text: # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
  """
  YOLO-specific modules
  
  Usage:
      $ python path/to/models/yolo.py --cfg yolov5s.yaml
  """
  
  import argparse
  import sys
  from copy import deepcopy
  from pathlib import Path
  
  FILE = Path(__file__).resolve()
  sys.path.append(FILE.parents[1].as_posix())  # add yolov5/ to path
  
  from models.common import *
  from models.experimental import *
  from utils.autoanchor import check_anchor_order
  from utils.general import check_yaml, make_divisible, set_logging
  from utils.plots import feature_visualization
  from utils.torch_utils import copy_attr, fuse_conv_and_bn, initialize_weights, model_info, scale_img, \
      select_device, time_sync
  
  try:
      import thop  # for FLOPs computation
  except ImportError:
      thop = None
  
  LOGGER = logging.getLogger(__name__)
  
  
  class Detect(nn.Module):
      stride = None  # strides computed during build
      onnx_dynamic = False  # ONNX export parameter
  
      def __init__(self, nc=80, anchors=(), ch=(), inplace=True):  # detection layer
          super().__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
          self.inplace = inplace  # use in-place ops (e.g. slice assignment)
  
      def forward(self, x):
          z = []  # inference output
          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] or self.onnx_dynamic:
                      self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
  
                  y = x[i].sigmoid()
                  if self.inplace:
                      y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
                      y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                  else:  # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
                      xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
                      wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i].view(1, self.na, 1, 1, 2)  # wh
                      y = torch.cat((xy, wh, y[..., 4:]), -1)
                  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, anchors=None):  # model, input channels, number of classes
          super().__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.safe_load(f)  # model dict
  
          # Define model
          ch = self.yaml['ch'] = self.yaml.get('ch', ch)  # input channels
          if nc and nc != self.yaml['nc']:
              LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
              self.yaml['nc'] = nc  # override yaml value
          if anchors:
              LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}')
              self.yaml['anchors'] = round(anchors)  # 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
          self.inplace = self.yaml.get('inplace', True)
          # LOGGER.info([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 = 256  # 2x min stride
              m.inplace = self.inplace
              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
              # LOGGER.info('Strides: %s' % m.stride.tolist())
  
          # Init weights, biases
          initialize_weights(self)
          self.info() 
          LOGGER.info('')
  
      def forward(self, x, augment=False, profile=False, visualize=False):
          if augment:
              return self.forward_augment(x)  # augmented inference, None
          return self.forward_once(x, profile, visualize)  # single-scale inference, train
  
      def forward_augment(self, x):
          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, gs=int(self.stride.max()))
              yi = self.forward_once(xi)[0]  # forward
              # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
              yi = self._descale_pred(yi, fi, si, img_size)
              y.append(yi)
          return torch.cat(y, 1), None  # augmented inference, train
  
      def forward_once(self, x, profile=False, visualize=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:
                  c = isinstance(m, Detect)  # copy input as inplace fix
                  o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
                  t = time_sync()
                  for _ in range(10):
                      m(x.copy() if c else x)
                  dt.append((time_sync() - t) * 100)
                  if m == self.model[0]:
                      LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  {'module'}")
                  LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f}  {m.type}')
  
              x = m(x)  # run
              y.append(x if m.i in self.save else None)  # save output
  
              if visualize:
                  feature_visualization(x, m.type, m.i, save_dir=visualize)
  
          if profile:
              LOGGER.info('%.1fms total' % sum(dt))
          return x
  
      def _descale_pred(self, p, flips, scale, img_size):
          # de-scale predictions following augmented inference (inverse operation)
          if self.inplace:
              p[..., :4] /= scale  # de-scale
              if flips == 2:
                  p[..., 1] = img_size[0] - p[..., 1]  # de-flip ud
              elif flips == 3:
                  p[..., 0] = img_size[1] - p[..., 0]  # de-flip lr
          else:
              x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale  # de-scale
              if flips == 2:
                  y = img_size[0] - y  # de-flip ud
              elif flips == 3:
                  x = img_size[1] - x  # de-flip lr
              p = torch.cat((x, y, wh, p[..., 4:]), -1)
          return p
  
      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)
              LOGGER.info(
                  ('%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:
      #             LOGGER.info('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weights
  
      def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
          LOGGER.info('Fusing layers... ')
          for m in self.model.modules():
              if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
                  m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
                  delattr(m, 'bn')  # remove batchnorm
                  m.forward = m.forward_fuse  # update forward
          self.info()
          return self
  
      def autoshape(self):  # add AutoShape module
          LOGGER.info('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 = n_ = max(round(n * gd), 1) if n > 1 else n  # depth gain
          if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
                   BottleneckCSP, C3, C3TR, C3SPP, C3Ghost]:
              c1, c2 = ch[f], args[0]
              if c2 != no:  # if not output
                  c2 = make_divisible(c2 * gw, 8)
  
              args = [c1, c2, *args[1:]]
              if m in [BottleneckCSP, C3, C3TR, C3Ghost]:
                  args.insert(2, n)  # number of repeats
                  n = 1
          elif m is nn.BatchNorm2d:
              args = [ch[f]]
          elif m is Concat:
              c2 = sum([ch[x] for x in f])
          elif m is Detect:
              args.append([ch[x] for x in f])
              if isinstance(args[1], int):  # number of anchors
                  args[1] = [list(range(args[1] * 2))] * len(f)
          elif m is Contract:
              c2 = ch[f] * args[0] ** 2
          elif m is Expand:
              c2 = ch[f] // args[0] ** 2
          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_)
          if i == 0:
              ch = []
          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')
      parser.add_argument('--profile', action='store_true', help='profile model speed')
      opt = parser.parse_args()
      opt.cfg = check_yaml(opt.cfg)  # check YAML
      set_logging()
      device = select_device(opt.device)
  
      # Create model
      model = Model(opt.cfg).to(device)
      model.train()
  
      # Profile
      if opt.profile:
          img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
          y = model(img, profile=True)
  
      # Tensorboard (not working https://github.com/ultralytics/yolov5/issues/2898)
      # from torch.utils.tensorboard import SummaryWriter
      # tb_writer = SummaryWriter('.')
      # LOGGER.info("Run 'tensorboard --logdir=models' to view tensorboard at http://localhost:6006/")
      # tb_writer.add_graph(torch.jit.trace(model, img, strict=False), [])  # add model graph
  