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439c523 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | import torch.nn.functional as F
from .evolution_module import *
class Evolution_Network(nn.Module):
def __init__(self, n_channels, n_classes, base_c=64, bilinear=True):
super(Evolution_Network, self).__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.bilinear = bilinear
base_c = base_c
self.inc = DoubleConv(n_channels, base_c)
self.down1 = Down(base_c * 1, base_c * 2)
self.down2 = Down(base_c * 2, base_c * 4)
self.down3 = Down(base_c * 4, base_c * 8)
factor = 2 if bilinear else 1
self.down4 = Down(base_c * 8, base_c * 16 // factor)
self.up1 = Up(base_c * 16, base_c * 8 // factor, bilinear)
self.up2 = Up(base_c * 8, base_c * 4 // factor, bilinear)
self.up3 = Up(base_c * 4, base_c * 2 // factor, bilinear)
self.up4 = Up(base_c * 2, base_c * 1, bilinear)
self.outc = OutConv(base_c * 1, n_classes)
self.gamma = nn.Parameter(torch.zeros(1, n_classes, 1, 1), requires_grad=True)
self.up1_v = Up(base_c * 16, base_c * 8 // factor, bilinear)
self.up2_v = Up(base_c * 8, base_c * 4 // factor, bilinear)
self.up3_v = Up(base_c * 4, base_c * 2 // factor, bilinear)
self.up4_v = Up(base_c * 2, base_c * 1, bilinear)
self.outc_v = OutConv(base_c * 1, n_classes * 2)
def forward(self, x):
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x = self.up1(x5, x4)
x = self.up2(x, x3)
x = self.up3(x, x2)
x = self.up4(x, x1)
x = self.outc(x) * self.gamma
v = self.up1_v(x5, x4)
v = self.up2_v(v, x3)
v = self.up3_v(v, x2)
v = self.up4_v(v, x1)
v = self.outc_v(v)
return x, v
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