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import torch
import torch.nn as nn
import torch.nn.functional as F
import pytorch_lightning as L
class DoubleConv(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.double_conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
)
def forward(self, x):
return self.double_conv(x)
class Down(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.maxpool_conv = nn.Sequential(
nn.MaxPool2d(2),
DoubleConv(in_channels, out_channels)
)
def forward(self, x):
return self.maxpool_conv(x)
class Up(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.up = nn.Upsample(
scale_factor = 2,
mode = 'bilinear',
align_corners = True
)
self.conv = DoubleConv(in_channels, out_channels)
def forward(self, x1, x2):
# x1, x2 -> upsampled tensor, skip-connection tensor
x1 = self.up(x1)
# Input -> CHW
diffY = x2.size()[2] - x1.size()[2] # Height difference
diffX = x2.size()[3] - x1.size()[3] # Width difference
# [left, right, top , bottom]
x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2])
x = torch.cat([x2, x1], dim=1) # channel-wise dim increase
return self.conv(x)
class OutConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(OutConv, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
def forward(self, x):
return self.conv(x)
class mIoULoss(nn.Module):
def __init__(self, weight=None, size_average=True, n_classes=2):
super().__init__()
self.classes = n_classes
def to_one_hot(self, tensor):
tensor = tensor.long() # Ensure tensor is a LongTensor
n,c,h,w = tensor.size()
one_hot = torch.zeros(n,self.classes,h,w).to(tensor.device).scatter_(1,tensor.view(n,1,h,w),1)
return one_hot
def forward(self, inputs, target):
# inputs => N x Classes x H x W
# target_oneHot => N x Classes x H x W
N = inputs.size()[0]
# predicted probabilities for each pixel along channel
inputs = F.softmax(inputs,dim=1)
# Numerator Product
target_oneHot = self.to_one_hot(target)
inter = inputs * target_oneHot
## Sum over all pixels N x C x H x W => N x C
inter = inter.view(N,self.classes,-1).sum(2)
#Denominator
union= inputs + target_oneHot - (inputs*target_oneHot)
## Sum over all pixels N x C x H x W => N x C
union = union.view(N,self.classes,-1).sum(2)
loss = inter/union
## Return average loss over classes and batch
return 1-loss.mean()
class UNet(L.LightningModule):
def __init__(self, n_channels=3, n_classes=6):
super().__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.inc = DoubleConv(n_channels, 64)
self.down1 = Down(64, 128)
self.down2 = Down(128, 256)
self.down3 = Down(256, 512)
self.down4 = Down(512, 512)
self.up1 = Up(1024, 256)
self.up2 = Up(512, 128)
self.up3 = Up(256, 64)
self.up4 = Up(128, 64)
self.outc = OutConv(64, n_classes)
self.criterion = mIoULoss(n_classes=n_classes)
def forward(self, x):
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x = self.up1(x5, x4)
x = self.up2(x, x3)
x = self.up3(x, x2)
x = self.up4(x, x1)
logits = self.outc(x)
return logits
def training_step(self, batch, batch_idx):
images, masks = batch
logits = self(images)
loss = self.criterion(logits, masks)
self.log('train_loss', loss)
print('train_loss', loss)
return loss
def validation_step(self, batch, batch_idx):
images, masks = batch
logits = self(images)
loss = self.criterion(logits, masks)
self.log('val_loss', loss)
print('val_loss', loss)
return loss
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
return optimizer
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