import argparse import json from torch.utils.data import DataLoader from models import * from utils.datasets import * from utils.utils import * def test(cfg, data, weights=None, batch_size=16, img_size=416, conf_thres=0.001, iou_thres=0.6, # for nms save_json=False, single_cls=False, model=None, dataloader=None): # Initialize/load model and set device if model is None: device = torch_utils.select_device(opt.device, batch_size=batch_size) verbose = opt.task == 'test' # Remove previous for f in glob.glob('test_batch*.png'): os.remove(f) # Initialize model model = Darknet(cfg, img_size) # Load weights attempt_download(weights) if weights.endswith('.pt'): # pytorch format model.load_state_dict(torch.load(weights, map_location=device)['model']) else: # darknet format load_darknet_weights(model, weights) # Fuse model.fuse() model.to(device) if device.type != 'cpu' and torch.cuda.device_count() > 1: model = nn.DataParallel(model) else: # called by train.py device = next(model.parameters()).device # get model device verbose = False # Configure run data = parse_data_cfg(data) nc = 1 if single_cls else int(data['classes']) # number of classes path = data['valid'] # path to test images names = load_classes(data['names']) # class names iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95 iouv = iouv[0].view(1) # comment for mAP@0.5:0.95 niou = iouv.numel() # Dataloader if dataloader is None: dataset = LoadImagesAndLabels(path, img_size, batch_size, rect=True, single_cls=opt.single_cls) batch_size = min(batch_size, len(dataset)) dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]), pin_memory=True, collate_fn=dataset.collate_fn) seen = 0 model.eval() coco91class = coco80_to_coco91_class() s = ('%20s' + '%10s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@0.5', 'F1') p, r, f1, mp, mr, map, mf1, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0. loss = torch.zeros(3, device=device) jdict, stats, ap, ap_class = [], [], [], [] for batch_i, (imgs, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)): imgs = imgs.to(device).float() / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0 targets = targets.to(device) nb, _, height, width = imgs.shape # batch size, channels, height, width whwh = torch.Tensor([width, height, width, height]).to(device) # Plot images with bounding boxes f = 'test_batch%g.png' % batch_i # filename if batch_i < 1 and not os.path.exists(f): plot_images(imgs=imgs, targets=targets, paths=paths, fname=f) # Disable gradients with torch.no_grad(): if opt.augment: # augmented testing https://github.com/ultralytics/yolov3/issues/931 imgs = torch.cat((imgs, imgs.flip(3), # flip-lr torch_utils.scale_img(imgs, 0.7), # scale ), 0) # Run model t = torch_utils.time_synchronized() inf_out, train_out = model(imgs) # inference and training outputs t0 += torch_utils.time_synchronized() - t if opt.augment: x = torch.split(inf_out, nb, dim=0) x[1][..., 0] = width - x[1][..., 0] # flip lr x[2][..., :4] /= 0.7 # scale inf_out = torch.cat(x, 1) # Compute loss if hasattr(model, 'hyp'): # if model has loss hyperparameters loss += compute_loss(train_out, targets, model)[1][:3] # GIoU, obj, cls # Run NMS t = torch_utils.time_synchronized() output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres) # nms t1 += torch_utils.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 # with open('test.txt', 'a') as file: # [file.write('%11.5g' * 7 % tuple(x) + '\n') for x in pred] # 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 = int(Path(paths[si]).stem.split('_')[-1]) box = pred[:, :4].clone() # xyxy scale_coords(imgs[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()): jdict.append({'image_id': image_id, 'category_id': coco91class[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]) * whwh # Per target class for cls in torch.unique(tcls_tensor): ti = (cls == tcls_tensor).nonzero().view(-1) # prediction indices pi = (cls == pred[:, 5]).nonzero().