text: #
  # SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  # SPDX-License-Identifier: Apache-2.0
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  # http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  
  import os
  import sys
  import argparse
  
  import numpy as np
  
  from infer import TensorRTInfer
  from image_batcher import ImageBatcher
  
  
  def main(args):
      annotations = {}
      for line in open(args.annotations, "r"):
          line = line.strip().split(args.separator)
          if len(line) < 2 or not line[1].isnumeric():
              print("Could not parse the annotations file correctly, make sure the correct separator is used")
              sys.exit(1)
          annotations[os.path.basename(line[0])] = int(line[1])
  
      trt_infer = TensorRTInfer(args.engine)
      batcher = ImageBatcher(args.input, *trt_infer.input_spec(), preprocessor=args.preprocessor)
      top1 = 0
      top5 = 0
      total = 0
      for batch, images in batcher.get_batch():
          classes, scores, top = trt_infer.infer(batch, top=5)
          for i in range(len(images)):
              image = os.path.basename(images[i])
              if image not in annotations.keys():
                  print("Image '{}' does not appear in the annotations file, please make sure all evaluated "
                        "images have a corresponding ground truth label".format(image))
                  sys.exit(1)
              if annotations[image] == classes[i]:
                  top1 += 1
              if annotations[image] in top[0][i]:
                  top5 += 1
              total += 1
              top1_acc = 100 * (top1 / total)
              top5_acc = 100 * (top5 / total)
              print("Processing {} / {} : Top-1 {:0.1f}% , Top-5: {:0.1f}%     ".format(total, batcher.num_images,
                                                                                        top1_acc, top5_acc), end="\r")
      print()
      print("Top-1 Accuracy: {:0.3f}%".format(top1_acc))
      print("Top-5 Accuracy: {:0.3f}%".format(top5_acc))
  
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser()
      parser.add_argument("-e", "--engine", help="The TensorRT engine to infer with")
      parser.add_argument("-i", "--input",
                          help="The input to infer, either a single image path, or a directory of images")
      parser.add_argument("-a", "--annotations", help="Set the file to use for classification ground truth annotations")
      parser.add_argument("-s", "--separator", default=" ",
                          help="Separator to use between columns when parsing the annotations file, default: ' ' (space)")
      parser.add_argument("-p", "--preprocessor", default="V2", choices=["V1", "V1MS", "V2"],
                          help="Select the image preprocessor to use, either 'V2', 'V1' or 'V1MS', default: V2")
      args = parser.parse_args()
      if not all([args.engine, args.input, args.annotations]):
          parser.print_help()
          print("\nThese arguments are required: --engine  --input and --annotations")
          sys.exit(1)
      main(args)
  