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Model.py
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import torch
import torch.nn as nn
class FeaturePyramidNetwork(nn.Module):
def __init__(self):
super(FeaturePyramidNetwork, self).__init__()
self.name = "AEFPNC"
self.bn0 = nn.BatchNorm2d(3)
self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, padding=1) # 3x256x256 -> 16x256x256
self.bn1 = nn.BatchNorm2d(num_features=16)
self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding=1) # 16x256x256 -> 32x256x256
self.bn2 = nn.BatchNorm2d(num_features=32)
self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1) # 32x256x256 -> 64x256x256
self.bn3 = nn.BatchNorm2d(num_features=64)
self.conv4 = nn.Conv2d(in_channels=64, out_channels=48, kernel_size=3, padding=1) # 64x256x256 -> 48x256x256
self.bn4 = nn.BatchNorm2d(num_features=48)
self.conv5 = nn.Conv2d(in_channels=48, out_channels=32, kernel_size=3, padding=1) # 48x256x256 -> 32x256x256
self.bn5 = nn.BatchNorm2d(num_features=32)
self.conv6 = nn.Conv2d(in_channels=32, out_channels=24, kernel_size=3, padding=1) # 32x256x256 -> 24x256x256
self.bn6 = nn.BatchNorm2d(num_features=24)
self.conv7 = nn.Conv2d(in_channels=24, out_channels=16, kernel_size=3, padding=1) # 24x256x256 -> 16x256x256
self.bn7 = nn.BatchNorm2d(num_features=16)
self.down = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=2)
self.conv_smooth1 = nn.Conv2d(in_channels=64, out_channels=32, kernel_size=3, padding=1)
self.conv_smooth2 = nn.Conv2d(in_channels=32, out_channels=16, kernel_size=3, padding=1)
self.conv_smooth3 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, padding=1)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
self.convtrans1 = nn.ConvTranspose2d(in_channels=16, out_channels=24, kernel_size=3, padding=1)
self.convtrans2 = nn.ConvTranspose2d(in_channels=24, out_channels=32, kernel_size=3, padding=1)
self.convtrans3 = nn.ConvTranspose2d(in_channels=32, out_channels=48, kernel_size=3, padding=1)
self.convtrans4 = nn.ConvTranspose2d(in_channels=48, out_channels=64, kernel_size=3, padding=1)
self.convtrans5 = nn.ConvTranspose2d(in_channels=64, out_channels=32, kernel_size=3, padding=1)
self.convtrans6 = nn.ConvTranspose2d(in_channels=32, out_channels=16, kernel_size=3, padding=1)
self.convtrans7 = nn.ConvTranspose2d(in_channels=16, out_channels=3, kernel_size=3, padding=1)
def forward(self, x):
# ============ Encoder ===========
# ====== Bottom Up Layers =====
x = self.bn0(x)
x = self.conv1(x)
x = self.relu(x)
x = self.bn1(x)
x = self.conv2(x)
x = self.relu(x)
x = self.bn2(x)
x = self.conv3(x)
x = self.relu(x)
x = self.bn3(x)
_, _, H1, W1 = x.size()
# ======= Branch network ======
x_d1 = self.down(x) # 128x128
_, _, H2, W2 = x_d1.size()
x_d2 = self.down(x_d1) # 64x64
_, _, H3, W3 = x_d2.size()
x_d3 = self.down(x_d2) # 32x32
# ======= First Branch =======
x = self.conv4(x)
x = self.relu(x)
x = self.bn4(x)
x = self.conv5(x)
x = self.relu(x)
x = self.bn5(x)
x = self.conv6(x)
x = self.relu(x)
x = self.bn6(x)
x = self.conv7(x)
x = self.relu(x)
x = self.bn7(x)
# ======= Second Branch ========
x_d1 = self.conv4(x_d1)
x_d1 = self.relu(x_d1)
