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SCConv.py
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SCConv.py
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"""
Description:
Date: 2023-07-21 14:36:27
LastEditTime: 2023-07-27 18:41:47
FilePath: /chengdongzhou/ScConv.py
"""
import torch
import torch.nn.functional as F
import torch.nn as nn
class GroupBatchnorm2d(nn.Module):
def __init__(self, c_num: int, group_num: int = 16, eps: float = 1e-10):
super(GroupBatchnorm2d, self).__init__()
assert c_num >= group_num
self.group_num = group_num
self.weight = nn.Parameter(torch.randn(c_num, 1, 1))
self.bias = nn.Parameter(torch.zeros(c_num, 1, 1))
self.eps = eps
def forward(self, x):
N, C, H, W = x.size()
x = x.view(N, self.group_num, -1)
mean = x.mean(dim=2, keepdim=True)
std = x.std(dim=2, keepdim=True)
x = (x - mean) / (std + self.eps)
x = x.view(N, C, H, W)
return x * self.weight + self.bias
class SRU(nn.Module):
def __init__(
self,
oup_channels: int,
group_num: int = 16,
gate_treshold: float = 0.5,
torch_gn: bool = True,
):
super().__init__()
self.gn = (
nn.GroupNorm(num_channels=oup_channels, num_groups=group_num)
if torch_gn
else GroupBatchnorm2d(c_num=oup_channels, group_num=group_num)
)
self.gate_treshold = gate_treshold
self.sigomid = nn.Sigmoid()
def forward(self, x):
gn_x = self.gn(x)
w_gamma = self.gn.weight / sum(self.gn.weight)
w_gamma = w_gamma.view(1, -1, 1, 1)
reweigts = self.sigomid(gn_x * w_gamma)
# Gate
w1 = torch.where(
reweigts > self.gate_treshold, torch.ones_like(reweigts), reweigts
) # 大于门限值的设为1,否则保留原值
w2 = torch.where(
reweigts > self.gate_treshold, torch.zeros_like(reweigts), reweigts
) # 大于门限值的设为0,否则保留原值
x_1 = w1 * x
x_2 = w2 * x
y = self.reconstruct(x_1, x_2)
return y
def reconstruct(self, x_1, x_2):
x_11, x_12 = torch.split(x_1, x_1.size(1) // 2, dim=1)
x_21, x_22 = torch.split(x_2, x_2.size(1) // 2, dim=1)
return torch.cat([x_11 + x_22, x_12 + x_21], dim=1)
class CRU(nn.Module):
"""
alpha: 0<alpha<1
"""
def __init__(
self,
op_channel: int,
alpha: float = 1 / 2,
squeeze_radio: int = 2,
group_size: int = 2,
group_kernel_size: int = 3,
):
super().__init__()
self.up_channel = up_channel = int(alpha * op_channel)
self.low_channel = low_channel = op_channel - up_channel
self.squeeze1 = nn.Conv2d(
up_channel, up_channel // squeeze_radio, kernel_size=1, bias=False
)
self.squeeze2 = nn.Conv2d(
low_channel, low_channel // squeeze_radio, kernel_size=1, bias=False
)
# up
self.GWC = nn.Conv2d(
up_channel // squeeze_radio,
op_channel,
kernel_size=group_kernel_size,
stride=1,
padding=group_kernel_size // 2,
groups=group_size,
)
self.PWC1 = nn.Conv2d(
up_channel // squeeze_radio, op_channel, kernel_size=1, bias=False
)
# low
self.PWC2 = nn.Conv2d(
low_channel // squeeze_radio,
op_channel - low_channel // squeeze_radio,
kernel_size=1,
bias=False,
)
self.advavg = nn.AdaptiveAvgPool2d(1)
def forward(self, x):
# Split
up, low = torch.split(x, [self.up_channel, self.low_channel], dim=1)
up, low = self.squeeze1(up), self.squeeze2(low)
# Transform
Y1 = self.GWC(up) + self.PWC1(up)
Y2 = torch.cat([self.PWC2(low), low], dim=1)
# Fuse
out = torch.cat([Y1, Y2], dim=1)
out = F.softmax(self.advavg(out), dim=1) * out
out1, out2 = torch.split(out, out.size(1) // 2, dim=1)
return out1 + out2
class ScConv(nn.Module):
def __init__(
self,
op_channel: int,
group_num: int = 4,
gate_treshold: float = 0.5,
alpha: float = 1 / 2,
squeeze_radio: int = 2,
group_size: int = 2,
group_kernel_size: int = 3,
):
super().__init__()
self.SRU = SRU(op_channel, group_num=group_num, gate_treshold=gate_treshold)
self.CRU = CRU(
op_channel,
alpha=alpha,
squeeze_radio=squeeze_radio,
group_size=group_size,
group_kernel_size=group_kernel_size,
)
def forward(self, x):
x = self.SRU(x)
x = self.CRU(x)
return x
if __name__ == "__main__":
x = torch.randn(1, 32, 16, 16)
model = ScConv(32)
print(model(x).shape)