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segnet_model.py
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segnet_model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
IN_CHANNELS=3
N_CLASSES=2
class SegNet(nn.Module):
# SegNet network
@staticmethod
def weight_init(m):
if isinstance(m, nn.Linear):
torch.nn.init.kaiming_normal(m.weight.data)
def __init__(self, in_channels=IN_CHANNELS, out_channels=N_CLASSES):
super(SegNet, self).__init__()
self.pool = nn.MaxPool2d(2, return_indices=True)
self.unpool = nn.MaxUnpool2d(2)
self.conv1_1 = nn.Conv2d(in_channels, 64, 3, padding=1)
self.conv1_1_bn = nn.BatchNorm2d(64)
self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1)
self.conv1_2_bn = nn.BatchNorm2d(64)
self.conv2_1 = nn.Conv2d(64, 128, 3, padding=1)
self.conv2_1_bn = nn.BatchNorm2d(128)
self.conv2_2 = nn.Conv2d(128, 128, 3, padding=1)
self.conv2_2_bn = nn.BatchNorm2d(128)
self.conv3_1 = nn.Conv2d(128, 256, 3, padding=1)
self.conv3_1_bn = nn.BatchNorm2d(256)
self.conv3_2 = nn.Conv2d(256, 256, 3, padding=1)
self.conv3_2_bn = nn.BatchNorm2d(256)
self.conv3_3 = nn.Conv2d(256, 256, 3, padding=1)
self.conv3_3_bn = nn.BatchNorm2d(256)
self.conv4_1 = nn.Conv2d(256, 512, 3, padding=1)
self.conv4_1_bn = nn.BatchNorm2d(512)
self.conv4_2 = nn.Conv2d(512, 512, 3, padding=1)
self.conv4_2_bn = nn.BatchNorm2d(512)
self.conv4_3 = nn.Conv2d(512, 512, 3, padding=1)
self.conv4_3_bn = nn.BatchNorm2d(512)
self.conv5_1 = nn.Conv2d(512, 512, 3, padding=1)
self.conv5_1_bn = nn.BatchNorm2d(512)
self.conv5_2 = nn.Conv2d(512, 512, 3, padding=1)
self.conv5_2_bn = nn.BatchNorm2d(512)
self.conv5_3 = nn.Conv2d(512, 512, 3, padding=1)
self.conv5_3_bn = nn.BatchNorm2d(512)
self.conv5_3_D = nn.Conv2d(512, 512, 3, padding=1)
self.conv5_3_D_bn = nn.BatchNorm2d(512)
self.conv5_2_D = nn.Conv2d(512, 512, 3, padding=1)
self.conv5_2_D_bn = nn.BatchNorm2d(512)
self.conv5_1_D = nn.Conv2d(512, 512, 3, padding=1)
self.conv5_1_D_bn = nn.BatchNorm2d(512)
self.conv4_3_D = nn.Conv2d(512, 512, 3, padding=1)
self.conv4_3_D_bn = nn.BatchNorm2d(512)
self.conv4_2_D = nn.Conv2d(512, 512, 3, padding=1)
self.conv4_2_D_bn = nn.BatchNorm2d(512)
self.conv4_1_D = nn.Conv2d(512, 256, 3, padding=1)
self.conv4_1_D_bn = nn.BatchNorm2d(256)
self.conv3_3_D = nn.Conv2d(256, 256, 3, padding=1)
self.conv3_3_D_bn = nn.BatchNorm2d(256)
self.conv3_2_D = nn.Conv2d(256, 256, 3, padding=1)
self.conv3_2_D_bn = nn.BatchNorm2d(256)
self.conv3_1_D = nn.Conv2d(256, 128, 3, padding=1)
self.conv3_1_D_bn = nn.BatchNorm2d(128)
self.conv2_2_D = nn.Conv2d(128, 128, 3, padding=1)
self.conv2_2_D_bn = nn.BatchNorm2d(128)
self.conv2_1_D = nn.Conv2d(128, 64, 3, padding=1)
self.conv2_1_D_bn = nn.BatchNorm2d(64)
self.conv1_2_D = nn.Conv2d(64, 64, 3, padding=1)
self.conv1_2_D_bn = nn.BatchNorm2d(64)
self.conv1_1_D = nn.Conv2d(64, out_channels, 3, padding=1)
self.apply(self.weight_init)
def forward(self, x):
# Encoder block 1
x = F.relu(self.conv1_1_bn(self.conv1_1(x)))
x = F.relu(self.conv1_2_bn(self.conv1_2(x)))
x, mask1 = self.pool(x)
# Encoder block 2
x = F.relu(self.conv2_1_bn(self.conv2_1(x)))
x = F.relu(self.conv2_2_bn(self.conv2_2(x)))
