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- from models.common import *
-
-
- 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):
- 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):
- 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)
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