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model.py
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#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
'''
Created on :2021/02/18 20:28:15
@author :Caihao (Chris) Cui
@file :model.py
@content :xxx xxx xxx
@version :0.1
@License : (C)Copyright 2020 MIT
'''
# here put the import lib
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
# TODO: Use Transposed Convolution for upsampling
# https://towardsdatascience.com/up-sampling-with-transposed-convolution-9ae4f2df52d0
class FCNN(nn.Module):
def __init__(self, num_classes=2):
super(FCNN, self).__init__()
# 1 input image channel, 6 output channels, 3x3 square convolution
# kernel : N C H W
# https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html?highlight=nn%20conv2d#torch.nn.Conv2d
nc = num_classes
# 1 Layer Features
# 1x1 filter net-in-net
self.conv1a = nn.Conv2d(3, 16, (1, 1))
self.bn1a = nn.BatchNorm2d(16)
# 3x3 filter
self.conv1b = nn.Conv2d(3, 16, (3, 3), padding=1)
self.bn1b = nn.BatchNorm2d(16)
# 5x5 filter with atrous algorithm, looking large area and keep in and out same size.
self.conv1c = nn.Conv2d(3, 16, (5, 5), stride=1, padding=4, dilation=2)
self.bn1c = nn.BatchNorm2d(16)
# 2 Layer Features
self.conv2 = nn.Conv2d(48, 96, (1, 1))
self.bn2 = nn.BatchNorm2d(96)
# 3 Layer Features
self.conv3 = nn.Conv2d(96, 48, (3, 3), padding=1)
self.bn3 = nn.BatchNorm2d(48)
# 4 Layer Features
self.conv4 = nn.Conv2d(16 + 48, 16, (1, 1))
self.bn4 = nn.BatchNorm2d(16)
# 5 Layer Features
self.conv5 = nn.Conv2d(16, nc, (3, 3), padding=1)
self.bn5 = nn.BatchNorm2d(nc)
def forward(self, x):
input_size = x.size()[2:]
# Layer 1
xa = F.relu(self.bn1a(self.conv1a(x)))
xb = F.relu(self.bn1b(self.conv1b(x)))
xc = F.relu(self.bn1c(self.conv1c(x)))
# Layer 2
xabc = torch.cat((xa, xb, xc), 1) # concatenated on channel
xabc = F.relu(self.bn2(self.conv2(xabc)))
xabc = F.max_pool2d(xabc, (2, 2)) # 0.5x
# Layer 3
xabc = F.relu(self.bn3(self.conv3(xabc)))
# x = F.interpolate(x, scale_factor=(2, 2)) # 2.0x nearest neightbour lead to jaggedness
xabc = F.interpolate(
xabc, scale_factor=(2, 2), mode="bicubic", align_corners=True
) # make the image smooth and reduce sharpness
# Layer 4
x = torch.cat((xa, xabc), 1)
x = self.bn4(self.conv4(x))
# Layer 5
x = self.bn5(self.conv5(x))
return x
class UNet(nn.Module):
def __init__(self, n_channels=3, n_classes=2, bilinear=True):
super(UNet, self).__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.bilinear = bilinear
self.inc = DoubleConv(n_channels, 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
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),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(mid_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):
"""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 CHW
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)