text: from __future__ import print_function
  import caffe
  from caffe.model_libs import *
  from google.protobuf import text_format
  
  import math
  import os
  import shutil
  import stat
  import subprocess
  import sys
  
  # Add extra layers on top of a "base" network (e.g. VGGNet or Inception).
  def AddExtraLayers(net, use_batchnorm=True, lr_mult=1):
      use_relu = True
  
      # Add additional convolutional layers.
      # 19 x 19
      from_layer = net.keys()[-1]
  
      # TODO(weiliu89): Construct the name using the last layer to avoid duplication.
      # 10 x 10
      out_layer = "conv6_1"
      ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 1, 0, 1,
          lr_mult=lr_mult)
  
      from_layer = out_layer
      out_layer = "conv6_2"
      ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 512, 3, 1, 2,
          lr_mult=lr_mult)
  
      # 5 x 5
      from_layer = out_layer
      out_layer = "conv7_1"
      ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 128, 1, 0, 1,
        lr_mult=lr_mult)
  
      from_layer = out_layer
      out_layer = "conv7_2"
      ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 3, 1, 2,
        lr_mult=lr_mult)
  
      # 3 x 3
      from_layer = out_layer
      out_layer = "conv8_1"
      ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 128, 1, 0, 1,
        lr_mult=lr_mult)
  
      from_layer = out_layer
      out_layer = "conv8_2"
      ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 3, 0, 1,
        lr_mult=lr_mult)
  
      # 1 x 1
      from_layer = out_layer
      out_layer = "conv9_1"
      ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 128, 1, 0, 1,
        lr_mult=lr_mult)
  
      from_layer = out_layer
      out_layer = "conv9_2"
      ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 3, 0, 1,
        lr_mult=lr_mult)
  
      return net
  
  
  ### Modify the following parameters accordingly ###
  # The directory which contains the caffe code.
  # We assume you are running the script at the CAFFE_ROOT.
  caffe_root = os.getcwd()
  
  # Set true if you want to start training right after generating all files.
  run_soon = True
  # The device id for webcam
  webcam_id = 0
  # Number of frames to be skipped.
  skip_frames = 0
  
  # The parameters for the webcam demo
  
  # Key parameters used in training
  # If true, use batch norm for all newly added layers.
  # Currently only the non batch norm version has been tested.
  use_batchnorm = False
  num_classes = 21
  share_location = True
  background_label_id=0
  conf_loss_type = P.MultiBoxLoss.SOFTMAX
  code_type = P.PriorBox.CENTER_SIZE
  lr_mult = 1.
  # Stores LabelMapItem.
  label_map_file = "data/VOC0712/labelmap_voc.prototxt"
  # The resized image size
  resize_width = 300
  resize_height = 300
  
  # Parameters needed for test.
  # Set the number of test iterations to the maximum integer number.
  test_iter = int(math.pow(2, 29) - 1)
  # Use GPU or CPU
  solver_mode = P.Solver.GPU
  # Defining which GPUs to use.
  gpus = "0"
  # Number of frames to be processed per batch.
  test_batch_size = 1
  # Only display high quality detections whose scores are higher than a threshold.
  visualize_threshold = 0.6
  # Size of webcam image.
  webcam_width = 640
  webcam_height = 480
  # Scale the image size for display.
  scale = 1.5
  
  ### Hopefully you don't need to change the following ###
  resize = "{}x{}".format(resize_width, resize_height)
  video_data_param = {
          'video_type': P.VideoData.WEBCAM,
          'device_id': webcam_id,
          'skip_frames': skip_frames,
          }
  test_transform_param = {
          'mean_value': [104, 117, 123],
          'resize_param': {
                  'prob': 1,
                  'resize_mode': P.Resize.WARP,
                  'height': resize_height,
                  'width': resize_width,
                  'interp_mode': [P.Resize.LINEAR],
                  },
          }
  output_transform_param = {
          'mean_value': [104, 117, 123],
          'resize_param': {
                  'prob': 1,
                  'resize_mode': P.Resize.WARP,
                  'height': int(webcam_height * scale),
                  'width': int(webcam_width * scale),
                  'interp_mode': [P.Resize.LINEAR],
                  },
          }
  # parameters for generating detection output.
  det_out_param = {
      'num_classes': num_classes,
      'share_location': share_location,
      'background_label_id': background_label_id,
      'nms_param': {'nms_threshold': 0.45, 'top_k': 400},
      'save_output_param': {
              'label_map_file': label_map_file,
              },
      'keep_top_k': 200,
      'confidence_threshold': 0.01,
      'code_type': code_type,
      'visualize': True,
      'visualize_threshold': visualize_threshold,
      }
  
  # The job name should be same as the name used in examples/ssd/ssd_pascal.py.
  job_name = "SSD_{}".format(resize)
  # The name of the model. Modify it if you want.
  model_name = "VGG_VOC0712_{}".format(job_name)
  
  # Directory which stores the model .prototxt file.
  save_dir = "models/VGGNet/VOC0712/{}_webcam".format(job_name)
  # Directory which stores the snapshot of trained models.
  snapshot_dir = "models/VGGNet/VOC0712/{}".format(job_name)
  # Directory which stores the job script and log file.
  job_dir = "jobs/VGGNet/VOC0712/{}_webcam".format(job_name)
  
  # model definition files.
  test_net_file = "{}/test.prototxt".format(save_dir)
  # snapshot prefix.
  snapshot_prefix = "{}/{}".format(snapshot_dir, model_name)
  # job script path.
  job_file = "{}/{}.sh".format(job_dir, model_name)
  
