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ganComponents.py
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ganComponents.py
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'''
Basic Components for GAN: Regressor,Segmentor, Discriminator
Dong Nie
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from nnBuildUnits import *
from utils import weights_init
class Segmentor(nn.Module):
def __init__(self):
super(Segmentor,self).__init__()
class UNet(nn.Module):
def __init__(self):
super(UNet, self).__init__()
#self.imsize = imsize
self.activation = F.relu
self.pool1 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
self.pool2 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
self.pool3 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
#self.pool4 = nn.MaxPool2d(2)
self.conv_block1_64 = unetConvUnit(3, 64)
self.conv_block64_128 = unetConvUnit(64, 128)
self.conv_block128_256 = unetConvUnit(128, 256)
self.conv_block256_512 = unetConvUnit(256, 512)
#self.conv_block512_1024 = unetConvUnit(512, 1024)
#self.up_block1024_512 = unetUpUnit(1024, 512)
self.up_block512_256 = unetUpUnit(512, 256)
self.up_block256_128 = unetUpUnit(256, 128)
self.up_block128_64 = unetUpUnit(128, 64)
self.last = nn.Conv2d(64, 4, 1)
def forward(self, x):
#print 'x.shape is, ',x.shape
block1 = self.conv_block1_64(x)
# print 'block1 size is ',block1.size()
pool1 = self.pool1(block1)
# print 'pool1 size is ',pool1.size()
block2 = self.conv_block64_128(pool1)
# print 'block2 size is ',block2.size()
pool2 = self.pool2(block2)
# print 'pool2 size is ',pool2.size()
block3 = self.conv_block128_256(pool2)
# print 'block3 size is ',block3.size()
pool3 = self.pool3(block3)
# print 'pool3 size is ',pool3.size()
block4 = self.conv_block256_512(pool3)
# print 'block4 size is ',block4.size()
# pool4 = self.pool4(block4)1
# print 'pool4 shape ',pool4.shape
# block5 = self.conv_block512_1024(pool4)
# up1 = self.up_block1024_512(block5, block4)
up2 = self.up_block512_256(block4, block3)
# print 'up2 size is ',up2.size()
up3 = self.up_block256_128(up2, block2)
# print 'up3 size is ',up3.size()
up4 = self.up_block128_64(up3, block1)
# print 'up4 size is ',up4.size()
# return F.log_softmax(self.last(up4))
return self.last(up4)
'''
Residual Learning for FCN: short-range residual connection and long-range residual connection
We use more residual layers, especially one more layer after each residual layer which have a non-empty branch1
We use spatial dropout after each layer group
We use stochastic long-range connection
We use small dilation in the middle layers
'''
class DeeperResSegNet(nn.Module):
def __init__(self, isRandomConnection = False, isSmallDilation = True, isSpatialDropOut = True, dropoutRate=0.25):
super(DeeperResSegNet, self).__init__()
#self.imsize = imsize
self.isSpatialDropOut = isSpatialDropOut
self.activation = F.relu
self.pool1 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
self.pool2 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
self.pool3 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
#self.pool4 = nn.MaxPool2d(2)
self.conv1_block1_64 = convUnit(3, 64)
self.conv1_block64_64 = residualUnit3(64, 64, isEmptyBranch1=False)
self.conv2_block64_128 = residualUnit3(64, 128)
self.conv2_block128_128 = residualUnit3(128, 128, isEmptyBranch1=False)
self.conv3_block128_256 = residualUnit3(128, 256)
self.conv3_block256_256 = residualUnit3(256, 256, isEmptyBranch1=False)
#dilated on the smallest resolution
self.conv4_block256_512 = residualUnit3(256, 512, isDilation=isSmallDilation)