x, mask2 = self.pool(x)
# Encoder block 3
x = F.relu(self.conv3_1_bn(self.conv3_1(x)))
x = F.relu(self.conv3_2_bn(self.conv3_2(x)))
x = F.relu(self.conv3_3_bn(self.conv3_3(x)))
x, mask3 = self.pool(x)
# Encoder block 4
x = F.relu(self.conv4_1_bn(self.conv4_1(x)))
x = F.relu(self.conv4_2_bn(self.conv4_2(x)))
x = F.relu(self.conv4_3_bn(self.conv4_3(x)))
x, mask4 = self.pool(x)
# Encoder block 5
x = F.relu(self.conv5_1_bn(self.conv5_1(x)))
x = F.relu(self.conv5_2_bn(self.conv5_2(x)))
x = F.relu(self.conv5_3_bn(self.conv5_3(x)))
x, mask5 = self.pool(x)
# Decoder block 5
x = self.unpool(x, mask5)
x = F.relu(self.conv5_3_D_bn(self.conv5_3_D(x)))
x = F.relu(self.conv5_2_D_bn(self.conv5_2_D(x)))
x = F.relu(self.conv5_1_D_bn(self.conv5_1_D(x)))
# Decoder block 4
x = self.unpool(x, mask4)
x = F.relu(self.conv4_3_D_bn(self.conv4_3_D(x)))
x = F.relu(self.conv4_2_D_bn(self.conv4_2_D(x)))
x = F.relu(self.conv4_1_D_bn(self.conv4_1_D(x)))
# Decoder block 3
x = self.unpool(x, mask3)
x = F.relu(self.conv3_3_D_bn(self.conv3_3_D(x)))
x = F.relu(self.conv3_2_D_bn(self.conv3_2_D(x)))
x = F.relu(self.conv3_1_D_bn(self.conv3_1_D(x)))
# Decoder block 2
x = self.unpool(x, mask2)
x = F.relu(self.conv2_2_D_bn(self.conv2_2_D(x)))
x = F.relu(self.conv2_1_D_bn(self.conv2_1_D(x)))
# Decoder block 1
x = self.unpool(x, mask1)
x = F.relu(self.conv1_2_D_bn(self.conv1_2_D(x)))
x = F.log_softmax(self.conv1_1_D(x))
return x
def forward_old(self, x):
# Encoder block 1
x = self.conv1_1_bn(F.relu(self.conv1_1(x)))
x = self.conv1_2_bn(F.relu(self.conv1_2(x)))
x, mask1 = self.pool(x)
# Encoder block 2
x = self.conv2_1_bn(F.relu(self.conv2_1(x)))
x = self.conv2_2_bn(F.relu(self.conv2_2(x)))
x, mask2 = self.pool(x)
# Encoder block 3
x = self.conv3_1_bn(F.relu(self.conv3_1(x)))
x = self.conv3_2_bn(F.relu(self.conv3_2(x)))
x = self.conv3_3_bn(F.relu(self.conv3_3(x)))
x, mask3 = self.pool(x)
# Encoder block 4
x = self.conv4_1_bn(F.relu(self.conv4_1(x)))
x = self.conv4_2_bn(F.relu(self.conv4_2(x)))
x = self.conv4_3_bn(F.relu(self.conv4_3(x)))
x, mask4 = self.pool(x)
# Encoder block 5
x = self.conv5_1_bn(F.relu(self.conv5_1(x)))
x = self.conv5_2_bn(F.relu(self.conv5_2(x)))
x = self.conv5_3_bn(F.relu(self.conv5_3(x)))
x, mask5 = self.pool(x)
# Decoder block 5
x = self.unpool(x, mask5)
x = self.conv5_3_D_bn(F.relu(self.conv5_3_D(x)))
x = self.conv5_2_D_bn(F.relu(self.conv5_2_D(x)))
x = self.conv5_1_D_bn(F.relu(self.conv5_1_D(x)))
# Decoder block 4
x = self.unpool(x, mask4)
x = self.conv4_3_D_bn(F.relu(self.conv4_3_D(x)))
x = self.conv4_2_D_bn(F.relu(self.conv4_2_D(x)))
x = self.conv4_1_D_bn(F.relu(self.conv4_1_D(x)))
# Decoder block 3
x = self.unpool(x, mask3)
x = self.conv3_3_D_bn(F.relu(self.conv3_3_D(x)))
x = self.conv3_2_D_bn(F.relu(self.conv3_2_D(x)))
x = self.conv3_1_D_bn(F.relu(self.conv3_1_D(x)))
# Decoder block 2
x = self.unpool(x, mask2)
x = self.conv2_2_D_bn(F.relu(self.conv2_2_D(x)))
x = self.conv2_1_D_bn(F.relu(self.conv2_1_D(x)))
# Decoder block 1
x = self.unpool(x, mask1)
x = self.conv1_2_D_bn(F.relu(self.conv1_2_D(x)))
x = F.log_softmax(self.conv1_1_D(x))
return x