text: # This file contains modules common to various models
  
  from utils.utils import *
  
  
  def autopad(k, p=None):  # kernel, padding
      # Pad to 'same'
      if p is None:
          p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
      return p
  
  
  def DWConv(c1, c2, k=1, s=1, act=True):
      # Depthwise convolution
      return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
  
  
  class Conv(nn.Module):
      # Standard convolution
      def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
          super(Conv, self).__init__()
          self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
          self.bn = nn.BatchNorm2d(c2)
          self.act = nn.LeakyReLU(0.1, inplace=True) if act else nn.Identity()
  
      def forward(self, x):
          return self.act(self.bn(self.conv(x)))
  
      def fuseforward(self, x):
          return self.act(self.conv(x))
  
  
  class Bottleneck(nn.Module):
      # Standard bottleneck
      def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
          super(Bottleneck, self).__init__()
          c_ = int(c2 * e)  # hidden channels
          self.cv1 = Conv(c1, c_, 1, 1)
          self.cv2 = Conv(c_, c2, 3, 1, g=g)
          self.add = shortcut and c1 == c2
  
      def forward(self, x):
          return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
  
  
  class BottleneckCSP(nn.Module):
      # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
      def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
          super(BottleneckCSP, self).__init__()
          c_ = int(c2 * e)  # hidden channels
          self.cv1 = Conv(c1, c_, 1, 1)
          self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
          self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
          self.cv4 = Conv(2 * c_, c2, 1, 1)
          self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
          self.act = nn.LeakyReLU(0.1, inplace=True)
          self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
  
      def forward(self, x):
          y1 = self.cv3(self.m(self.cv1(x)))
          y2 = self.cv2(x)
          return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
  
  
  class SPP(nn.Module):
      # Spatial pyramid pooling layer used in YOLOv3-SPP
      def __init__(self, c1, c2, k=(5, 9, 13)):
          super(SPP, self).__init__()
          c_ = c1 // 2  # hidden channels
          self.cv1 = Conv(c1, c_, 1, 1)
          self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
          self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
  
      def forward(self, x):
          x = self.cv1(x)
          return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
  
  
  class Flatten(nn.Module):
      # Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions
      def forward(self, x):
          return x.view(x.size(0), -1)
  
  
  class Focus(nn.Module):
      # Focus wh information into c-space
      def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
          super(Focus, self).__init__()
          self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
  
      def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
          return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
  
  
  class Concat(nn.Module):
      # Concatenate a list of tensors along dimension
      def __init__(self, dimension=1):
          super(Concat, self).__init__()
          self.d = dimension
  
      def forward(self, x):
          return torch.cat(x, self.d)
  