text: import argparse
  import logging
  import math
  import sys
  from copy import deepcopy
  from pathlib import Path
  
  import torch
  import torch.nn as nn
  
  sys.path.append('./')  # to run '$ python *.py' files in subdirectories
  logger = logging.getLogger(__name__)
  
  from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, C3, Concat, NMS, autoShape
  from models.experimental import MixConv2d, CrossConv
  from utils.autoanchor import check_anchor_order
  from utils.general import make_divisible, check_file, set_logging
  from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
      select_device, copy_attr
  
  try:
      import thop  # for FLOPS computation
  except ImportError:
      thop = None
  
  
  class Detect(nn.Module):
      stride = None  # strides computed during build
      export = False  # onnx export
  
      def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
          super(Detect, self).__init__()
          self.nc = nc  # number of classes
          self.no = nc + 5  # number of outputs per anchor
          self.nl = len(anchors)  # number of detection layers
          self.na = len(anchors[0]) // 2  # number of anchors
          self.grid = [torch.zeros(1)] * self.nl  # init grid
          a = torch.tensor(anchors).float().view(self.nl, -1, 2)
          self.register_buffer('anchors', a)  # shape(nl,na,2)
          self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl,1,na,1,1,2)
          self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv
  
      def forward(self, x):
          # x = x.copy()  # for profiling
          z = []  # inference output
          self.training |= self.export
          for i in range(self.nl):
              x[i] = self.m[i](x[i])  # conv
              bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
              x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
  
              if not self.training:  # inference
                  if self.grid[i].shape[2:4] != x[i].shape[2:4]:
                      self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
  
                  y = x[i].sigmoid()
                  y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i]  # xy
                  y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                  z.append(y.view(bs, -1, self.no))
  
          return x if self.training else (torch.cat(z, 1), x)
  
      @staticmethod
      def _make_grid(nx=20, ny=20):
          yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
          return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
  
  
  class Model(nn.Module):
      def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None):  # model, input channels, number of classes
          super(Model, self).__init__()
          if isinstance(cfg, dict):
              self.yaml = cfg  # model dict
          else:  # is *.yaml
              import yaml  # for torch hub
              self.yaml_file = Path(cfg).name
              with open(cfg) as f:
                  self.yaml = yaml.load(f, Loader=yaml.FullLoader)  # model dict
  
          # Define model
          ch = self.yaml['ch'] = self.yaml.get('ch', ch)  # input channels
          if nc and nc != self.yaml['nc']:
              logger.info('Overriding model.yaml nc=%g with nc=%g' % (self.yaml['nc'], nc))
              self.yaml['nc'] = nc  # override yaml value
          self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist
          self.names = [str(i) for i in range(self.yaml['nc'])]  # default names
          # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
  
          # Build strides, anchors
          m = self.model[-1]  # Detect()
          if isinstance(m, Detect):
              s = 128  # 2x min stride
              m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forward
              m.anchors /= m.stride.view(-1, 1, 1)
              check_anchor_order(m)
              self.stride = m.stride
              self._initialize_biases()  # only run once
              # print('Strides: %s' % m.stride.tolist())
  
          # Init weights, biases
          initialize_weights(self)
          self.info()
          logger.info('')
  
      def forward(self, x, augment=False, profile=False):
          if augment:
              img_size = x.shape[-2:]  # height, width
              s = [1, 0.83, 0.67]  # scales
              f = [None, 3, None]  # flips (2-ud, 3-lr)
              y = []  # outputs
              for si, fi in zip(s, f):
                  xi = scale_img(x.flip(fi) if fi else x, si)
                  yi = self.forward_once(xi)[0]  # forward
                  # cv2.imwrite('img%g.jpg' % s, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
                  yi[..., :4] /= si  # de-scale
                  if fi == 2:
                      yi[..., 1] = img_size[0] - yi[..., 1]  # de-flip ud
                  elif fi == 3:
                      yi[..., 0] = img_size[1] - yi[..., 0]  # de-flip lr
                  y.append(yi)
              return torch.cat(y, 1), None  # augmented inference, train
          else:
              return self.forward_once(x, profile)  # single-scale inference, train
  
      def forward_once(self, x, profile=False):
          y, dt = [], []  # outputs
          for m in self.model:
              if m.f != -1:  # if not from previous layer
                  x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
  
              if profile:
                  o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPS
                  t = time_synchronized()
                  for _ in range(10):
                      _ = m(x)
                  dt.append((time_synchronized() - t) * 100)
                  print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type))
  
              x = m(x)  # run
              y.append(x if m.i in self.save else None)  # save output
  
          if profile:
              print('%.1fms total' % sum(dt))
          return x
  
      def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
          # https://arxiv.org/abs/1708.02002 section 3.3
          # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
          m = self.model[-1]  # Detect() module
          for mi, s in zip(m.m, m.stride):  # from
              b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
              b.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
              b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum())  # cls
              mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
  
      def _print_biases(self):
          m = self.model[-1]  # Detect() module
          for mi in m.m:  # from
              b = mi.bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)
              print(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
  
