text: import argparse
  import glob
  import json
  import os
  import shutil
  from pathlib import Path
  
  import numpy as np
  import torch
  import yaml
  from tqdm import tqdm
  
  from models.experimental import attempt_load
  from utils.datasets import create_dataloader
  from utils.general import (
      coco80_to_coco91_class, check_dataset, check_file, check_img_size, compute_loss, non_max_suppression, scale_coords, 
      xyxy2xywh, clip_coords, plot_images, xywh2xyxy, box_iou, output_to_target, ap_per_class, set_logging)
  from utils.torch_utils import select_device, time_synchronized
  
  
  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='',
           merge=False,
           save_txt=False):
      # 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)
          merge, save_txt = opt.merge, opt.save_txt  # use Merge NMS, save *.txt labels
          if save_txt:
              out = Path('inference/output')
              if os.path.exists(out):
                  shutil.rmtree(out)  # delete output folder
              os.makedirs(out)  # make new output folder
  
          # Remove previous
          for f in glob.glob(str(Path(save_dir) / 'test_batch*.jpg')):
              os.remove(f)
  
          # Load model
          model = attempt_load(weights, map_location=device)  # load FP32 model
          imgsz = check_img_size(imgsz, s=model.stride.max())  # check img_size
  
          # Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99
          # if device.type != 'cpu' and torch.cuda.device_count() > 1:
          #     model = nn.DataParallel(model)
  
      # Half
      half = device.type != 'cpu'  # half precision only supported on CUDA
      if half:
          model.half()
  
      # Configure
      model.eval()
      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()
  
      # 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, model.stride.max(), opt,
                                         hyp=None, augment=False, cache=False, pad=0.5, rect=True)[0]
  
      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, 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)
  
          # 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]  # GIoU, obj, cls
  
              # Run NMS
              t = time_synchronized()
              output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, merge=merge)
              t1 += 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
              if save_txt:
                  gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]]  # normalization gain whwh
                  txt_path = str(out / Path(paths[si]).stem)
                  pred[:, :4] = scale_coords(img[si].shape[1:], pred[:, :4], shapes[si][0], shapes[si][1])  # to original
                  for *xyxy, conf, cls in pred:
                      xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                      with open(txt_path + '.txt', 'a') as f:
                          f.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format
  
              # 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 = Path(paths[si]).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': int(image_id) if image_id.isnumeric() else 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(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
                          for j in (ious > iouv[0]).nonzero(as_tuple=False):
                              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 = Path(save_dir) / ('test_batch%g_gt.jpg' % batch_i)  # filename
              plot_images(img, targets, paths, str(f), names)  # ground truth
              f = Path(save_dir) / ('test_batch%g_pred.jpg' % batch_i)
              plot_images(img, output_to_target(output, width, height), paths, str(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)
          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 len(jdict):
          f = 'detections_val2017_%s_results.json' % \
              (weights.split(os.sep)[-1].replace('.pt', '') if isinstance(weights, str) else '')  # filename
          print('\nCOCO mAP with pycocotools... saving %s...' % f)
          with open(f, 'w') as file:
              json.dump(jdict, file)
  
          try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
              from pycocotools.coco import COCO
              from pycocotools.cocoeval import COCOeval
  
              imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files]
              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 Exception as e:
              print('ERROR: pycocotools unable to run: %s' % e)
  
      # Return results
      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='yolov5s.pt', help='model.pt path(s)')
      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')
      parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
      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)
  
      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
  