self.dropout2d = nn.Dropout2d(dropoutRate)
#self.conv_block512_1024 = unetConvUnit(512, 1024)
#self.up_block1024_512 = unetUpUnit(1024, 512)
# print 'line 107'
if isRandomConnection:
self.up_block512_256 = ResUpUnit(512, 256, spatial_dropout_rate = 0.1)
# print 'line 109'
self.up_block256_128 = ResUpUnit(256, 128, spatial_dropout_rate = 0.05)
self.up_block128_64 = ResUpUnit(128, 64, spatial_dropout_rate = 0.01)
else:
self.up_block512_256 = ResUpUnit(512, 256, spatial_dropout_rate = 0)
# print 'line 109'
self.up_block256_128 = ResUpUnit(256, 128, spatial_dropout_rate = 0)
self.up_block128_64 = ResUpUnit(128, 64, spatial_dropout_rate = 0)
self.last = nn.Conv2d(64, 4, 1)
def forward(self, x):
# print 'line 128: ','x.shape is, ',x.shape, 'type(x) is ',type(x)
block0 = self.conv1_block1_64(x)
# print 'block0 size is ', block0.size()
block1 = self.conv1_block64_64(block0)
pool1 = self.pool1(block1)
# print 'pool1 size is ',pool1.size()
block2 = self.conv2_block64_128(pool1)
block2a = self.conv2_block128_128(block2)
if self.isSpatialDropOut:
block2a = self.dropout2d(block2a)
# print 'block2 size is ',block2.size()
pool2 = self.pool2(block2a)
# print 'pool2 size is ',pool2.size()
block3 = self.conv3_block128_256(pool2)
block3a = self.conv3_block256_256(block3)
if self.isSpatialDropOut:
block3a = self.dropout2d(block3a)
# print 'block3 size is ',block3.size()
pool3 = self.pool3(block3a)
# print 'pool3 size is ',pool3.size()
block4 = self.conv4_block256_512(pool3)
#actually, I donot think this is good for this layer, if we do dropout here, then block4 and block3 are all randomized
if self.isSpatialDropOut:
block4 = self.dropout2d(block4)
# print 'block4 size is ',block4.size()
# pool4 = self.pool4(block4)1
# print 'pool4 shape ',pool4.shape
# block5 = self.conv_block512_1024(pool4)
# up1 = self.up_block1024_512(block5, block4)
up2 = self.up_block512_256(block4, block3)
# print 'up2 size is ',up2.size()
up3 = self.up_block256_128(up2, block2)
# print 'up3 size is ',up3.size()
up4 = self.up_block128_64(up3, block1)
# print 'up4 size is ',up4.size()
# return F.log_softmax(self.last(up4))
return self.last(up4)
'''
Dilated network:
a segmentation network introduced in "Adversarial training and dilated convolutions for brain MRI segmentation"
from url: https://arxiv.org/pdf/1707.03195.pdf
'''
class DilatedNetwork(nn.Module):
def __init__(self):
super(DilatedNetwork, self).__init__()
#self.imsize = imsize
self.activation = F.relu
self.pool1 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
self.pool2 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
self.pool3 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
#self.pool4 = nn.MaxPool2d(2)
self.conv_block1_64 = unetConvUnit(3, 64)
self.conv_block64_128 = unetConvUnit(64, 128)
self.conv_block128_256 = unetConvUnit(128, 256)
self.conv_block256_512 = unetConvUnit(256, 512)
#self.conv_block512_1024 = unetConvUnit(512, 1024)
#self.up_block1024_512 = unetUpUnit(1024, 512)
self.up_block512_256 = unetUpUnit(512, 256)
self.up_block256_128 = unetUpUnit(256, 128)
self.up_block128_64 = unetUpUnit(128, 64)
self.last = nn.Conv2d(64, 4, 1)
def forward(self, x):
#print 'x.shape is, ',x.shape
block1 = self.conv_block1_64(x)
# print 'block1 size is ',block1.size()
pool1 = self.pool1(block1)
# print 'pool1 size is ',pool1.size()
block2 = self.conv_block64_128(pool1)
# print 'block2 size is ',block2.size()
pool2 = self.pool2(block2)
# print 'pool2 size is ',pool2.size()
block3 = self.conv_block128_256(pool2)
# print 'block3 size is ',block3.size()
pool3 = self.pool3(block3)