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 for j in (ious > iouv[0]).nonzero(): d = ti[i[j]] # detected target if d not in detected: 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)) # Compute statistics stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy if len(stats): p, r, ap, f1, ap_class = ap_per_class(*stats) if niou > 1: p, r, ap, f1 = p[:, 0], r[:, 0], ap.mean(1), ap[:, 0] # [P, R, AP@0.5:0.95, AP@0.5] mp, mr, map, mf1 = p.mean(), r.mean(), ap.mean(), f1.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' + '%10.3g' * 6 # print format print(pf % ('all', seen, nt.sum(), mp, mr, map, mf1)) # 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], ap[i], f1[i])) # Print speeds if verbose: t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (img_size, img_size, batch_size) # tuple print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t) # Save JSON if save_json and map and len(jdict): print('\nCOCO mAP with pycocotools...') imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataloader.dataset.img_files] with open('results.json', 'w') as file: json.dump(jdict, file) try: from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval except: print('WARNING: missing pycocotools package, can not compute official COCO mAP. See requirements.txt.') # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb cocoGt = COCO(glob.glob('../coco/annotations/instances_val*.json')[0]) # initialize COCO ground truth api cocoDt = cocoGt.loadRes('results.json') # initialize COCO pred api cocoEval = COCOeval(cocoGt, cocoDt, 'bbox') cocoEval.params.imgIds = imgIds # [:32] # only evaluate these images cocoEval.evaluate() cocoEval.accumulate() cocoEval.summarize() mf1, map = cocoEval.stats[:2] # update to pycocotools results (mAP@0.5:0.95, mAP@0.5) # Return results maps = np.zeros(nc) + map for i, c in enumerate(ap_class): maps[c] = ap[i] return (mp, mr, map, mf1, *(loss.cpu() / len(dataloader)).tolist()), maps if __name__ == '__main__': parser = argparse.ArgumentParser(prog='test.py') parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path') parser.add_argument('--data', type=str, default='data/coco2014.data', help='*.data path') parser.add_argument('--weights', type=str, default='weights/yolov3-spp-ultralytics.pt', help='weights path') parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch') parser.add_argument('--img-size', type=int, default=416, 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=0.6, 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='test', help="'test', 'study', 'benchmark'") parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu') parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset') parser.add_argument('--augment', action='store_true', help='augmented testing') opt = parser.parse_args() opt.save_json = opt.save_json or any([x in opt.data for x in ['coco.data', 'coco2014.data', 'coco2017.data']]) print(opt) # task = 'test', 'study', 'benchmark' if opt.task == 'test': # (default) test normally test(opt.cfg, opt.data, opt.weights, opt.batch_size, opt.img_size, opt.conf_thres, opt.iou_thres, opt.save_json, opt.single_cls) elif opt.task == 'benchmark': # mAPs at 320-608 at conf 0.5 and 0.7 y = [] for i in [320, 416, 512, 608]: # img-size for j in [0.5, 0.7]: # iou-thres t = time.time() r = test(opt.cfg, opt.data, opt.weights, opt.batch_size, i, opt.conf_thres, j, opt.save_json)[0] y.append(r + (time.time() - t,)) np.savetxt('benchmark.txt', y, fmt='%10.4g') # y = np.loadtxt('study.txt') elif opt.task == 'study': # Parameter study y = [] x = np.arange(0.4, 0.9, 0.05) # iou-thres for i in x: t = time.time() r = test(opt.cfg, opt.data, opt.weights, opt.batch_size, opt.img_size, opt.conf_thres, i, opt.save_json)[0] y.append(r + (time.time() - t,)) np.savetxt('study.txt', y, fmt='%10.4g') # y = np.loadtxt('study.txt') # Plot fig, ax = plt.subplots(3, 1, figsize=(6, 6)) y = np.stack(y, 0) ax[0].plot(x, y[:, 2], marker='.', label='mAP@0.5') ax[0].set_ylabel('mAP') ax[1].plot(x, y[:, 3], marker='.', label='mAP@0.5:0.95') ax[1].set_ylabel('mAP') ax[2].plot(x, y[:, -1], marker='.', label='time') ax[2].set_ylabel('time (s)') for i in range(3): ax[i].legend() ax[i].set_xlabel('iou_thr') fig.tight_layout() plt.savefig('study.jpg', dpi=200)