text: import argparse
  import glob
  import json
  import os
  from pathlib import Path
  
  import numpy as np
  import torch
  import yaml
  from tqdm import tqdm
  
  from yolor.utils.google_utils import attempt_load
  from yolor.utils.datasets import create_dataloader
  from yolor.utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, box_iou, \
      non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, clip_coords, set_logging, increment_path
  from yolor.utils.loss import compute_loss
  from yolor.utils.metrics import ap_per_class
  from yolor.utils.plots import plot_images, output_to_target
  from yolor.utils.torch_utils import select_device, time_synchronized
  
  from yolor.models.models import *
  
  #from utils.augmentation import mask_augment
  def load_classes(path):
      # Loads *.names file at 'path'
      with open(path, 'r') as f:
          names = f.read().split('\n')
      return list(filter(None, names))  # filter removes empty strings (such as last line)
  
  
  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=Path(''),  # for saving images
           save_txt=False,  # for auto-labelling
           save_conf=False,
           plots=True,
           log_imgs=0):  # number of logged images
  
      # 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)
          save_txt = opt.save_txt  # save *.txt labels
  
          # Directories
          save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
          (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
  
          # Load model
          model = Darknet(opt.cfg).to(device)
  
          # load model
          try:
              ckpt = torch.load(weights[0], map_location=device)  # load checkpoint
              ckpt['model'] = {k: v for k, v in ckpt['model'].items() if model.state_dict()[k].numel() == v.numel()}
              model.load_state_dict(ckpt['model'], strict=False)
          except:
              load_darknet_weights(model, weights[0])
          imgsz = check_img_size(imgsz, s=64)  # check img_size
  
      # Half
      half = device.type != 'cpu'  # half precision only supported on CUDA
      if half:
          model.half()
  
      # Configure
      model.eval()
      is_coco = data.endswith('coco.yaml')  # is COCO dataset
      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()
  
      # Logging
      log_imgs, wandb = min(log_imgs, 100), None  # ceil
      try:
          import wandb  # Weights & Biases
      except ImportError:
          log_imgs = 0
  
      # 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, 64, opt, pad=0.5, rect=True)[0]
  
      seen = 0
      try:
          names = model.names if hasattr(model, 'names') else model.module.names
      except:
          names = load_classes(opt.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, wandb_images = [], [], [], [], []
      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)
  
          #img,targets = mask_augment(img,targets)
          # 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]  # box, obj, cls
  
              # Run NMS
              t = time_synchronized()
              output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres)
              t1 += time_synchronized() - t
  
              #output = [torch.cat([x,torch.tensor([[0,0,width, height,1,1]]).to(device)]) for x in output]
          # 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 len(pred) == 0:
                  if nl:
                      stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
                  continue
  
              # Append to text file
              path = Path(paths[si])
              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
                      #line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
                      if 'voc' in opt.data and cls>19:
                          continue
                      if 'kitti' in opt.data and cls>3:
                          continue
                      xyxy = torch.tensor(xyxy).tolist()
                      line = (cls, conf,*xyxy)
                      with open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f:
                          f.write(('%g ' * len(line)).rstrip() % line + '\n')
  
              # W&B logging
              if plots and len(wandb_images) < log_imgs:
                  box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
                               "class_id": int(cls),
                               "box_caption": "%s %.3f" % (names[cls], conf),
                               "scores": {"class_score": conf},
                               "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
                  boxes = {"predictions": {"box_data": box_data, "class_labels": names}}
                  wandb_images.append(wandb.Image(img[si], boxes=boxes, caption=path.name))
  
              # 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.stem) if path.stem.isnumeric() else path.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()):
                      jdict.append({'image_id': image_id,
                                    'category_id': coco91class[int(p[5])] if is_coco else 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(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 plots and batch_i < 0:
              f = save_dir / f'test_batch{batch_i}_labels.jpg'  # filename
              plot_images(img, targets, paths, f, names)  # labels
              f = save_dir / f'test_batch{batch_i}_pred.jpg'
              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) and stats[0].any():
          p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, fname=save_dir / 'precision-recall_curve.png')
          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)
  
      # W&B logging
      if plots and wandb:
          wandb.log({"Images": wandb_images})
          wandb.log({"Validation": [wandb.Image(str(x), caption=x.name) for x in sorted(save_dir.glob('test*.jpg'))]})
  
      # 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):
          w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ''  # weights
          anno_json = glob.glob('../data/coco/annotations/instances_val*.json')[0]  # annotations json
          pred_json = str(save_dir / f"{w}_predictions.json")  # predictions json
          print('\nEvaluating pycocotools mAP... saving %s...' % pred_json)
          with open(pred_json, 'w') as f:
              json.dump(jdict, f)
  
          try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
              from pycocotools.coco import COCO
              from pycocotools.cocoeval import COCOeval
  
              anno = COCO(anno_json)  # init annotations api
              pred = anno.loadRes(pred_json)  # init predictions api
              eval = COCOeval(anno, pred, 'bbox')
              if is_coco:
                  eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files]  # image IDs to evaluate
              eval.evaluate()
              eval.accumulate()
              eval.summarize()
              map, map50 = eval.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
      if not training:
          print('Results saved to %s' % save_dir)
      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='yolor_p6.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=8, help='size of each image batch')
      parser.add_argument('--img-size', type=int, default=1280, 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('--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('--verbose', action='store_true', help='report mAP by class')
      parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
      parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
      parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
      parser.add_argument('--project', default='yolor/runs/test', help='save to project/name')
      parser.add_argument('--name', default='exp', help='save to project/name')
      parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
      parser.add_argument('--cfg', type=str, default='yolor/cfg/yolor_p6.cfg', help='*.cfg path')
      parser.add_argument('--names', type=str, default='data/coco.names', help='*.cfg path')
      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,
               save_txt=opt.save_txt,
               save_conf=opt.save_conf,
               )
  
      elif opt.task == 'study':  # run over a range of settings and save/plot
          for weights in ['yolor_p6.pt', 'yolor_w6.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
  