# print 'pool3 size is ',pool3.size()
block4 = self.conv_block256_512(pool3)
# print 'block4 size is ',block4.size()
# pool4 = self.pool4(block4)1
# print 'pool4 shape ',pool4.shape
# block5 = self.conv_block512_1024(pool4)
# up1 = self.up_block1024_512(block5, block4)
up2 = self.up_block512_256(block4, block3)
# print 'up2 size is ',up2.size()
up3 = self.up_block256_128(up2, block2)
# print 'up3 size is ',up3.size()
up4 = self.up_block128_64(up3, block1)
# print 'up4 size is ',up4.size()
# return F.log_softmax(self.last(up4))
return self.last(up4)
'''
Residual Learning for FCN: short-range residual connection and long-range residual connection
We use spatial dropout after each layer group
We use stochastic long-range connection (short-range connection can be directly set in the residual module)
We use small dilation in the middle layers
We use local conv in the network
'''
class ResSegNet_v1(nn.Module):
def __init__(self, in_channel = 3, n_classes = 4, isRandomConnection=None, isSmallDilation=None, isSpatialDropOut=None, dropoutRate=0.25):
super(ResSegNet_v1, self).__init__()
#self.imsize = imsize
self.isSpatialDropOut = isSpatialDropOut
self.isRandomConnection = isRandomConnection
self.isSmallDilation = isSmallDilation
self.activation = F.relu
self.pool1 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
self.pool2 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
self.pool3 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
#self.pool4 = nn.MaxPool2d(2)
self.conv_block1_64 = convUnit(in_channel, 64)
self.conv_block64_64 = residualUnit3(64, 64, isDilation=False,isEmptyBranch1=False, spatial_dropout_rate=0.01)
self.conv_block64_128 = residualUnit3(64, 128, isDilation=False,isEmptyBranch1=False,spatial_dropout_rate=0.01)
self.conv_block128_256 = residualUnit3(128, 256, isDilation=False,isEmptyBranch1=False,spatial_dropout_rate=0.01)
#the residual layers on the smallest resolution
self.conv_block256_512 = residualUnit3(256, 512, isDilation=isSmallDilation, isEmptyBranch1=False)
#self.conv_block512_1024 = unetConvUnit(512, 1024)
#self.up_block1024_512 = unetUpUnit(1024, 512)
# print 'line 107'
self.dropout2d = nn.Dropout2d(dropoutRate)
# self.up_block512_256 = ResUpUnit(512, 256)
# # print 'line 109'
# self.up_block256_128 = ResUpUnit(256, 128)
# self.up_block128_64 = ResUpUnit(128, 64)
if isRandomConnection:
# print 'line 233: isRandomConnection'
self.up_block512_256 = ResUpUnit(512, 256, spatial_dropout_rate = 0.1)
# print 'line 109'
self.up_block256_128 = ResUpUnit(256, 128, spatial_dropout_rate = 0.1)
self.up_block128_64 = ResUpUnit(128, 64, spatial_dropout_rate = 0.1)
else:
# print 'line 240: isRandomConnection'
self.up_block512_256 = ResUpUnit(512, 256, spatial_dropout_rate = 0)
# print 'line 109'
self.up_block256_128 = ResUpUnit(256, 128, spatial_dropout_rate = 0)
self.up_block128_64 = ResUpUnit(128, 64, spatial_dropout_rate = 0)
self.localconv = nn.Conv2dLocal(64, 64, 168, 112, kernel_size=3, stride=1, padding=1)
self.last = nn.Conv2d(64, n_classes, 1)
def forward(self, x):
#print 'x.shape is, ',x.shape
block0 = self.conv_block1_64(x)
# print 'block0 size is ', block0.size()
block1 = self.conv_block64_64(block0)
pool1 = self.pool1(block1)
# print 'pool1 size is ',pool1.size()
block2 = self.conv_block64_128(pool1)
if self.isSpatialDropOut:
block2 = self.dropout2d(block2)
# print 'block2 size is ',block2.size()
pool2 = self.pool2(block2)
# print 'pool2 size is ',pool2.size()
block3 = self.conv_block128_256(pool2)
if self.isSpatialDropOut:
block3 = self.dropout2d(block3)
