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- import argparse
- import json
- from torch.utils.data import DataLoader
- from utils import google_utils
- from utils.datasets import *
- from utils.utils import *
- 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,
- merge=False):
- # Initialize/load model and set device
- if model is None:
- training = False
- device = torch_utils.select_device(opt.device, batch_size=batch_size)
- half = device.type != 'cpu' # half precision only supported on CUDA
- # Remove previous
- for f in glob.glob('test_batch*.jpg'):
- os.remove(f)
- # Load model
- google_utils.attempt_download(weights)
- model = torch.load(weights, map_location=device)['model'].float() # load to FP32
- torch_utils.model_info(model)
- model.fuse()
- model.to(device)
- if half:
- model.half() # to FP16
- # Multi-GPU disabled, incompatible with .half()
- # if device.type != 'cpu' and torch.cuda.device_count() > 1:
- # model = nn.DataParallel(model)
- else: # called by train.py
- training = True
- device = next(model.parameters()).device # get model device
- # half disabled https://github.com/ultralytics/yolov5/issues/99
- half = False # device.type != 'cpu' and torch.cuda.device_count() == 1
- if half:
- model.half() # to FP16
- # Configure
- model.eval()
- with open(data) as f:
- data = yaml.load(f, Loader=yaml.FullLoader) # model dict
- 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
- # iouv = iouv[0].view(1) # comment for mAP@0.5:0.95
- niou = iouv.numel()
- # Dataloader
- if dataloader is None: # 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
- merge = opt.merge # use Merge NMS
- path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images
- dataset = LoadImagesAndLabels(path,
- imgsz,
- batch_size,
- rect=True, # rectangular inference
- single_cls=opt.single_cls, # single class mode
- stride=int(max(model.stride)), # model stride
- pad=0.5) # padding
- batch_size = min(batch_size, len(dataset))
- nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) # number of workers
- dataloader = DataLoader(dataset,
- batch_size=batch_size,
- num_workers=nw,
- pin_memory=True,
- collate_fn=dataset.collate_fn)
- 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 = [], [], [], []
- for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
- img = img.to(device)
- 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 = torch_utils.time_synchronized()
- inf_out, train_out = model(img, augment=augment) # inference and training outputs
- t0 += torch_utils.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 = torch_utils.time_synchronized()
- output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, merge=merge)
- 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(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()):
- 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))
- # Plot images
- if batch_i < 1:
- f = 'test_batch%g_gt.jpg' % batch_i # filename
- plot_images(img, targets, paths, f, names) # ground truth
- f = 'test_batch%g_pred.jpg' % batch_i
- plot_images(img, output_to_target(output, width, height), paths, f, names) # predictions
- # 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)
- 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 map50 and len(jdict):
- imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataloader.dataset.img_files]
- f = 'detections_val2017_%s_results.json' % \
- (weights.split(os.sep)[-1].replace('.pt', '') if weights else '') # filename
- print('\nCOCO mAP with pycocotools... saving %s...' % f)
- with open(f, 'w') as file:
- json.dump(jdict, file)
- try:
- from pycocotools.coco import COCO
- from pycocotools.cocoeval import COCOeval
- # 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(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:
- print('WARNING: pycocotools must be installed with numpy==1.17 to run correctly. '
- 'See https://github.com/cocodataset/cocoapi/issues/356')
- # Return results
- 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', type=str, default='weights/yolov5s.pt', help='model.pt path')
- parser.add_argument('--data', type=str, default='data/coco128.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')
- opt = parser.parse_args()
- opt.img_size = check_img_size(opt.img_size)
- opt.save_json = opt.save_json or opt.data.endswith('coco.yaml')
- opt.data = check_file(opt.data) # check file
- print(opt)
- # task = 'val', 'test', 'study'
- if opt.task in ['val', 'test']: # (default) 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', 'yolov3-spp.pt']:
- f = 'study_%s_%s.txt' % (Path(opt.data).stem, Path(weights).stem) # filename to save to
- x = list(range(352, 832, 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')
- # plot_study_txt(f, x) # plot
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