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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):
- # 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 ConvPlus(nn.Module):
- # Plus-shaped convolution
- def __init__(self, c1, c2, k=3, s=1, g=1, bias=True): # ch_in, ch_out, kernel, stride, groups
- super(ConvPlus, self).__init__()
- self.cv1 = nn.Conv2d(c1, c2, (k, 1), s, (k // 2, 0), groups=g, bias=bias)
- self.cv2 = nn.Conv2d(c1, c2, (1, k), s, (0, k // 2), groups=g, bias=bias)
-
- def forward(self, x):
- return self.cv1(x) + self.cv2(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)))
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