# print 'line 264: spatial dropout done'
# if self.isRandomConnection:
# print 'line 201: random connection'
# if self.isSmallDilation:
# print 'line 203: small dilation'
# if self.isSpatialDropOut:
# print 'line 205: spatial dropout'
# print 'block3 size is ',block3.size()
pool3 = self.pool3(block3)
# print 'pool3 size is ',pool3.size()
block4 = self.conv_block256_512(pool3)
# if self.isSpatialDropOut:
# block4 = self.dropout2d(block4)
# print 'block4 size is ',block4.size()
# pool4 = self.pool4(block4)1
# print 'pool4 shape ',pool4.shape
# block5 = self.conv_block512_1024(pool4)
# up1 = self.up_block1024_512(block5, block4)
up2 = self.up_block512_256(block4, block3)
# print 'up2 size is ',up2.size()
up3 = self.up_block256_128(up2, block2)
# print 'up3 size is ',up3.size()
up4 = self.up_block128_64(up3, block1)
# print 'up4 size is ',up4.size()
# return F.log_softmax(self.last(up4))
localconv = self.localconv(up4)
return self.last(localconv)
'''
Residual Learning for FCN: short-range residual connection and long-range residual connection
We use spatial dropout after each layer group
We use stochastic long-range connection (short-range connection can be directly set in the residual module)
We use small dilation in the middle layers
'''
class ResSegNet(nn.Module):
def __init__(self, in_channel = 3, n_classes = 4, isRandomConnection=None, isSmallDilation=None, isSpatialDropOut=None, dropoutRate=0.25):
super(ResSegNet, self).__init__()
#self.imsize = imsize
self.isSpatialDropOut = isSpatialDropOut
self.isRandomConnection = isRandomConnection
self.isSmallDilation = isSmallDilation
self.activation = F.relu
self.pool1 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
self.pool2 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
self.pool3 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
#self.pool4 = nn.MaxPool2d(2)
self.conv_block1_64 = convUnit(in_channel, 64)
self.conv_block64_64 = residualUnit3(64, 64, isDilation=False,isEmptyBranch1=False, spatial_dropout_rate=0.01)
self.conv_block64_128 = residualUnit3(64, 128, isDilation=False,isEmptyBranch1=False,spatial_dropout_rate=0.01)
self.conv_block128_256 = residualUnit3(128, 256, isDilation=False,isEmptyBranch1=False,spatial_dropout_rate=0.01)
#the residual layers on the smallest resolution
self.conv_block256_512 = residualUnit3(256, 512, isDilation=isSmallDilation, isEmptyBranch1=False)
#self.conv_block512_1024 = unetConvUnit(512, 1024)
#self.up_block1024_512 = unetUpUnit(1024, 512)
# print 'line 107'
self.dropout2d = nn.Dropout2d(dropoutRate)
# self.up_block512_256 = ResUpUnit(512, 256)
# # print 'line 109'
# self.up_block256_128 = ResUpUnit(256, 128)
# self.up_block128_64 = ResUpUnit(128, 64)
if isRandomConnection:
# print 'line 233: isRandomConnection'
self.up_block512_256 = ResUpUnit(512, 256, spatial_dropout_rate = 0.1)
# print 'line 109'
self.up_block256_128 = ResUpUnit(256, 128, spatial_dropout_rate = 0.1)
self.up_block128_64 = ResUpUnit(128, 64, spatial_dropout_rate = 0.1)
else:
# print 'line 240: isRandomConnection'
self.up_block512_256 = ResUpUnit(512, 256, spatial_dropout_rate = 0)
# print 'line 109'
self.up_block256_128 = ResUpUnit(256, 128, spatial_dropout_rate = 0)
self.up_block128_64 = ResUpUnit(128, 64, spatial_dropout_rate = 0)
self.last = nn.Conv2d(64, n_classes, 1)
def forward(self, x):
#print 'x.shape is, ',x.shape
block0 = self.conv_block1_64(x)
# print 'block0 size is ', block0.size()
block1 = self.conv_block64_64(block0)
pool1 = self.pool1(block1)
# print 'pool1 size is ',pool1.size()
block2 = self.conv_block64_128(pool1)
