def __init__(self, nin, nout, nG=64):
super().__init__()
self.name = "UNet"
self.conv0 = nn.Sequential(convBatch(nin, nG),
convBatch(nG, nG))
self.conv1 = nn.Sequential(convBatch(nG * 1, nG * 2, stride=2),
convBatch(nG * 2, nG * 2))
self.conv2 = nn.Sequential(convBatch(nG * 2, nG * 4, stride=2),
convBatch(nG * 4, nG * 4))
self.bridge = nn.Sequential(convBatch(nG * 4, nG * 8, stride=2),
residualConv(nG * 8, nG * 8),
convBatch(nG * 8, nG * 8))
self.deconv1 = upSampleConv(nG * 8, nG * 8)
self.conv5 = nn.Sequential(convBatch(nG * 12, nG * 4),
convBatch(nG * 4, nG * 4))
self.deconv2 = upSampleConv(nG * 4, nG * 4)
self.conv6 = nn.Sequential(convBatch(nG * 6, nG * 2),
convBatch(nG * 2, nG * 2))
self.deconv3 = upSampleConv(nG * 2, nG * 2)
self.conv7 = nn.Sequential(convBatch(nG * 3, nG * 1),
convBatch(nG * 1, nG * 1))
self.final = nn.Conv2d(nG, nout, kernel_size=1)
def __init__(self, nin, nout, nG=32): # m changed the default from 64 to 32
super().__init__()
self.conv0 = nn.Sequential(convBatch(nin, nG),
convBatch(nG, nG))
self.conv1 = nn.Sequential(convBatch(nG * 1, nG * 2, stride=2),
convBatch(nG * 2, nG * 2))
self.conv2 = nn.Sequential(convBatch(nG * 2, nG * 4, stride=2),
convBatch(nG * 4, nG * 4))
self.bridge = nn.Sequential(convBatch(nG * 4, nG * 8, stride=2),
residualConv(nG * 8, nG * 8),
convBatch(nG * 8, nG * 8))
self.deconv1 = upSampleConv(nG * 8, nG * 8)
self.conv5 = nn.Sequential(convBatch(nG * 12, nG * 4),
convBatch(nG * 4, nG * 4))
self.deconv2 = upSampleConv(nG * 4, nG * 4)
self.conv6 = nn.Sequential(convBatch(nG * 6, nG * 2),
convBatch(nG * 2, nG * 2))
self.deconv3 = upSampleConv(nG * 2, nG * 2)
self.conv7 = nn.Sequential(convBatch(nG * 3, nG * 1),
convBatch(nG * 1, nG * 1))
self.final = nn.Conv2d(nG, nout, kernel_size=1)
def __init__(self, nin, nout):
super().__init__()
self.projecting_factor = 4
self.n_kernels = 16
# Initial
self.conv0 = nn.Conv2d(nin, 15, kernel_size=3, stride=2, padding=1)
self.maxpool0 = nn.MaxPool2d(2, return_indices=True)
# First group
self.bottleNeck1_0 = BottleNeckDownSampling(self.n_kernels,
self.projecting_factor,
self.n_kernels * 4)
self.bottleNeck1_1 = BottleNeckNormal(self.n_kernels * 4,
self.n_kernels * 4,
self.projecting_factor, 0.01)
self.bottleNeck1_2 = BottleNeckNormal(self.n_kernels * 4,
self.n_kernels * 4,
self.projecting_factor, 0.01)
self.bottleNeck1_3 = BottleNeckNormal(self.n_kernels * 4,
self.n_kernels * 4,
self.projecting_factor, 0.01)
self.bottleNeck1_4 = BottleNeckNormal(self.n_kernels * 4,
self.n_kernels * 4,
self.projecting_factor, 0.01)
# Second group
self.bottleNeck2_0 = BottleNeckDownSampling(self.n_kernels * 4,
self.projecting_factor,
self.n_kernels * 8)
self.bottleNeck2_1 = BottleNeckNormal(self.n_kernels * 8,
self.n_kernels * 8,
self.projecting_factor, 0.1)
