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import argparse |
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import json |
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import yaml |
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from torch.utils.data import DataLoader |
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from utils.datasets import * |
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from utils.utils import * |
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def test(data, |
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weights=None, |
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batch_size=16, |
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imgsz=640, |
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conf_thres=0.001, |
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iou_thres=0.6, |
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save_json=False, |
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single_cls=False, |
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augment=False, |
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model=None, |
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dataloader=None, |
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fast=False, |
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verbose=False): |
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if model is None: |
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device = torch_utils.select_device(opt.device, batch_size=batch_size) |
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for f in glob.glob('test_batch*.jpg'): |
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os.remove(f) |
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google_utils.attempt_download(weights) |
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model = torch.load(weights, map_location=device)['model'] |
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torch_utils.model_info(model) |
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model.to(device) |
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if device.type != 'cpu' and torch.cuda.device_count() > 1: |
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model = nn.DataParallel(model) |
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training = False |
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else: |
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device = next(model.parameters()).device |
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training = True |
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with open(data) as f: |
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data = yaml.load(f, Loader=yaml.FullLoader) |
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nc = 1 if single_cls else int(data['nc']) |
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iouv = torch.linspace(0.5, 0.95, 10).to(device) |
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niou = iouv.numel() |
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if dataloader is None: |
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fast |= conf_thres > 0.001 |
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path = data['test'] if opt.task == 'test' else data['val'] |
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dataset = LoadImagesAndLabels(path, |
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imgsz, |
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batch_size, |
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rect=True, |
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single_cls=opt.single_cls, |
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pad=0.0 if fast else 0.5) |
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batch_size = min(batch_size, len(dataset)) |
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nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) |
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dataloader = DataLoader(dataset, |
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batch_size=batch_size, |
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num_workers=nw, |
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pin_memory=True, |
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collate_fn=dataset.collate_fn) |
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seen = 0 |
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model.eval() |
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_ = model(torch.zeros((1, 3, imgsz, imgsz), device=device)) if device.type != 'cpu' else None |
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names = model.names if hasattr(model, 'names') else model.module.names |
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coco91class = coco80_to_coco91_class() |
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s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95') |
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p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0. |
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loss = torch.zeros(3, device=device) |
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jdict, stats, ap, ap_class = [], [], [], [] |
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for batch_i, (imgs, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)): |
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imgs = imgs.to(device).float() / 255.0 |
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targets = targets.to(device) |
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nb, _, height, width = imgs.shape |
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whwh = torch.Tensor([width, height, width, height]).to(device) |
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with torch.no_grad(): |
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t = torch_utils.time_synchronized() |
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inf_out, train_out = model(imgs, augment=augment) |
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t0 += torch_utils.time_synchronized() - t |
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if training: |
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loss += compute_loss(train_out, targets, model)[1][:3] |
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t = torch_utils.time_synchronized() |
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output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, fast=fast) |
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t1 += torch_utils.time_synchronized() - t |
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for si, pred in enumerate(output): |
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labels = targets[targets[:, 0] == si, 1:] |
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nl = len(labels) |
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tcls = labels[:, 0].tolist() if nl else [] |
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seen += 1 |
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if pred is None: |
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if nl: |
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stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls)) |
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continue |
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clip_coords(pred, (height, width)) |
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if save_json: |
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image_id = int(Path(paths[si]).stem.split('_')[-1]) |
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box = pred[:, :4].clone() |
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scale_coords(imgs[si].shape[1:], box, shapes[si][0], shapes[si][1]) |
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box = xyxy2xywh(box) |
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box[:, :2] -= box[:, 2:] / 2 |
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for p, b in zip(pred.tolist(), box.tolist()): |
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jdict.append({'image_id': image_id, |
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'category_id': coco91class[int(p[5])], |
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'bbox': [round(x, 3) for x in b], |
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'score': round(p[4], 5)}) |
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correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device) |
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if nl: |
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detected = [] |
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tcls_tensor = labels[:, 0] |
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tbox = xywh2xyxy(labels[:, 1:5]) * whwh |
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for cls in torch.unique(tcls_tensor): |
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ti = (cls == tcls_tensor).nonzero().view(-1) |