if self.isSpatialDropOut:
block2 = self.dropout2d(block2)
# print 'block2 size is ',block2.size()
pool2 = self.pool2(block2)
# print 'pool2 size is ',pool2.size()
block3 = self.conv_block128_256(pool2)
if self.isSpatialDropOut:
block3 = self.dropout2d(block3)
# print 'line 264: spatial dropout done'
# if self.isRandomConnection:
# print 'line 201: random connection'
# if self.isSmallDilation:
# print 'line 203: small dilation'
# if self.isSpatialDropOut:
# print 'line 205: spatial dropout'
# print 'block3 size is ',block3.size()
pool3 = self.pool3(block3)
# print 'pool3 size is ',pool3.size()
block4 = self.conv_block256_512(pool3)
# if self.isSpatialDropOut:
# block4 = self.dropout2d(block4)
# print 'block4 size is ',block4.size()
# pool4 = self.pool4(block4)1
# print 'pool4 shape ',pool4.shape
# block5 = self.conv_block512_1024(pool4)
# up1 = self.up_block1024_512(block5, block4)
up2 = self.up_block512_256(block4, block3)
# print 'up2 size is ',up2.size()
up3 = self.up_block256_128(up2, block2)
# print 'up3 size is ',up3.size()
up4 = self.up_block128_64(up3, block1)
# print 'up4 size is ',up4.size()
# return F.log_softmax(self.last(up4))
return self.last(up4)
'''
Residual Learning for FCN: short-range residual connection and long-range residual connection
Besides the segmentation maps, we also return the contours
We use spatial dropout after each layer group
We use stochastic long-range connection
We use small dilation in the middle layers
'''
class ResSegContourNet(nn.Module):
def __init__(self, in_channel = 3, n_classes = 4, isRandomConnection=None, isSmallDilation=None, isSpatialDropOut=None, dropoutRate=0.25):
super(ResSegContourNet, self).__init__()
#self.imsize = imsize
self.isSpatialDropOut = isSpatialDropOut
self.isRandomConnection = isRandomConnection
self.isSmallDilation = isSmallDilation
self.activation = F.relu
self.pool1 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
self.pool2 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
self.pool3 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
#self.pool4 = nn.MaxPool2d(2)
self.conv_block1_64 = convUnit(in_channel, 64)
self.conv_block64_64 = residualUnit3(64, 64, isDilation=False,isEmptyBranch1=False)
self.conv_block64_128 = residualUnit3(64, 128, isDilation=False,isEmptyBranch1=False)
self.conv_block128_256 = residualUnit3(128, 256, isDilation=False,isEmptyBranch1=False)
#the residual layers on the smallest resolution
self.conv_block256_512 = residualUnit3(256, 512, isDilation=isSmallDilation, isEmptyBranch1=False)
#self.conv_block512_1024 = unetConvUnit(512, 1024)
#self.up_block1024_512 = unetUpUnit(1024, 512)
# print 'line 107'
self.dropout2d = nn.Dropout2d(dropoutRate)
# self.up_block512_256 = ResUpUnit(512, 256)
# # print 'line 109'
# self.up_block256_128 = ResUpUnit(256, 128)
# self.up_block128_64 = ResUpUnit(128, 64)
if isRandomConnection:
# print 'line 233: isRandomConnection'
self.up_block512_256 = ResUpUnit(512, 256, spatial_dropout_rate = 0.1)
# print 'line 109'
self.up_block256_128 = ResUpUnit(256, 128, spatial_dropout_rate = 0.05)
# self.up_block128_64 = ResUpUnit(128, 64, spatial_dropout_rate = 0.01)
else:
# print 'line 240: isRandomConnection'
self.up_block512_256 = ResUpUnit(512, 256, spatial_dropout_rate = 0)
# print 'line 109'
self.up_block256_128 = ResUpUnit(256, 128, spatial_dropout_rate = 0)
# self.up_block128_64 = ResUpUnit(128, 64, spatial_dropout_rate = 0)
self.up_block128_64 = BaseResUpUnit(128, 64)
#### for segmentation
self.seg_conv1 = residualUnit3(64,64)
self.seg_conv2 = residualUnit3(64,32)
