text: import torch
  import numpy as np
  import torch.nn as nn
  import math
  import argparse
  from torch import optim
  
  def conv_bn(inp, oup, stride):
      return nn.Sequential(
          nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
          nn.BatchNorm2d(oup, track_running_stats=False),
          nn.ReLU(inplace=True)
      )
  
  
  def conv_1x1_bn(inp, oup):
      return nn.Sequential(
          nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
          nn.BatchNorm2d(oup, track_running_stats=False),
          nn.ReLU(inplace=True)
      )
  
  
  def make_divisible(x, divisible_by=8):
      import numpy as np
      return int(np.ceil(x * 1. / divisible_by) * divisible_by)
  
  
  class InvertedResidual(nn.Module):
      def __init__(self, inp, oup, stride, expand_ratio):
          super(InvertedResidual, self).__init__()
          self.stride = stride
          assert stride in [1, 2]
  
          hidden_dim = int(inp * expand_ratio)
          self.use_res_connect = self.stride == 1 and inp == oup
  
          if expand_ratio == 1:
              self.conv = nn.Sequential(
                  # dw
                  nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
                  nn.BatchNorm2d(hidden_dim, track_running_stats=False),
                  nn.ReLU(inplace=True),
                  # pw-linear
                  nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                  nn.BatchNorm2d(oup, track_running_stats=False),
              )
          else:
              self.conv = nn.Sequential(
                  # pw
                  nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
                  nn.BatchNorm2d(hidden_dim, track_running_stats=False),
                  nn.ReLU(inplace=True),
                  # dw
                  nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
                  nn.BatchNorm2d(hidden_dim, track_running_stats=False),
                  nn.ReLU(inplace=True),
                  # pw-linear
                  nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                  nn.BatchNorm2d(oup, track_running_stats=False),
              )
  
      def forward(self, x):
          if self.use_res_connect:
              return x + self.conv(x)
          else:
              return self.conv(x)
  
  
  class MobileNetV2(nn.Module):
      def __init__(self, n_class=1000, input_size=224, width_mult=1.):
          super(MobileNetV2, self).__init__()
          block = InvertedResidual
          input_channel = 32
          last_channel = 1280
          interverted_residual_setting = [
              # t, c, n, s
              [1, 16, 1, 1],
              [6, 24, 2, 2],
              [6, 32, 3, 2],
              [6, 64, 4, 2],
              [6, 96, 3, 1],
              [6, 160, 3, 2],
              [6, 320, 1, 1],
          ]
  
          # building first layer
          assert input_size % 32 == 0
          # input_channel = make_divisible(input_channel * width_mult)  # first channel is always 32!
          self.last_channel = make_divisible(last_channel * width_mult) if width_mult > 1.0 else last_channel
          self.features = [conv_bn(3, input_channel, 2)]
          # building inverted residual blocks
          for t, c, n, s in interverted_residual_setting:
              output_channel = make_divisible(c * width_mult) if t > 1 else c
              for i in range(n):
                  if i == 0:
                      self.features.append(block(input_channel, output_channel, s, expand_ratio=t))
                  else:
                      self.features.append(block(input_channel, output_channel, 1, expand_ratio=t))
                  input_channel = output_channel
          # building last several layers
          self.features.append(conv_1x1_bn(input_channel, self.last_channel))
          # make it nn.Sequential
          self.features = nn.Sequential(*self.features)
  
          # building classifier
          self.classifier = nn.Linear(self.last_channel, n_class)
  
          self._initialize_weights()
  
      def forward(self, x):
          x = self.features(x)
          x = x.mean(3).mean(2)
          x = self.classifier(x)
          return x
  
      def _initialize_weights(self):
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                  m.weight.data.normal_(0, math.sqrt(2. / n))
                  if m.bias is not None:
                      m.bias.data.zero_()
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
              elif isinstance(m, nn.Linear):
                  n = m.weight.size(1)
                  m.weight.data.normal_(0, 0.01)
                  m.bias.data.zero_()
  
  example_text = """
      example:
          python mobile_v2.py --batch 16--enable_cudnn --number 5 --repeats 5
          python mobile_v2.py --batch 1 --number 8 --repeats 8
  """
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser(
          prog="base_maker",
          description="template maker",
          epilog=example_text,
          formatter_class=argparse.RawDescriptionHelpFormatter,
      )
      parser.add_argument('--batch', type=int, default=1)
      parser.add_argument('--enable_cudnn', action='store_true')
      parser.add_argument('--number', type=int, default=10)
      parser.add_argument('--repeats', type=int, default=10)
  
      args = parser.parse_args()
  
      if args.enable_cudnn:
          assert torch.backends.cudnn.is_available()
          torch.backends.cudnn.enabled = True
      else:
          torch.backends.cudnn.enabled = False
  
      batch_size = args.batch
      
      # The following code is used for profiling with dummy data input on CUDA 0
      model = MobileNetV2().cuda().half()
      model.eval()
  
      img_tensor = torch.rand([batch_size, 3, 224, 224], dtype=torch.float16).cuda()
      
      # warm up
      out = model(img_tensor)
  
      number = args.number
      repeats = args.repeats
      for i in range(repeats):
          time_record = []
          for j in range(number):
              torch.cuda.synchronize()
              start = torch.cuda.Event(enable_timing=True)
              end = torch.cuda.Event(enable_timing=True)
              start.record()
  
              output = model(img_tensor)
  
              end.record()
              torch.cuda.synchronize()
              total = start.elapsed_time(end)
              time_record.append(total)
          if i == repeats - 1:
              print("Average inference latency for mobile_v1 fp16", np.mean(time_record))
      print("batch = ", batch_size)
  