import argparse import glob import json import os import shutil from pathlib import Path 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, compute_loss, non_max_suppression, scale_coords, xyxy2xywh, clip_coords, plot_images, xywh2xyxy, box_iou, output_to_target, ap_per_class, set_logging) from utils.torch_utils import select_device, time_synchronized from sotabencheval.object_detection import COCOEvaluator from sotabencheval.utils import is_server DATA_ROOT = './.data/vision/coco' if is_server() else '../coco' # sotabench data dir def test(data, weights=None, batch_size=16, 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='', merge=False, save_txt=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) merge, save_txt = opt.merge, opt.save_txt # use Merge NMS, save *.txt labels if save_txt: out = Path('inference/output') if os.path.exists(out): shutil.rmtree(out) # delete output folder os.makedirs(out) # make new output folder # Remove previous for f in glob.glob(str(Path(save_dir) / 'test_batch*.jpg')): os.remove(f) # Load model model = attempt_load(weights, map_location=device) # load FP32 model imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_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) # Half half = device.type != 'cpu' # half precision only supported on CUDA if half: model.half() # Configure model.eval() with open(data) as f: data = yaml.load(f, Loader=yaml.FullLoader) # model dict 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() # Dataloader if not training: img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt, hyp=None, augment=False, cache=True, pad=0.5, rect=True)[0] seen = 0 names = model.names if hasattr(model, 'names') else model.module.names coco91class = coco80_to_coco91_class() s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', '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 = [], [], [], [] evaluator = COCOEvaluator(root=DATA_ROOT, model_name=opt.weights.replace('.pt', '')) 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 whwh = torch.Tensor([width, height, width, height]).to(device) # Disable gradients with torch.no_grad(): # Run model t = time_synchronized() inf_out, train_out = model(img, augment=augment) # inference and training outputs t0 += time_synchronized() - t # Compute loss if training: # if model has loss hyperparameters loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3] # GIoU, obj, cls # Run NMS t = time_synchronized() output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, merge=merge) t1 += time_synchronized() - t # Statistics per image for si, pred in enumerate(output): labels = targets[targets[:, 0] == si, 1:] nl = len(labels) tcls = labels[:, 0].tolist() if nl else [] # target class seen += 1 if pred is None: if nl: stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls)) continue # Append to text file if save_txt: gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]] # normalization gain whwh x = pred.clone() x[:, :4] = scale_coords(img[si].shape[1:], x[:, :4], shapes[si][0], shapes[si][1]) # to original for *xyxy, conf, cls in x: xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh with open(str(out / Path(paths[si]).stem) + '.txt', 'a') as f: f.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format # Clip boxes to image bounds clip_coords(pred, (height, width)) # 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 = Path(paths[si]).stem box = pred[:, :4].clone() # xyxy scale_coords(img[si].shape[1:], box, shapes[si][0], shapes[si][1]) # to original shape box = xyxy2xywh(box) # xywh box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner for p, b in zip(pred.tolist(), box.tolist()): result = {'image_id': int(image_id) if image_id.isnumeric() else image_id, 'category_id': coco91class[int(p[5])], 'bbox': [round(x, 3) for x in b], 'score': round(p[4], 5)} jdict.append(result) #evaluator.add([result]) #if evaluator.cache_exists: # break # # 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]) * whwh # # # 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(pred[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 batch_i < 1: # f = Path(save_dir) / ('test_batch%g_gt.jpg' % batch_i) # filename # plot_images(img, targets, paths, str(f), names) # ground truth # f = Path(save_dir) / ('test_batch%g_pred.jpg' % batch_i) # plot_images(img, output_to_target(output, width, height), paths, str(f), names) # predictions evaluator.add(jdict) evaluator.save() # # 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) # p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1) # [P, R, 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' + '%12.3g' * 6 # print format # print(pf % ('all', seen, nt.sum(), mp, mr, map50, map)) # # # Print results per class # if verbose 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) # # # Save JSON # if save_json and len(jdict): # f = 'detections_val2017_%s_results.json' % \ # (weights.split(os.sep)[-1].replace('.pt', '') if isinstance(weights, str) else '') # filename # print('\nCOCO mAP with pycocotools... saving %s...' % f) # with open(f, 'w') as file: # json.dump(jdict, file) # # try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb # from pycocotools.coco import COCO # from pycocotools.cocoeval import COCOeval # # imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files] # cocoGt = COCO(glob.glob('../coco/annotations/instances_val*.json')[0]) # initialize COCO ground truth api # cocoDt = cocoGt.loadRes(f) # initialize COCO pred api # cocoEval = COCOeval(cocoGt, cocoDt, 'bbox') # cocoEval.params.imgIds = imgIds # image IDs to evaluate # cocoEval.evaluate() # cocoEval.accumulate() # cocoEval.summarize() # map, map50 = cocoEval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5) # except Exception as e: # print('ERROR: pycocotools unable to run: %s' % e) # # # Return results # model.float() # for training # 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='yolov5s.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('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file') parser.add_argument('--task', default='val', help="'val', 'test', '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('--merge', action='store_true', help='use Merge NMS') 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') opt = parser.parse_args() opt.save_json |= opt.data.endswith('coco.yaml') opt.data = check_file(opt.data) # check file print(opt) if opt.task in ['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) elif opt.task == 'study': # run over a range of settings and save/plot for weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']: f = 'study_%s_%s.txt' % (Path(opt.data).stem, Path(weights).stem) # filename to save to x = list(range(320, 800, 64)) # x axis y = [] # y axis for i in x: # img-size print('\nRunning %s point %s...' % (f, i)) r, _, t = test(opt.data, weights, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json) y.append(r + t) # results and times np.savetxt(f, y, fmt='%10.4g') # save os.system('zip -r study.zip study_*.txt') # utils.general.plot_study_txt(f, x) # plot