self.seg_last = nn.Conv2d(32, n_classes, 1)
##### for contour classification
self.contour_conv1 = residualUnit3(64, 64)
self.contour_conv2 = residualUnit3(64, 32)
self.contour_last = nn.Conv2d(32, 2, 1)
# self.last = nn.Conv2d(64, n_classes, 1)
def forward(self, x):
#print 'x.shape is, ',x.shape
block0 = self.conv_block1_64(x)
# print 'block0 size is ', block0.size()
block1 = self.conv_block64_64(block0)
pool1 = self.pool1(block1)
# print 'pool1 size is ',pool1.size()
block2 = self.conv_block64_128(pool1)
if self.isSpatialDropOut:
block2 = self.dropout2d(block2)
# print 'block2 size is ',block2.size()
pool2 = self.pool2(block2)
# print 'pool2 size is ',pool2.size()
block3 = self.conv_block128_256(pool2)
if self.isSpatialDropOut:
block3 = self.dropout2d(block3)
# print 'line 264: spatial dropout done'
# if self.isRandomConnection:
# print 'line 201: random connection'
# if self.isSmallDilation:
# print 'line 203: small dilation'
# if self.isSpatialDropOut:
# print 'line 205: spatial dropout'
# print 'block3 size is ',block3.size()
pool3 = self.pool3(block3)
# print 'pool3 size is ',pool3.size()
block4 = self.conv_block256_512(pool3)
# if self.isSpatialDropOut:
# block4 = self.dropout2d(block4)
# print 'block4 size is ',block4.size()
# pool4 = self.pool4(block4)1
# print 'pool4 shape ',pool4.shape
# block5 = self.conv_block512_1024(pool4)
# up1 = self.up_block1024_512(block5, block4)
up2 = self.up_block512_256(block4, block3)
# print 'up2 size is ',up2.size()
up3 = self.up_block256_128(up2, block2)
# print 'up3 size is ',up3.size()
up4 = self.up_block128_64(up3, block1)
# print 'up4 size is ',up4.size()
# return F.log_softmax(self.last(up4))
# seg conv1
seg_conv1 = self.seg_conv1(up4)
seg_conv2 = self.seg_conv2(seg_conv1)
# regression task1
contour_conv1 = self.contour_conv1(up4)
contour_conv2 = self.contour_conv2(contour_conv1)
# return F.log_softmax(self.last(up4))
return self.seg_last(seg_conv2), self.contour_last(contour_conv2)
'''
Residual Learning for FCN: short-range residual connection and long-range residual connection
Multi-task Learning: Using segmentation and regression for the same network
'''
class ResSegRegNet(nn.Module):
def __init__(self):
super(ResSegNet, self).__init__()
#self.imsize = imsize
self.activation = F.relu
self.pool1 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
self.pool2 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
self.pool3 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
#self.pool4 = nn.MaxPool2d(2)
self.conv_block1_64 = convUnit(3, 64)
self.conv_block64_64 = residualUnit3(64, 64)
self.conv_block64_128 = residualUnit3(64, 128)
self.conv_block128_256 = residualUnit3(128, 256)
self.conv_block256_512 = residualUnit3(256, 512)
#self.conv_block512_1024 = unetConvUnit(512, 1024)
#self.up_block1024_512 = unetUpUnit(1024, 512)
# print 'line 107'
self.up_block512_256 = ResUpUnit(512, 256)
# print 'line 109'
self.up_block256_128 = ResUpUnit(256, 128)
self.up_block128_64 = BaseResUpUnit(128, 64)
#### for segmentation
self.seg_conv1 = residualUnit3(64,64)
self.seg_conv2 = residualUnit3(64,32)
self.seg_last = nn.Conv2d(32, 4, 1)
##### for regression 1
self.reg1_conv1 = residualUnit3(64, 64)
self.reg1_conv2 = residualUnit3(64, 32)
self.reg1_last = nn.Conv2d(32, 1, 1)
##### for regression 2
self.reg2_conv1 = residualUnit3(64, 64)
self.reg2_conv2 = residualUnit3(64, 32)
self.reg2_last = nn.Conv2d(32, 1, 1)
##### for regression 3
self.reg3_conv1 = residualUnit3(64, 64)
self.reg3_conv2 = residualUnit3(64, 32)
self.reg3_last = nn.Conv2d(32, 1, 1)
#....
