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import argparse
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import json
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from models.experimental import *
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from utils.datasets 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, # for NMS
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save_json=False,
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single_cls=False,
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augment=False,
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verbose=False,
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model=None,
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dataloader=None,
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save_dir='',
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merge=False,
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save_txt=False):
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# Initialize/load model and set device
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training = model is not None
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if training: # called by train.py
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device = next(model.parameters()).device # get model device
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else: # called directly
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device = torch_utils.select_device(opt.device, batch_size=batch_size)
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merge, save_txt = opt.merge, opt.save_txt # use Merge NMS, save *.txt labels
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if save_txt:
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out = Path('inference/output')
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if os.path.exists(out):
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shutil.rmtree(out) # delete output folder
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os.makedirs(out) # make new output folder
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# Remove previous
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for f in glob.glob(str(Path(save_dir) / 'test_batch*.jpg')):
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os.remove(f)
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# Load model
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model = attempt_load(weights, map_location=device) # load FP32 model
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imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size
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# Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99
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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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# Half
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half = device.type != 'cpu' # half precision only supported on CUDA
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if half:
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model.half()
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# Configure
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model.eval()
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with open(data) as f:
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data = yaml.load(f, Loader=yaml.FullLoader) # model dict
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nc = 1 if single_cls else int(data['nc']) # number of classes
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iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95
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niou = iouv.numel()
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# Dataloader
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if not training:
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img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
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_ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
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path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images
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dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt,
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hyp=None, augment=False, cache=False, pad=0.5, rect=True)[0]
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seen = 0
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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, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
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img = img.to(device, non_blocking=True)
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img = img.half() if half else img.float() # uint8 to fp16/32
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img /= 255.0 # 0 - 255 to 0.0 - 1.0
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targets = targets.to(device)
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nb, _, height, width = img.shape # batch size, channels, height, width
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whwh = torch.Tensor([width, height, width, height]).to(device)
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# Disable gradients
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with torch.no_grad():
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# Run model
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t = torch_utils.time_synchronized()
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inf_out, train_out = model(img, augment=augment) # inference and training outputs
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t0 += torch_utils.time_synchronized() - t
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# Compute loss
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if training: # if model has loss hyperparameters
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loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3] # GIoU, obj, cls
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# Run NMS
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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, merge=merge)
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t1 += torch_utils.time_synchronized() - t
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# Statistics per image
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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 [] # target class
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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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# Append to text file
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if save_txt:
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gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]] # normalization gain whwh
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txt_path = str(out / Path(paths[si]).stem)
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pred[:, :4] = scale_coords(img[si].shape[1:], pred[:, :4], shapes[si][0], shapes[si][1]) # to original
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for *xyxy, conf, cls in pred:
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xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
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with open(txt_path + '.txt', 'a') as f:
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f.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format
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# Clip boxes to image bounds
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clip_coords(pred, (height, width))
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# Append to pycocotools JSON dictionary
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if save_json:
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# [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
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image_id = Path(paths[si]).stem
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box = pred[:, :4].clone() # xyxy
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scale_coords(img[si].shape[1:], box, shapes[si][0], shapes[si][1]) # to original shape
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box = xyxy2xywh(box) # xywh
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box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
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for p, b in zip(pred.tolist(), box.tolist()):
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jdict.append({'image_id': int(image_id) if image_id.isnumeric() else 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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# Assign all predictions as incorrect
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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 = [] # target indices
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tcls_tensor = labels[:, 0]
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# target boxes
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tbox = xywh2xyxy(labels[:, 1:5]) * whwh
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# Per target class
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for cls in torch.unique(tcls_tensor):
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ti = (cls == tcls_tensor).nonzero().view(-1) # prediction indices
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pi = (cls == pred[:, 5]).nonzero().view(-1) # target indices
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# Search for detections
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if pi.shape[0]:
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# Prediction to target ious
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ious, i = box_iou(pred[pi, :4], tbox[ti]).max(1) # best ious, indices
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# Append detections
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for j in (ious > iouv[0]).nonzero():
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d = ti[i[j]] # detected target
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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 # iou_thres is 1xn
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if len(detected) == nl: # all targets already located in image
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break
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# Append statistics (correct, conf, pcls, tcls)
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stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
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# Plot images
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if batch_i < 1:
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f = Path(save_dir) / ('test_batch%g_gt.jpg' % batch_i) # filename
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plot_images(img, targets, paths, str(f), names) # ground truth
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f = Path(save_dir) / ('test_batch%g_pred.jpg' % batch_i)
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plot_images(img, output_to_target(output, width, height), paths, str(f), names) # predictions
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# Compute statistics
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stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
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if len(stats) and stats[0].any():
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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) # [P, R, AP@0.5, AP@0.5:0.95]
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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) # number of targets per class
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else:
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nt = torch.zeros(1)
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# Print results
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pf = '%20s' + '%12.3g' * 6 # print format
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print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
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# Print results per class
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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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# Print speeds
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t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size) # tuple
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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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# Save JSON
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if save_json and len(jdict):
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f = 'detections_val2017_%s_results.json' % \
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(weights.split(os.sep)[-1].replace('.pt', '') if isinstance(weights, str) else '') # filename
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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: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
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from pycocotools.coco import COCO
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from pycocotools.cocoeval import COCOeval
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imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files]
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cocoGt = COCO(glob.glob('../coco/annotations/instances_val*.json')[0]) # initialize COCO ground truth api
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cocoDt = cocoGt.loadRes(f) # initialize COCO pred api
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cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
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cocoEval.params.imgIds = imgIds # image IDs to evaluate
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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] # update results (mAP@0.5:0.95, mAP@0.5)
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except Exception as e:
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print('ERROR: pycocotools unable to run: %s' % e)
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# Return results
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model.float() # for training
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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', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
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parser.add_argument('--data', type=str, default='data/coco128.yaml', help='*.data path')
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parser.add_argument('--batch-size', type=int, default=32, 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('--merge', action='store_true', help='use Merge NMS')
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parser.add_argument('--verbose', action='store_true', help='report mAP by class')
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parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
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opt = parser.parse_args()
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opt.save_json |= opt.data.endswith('coco.yaml')
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opt.data = check_file(opt.data) # check file
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print(opt)
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if opt.task in ['val', 'test']: # run normally
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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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opt.verbose)
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elif opt.task == 'study': # run over a range of settings and save/plot
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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) # filename to save to
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x = list(range(352, 832, 64)) # x axis
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y = [] # y axis
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for i in x: # img-size
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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) # results and times
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np.savetxt(f, y, fmt='%10.4g') # save
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os.system('zip -r study.zip study_*.txt')
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# plot_study_txt(f, x) # plot
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