opt, 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)} | |
| 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() # 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 | |
| with torch.no_grad(): | |
| # Run model | |
| t = time_synchronized() | |
| out, train_out = model(img, augment=augment) # inference and training outputs | |
| t0 += time_synchronized() - 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_synchronized() | |
| out = non_max_suppression(out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb, multi_label=True) | |
| t1 += time_synchronized() - 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 = 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 | |
| # Predictions | |
| predn = pred.clone() | |
| scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1]) # native-space pred | |
| # Append to text file | |
| if save_txt: | |
| gn = torch.tensor(shapes[si][0])[[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(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f: | |
| f.write(('%g ' * len(line)).rstrip() % line + '\n') | |
| # W&B logging - Media Panel Plots | |
| if len(wandb_images) < log_imgs and wandb_logger.current_epoch > 0: # Check for test operation | |
| 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}} # inference-space | |
| 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 | |
| # Append to pycocotools JSON dictionary | |
| if save_json: | |
| # [{"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(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)}) | |
| # Assign all predictions as incorrect | |
| correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device) | |
| if nl: | |
| detected = [] # target indices | |
| tcls_tensor = labels[:, 0] | |
| # target boxes | |
| tbox = xywh2xyxy(labels[:, 1:5]) | |
| scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1]) # native-space labels | |
| if plots: | |
| confusion_matrix.process_batch(predn, torch.cat((labels[:, 0:1], tbox), 1)) | |
| # Per target class | |
| for cls in torch.unique(tcls_tensor): | |
| ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1) # prediction indices | |
| pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1) # target indices | |
| # Search for detections | |
| if pi.shape[0]: | |
| # Prediction to target ious | |
| ious, i = box_iou(predn[pi, :4], tbox[ti]).max(1) # best ious, indices | |
| # Append detections | |
| detected_set = set() | |
| for j in (ious > iouv[0]).nonzero(as_tuple=False): | |
| d = ti[i[j]] # detected target | |
| if d.item() not in detected_set: | |
| detected_set.add(d.item()) | |
| detected.append(d) | |
| correct[pi[j]] = ious[j] > iouv # iou_thres is 1xn | |
| if len(detected) == nl: # all targets already located in image | |
| break | |
| # Append statistics (correct, conf, pcls, tcls) | |
| stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls)) | |
| # Plot images | |
| if plots and batch_i < 3: | |
| f = save_dir / f'test_batch{batch_i}_labels.jpg' # labels | |
| Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start() | |
| f = save_dir / f'test_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' + '%12i' * 2 + '%12.3g' * 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, t0 + t1)) + (imgsz, imgsz, batch_size) # tuple | |
| if not training: | |
| print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t) | |
| # Plots | |
| 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}) | |
| # 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 = '../coco/annotations/instances_val2017.json' # annotations json | |
| pred_json = str(save_dir / f"{w}_predictions.json") # predictions json | |
| print('\nEvaluating pycocotools mAP... saving %s...' % pred_json) | |
| with open(pred_json, 'w') as f: | |
| json.dump(jdict, f) | |
| try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb | |
| 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 | |
| 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('--trace', action='store_true', help='trace model') | |
| opt = parser.parse_args() | |
| opt.save_json |= opt.data.endswith('coco.yaml') | |
| opt.data = check_file(opt.data) # check file | |
| print(opt) | |
| #check_requirements() | |
| if opt.task in ('train', 'val', 'test'): # run normally | |
| 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=opt.trace, | |
| ) | |
| elif opt.task == 'speed': # speed benchmarks | |
| for w in opt.weights: | |
| test(opt.data, w, opt.batch_size, opt.img_size, 0.25, 0.45, save_json=False, plots=False) | |
| elif opt.task == 'study': # run over a range of settings and save/plot | |
| # python test.py --task study --data coco.yaml --iou 0.65 --weights yolov7.pt | |
| x = list(range(256, 1536 + 128, 128)) # x axis (image sizes) | |
| for w in 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 = test(opt.data, w, opt.batch_size, i, opt.conf_thres, opt.iou_thres, 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 | |