code: def evaluate(model, coco, cocoGt, encoder, inv_map, args):
      if args.distributed:
          N_gpu = torch.distributed.get_world_size()
      else:
          N_gpu = 1
  
      model.eval()
      model.cuda()
  
      ret = []
      start = time.time()
  
      # for idx, image_id in enumerate(coco.img_keys):
      for nbatch, (img, img_id, img_size, _, _) in enumerate(coco):
          print(
              "Parsing batch: {}/{}".format(nbatch, len(coco)), end="\r"
          )
          with torch.no_grad():
              inp = img.cuda()
              with torch.cuda.amp.autocast(enabled=args.fp16_mode):
                  # Get predictions
                  ploc, plabel = model(inp)
              ploc, plabel = ploc.float(), plabel.float()
  
              # Handle the batch of predictions produced
              # This is slow, but consistent with old implementation.
              for idx in range(ploc.shape[0]):
                  # ease-of-use for specific predictions
                  ploc_i = ploc[idx, :, :].unsqueeze(0)
                  plabel_i = plabel[idx, :, :].unsqueeze(0)
  
                  try:
                      result = encoder.decode_batch(
                          ploc_i, plabel_i, 0.50, 200
                      )[0]
                  except:
                      # raise
                      print("")
                      print("No object detected in idx: {}".format(idx))
                      continue
  
                  htot, wtot = (
                      img_size[0][idx].item(),
                      img_size[1][idx].item(),
                  )
                  loc, label, prob = [r.cpu().numpy() for r in result]
                  for loc_, label_, prob_ in zip(loc, label, prob):
                      ret.append(
                          [
                              img_id[idx],
                              loc_[0] * wtot,
                              loc_[1] * htot,
                              (loc_[2] - loc_[0]) * wtot,
                              (loc_[3] - loc_[1]) * htot,
                              prob_,
                              inv_map[label_],
                          ]
                      )
  
      # Now we have all predictions from this rank, gather them all together
      # if necessary
      ret = np.array(ret).astype(np.float32)
  
      # Multi-GPU eval
      if args.distributed:
          # NCCL backend means we can only operate on GPU tensors
          ret_copy = torch.tensor(ret).cuda()
          # Everyone exchanges the size of their results
          ret_sizes = [torch.tensor(0).cuda() for _ in range(N_gpu)]
  
          torch.cuda.synchronize()
          torch.distributed.all_gather(
              ret_sizes, torch.tensor(ret_copy.shape[0]).cuda()
          )
          torch.cuda.synchronize()
  
          # Get the maximum results size, as all tensors must be the same shape for
          # the all_gather call we need to make
          max_size = 0
          sizes = []
          for s in ret_sizes:
              max_size = max(max_size, s.item())
              sizes.append(s.item())
  
          # Need to pad my output to max_size in order to use in all_gather
          ret_pad = torch.cat(
              [
                  ret_copy,
                  torch.zeros(
                      max_size - ret_copy.shape[0],
                      7,
                      dtype=torch.float32,
                  ).cuda(),
              ]
          )
  
          # allocate storage for results from all other processes
          other_ret = [
              torch.zeros(max_size, 7, dtype=torch.float32).cuda()
              for i in range(N_gpu)
          ]
          # Everyone exchanges (padded) results
  
          torch.cuda.synchronize()
          torch.distributed.all_gather(other_ret, ret_pad)
          torch.cuda.synchronize()
  
          # Now need to reconstruct the _actual_ results from the padded set using slices.
          cat_tensors = []
          for i in range(N_gpu):
              cat_tensors.append(other_ret[i][: sizes[i]][:])
  
          final_results = torch.cat(cat_tensors).cpu().numpy()
      else:
          # Otherwise full results are just our results
          final_results = ret
  
      if args.local_rank == 0:
          print("")
          print(
              "Predicting Ended, total time: {:.2f} s".format(
                  time.time() - start
              )
          )
  
      cocoDt = cocoGt.loadRes(final_results)
  
      E = COCOeval(cocoGt, cocoDt, iouType="bbox")
      E.evaluate()
      E.accumulate()
      if args.local_rank == 0:
          E.summarize()
          print("Current AP: {:.5f}".format(E.stats[0]))
      else:
          # fix for cocoeval indiscriminate prints
          with redirect_stdout(io.StringIO()):
              E.summarize()
  
      # put your model in training mode back on
      model.train()
  
      return E.stats[0]
  
imports: import torch
  import time
  import numpy
  from contextlib import redirect_stdout
  import io
  from pycocotools.cocoeval import COCOeval
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(model, coco, cocoGt, encoder, inv_map, args) and returns: E