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# This is a UNet model for CARRADA and KuRALS
import torch
import torch.nn as nn
import torch.nn.functional as F
class DoubleConv(nn.Module):
"""(convolution => [BN] => ReLU) * 2"""
def __init__(self, in_channels, out_channels, mid_channels=None):
super().__init__()
if not mid_channels:
mid_channels = out_channels
self.double_conv = nn.Sequential(
nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.double_conv(x)
class Down(nn.Module):
"""Downscaling with maxpool then double conv"""
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):
"""Upscaling then double conv"""
def __init__(self, in_channels, out_channels, bilinear=True):
super().__init__()
# if bilinear, use the normal convolutions to reduce the number of channels
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
else:
self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
self.conv = DoubleConv(in_channels, out_channels)
def forward(self, x1, x2):
x1 = self.up(x1)
# input is BCHW
diffY = x2.size()[2] - x1.size()[2]
diffX = x2.size()[3] - x1.size()[3]
x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
diffY // 2, diffY - diffY // 2])
# if you have padding issues, see
# https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
# https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
x = torch.cat([x2, x1], dim=1)
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 UNet(nn.Module):
def __init__(self, n_classes, n_frames=5, bilinear=True):
"""
Args:
n_classes : _description_
n_frames : _description_. Defaults to 5.
bilinear : Bilinear interpolation or ConvTranspose2d.
"""
super(UNet, self).__init__()
self.n_channels = n_frames
self.n_classes = n_classes
self.bilinear = bilinear
# lt@20240406 reduce resolution
# self.pool0 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.inc = (DoubleConv(n_frames, 64))
self.down1 = (Down(64, 128))
self.down2 = (Down(128, 256))
self.down3 = (Down(256, 512))
factor = 2 if bilinear else 1
self.down4 = (Down(512, 1024 // factor))
self.up1 = (Up(1024, 512 // factor, bilinear))
self.up2 = (Up(512, 256 // factor, bilinear))
self.up3 = (Up(256, 128 // factor, bilinear))
self.up4 = (Up(128, 64, bilinear))
self.outc = (OutConv(64, n_classes))
def forward(self, x):
# lt@20240406 reduce resolution
# x = self.pool0(x)
# lt@20240408 add resolution
# x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True)
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)
# lt@20240406 recover resolution
# logits = F.interpolate(logits, scale_factor=2, mode='bilinear', align_corners=True)
# lt@20240408 add resolution
# logits = F.interpolate(logits, scale_factor=0.5, mode='bilinear', align_corners=True)
return logits
# from https://github.com/milesial/Pytorch-UNet/tree/master
# import torch
# import torch.nn as nn
# import torch.nn.functional as F
# lt@20240315 spatial group normalization
# lt@20240316 change fixed value 200 to w//4.
# class DoubleConv(nn.Module):
# """(convolution => [SGN] => ReLU) * 2"""
# def __init__(self, in_channels, out_channels, mid_channels=None):
# super().__init__()
# if not mid_channels:
# mid_channels = out_channels
# self.conv1 = nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False)
# self.bn1_1 = nn.BatchNorm2d(mid_channels)
# self.bn1_2 = nn.BatchNorm2d(mid_channels)
# self.relu = nn.ReLU(inplace=True)
# self.conv2 = nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False)
# self.bn2_1 = nn.BatchNorm2d(out_channels)
# self.bn2_2 = nn.BatchNorm2d(out_channels)
# def forward(self, x):
# b, c, h, w = x.shape
# x = self.conv1(x)
# x11 = self.bn1_1(x[:, :, :, :(w//4)])
# x12 = self.bn1_2(x[:, :, :, (w//4):])
# x1 = self.relu(torch.concat([x11, x12], dim=-1))
# x2 = self.conv2(x1)
# x21 = self.bn2_1(x2[:, :, :, :(w//4)])
# x22 = self.bn2_2(x2[:, :, :, (w//4):])
# x2 = self.relu(torch.concat([x21, x22], dim=-1))
# return x2
# lt@20240315 Group Normalization
# class DoubleConv(nn.Module):
# """(convolution => [GN] => ReLU) * 2"""
# def __init__(self, in_channels, out_channels, mid_channels=None):
# super().__init__()
# if not mid_channels:
# mid_channels = out_channels
# self.double_conv = nn.Sequential(
# nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False),
# nn.GroupNorm(mid_channels//16, mid_channels),
# nn.ReLU(inplace=True),
# nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False),
# nn.GroupNorm(out_channels//16, out_channels),
# nn.ReLU(inplace=True)
# )
# def forward(self, x):
# return self.double_conv(x)
# class Down(nn.Module):
# """Downscaling with maxpool then double conv"""
# 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):
# """Upscaling then double conv"""
# def __init__(self, in_channels, out_channels, bilinear=True):
# super().__init__()
# # if bilinear, use the normal convolutions to reduce the number of channels
# if bilinear:
# self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
# self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
# else:
# self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
# self.conv = DoubleConv(in_channels, out_channels)
# def forward(self, x1, x2):
# x1 = self.up(x1)
# # input is BCHW
# diffY = x2.size()[2] - x1.size()[2]
# diffX = x2.size()[3] - x1.size()[3]
# x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
# diffY // 2, diffY - diffY // 2])
# # if you have padding issues, see
# # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
# # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
# x = torch.cat([x2, x1], dim=1)
# 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 UNet(nn.Module):
# def __init__(self, n_classes, n_frames=5, bilinear=True):
# """
# Args:
# n_classes : _description_
# n_frames : _description_. Defaults to 5.
# bilinear : Bilinear interpolation or ConvTranspose2d.
# """
# super(UNet, self).__init__()
# self.n_channels = n_frames
# self.n_classes = n_classes
# self.bilinear = bilinear
# self.inc = (DoubleConv(n_frames, 64))
# self.down1 = (Down(64, 128))
# self.down2 = (Down(128, 256))
# self.down3 = (Down(256, 512))
# factor = 2 if bilinear else 1
# self.down4 = (Down(512, 1024 // factor))
# self.up1 = (Up(1024, 512 // factor, bilinear))
# self.up2 = (Up(512, 256 // factor, bilinear))
# self.up3 = (Up(256, 128 // factor, bilinear))
# self.up4 = (Up(128, 64, bilinear))
# self.outc = (OutConv(64, 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 |