      # def _print_weights(self):
      #     for m in self.model.modules():
      #         if type(m) is Bottleneck:
      #             print('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weights
  
      def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
          # print('Fusing layers... ')
          for m in self.model.modules():
              if type(m) is Conv and hasattr(m, 'bn'):
                  m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
                  delattr(m, 'bn')  # remove batchnorm
                  m.forward = m.fuseforward  # update forward
          self.info()
          return self
  
      def nms(self, mode=True):  # add or remove NMS module
          present = type(self.model[-1]) is NMS  # last layer is NMS
          if mode and not present:
              print('Adding NMS... ')
              m = NMS()  # module
              m.f = -1  # from
              m.i = self.model[-1].i + 1  # index
              self.model.add_module(name='%s' % m.i, module=m)  # add
              self.eval()
          elif not mode and present:
              print('Removing NMS... ')
              self.model = self.model[:-1]  # remove
          return self
  
      def autoshape(self):  # add autoShape module
          print('Adding autoShape... ')
          m = autoShape(self)  # wrap model
          copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=())  # copy attributes
          return m
  
      def info(self, verbose=False, img_size=640):  # print model information
          model_info(self, verbose, img_size)
  
  
  def parse_model(d, ch):  # model_dict, input_channels(3)
      logger.info('\n%3s%18s%3s%10s  %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
      anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
      na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
      no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
  
      layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
      for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
          m = eval(m) if isinstance(m, str) else m  # eval strings
          for j, a in enumerate(args):
              try:
                  args[j] = eval(a) if isinstance(a, str) else a  # eval strings
              except:
                  pass
  
          n = max(round(n * gd), 1) if n > 1 else n  # depth gain
          if m in [Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]:
              c1, c2 = ch[f], args[0]
  
              # Normal
              # if i > 0 and args[0] != no:  # channel expansion factor
              #     ex = 1.75  # exponential (default 2.0)
              #     e = math.log(c2 / ch[1]) / math.log(2)
              #     c2 = int(ch[1] * ex ** e)
              # if m != Focus:
  
              c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
  
              # Experimental
              # if i > 0 and args[0] != no:  # channel expansion factor
              #     ex = 1 + gw  # exponential (default 2.0)
              #     ch1 = 32  # ch[1]
              #     e = math.log(c2 / ch1) / math.log(2)  # level 1-n
              #     c2 = int(ch1 * ex ** e)
              # if m != Focus:
              #     c2 = make_divisible(c2, 8) if c2 != no else c2
  
              args = [c1, c2, *args[1:]]
              if m in [BottleneckCSP, C3]:
                  args.insert(2, n)
                  n = 1
          elif m is nn.BatchNorm2d:
              args = [ch[f]]
          elif m is Concat:
              c2 = sum([ch[-1 if x == -1 else x + 1] for x in f])
          elif m is Detect:
              args.append([ch[x + 1] for x in f])
              if isinstance(args[1], int):  # number of anchors
                  args[1] = [list(range(args[1] * 2))] * len(f)
          else:
              c2 = ch[f]
  
          m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args)  # module
          t = str(m)[8:-2].replace('__main__.', '')  # module type
          np = sum([x.numel() for x in m_.parameters()])  # number params
          m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
          logger.info('%3s%18s%3s%10.0f  %-40s%-30s' % (i, f, n, np, t, args))  # print
          save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
          layers.append(m_)
          ch.append(c2)
      return nn.Sequential(*layers), sorted(save)
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
      parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml')
      parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
      opt = parser.parse_args()
      opt.cfg = check_file(opt.cfg)  # check file
      set_logging()
      device = select_device(opt.device)
  
      # Create model
      model = Model(opt.cfg).to(device)
      model.train()
  
      # Profile
      # img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
      # y = model(img, profile=True)
  
      # Tensorboard
      # from torch.utils.tensorboard import SummaryWriter
      # tb_writer = SummaryWriter()
      # print("Run 'tensorboard --logdir=models/runs' to view tensorboard at http://localhost:6006/")
      # tb_writer.add_graph(model.model, img)  # add model to tensorboard
      # tb_writer.add_image('test', img[0], dataformats='CWH')  # add model to tensorboard
  