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WNet.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Oct 15 17:38:02 2018
@author: Tao Lin
Implementation of the W-Net unsupervised image segmentation architecture
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class Block(nn.Module):
def __init__(self, in_filters, out_filters, seperable=True):
super(Block, self).__init__()
if seperable:
self.spatial1=nn.Conv2d(in_filters, in_filters, kernel_size=3, groups=in_filters, padding=1)
self.depth1=nn.Conv2d(in_filters, out_filters, kernel_size=1)
self.conv1=lambda x: self.depth1(self.spatial1(x))
self.spatial2=nn.Conv2d(out_filters, out_filters, kernel_size=3, padding=1, groups=out_filters)
self.depth2=nn.Conv2d(out_filters, out_filters, kernel_size=1)
self.conv2=lambda x: self.depth2(self.spatial2(x))
else:
self.conv1=nn.Conv2d(in_filters, out_filters, kernel_size=3, padding=1)
self.conv2=nn.Conv2d(out_filters, out_filters, kernel_size=3, padding=1)
self.batchnorm1=nn.BatchNorm2d(out_filters)
self.batchnorm2=nn.BatchNorm2d(out_filters)
def forward(self, x):
x=self.batchnorm1(self.conv1(x)).clamp(0)
x=self.batchnorm2(self.conv2(x)).clamp(0)
return x
class UEnc(nn.Module):
def __init__(self, squeeze, ch_mul=64, in_chans=3):
super(UEnc, self).__init__()
self.enc1=Block(in_chans, ch_mul, seperable=False)
self.enc2=Block(ch_mul, 2*ch_mul)
self.enc3=Block(2*ch_mul, 4*ch_mul)
self.enc4=Block(4*ch_mul, 8*ch_mul)
self.middle=Block(8*ch_mul, 16*ch_mul)
self.up1=nn.ConvTranspose2d(16*ch_mul, 8*ch_mul, kernel_size=3, stride=2, padding=1, output_padding=1)
self.dec1=Block(16*ch_mul, 8*ch_mul)
self.up2=nn.ConvTranspose2d(8*ch_mul, 4*ch_mul, kernel_size=3, stride=2, padding=1, output_padding=1)
self.dec2=Block(8*ch_mul, 4*ch_mul)
self.up3=nn.ConvTranspose2d(4*ch_mul, 2*ch_mul, kernel_size=3, stride=2, padding=1, output_padding=1)
self.dec3=Block(4*ch_mul, 2*ch_mul)
self.up4=nn.ConvTranspose2d(2*ch_mul, ch_mul, kernel_size=3, stride=2, padding=1, output_padding=1)
self.dec4=Block(2*ch_mul, ch_mul, seperable=False)
self.final=nn.Conv2d(ch_mul, squeeze, kernel_size=(1, 1))
def forward(self, x):
enc1=self.enc1(x)
enc2=self.enc2(F.max_pool2d(enc1, (2, 2)))
enc3=self.enc3(F.max_pool2d(enc2, (2,2)))
enc4=self.enc4(F.max_pool2d(enc3, (2,2)))
middle=self.middle(F.max_pool2d(enc4, (2,2)))
up1=torch.cat([enc4, self.up1(middle)], 1)
dec1=self.dec1(up1)
up2=torch.cat([enc3, self.up2(dec1)], 1)
dec2=self.dec2(up2)
up3=torch.cat([enc2, self.up3(dec2)], 1)
dec3=self.dec3(up3)
up4=torch.cat([enc1, self.up4(dec3)], 1)
dec4=self.dec4(up4)
final=self.final(dec4)
return final
class UDec(nn.Module):
def __init__(self, squeeze, ch_mul=64, in_chans=3):
super(UDec, self).__init__()
self.enc1=Block(squeeze, ch_mul, seperable=False)
self.enc2=Block(ch_mul, 2*ch_mul)
self.enc3=Block(2*ch_mul, 4*ch_mul)
self.enc4=Block(4*ch_mul, 8*ch_mul)
self.middle=Block(8*ch_mul, 16*ch_mul)
self.up1=nn.ConvTranspose2d(16*ch_mul, 8*ch_mul, kernel_size=3, stride=2, padding=1, output_padding=1)
self.dec1=Block(16*ch_mul, 8*ch_mul)
self.up2=nn.ConvTranspose2d(8*ch_mul, 4*ch_mul, kernel_size=3, stride=2, padding=1, output_padding=1)
self.dec2=Block(8*ch_mul, 4*ch_mul)
self.up3=nn.ConvTranspose2d(4*ch_mul, 2*ch_mul, kernel_size=3, stride=2, padding=1, output_padding=1)
self.dec3=Block(4*ch_mul, 2*ch_mul)
self.up4=nn.ConvTranspose2d(2*ch_mul, ch_mul, kernel_size=3, stride=2, padding=1, output_padding=1)
self.dec4=Block(2*ch_mul, ch_mul, seperable=False)
self.final=nn.Conv2d(ch_mul, in_chans, kernel_size=(1, 1))
def forward(self, x):
enc1 = self.enc1(x)
enc2 = self.enc2(F.max_pool2d(enc1, (2, 2)))
enc3 = self.enc3(F.max_pool2d(enc2, (2,2)))
enc4 = self.enc4(F.max_pool2d(enc3, (2,2)))
middle = self.middle(F.max_pool2d(enc4, (2,2)))
up1 = torch.cat([enc4, self.up1(middle)], 1)
dec1 = self.dec1(up1)
up2 = torch.cat([enc3, self.up2(dec1)], 1)
dec2 = self.dec2(up2)
up3 = torch.cat([enc2, self.up3(dec2)], 1)
dec3 =self.dec3(up3)
up4 = torch.cat([enc1, self.up4(dec3)], 1)
dec4 = self.dec4(up4)
final=self.final(dec4)
return final
class WNet(nn.Module):
def __init__(self, squeeze, ch_mul=64, in_chans=3, out_chans=1000):
super(WNet, self).__init__()
if out_chans==1000:
out_chans=in_chans
self.UEnc=UEnc(squeeze, ch_mul, in_chans)
self.UDec=UDec(squeeze, ch_mul, out_chans)
def forward(self, x, returns='both'):
enc = self.UEnc(x)
if returns=='enc':
return enc
dec=self.UDec(F.softmax(enc, 1))
if returns=='dec':
return dec
if returns=='both':
return enc, dec
else:
raise ValueError('Invalid returns, returns must be in [enc dec both]')