text: import functools
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.nn.init as init
  import cv2
  import re
  import argparse
  
  def str2bool(v):
      if isinstance(v, bool):
          return v
      if v.lower() in ('yes', 'true', 't', 'y', '1'):
          return True
      elif v.lower() in ('no', 'false', 'f', 'n', '0'):
          return False
      else:
          raise argparse.ArgumentTypeError('Boolean value expected.')
  
  parser = argparse.ArgumentParser()
  parser.add_argument('--image_path', type=str,required=True,help = 'root directory of the image')
  parser.add_argument('--sr_ratio', type=int, default=4,choices=[2,4],help = 'mutiple value of super resolution')
  parser.add_argument('--device', type=str, default='cuda',help = 'device to use for computation')
  parser.add_argument('--use_half', type=str2bool, default=False,help = 'use float16 for computation')
  args = parser.parse_args()
  
  save_path = ''.join(re.split('\.',args.image_path)[:-1]) + f'_x{args.sr_ratio}.png'
  
  def initialize_weights(net_l, scale=1):
      if not isinstance(net_l, list):
          net_l = [net_l]
      for net in net_l:
          for m in net.modules():
              if isinstance(m, nn.Conv2d):
                  init.kaiming_normal_(m.weight, a=0, mode='fan_in')
                  m.weight.data *= scale  # for residual block
                  if m.bias is not None:
                      m.bias.data.zero_()
              elif isinstance(m, nn.Linear):
                  init.kaiming_normal_(m.weight, a=0, mode='fan_in')
                  m.weight.data *= scale
                  if m.bias is not None:
                      m.bias.data.zero_()
              elif isinstance(m, nn.BatchNorm2d):
                  init.constant_(m.weight, 1)
                  init.constant_(m.bias.data, 0.0)
  
  
  def make_layer(block, n_layers):
      layers = []
      for _ in range(n_layers):
          layers.append(block())
      return nn.Sequential(*layers)
  
  
  class ResidualDenseBlock_5C(nn.Module):
      def __init__(self, nf=64, gc=32, bias=True):
          super(ResidualDenseBlock_5C, self).__init__()
          # gc: growth channel, i.e. intermediate channels
          self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
          self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
          self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
          self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
          self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
          self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
  
          # initialization
          initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
  
      def forward(self, x):
          x1 = self.lrelu(self.conv1(x))
          x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
          x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
          x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
          x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
          return x5 * 0.2 + x
  
  
  class RRDB(nn.Module):
      '''Residual in Residual Dense Block'''
  
      def __init__(self, nf, gc=32):
          super(RRDB, self).__init__()
          self.RDB1 = ResidualDenseBlock_5C(nf, gc)
          self.RDB2 = ResidualDenseBlock_5C(nf, gc)
          self.RDB3 = ResidualDenseBlock_5C(nf, gc)
  
      def forward(self, x):
          out = self.RDB1(x)
          out = self.RDB2(out)
          out = self.RDB3(out)
          return out * 0.2 + x
  
  
  class RRDBNet(nn.Module):
      def __init__(self, in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4):
          super(RRDBNet, self).__init__()
          RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
          self.sf = sf
  
          self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
          self.RRDB_trunk = make_layer(RRDB_block_f, nb)
          self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
          #### upsampling
          self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
          if self.sf==4:
              self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
          self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
          self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
  
          self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
  
      def forward(self, x):
          fea = self.conv_first(x)
          trunk = self.trunk_conv(self.RRDB_trunk(fea))
          fea = fea + trunk
  
          fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
          if self.sf==4:
              fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
          out = self.conv_last(self.lrelu(self.HRconv(fea)))
  
          return out
      
  model = RRDBNet(sf = args.sr_ratio).eval().to(args.device)
  if args.sr_ratio == 4:
      model.load_state_dict(torch.load('.\\BSRGAN.pth'))
  else:
      model.load_state_dict(torch.load('.\\BSRGANx2.pth'))    
  image = torch.from_numpy(cv2.cvtColor(cv2.imread(args.image_path),cv2.COLOR_BGR2RGB)/255)[None].permute(0,3,1,2).to(args.device).float()
  
  if args.use_half:
      model = model.half()
      image = image.half()
  
  with torch.no_grad():
      image = model(image)
  cv2.imwrite(save_path,cv2.cvtColor(torch.clip(image[0].permute(1,2,0)*255,0,255).to(torch.uint8).cpu().numpy(),cv2.COLOR_RGB2BGR))
  