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49 lines
1.7 KiB
49 lines
1.7 KiB
from models.common import *
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class Sum(nn.Module):
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# weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
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def __init__(self, n, weight=False): # n: number of inputs
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super(Sum, self).__init__()
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self.weight = weight # apply weights boolean
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self.iter = range(n - 1) # iter object
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if weight:
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self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True) # layer weights
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def forward(self, x):
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y = x[0] # no weight
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if self.weight:
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w = torch.sigmoid(self.w) * 2
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for i in self.iter:
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y = y + x[i + 1] * w[i]
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else:
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for i in self.iter:
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y = y + x[i + 1]
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return y
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class GhostConv(nn.Module):
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def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups
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super(GhostConv, self).__init__()
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c_ = c2 // 2 # hidden channels
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self.cv1 = Conv(c1, c_, k, s, g, act)
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self.cv2 = Conv(c_, c_, 5, 1, c_, act)
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def forward(self, x):
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y = self.cv1(x)
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return torch.cat([y, self.cv2(y)], 1)
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class GhostBottleneck(nn.Module):
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def __init__(self, c1, c2, k, s):
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super(GhostBottleneck, self).__init__()
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c_ = c2 // 2
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self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw
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DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
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GhostConv(c_, c2, 1, 1, act=False)) # pw-linear
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self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
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Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
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def forward(self, x):
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return self.conv(x) + self.shortcut(x)
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