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pi = (cls == pred[:, 5]).nonzero().view(-1) |
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if pi.shape[0]: |
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ious, i = box_iou(pred[pi, :4], tbox[ti]).max(1) |
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for j in (ious > iouv[0]).nonzero(): |
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d = ti[i[j]] |
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if d not in detected: |
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detected.append(d) |
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correct[pi[j]] = ious[j] > iouv |
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if len(detected) == nl: |
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break |
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stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls)) |
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if batch_i < 1: |
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f = 'test_batch%g_gt.jpg' % batch_i |
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plot_images(imgs, targets, paths, f, names) |
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f = 'test_batch%g_pred.jpg' % batch_i |
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plot_images(imgs, output_to_target(output, width, height), paths, f, names) |
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stats = [np.concatenate(x, 0) for x in zip(*stats)] |
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if len(stats): |
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p, r, ap, f1, ap_class = ap_per_class(*stats) |
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p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1) |
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mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean() |
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nt = np.bincount(stats[3].astype(np.int64), minlength=nc) |
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else: |
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nt = torch.zeros(1) |
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pf = '%20s' + '%12.3g' * 6 |
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print(pf % ('all', seen, nt.sum(), mp, mr, map50, map)) |
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if verbose and nc > 1 and len(stats): |
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for i, c in enumerate(ap_class): |
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print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i])) |
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t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size) |
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if not training: |
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print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t) |
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if save_json and map50 and len(jdict): |
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imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataloader.dataset.img_files] |
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f = 'detections_val2017_%s_results.json' % \ |
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(weights.split(os.sep)[-1].replace('.pt', '') if weights else '') |
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print('\nCOCO mAP with pycocotools... saving %s...' % f) |
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with open(f, 'w') as file: |
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json.dump(jdict, file) |
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try: |
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from pycocotools.coco import COCO |
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from pycocotools.cocoeval import COCOeval |
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cocoGt = COCO(glob.glob('../coco/annotations/instances_val*.json')[0]) |
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cocoDt = cocoGt.loadRes(f) |
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cocoEval = COCOeval(cocoGt, cocoDt, 'bbox') |
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cocoEval.params.imgIds = imgIds |
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cocoEval.evaluate() |
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cocoEval.accumulate() |
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cocoEval.summarize() |
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map, map50 = cocoEval.stats[:2] |
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except: |
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print('WARNING: pycocotools must be installed with numpy==1.17 to run correctly. ' |
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'See https://github.com/cocodataset/cocoapi/issues/356') |
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maps = np.zeros(nc) + map |
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for i, c in enumerate(ap_class): |
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maps[c] = ap[i] |
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return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t |
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if __name__ == '__main__': |
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parser = argparse.ArgumentParser(prog='test.py') |
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parser.add_argument('--weights', type=str, default='weights/yolov5s.pt', help='model.pt path') |
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parser.add_argument('--data', type=str, default='data/coco.yaml', help='*.data path') |
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parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch') |
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parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') |
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parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold') |
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parser.add_argument('--iou-thres', type=float, default=0.65, help='IOU threshold for NMS') |
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parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file') |
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parser.add_argument('--task', default='val', help="'val', 'test', 'study'") |
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parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') |
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parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset') |
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parser.add_argument('--augment', action='store_true', help='augmented inference') |
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parser.add_argument('--verbose', action='store_true', help='report mAP by class') |
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opt = parser.parse_args() |
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opt.save_json = opt.save_json or opt.data.endswith('coco.yaml') |
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opt.data = glob.glob('./**/' + opt.data, recursive=True)[0] |
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print(opt) |
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if opt.task in ['val', 'test']: |
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test(opt.data, |
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opt.weights, |
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opt.batch_size, |
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opt.img_size, |
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opt.conf_thres, |
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opt.iou_thres, |
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opt.save_json, |
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opt.single_cls, |
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opt.augment) |
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elif opt.task == 'study': |
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for weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt', 'yolov3-spp.pt']: |
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f = 'study_%s_%s.txt' % (Path(opt.data).stem, Path(weights).stem) |
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x = list(range(256, 1024, 64)) |
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y = [] |
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for i in x: |
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print('\nRunning %s point %s...' % (f, i)) |
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r, _, t = test(opt.data, weights, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json) |
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y.append(r + t) |
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np.savetxt(f, y, fmt='%10.4g') |
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plot_study_txt(f, x) |
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