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modelNetM.py
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modelNetM.py
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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class plainEncoderBlock(nn.Module):
def __init__(self, inChannel, outChannel, stride):
super(plainEncoderBlock, self).__init__()
self.conv1 = nn.Conv2d(inChannel, outChannel, kernel_size=3, stride=stride, padding=1)
self.bn1 = nn.BatchNorm2d(outChannel)
self.conv2 = nn.Conv2d(outChannel, outChannel, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(outChannel)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
return x
class plainDecoderBlock(nn.Module):
def __init__(self, inChannel, outChannel, stride):
super(plainDecoderBlock, self).__init__()
self.conv1 = nn.Conv2d(inChannel, inChannel, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(inChannel)
if stride == 1:
self.conv2 = nn.Conv2d(inChannel, outChannel, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(outChannel)
else:
self.conv2 = nn.ConvTranspose2d(inChannel, outChannel, kernel_size=2, stride=2)
self.bn2 = nn.BatchNorm2d(outChannel)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
return x
class resEncoderBlock(nn.Module):
def __init__(self, inChannel, outChannel, stride):
super(resEncoderBlock, self).__init__()
self.conv1 = nn.Conv2d(inChannel, outChannel, kernel_size=3, stride=stride, padding=1)
self.bn1 = nn.BatchNorm2d(outChannel)
self.conv2 = nn.Conv2d(outChannel, outChannel, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(outChannel)
self.downsample = None
if stride != 1:
self.downsample = nn.Sequential(
nn.Conv2d(inChannel, outChannel, kernel_size=1, stride=stride),
nn.BatchNorm2d(outChannel))
def forward(self, x):
residual = x
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = F.relu(out)
return out
class resDecoderBlock(nn.Module):
def __init__(self, inChannel, outChannel, stride):
super(resDecoderBlock, self).__init__()
self.conv1 = nn.Conv2d(inChannel, inChannel, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(inChannel)
self.downsample = None
if stride == 1:
self.conv2 = nn.Conv2d(inChannel, outChannel, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(outChannel)
else:
self.conv2 = nn.ConvTranspose2d(inChannel, outChannel, kernel_size=2, stride=2)
self.bn2 = nn.BatchNorm2d(outChannel)
self.downsample = nn.Sequential(
nn.ConvTranspose2d(inChannel, outChannel, kernel_size=1, stride=2, output_padding=1),
nn.BatchNorm2d(outChannel))
def forward(self, x):
residual = x
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = F.relu(out)
return out
class EncoderNet(nn.Module):
def __init__(self, layers):
super(EncoderNet, self).__init__()
self.conv = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.bn = nn.BatchNorm2d(64)
self.en_layer1 = self.make_encoder_layer(plainEncoderBlock, 64, 64, layers[0], stride=1)
self.en_layer2 = self.make_encoder_layer(resEncoderBlock, 64, 128, layers[1], stride=2)
self.en_layer3 = self.make_encoder_layer(resEncoderBlock, 128, 256, layers[2], stride=2)
self.en_layer4 = self.make_encoder_layer(resEncoderBlock, 256, 512, layers[3], stride=2)
self.en_layer5 = self.make_encoder_layer(resEncoderBlock, 512, 512, layers[4], stride=2)
# weight initializaion with Kaiming method
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def make_encoder_layer(self, block, inChannel, outChannel, block_num, stride):
layers = []
layers.append(block(inChannel, outChannel, stride=stride))
for i in range(1, block_num):
layers.append(block(outChannel, outChannel, stride=1))
return nn.Sequential(*layers)
def forward(self, x):
x = F.relu(self.bn(self.conv(x)))
x = self.en_layer1(x) #128
x = self.en_layer2(x) #64
x = self.en_layer3(x) #32
x = self.en_layer4(x) #16
x = self.en_layer5(x) #8
return x
class DecoderNet(nn.Module):
def __init__(self, layers):
super(DecoderNet, self).__init__()
self.de_layer5 = self.make_decoder_layer(resDecoderBlock, 512, 512, layers[4], stride=2)
self.de_layer4 = self.make_decoder_layer(resDecoderBlock, 512, 256, layers[3], stride=2)
self.de_layer3 = self.make_decoder_layer(resDecoderBlock, 256, 128, layers[2], stride=2)
self.de_layer2 = self.make_decoder_layer(resDecoderBlock, 128, 64, layers[1], stride=2)
self.de_layer1 = self.make_decoder_layer(plainDecoderBlock, 64, 64, layers[0], stride=1)
self.conv_end = nn.Conv2d(64, 2, kernel_size=3, stride=1, padding=1)
# weight initializaion with Kaiming method
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def make_decoder_layer(self, block, inChannel, outChannel, block_num, stride):
layers = []
for i in range(0, block_num-1):
layers.append(block(inChannel, inChannel, stride=1))
layers.append(block(inChannel, outChannel, stride=stride))
return nn.Sequential(*layers)
def forward(self, x):
x = self.de_layer5(x) #8
x = self.de_layer4(x) #16
x = self.de_layer3(x) #32
x = self.de_layer2(x) #64
x = self.de_layer1(x) #128
x = self.conv_end(x)
return x
class ClassNet(nn.Module):
def __init__(self):
super(ClassNet, self).__init__()
self.conv1 = nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1)
self.bn1 = nn.BatchNorm2d(512)
self.conv2 = nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1)
self.bn2 = nn.BatchNorm2d(512)
self.fc = nn.Linear(512 * 4 * 4, 6)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
class EPELoss(nn.Module):
def __init__(self):
super(EPELoss, self).__init__()
def forward(self, output, target):
lossvalue = torch.norm(output - target + 1e-16, p=2, dim=1).mean()
return lossvalue