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import torch as T
import torch.nn as nn
from Networks.network import Network
class UNet5_Teacher(Network):
def __init__(self, base, expansion):
super(UNet5_Teacher, self).__init__()
self._build(base, expansion)
def forward(self, image, segbox):
x_new = T.cat((image, segbox), dim = 1)
layer_0, layer_1, layer_2, layer_3 = self._analysis(x_new)
return self._synthesis(layer_0, layer_1, layer_2, layer_3, self._bridge(layer_3))
def train_step(self, image, segment, criterion, segbox = None):
output = self.forward(image, segbox)
loss = criterion(output, segment)
return loss
def _analysis(self, x):
layer_0 = self.analysis_0(x)
layer_1 = self.analysis_1(layer_0)
layer_2 = self.analysis_2(layer_1)
layer_3 = self.analysis_3(layer_2)
return layer_0, layer_1, layer_2, layer_3
def _bridge(self, layer_3):
return self.bridge(layer_3)
def _synthesis(self, l0, l1, l2, l3, l4):
c_3 = T.cat((l3, l4), dim = 1)
c_2 = T.cat((l2, self.synthesis_3(c_3)), dim = 1)
c_1 = T.cat((l1, self.synthesis_2(c_2)), dim = 1)
c_0 = T.cat((l0, self.synthesis_1(c_1)), dim = 1)
return self.synthesis_0(c_0)
def _build(self, base, expansion):
fl_0 = int(base)
fl_1 = int(base * expansion)
fl_2 = int(base * (expansion ** 2))
fl_3 = int(base * (expansion ** 3))
fl_4 = int(base * (expansion ** 4))
self.analysis_0 = nn.Sequential(
nn.Conv3d(2, fl_0, 3, 1, 1),
nn.LeakyReLU(),
nn.Conv3d(fl_0, fl_0, 3, 1, 1),
nn.LeakyReLU(),
)
self.analysis_1 = nn.Sequential(
nn.MaxPool3d(2),
nn.Conv3d(fl_0, fl_1, 3, 1, 1),
nn.LeakyReLU(),
nn.Conv3d(fl_1, fl_1, 3, 1, 1),
nn.LeakyReLU(),
)
self.analysis_2 = nn.Sequential(
nn.MaxPool3d(2),
nn.Conv3d(fl_1, fl_2, 3, 1, 1),
nn.LeakyReLU(),
nn.Conv3d(fl_2, fl_2, 3, 1, 1),
nn.LeakyReLU(),
)
self.analysis_3 = nn.Sequential(
nn.MaxPool3d(2),
nn.Conv3d(fl_2, fl_3, 3, 1, 1),
nn.LeakyReLU(),
nn.Conv3d(fl_3, fl_3, 3, 1, 1),
nn.LeakyReLU(),
)
self.bridge = nn.Sequential(
nn.MaxPool3d(2),
nn.Conv3d(fl_3, fl_3, 3, 1, 1),
nn.LeakyReLU(),
nn.Conv3d(fl_3, fl_3, 3, 1, 1),
nn.LeakyReLU(),
nn.ConvTranspose3d(fl_3, fl_3, 2, 2, 0),
)
self.synthesis_3 = nn.Sequential(
nn.Conv3d(fl_3 + fl_3, fl_2, 3, 1, 1),
nn.LeakyReLU(),
nn.Conv3d(fl_2, fl_2, 3, 1, 1),
nn.LeakyReLU(),
nn.ConvTranspose3d(fl_2, fl_2, 2, 2, 0),
)
self.synthesis_2 = nn.Sequential(
nn.Conv3d(fl_2 + fl_2, fl_1, 3, 1, 1),
nn.LeakyReLU(),
nn.Conv3d(fl_1, fl_1, 3, 1, 1),
nn.LeakyReLU(),
nn.ConvTranspose3d(fl_1, fl_1, 2, 2, 0),
)
self.synthesis_1 = nn.Sequential(
nn.Conv3d(fl_1 + fl_1, fl_0, 3, 1, 1),
nn.LeakyReLU(),
nn.Conv3d(fl_0, fl_0, 3, 1, 1),
nn.LeakyReLU(),
nn.ConvTranspose3d(fl_0, fl_0, 2, 2, 0),
)
self.synthesis_0 = nn.Sequential(
nn.Conv3d(fl_0 + fl_0, fl_0, 3, 1, 1),
nn.LeakyReLU(),
nn.Conv3d(fl_0, fl_0, 3, 1, 1),
nn.LeakyReLU(),
nn.Conv3d(fl_0, 1, 3, 1, 1),
nn.Conv3d(1, 1, 1, 1, 0),
nn.Sigmoid()
)
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