text: # This file contains experimental modules
  import torch.nn as nn
  import numpy as np
  import torch
  
  from .common import Conv, DWConv
  
  
  class CrossConv(nn.Module):
      # Cross Convolution Downsample
      def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
          # ch_in, ch_out, kernel, stride, groups, expansion, shortcut
          super(CrossConv, self).__init__()
          c_ = int(c2 * e)  # hidden channels
          self.cv1 = Conv(c1, c_, (1, k), (1, s))
          self.cv2 = Conv(c_, c2, (k, 1), (s, 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 C3(nn.Module):
      # Cross Convolution CSP
      def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
          super(C3, 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(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) 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 Sum(nn.Module):
      # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
      def __init__(self, n, weight=False):  # n: number of inputs
          super(Sum, self).__init__()
          self.weight = weight  # apply weights boolean
          self.iter = range(n - 1)  # iter object
          if weight:
              self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True)  # layer weights
  
      def forward(self, x):
          y = x[0]  # no weight
          if self.weight:
              w = torch.sigmoid(self.w) * 2
              for i in self.iter:
                  y = y + x[i + 1] * w[i]
          else:
              for i in self.iter:
                  y = y + x[i + 1]
          return y
  
  
  class GhostConv(nn.Module):
      # Ghost Convolution https://github.com/huawei-noah/ghostnet
      def __init__(self, c1, c2, k=1, s=1, g=1, act=True):  # ch_in, ch_out, kernel, stride, groups
          super(GhostConv, self).__init__()
          c_ = c2 // 2  # hidden channels
          self.cv1 = Conv(c1, c_, k, s, g, act)
          self.cv2 = Conv(c_, c_, 5, 1, c_, act)
  
      def forward(self, x):
          y = self.cv1(x)
          return torch.cat([y, self.cv2(y)], 1)
  
  
  class GhostBottleneck(nn.Module):
      # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
      def __init__(self, c1, c2, k, s):
          super(GhostBottleneck, self).__init__()
          c_ = c2 // 2
          self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1),  # pw
                                    DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(),  # dw
                                    GhostConv(c_, c2, 1, 1, act=False))  # pw-linear
          self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
                                        Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
  
      def forward(self, x):
          return self.conv(x) + self.shortcut(x)
  
  
  class MixConv2d(nn.Module):
      # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595
      def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
          super(MixConv2d, self).__init__()
          groups = len(k)
          if equal_ch:  # equal c_ per group
              i = torch.linspace(0, groups - 1E-6, c2).floor()  # c2 indices
              c_ = [(i == g).sum() for g in range(groups)]  # intermediate channels
          else:  # equal weight.numel() per group
              b = [c2] + [0] * groups
              a = np.eye(groups + 1, groups, k=-1)
              a -= np.roll(a, 1, axis=1)
              a *= np.array(k) ** 2
              a[0] = 1
              c_ = np.linalg.lstsq(a, b, rcond=None)[0].round()  # solve for equal weight indices, ax = b
  
          self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)])
          self.bn = nn.BatchNorm2d(c2)
          self.act = nn.LeakyReLU(0.1, inplace=True)
  
      def forward(self, x):
          return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
  
  
  class Ensemble(nn.ModuleList):
      # Ensemble of models
      def __init__(self):
          super(Ensemble, self).__init__()
  
      def forward(self, x, augment=False):
          y = []
          for module in self:
              y.append(module(x, augment)[0])
          # y = torch.stack(y).max(0)[0]  # max ensemble
          # y = torch.cat(y, 1)  # nms ensemble
          y = torch.stack(y).mean(0)  # mean ensemble
          return y, None  # inference, train output
  
  
  def attempt_load(weights, map_location=None):
      # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
      model = Ensemble()
      for w in weights if isinstance(weights, list) else [weights]:
          model.append(torch.load(w, map_location=map_location)['model'].float().fuse().eval())  # load FP32 model
  
      if len(model) == 1:
          return model[-1]  # return model
      else:
          print('Ensemble created with %s\n' % weights)
          for k in ['names', 'stride']:
              setattr(model, k, getattr(model[-1], k))
          return model  # return ensemble
  