self.bottleNeck2_2 = BottleNeckDownSamplingDilatedConv(
self.n_kernels * 8, self.projecting_factor, self.n_kernels * 8, 2)
self.bottleNeck2_3 = BottleNeckNormal_Asym(self.n_kernels * 8,
self.n_kernels * 8,
self.projecting_factor, 0.1)
self.bottleNeck2_4 = BottleNeckDownSamplingDilatedConv(
self.n_kernels * 8, self.projecting_factor, self.n_kernels * 8, 4)
self.bottleNeck2_5 = BottleNeckNormal(self.n_kernels * 8,
self.n_kernels * 8,
self.projecting_factor, 0.1)
self.bottleNeck2_6 = BottleNeckDownSamplingDilatedConv(
self.n_kernels * 8, self.projecting_factor, self.n_kernels * 8, 8)
self.bottleNeck2_7 = BottleNeckNormal_Asym(self.n_kernels * 8,
self.n_kernels * 8,
self.projecting_factor, 0.1)
self.bottleNeck2_8 = BottleNeckDownSamplingDilatedConv(
self.n_kernels * 8, self.projecting_factor, self.n_kernels * 8, 16)
# Third group
self.bottleNeck3_1 = BottleNeckNormal(self.n_kernels * 8,
self.n_kernels * 8,
self.projecting_factor, 0.1)
self.bottleNeck3_2 = BottleNeckDownSamplingDilatedConv(
self.n_kernels * 8, self.projecting_factor, self.n_kernels * 8, 2)
self.bottleNeck3_3 = BottleNeckNormal_Asym(self.n_kernels * 8,
self.n_kernels * 8,
self.projecting_factor, 0.1)
self.bottleNeck3_4 = BottleNeckDownSamplingDilatedConv(
self.n_kernels * 8, self.projecting_factor, self.n_kernels * 8, 4)
self.bottleNeck3_5 = BottleNeckNormal(self.n_kernels * 8,
self.n_kernels * 8,
self.projecting_factor, 0.1)
self.bottleNeck3_6 = BottleNeckDownSamplingDilatedConv(
self.n_kernels * 8, self.projecting_factor, self.n_kernels * 8, 8)
self.bottleNeck3_7 = BottleNeckNormal_Asym(self.n_kernels * 8,
self.n_kernels * 8,
self.projecting_factor, 0.1)
self.bottleNeck3_8 = BottleNeckDownSamplingDilatedConvLast(
self.n_kernels * 8, self.projecting_factor, self.n_kernels * 4, 16)
# ### Decoding path ####
# Unpooling 1
self.unpool_0 = nn.MaxUnpool2d(2)
self.bottleNeck_Up_1_0 = BottleNeckUpSampling(self.n_kernels * 8,
self.projecting_factor,
self.n_kernels * 4)
self.PReLU_Up_1 = nn.PReLU()
self.bottleNeck_Up_1_1 = BottleNeckNormal(self.n_kernels * 4,
self.n_kernels * 4,
self.projecting_factor, 0.1)
self.bottleNeck_Up_1_2 = BottleNeckNormal(self.n_kernels * 4,
self.n_kernels,
self.projecting_factor, 0.1)
# Unpooling 2
self.unpool_1 = nn.MaxUnpool2d(2)
self.bottleNeck_Up_2_1 = BottleNeckUpSampling(self.n_kernels * 2,
self.projecting_factor,
self.n_kernels)
self.bottleNeck_Up_2_2 = BottleNeckNormal(self.n_kernels,
self.n_kernels,
self.projecting_factor, 0.1)
self.PReLU_Up_2 = nn.PReLU()
# Unpooling Last
self.deconv3 = upSampleConv(self.n_kernels, self.n_kernels)
self.out_025 = nn.Conv2d(self.n_kernels * 8,
nout,
kernel_size=3,
stride=1,
padding=1)
self.out_05 = nn.Conv2d(self.n_kernels,
nout,
kernel_size=3,
stride=1,
padding=1)
self.final = nn.Conv2d(self.n_kernels, nout, kernel_size=1)