x_d1 = self.bn4(x_d1)
x_d1 = self.conv5(x_d1)
x_d1 = self.relu(x_d1)
x_d1 = self.bn5(x_d1)
x_d1 = self.conv6(x_d1)
x_d1 = self.relu(x_d1)
x_d1 = self.bn6(x_d1)
x_d1 = self.conv7(x_d1)
x_d1 = self.relu(x_d1)
x_d1 = self.bn7(x_d1)
x_d1 = self.upsample(x_d1, size=(H1, W1))
# ======= Third Branch ========
x_d2 = self.conv4(x_d2)
x_d2 = self.relu(x_d2)
x_d2 = self.bn4(x_d2)
x_d2 = self.conv5(x_d2)
x_d2 = self.relu(x_d2)
x_d2 = self.bn5(x_d2)
x_d2 = self.conv6(x_d2)
x_d2 = self.relu(x_d2)
x_d2 = self.bn6(x_d2)
x_d2 = self.conv7(x_d2)
x_d2 = self.relu(x_d2)
x_d2 = self.bn7(x_d2)
x_d2 = self.upsample(x_d2, size=(H2, W2))
x_d2 = self.upsample(x_d2, size=(H1, W1))
# ======= Fourth Branch ========
x_d3 = self.conv4(x_d3)
x_d3 = self.relu(x_d3)
x_d3 = self.bn4(x_d3)
x_d3 = self.conv5(x_d3)
x_d3 = self.relu(x_d3)
x_d3 = self.bn5(x_d3)
x_d3 = self.conv6(x_d3)
x_d3 = self.relu(x_d3)
x_d3 = self.bn6(x_d3)
x_d3 = self.conv7(x_d3)
x_d3 = self.relu(x_d3)
x_d3 = self.bn7(x_d3)
x_d3 = self.upsample(x_d3, size=(H3, W3))
x_d3 = self.upsample(x_d3, size=(H2, W2))
x_d3 = self.upsample(x_d3, size=(H1, W1))
# ======= Concat maps ==========
x = torch.cat((x, x_d1, x_d2, x_d3), 1)
x = self.conv_smooth1(x)
x = self.conv_smooth2(x)
x = self.conv_smooth3(x)
# ============ Decoder ==========
x = self.convtrans1(x)
x = self.relu(x)
x = self.convtrans2(x)
x = self.relu(x)
x = self.convtrans3(x)
x = self.relu(x)
x = self.convtrans4(x)
x = self.relu(x)
x = self.convtrans5(x)
x = self.relu(x)
x = self.convtrans6(x)
x = self.relu(x)
x = self.convtrans7(x)
x = self.sigmoid(x)
return x
def upsample(self, x, size):
up = nn.Upsample(size=size, mode="bilinear")
return up(x)
class FPN_Gray(nn.Module):
def __init__(self):
super(FPN_Gray, self).__init__()
self.name = "AEFPNC_Gray"
self.bn0 = nn.BatchNorm2d(1)
self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3, padding=1) # 3x256x256 -> 16x256x256
self.bn1 = nn.BatchNorm2d(num_features=16)
self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding=1) # 16x256x256 -> 32x256x256
self.bn2 = nn.BatchNorm2d(num_features=32)
self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1) # 32x256x256 -> 64x256x256
self.bn3 = nn.BatchNorm2d(num_features=64)
self.conv4 = nn.Conv2d(in_channels=64, out_channels=48, kernel_size=3, padding=1) # 64x256x256 -> 48x256x256
self.bn4 = nn.BatchNorm2d(num_features=48)
self.conv5 = nn.Conv2d(in_channels=48, out_channels=32, kernel_size=3, padding=1) # 48x256x256 -> 32x256x256
self.bn5 = nn.BatchNorm2d(num_features=32)
self.conv6 = nn.Conv2d(in_channels=32, out_channels=24, kernel_size=3, padding=1) # 32x256x256 -> 24x256x256
self.bn6 = nn.BatchNorm2d(num_features=24)
self.conv7 = nn.Conv2d(in_channels=24, out_channels=16, kernel_size=3, padding=1) # 24x256x256 -> 16x256x256
self.bn7 = nn.BatchNorm2d(num_features=16)
self.down = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=2)
self.conv_smooth1 = nn.Conv2d(in_channels=64, out_channels=32, kernel_size=3, padding=1)
self.conv_smooth2 = nn.Conv2d(in_channels=32, out_channels=16, kernel_size=3, padding=1)
self.conv_smooth3 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, padding=1)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