def forward(self, x):
#print 'x.shape is, ',x.shape
block0 = self.conv_block1_64(x)
# print 'block0 size is ', block0.size()
block1 = self.conv_block64_64(block0)
pool1 = self.pool1(block1)
# print 'pool1 size is ',pool1.size()
block2 = self.conv_block64_128(pool1)
# print 'block2 size is ',block2.size()
pool2 = self.pool2(block2)
# print 'pool2 size is ',pool2.size()
block3 = self.conv_block128_256(pool2)
# print 'block3 size is ',block3.size()
pool3 = self.pool3(block3)
# print 'pool3 size is ',pool3.size()
block4 = self.conv_block256_512(pool3)
# print 'block4 size is ',block4.size()
# pool4 = self.pool4(block4)1
# print 'pool4 shape ',pool4.shape
# block5 = self.conv_block512_1024(pool4)
# up1 = self.up_block1024_512(block5, block4)
up2 = self.up_block512_256(block4, block3)
# print 'up2 size is ',up2.size()
up3 = self.up_block256_128(up2, block2)
# print 'up3 size is ',up3.size()
up4 = self.up_block128_64(up3, block1)
# seg conv1
seg_conv1 = self.seg_conv1(up4)
seg_conv2 = self.seg_conv2(seg_conv1)
# regression task1
reg1_conv1 = self.reg1_conv1(up4)
reg1_conv2 = self.reg1_conv2(reg1_conv1)
# regression task2
reg2_conv1 = self.reg2_conv1(up4)
reg2_conv2 = self.reg2_conv2(reg2_conv1)
# regression task3
reg3_conv1 = self.reg3_conv1(up4)
reg3_conv2 = self.reg3_conv2(reg3_conv1)
# print 'up4 size is ',up4.size()
# return F.log_softmax(self.last(up4))
return self.seg_last(seg_conv2), self.reg1_last(reg1_conv2), self.reg2_last(reg2_conv2), self.reg3_last(reg3_conv2)
class Generator(nn.Module):
def __init__(self, n_residual_blocks, upsample):
super(Generator, self).__init__()
self.n_residual_blocks = n_residual_blocks
self.upsample = upsample
self.conv1 = nn.Conv2d(3, 64, 9, stride=1, padding=1)
for i in range(self.n_residual_blocks):
self.add_module('res' + str(i+1), residualUnit(64))
self.conv2 = nn.Conv2d(64, 64, 3, stride=1, padding=1)
in_channels = 64
out_channels = 256
for i in range(self.upsample):
self.add_module('upscale' + str(i+1), upsampleUnit(in_channels, out_channels))
in_channels = out_channels
out_channels = out_channels/2
self.conv3 = nn.Conv2d(in_channels, 3, 9, stride=1, padding=1)
def forward(self, x):
x = F.elu(self.conv1(x))
y = self.__getattr__('res1')(x)
for i in range(1, self.n_residual_blocks):
y = self.__getattr__('res' + str(i+1))(y)
x = self.conv2(y) + x
for i in range(self.upsample):
x = self.__getattr__('upscale' + str(i+1))(x)
return F.sigmoid(self.conv3(x))
class regGenerator(nn.Module):
def __init__(self):
super(regGenerator, self).__init__()
self.conv_input = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
self.relu = nn.ReLU(inplace=True)
self.residual = self.make_layer(residualUnit3, 15)
self.upscale4x = nn.Sequential(
nn.Conv2d(in_channels=64, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False),
nn.PixelShuffle(2),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=64, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False),
nn.PixelShuffle(2),
nn.ReLU(inplace=True),
)
self.conv_output = nn.Conv2d(in_channels=64, out_channels=3, kernel_size=3, stride=1, padding=1, bias=False)
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))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
if m.bias is not None:
m.bias.data.zero_()
def make_layer(self, block, num_of_layer):
layers = []
for _ in range(num_of_layer):
layers.append(block())
return nn.Sequential(*layers)
def forward(self, x):
out = self.relu(self.conv_input(x))
residual = out
out = self.residual(out)
out = torch.add(out,residual)
out = self.upscale4x(out)
out = self.conv_output(out)
return out
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, stride=1, padding=1)
self.conv2 = nn.Conv2d(32, 32, 3, stride=2, padding=1)
self.conv3 = nn.Conv2d(32, 64, 3, stride=1, padding=1)
self.conv4 = nn.Conv2d(64, 64, 3, stride=2, padding=1)
self.conv5 = nn.Conv2d(64, 128, 3, stride=1, padding=1)
self.conv6 = nn.Conv2d(128, 128, 3, stride=2, padding=1)
self.conv7 = nn.Conv2d(128, 128, 3, stride=1, padding=1)
self.conv8 = nn.Conv2d(128, 128, 3, stride=2, padding=1)
self.fc1 = nn.Linear(9856, 1024)
self.fc2 = nn.Linear(1024, 1)
def forward(self, x):
x = F.elu(self.conv1(x))
# print 'line 260 ',x.size()
x = F.elu(self.conv2(x))
# print 'line 262 ',x.size()
x = F.elu(self.conv3(x))
x = F.elu(self.conv4(x))
# print 'line 265 ',x.size()
x = F.elu(self.conv5(x))
x = F.elu(self.conv6(x))
# print 'line 267 ',x.size()
x = F.elu(self.conv7(x))
x = F.elu(self.conv8(x))
# print 'line 271 ',x.size()
# Flatten
x = x.view(x.size(0), -1)
# print 'line 266 is ', x.size()
x = F.elu(self.fc1(x))
return F.sigmoid(self.fc2(x))