code: def eval_retinanet():
      # RetinaNet with ResNeXt50_32X4D
      from extra.models.resnet import ResNeXt50_32X4D
      from extra.models.retinanet import RetinaNet
  
      mdl = RetinaNet(ResNeXt50_32X4D())
      mdl.load_from_pretrained()
  
      input_mean = Tensor([0.485, 0.456, 0.406]).reshape(1, -1, 1, 1)
      input_std = Tensor([0.229, 0.224, 0.225]).reshape(1, -1, 1, 1)
  
      def input_fixup(x):
          x = x.permute([0, 3, 1, 2]) / 255.0
          x -= input_mean
          x /= input_std
          return x
  
      from extra.datasets.openimages import openimages, iterate
      from pycocotools.coco import COCO
      from pycocotools.cocoeval import COCOeval
      from contextlib import redirect_stdout
  
      coco = COCO(openimages())
      coco_eval = COCOeval(coco, iouType="bbox")
      coco_evalimgs, evaluated_imgs, ncats, narea = (
          [],
          [],
          len(coco_eval.params.catIds),
          len(coco_eval.params.areaRng),
      )
  
      from tinygrad.jit import TinyJit
  
      mdlrun = TinyJit(lambda x: mdl(input_fixup(x)).realize())
  
      n, bs = 0, 8
      st = time.perf_counter()
      for x, targets in iterate(coco, bs):
          dat = Tensor(x.astype(np.float32))
          mt = time.perf_counter()
          if dat.shape[0] == bs:
              outs = mdlrun(dat).numpy()
          else:
              mdlrun.jit_cache = None
              outs = mdl(input_fixup(dat)).numpy()
          et = time.perf_counter()
          predictions = mdl.postprocess_detections(
              outs,
              input_size=dat.shape[1:3],
              orig_image_sizes=[t["image_size"] for t in targets],
          )
          ext = time.perf_counter()
          n += len(targets)
          print(
              f"[{n}/{len(coco.imgs)}] == {(mt-st)*1000:.2f} ms loading data, {(et-mt)*1000:.2f} ms to run model, {(ext-et)*1000:.2f} ms for postprocessing"
          )
          img_ids = [t["image_id"] for t in targets]
          coco_results = [
              {
                  "image_id": targets[i]["image_id"],
                  "category_id": label,
                  "bbox": box,
                  "score": score,
              }
              for i, prediction in enumerate(predictions)
              for box, score, label in zip(*prediction.values())
          ]
          with redirect_stdout(None):
              coco_eval.cocoDt = coco.loadRes(coco_results)
              coco_eval.params.imgIds = img_ids
              coco_eval.evaluate()
          evaluated_imgs.extend(img_ids)
          coco_evalimgs.append(
              np.array(coco_eval.evalImgs).reshape(
                  ncats, narea, len(img_ids)
              )
          )
          st = time.perf_counter()
  
      coco_eval.params.imgIds = evaluated_imgs
      coco_eval._paramsEval.imgIds = evaluated_imgs
      coco_eval.evalImgs = list(
          np.concatenate(coco_evalimgs, -1).flatten()
      )
      coco_eval.accumulate()
      coco_eval.summarize()
  
file_path: tinygrad/examples/mlperf/model_eval.py
imports: import time
  from pathlib import Path
  import numpy
  from tinygrad import Tensor, Device, dtypes, GlobalCounters
  from tinygrad.jit import TinyJit
  from tinygrad.nn.state import get_parameters, load_state_dict, safe_load
  from tinygrad.helpers import getenv, Timing
  from examples.mlperf import helpers
label_desc: Use image node_id 4 for calling a global function with example usage: eval_retinanet() and returns: x