self.convtrans1 = nn.ConvTranspose2d(in_channels=16, out_channels=24, kernel_size=3, padding=1)
self.convtrans2 = nn.ConvTranspose2d(in_channels=24, out_channels=32, kernel_size=3, padding=1)
self.convtrans3 = nn.ConvTranspose2d(in_channels=32, out_channels=48, kernel_size=3, padding=1)
self.convtrans4 = nn.ConvTranspose2d(in_channels=48, out_channels=64, kernel_size=3, padding=1)
self.convtrans5 = nn.ConvTranspose2d(in_channels=64, out_channels=32, kernel_size=3, padding=1)
self.convtrans6 = nn.ConvTranspose2d(in_channels=32, out_channels=16, kernel_size=3, padding=1)
self.convtrans7 = nn.ConvTranspose2d(in_channels=16, out_channels=1, kernel_size=3, padding=1)
def forward(self, x):
# ============ Encoder ===========
# ====== Bottom Up Layers =====
x = self.bn0(x)
x = self.conv1(x)
x = self.relu(x)
x = self.bn1(x)
x = self.conv2(x)
x = self.relu(x)
x = self.bn2(x)
x = self.conv3(x)
x = self.relu(x)
x = self.bn3(x)
_, _, H1, W1 = x.size()
# ======= Branch network ======
x_d1 = self.down(x) # 128x128
_, _, H2, W2 = x_d1.size()
x_d2 = self.down(x_d1) # 64x64
_, _, H3, W3 = x_d2.size()
x_d3 = self.down(x_d2) # 32x32
# ======= First Branch =======
x = self.conv4(x)
x = self.relu(x)
x = self.bn4(x)
x = self.conv5(x)
x = self.relu(x)
x = self.bn5(x)
x = self.conv6(x)
x = self.relu(x)
x = self.bn6(x)
x = self.conv7(x)
x = self.relu(x)
x = self.bn7(x)
# ======= Second Branch ========
x_d1 = self.conv4(x_d1)
x_d1 = self.relu(x_d1)
x_d1 = self.bn4(x_d1)
x_d1 = self.conv5(x_d1)
x_d1 = self.relu(x_d1)
x_d1 = self.bn5(x_d1)
x_d1 = self.conv6(x_d1)
x_d1 = self.relu(x_d1)
x_d1 = self.bn6(x_d1)
x_d1 = self.conv7(x_d1)
x_d1 = self.relu(x_d1)
x_d1 = self.bn7(x_d1)
x_d1 = self.upsample(x_d1, size=(H1, W1))
# ======= Third Branch ========
x_d2 = self.conv4(x_d2)
x_d2 = self.relu(x_d2)
x_d2 = self.bn4(x_d2)
x_d2 = self.conv5(x_d2)
x_d2 = self.relu(x_d2)
x_d2 = self.bn5(x_d2)
x_d2 = self.conv6(x_d2)
x_d2 = self.relu(x_d2)
x_d2 = self.bn6(x_d2)
x_d2 = self.conv7(x_d2)
x_d2 = self.relu(x_d2)
x_d2 = self.bn7(x_d2)
x_d2 = self.upsample(x_d2, size=(H2, W2))
x_d2 = self.upsample(x_d2, size=(H1, W1))
# ======= Fourth Branch ========
x_d3 = self.conv4(x_d3)
x_d3 = self.relu(x_d3)
x_d3 = self.bn4(x_d3)
x_d3 = self.conv5(x_d3)
x_d3 = self.relu(x_d3)
x_d3 = self.bn5(x_d3)
x_d3 = self.conv6(x_d3)
x_d3 = self.relu(x_d3)
x_d3 = self.bn6(x_d3)
x_d3 = self.conv7(x_d3)
x_d3 = self.relu(x_d3)
x_d3 = self.bn7(x_d3)
x_d3 = self.upsample(x_d3, size=(H3, W3))
x_d3 = self.upsample(x_d3, size=(H2, W2))
x_d3 = self.upsample(x_d3, size=(H1, W1))
# ======= Concat maps ==========
x = torch.cat((x, x_d1, x_d2, x_d3), 1)
x = self.conv_smooth1(x)
x = self.conv_smooth2(x)
x = self.conv_smooth3(x)
# ============ Decoder ==========
x = self.convtrans1(x)
x = self.relu(x)
x = self.convtrans2(x)
x = self.relu(x)
x = self.convtrans3(x)
x = self.relu(x)
x = self.convtrans4(x)
x = self.relu(x)
x = self.convtrans5(x)
x = self.relu(x)
x = self.convtrans6(x)
x = self.relu(x)
x = self.convtrans7(x)
x = self.sigmoid(x)
return x
def upsample(self, x, size):
up = nn.Upsample(size=size, mode="bilinear")
return up(x)