  # Find most recent snapshot.
  max_iter = 0
  for file in os.listdir(snapshot_dir):
    if file.endswith(".caffemodel"):
      basename = os.path.splitext(file)[0]
      iter = int(basename.split("{}_iter_".format(model_name))[1])
      if iter > max_iter:
        max_iter = iter
  
  if max_iter == 0:
    print("Cannot find snapshot in {}".format(snapshot_dir))
    sys.exit()
  
  # The resume model.
  pretrain_model = "{}_iter_{}.caffemodel".format(snapshot_prefix, max_iter)
  
  # parameters for generating priors.
  # minimum dimension of input image
  min_dim = 300
  # conv4_3 ==> 38 x 38
  # fc7 ==> 19 x 19
  # conv6_2 ==> 10 x 10
  # conv7_2 ==> 5 x 5
  # conv8_2 ==> 3 x 3
  # conv9_2 ==> 1 x 1
  mbox_source_layers = ['conv4_3', 'fc7', 'conv6_2', 'conv7_2', 'conv8_2', 'conv9_2']
  # in percent %
  min_ratio = 20
  max_ratio = 90
  step = int(math.floor((max_ratio - min_ratio) / (len(mbox_source_layers) - 2)))
  min_sizes = []
  max_sizes = []
  for ratio in xrange(min_ratio, max_ratio + 1, step):
    min_sizes.append(min_dim * ratio / 100.)
    max_sizes.append(min_dim * (ratio + step) / 100.)
  min_sizes = [min_dim * 10 / 100.] + min_sizes
  max_sizes = [min_dim * 20 / 100.] + max_sizes
  steps = [8, 16, 32, 64, 100, 300]
  aspect_ratios = [[2], [2, 3], [2, 3], [2, 3], [2], [2]]
  # L2 normalize conv4_3.
  normalizations = [20, -1, -1, -1, -1, -1]
  # variance used to encode/decode prior bboxes.
  if code_type == P.PriorBox.CENTER_SIZE:
    prior_variance = [0.1, 0.1, 0.2, 0.2]
  else:
    prior_variance = [0.1]
  flip = True
  clip = False
  
  # Check file.
  check_if_exist(label_map_file)
  check_if_exist(pretrain_model)
  make_if_not_exist(save_dir)
  make_if_not_exist(job_dir)
  make_if_not_exist(snapshot_dir)
  
  # Create test net.
  net = caffe.NetSpec()
  net.data = L.VideoData(video_data_param=video_data_param,
          data_param=dict(batch_size=test_batch_size),
          transform_param=test_transform_param)
  
  VGGNetBody(net, from_layer='data', fully_conv=True, reduced=True, dilated=True,
      dropout=False)
  
  AddExtraLayers(net, use_batchnorm, lr_mult=lr_mult)
  
  mbox_layers = CreateMultiBoxHead(net, data_layer='data', from_layers=mbox_source_layers,
          use_batchnorm=use_batchnorm, min_sizes=min_sizes, max_sizes=max_sizes,
          aspect_ratios=aspect_ratios, steps=steps, normalizations=normalizations,
          num_classes=num_classes, share_location=share_location, flip=flip, clip=clip,
          prior_variance=prior_variance, kernel_size=3, pad=1, lr_mult=lr_mult)
  
  conf_name = "mbox_conf"
  if conf_loss_type == P.MultiBoxLoss.SOFTMAX:
    reshape_name = "{}_reshape".format(conf_name)
    net[reshape_name] = L.Reshape(net[conf_name], shape=dict(dim=[0, -1, num_classes]))
    softmax_name = "{}_softmax".format(conf_name)
    net[softmax_name] = L.Softmax(net[reshape_name], axis=2)
    flatten_name = "{}_flatten".format(conf_name)
    net[flatten_name] = L.Flatten(net[softmax_name], axis=1)
    mbox_layers[1] = net[flatten_name]
  elif conf_loss_type == P.MultiBoxLoss.LOGISTIC:
    sigmoid_name = "{}_sigmoid".format(conf_name)
    net[sigmoid_name] = L.Sigmoid(net[conf_name])
    mbox_layers[1] = net[sigmoid_name]
  
  mbox_layers.append(net.data)
  net.detection_out = L.DetectionOutput(*mbox_layers,
      detection_output_param=det_out_param,
      transform_param=output_transform_param,
      include=dict(phase=caffe_pb2.Phase.Value('TEST')))
  net.slience = L.Silence(net.detection_out, ntop=0,
      include=dict(phase=caffe_pb2.Phase.Value('TEST')))
  
  with open(test_net_file, 'w') as f:
      print('name: "{}_test"'.format(model_name), file=f)
      print(net.to_proto(), file=f)
  shutil.copy(test_net_file, job_dir)
  
  # Create job file.
  with open(job_file, 'w') as f:
    f.write('cd {}\n'.format(caffe_root))
    f.write('./build/tools/caffe test \\\n')
    f.write('--model="{}" \\\n'.format(test_net_file))
    f.write('--weights="{}" \\\n'.format(pretrain_model))
    f.write('--iterations="{}" \\\n'.format(test_iter))
    if solver_mode == P.Solver.GPU:
      f.write('--gpu {}\n'.format(gpus))
  
  # Copy the python script to job_dir.
  py_file = os.path.abspath(__file__)
  shutil.copy(py_file, job_dir)
  
  # Run the job.
  os.chmod(job_file, stat.S_IRWXU)
  if run_soon:
    subprocess.call(job_file, shell=True)
  