def __init__(self, in_channels=1, n_classes=2, feature_scale=2, is_deconv=True, is_batchnorm=True): super(UNet, self).__init__() self.in_channels = in_channels self.feature_scale = feature_scale self.is_deconv = is_deconv self.is_batchnorm = is_batchnorm filters = [64, 128, 256, 512, 1024] filters = [int(x / self.feature_scale) for x in filters] # downsampling self.maxpool = nn.MaxPool2d(kernel_size=2) self.conv1 = unetConv2(self.in_channels, filters[0], self.is_batchnorm) self.conv2 = unetConv2(filters[0], filters[1], self.is_batchnorm) self.conv3 = unetConv2(filters[1], filters[2], self.is_batchnorm) self.conv4 = unetConv2(filters[2], filters[3], self.is_batchnorm) self.center = unetConv2(filters[3], filters[4], self.is_batchnorm) # upsampling self.up_concat4 = unetUp(filters[4], filters[3], self.is_deconv) self.up_concat3 = unetUp(filters[3], filters[2], self.is_deconv) self.up_concat2 = unetUp(filters[2], filters[1], self.is_deconv) self.up_concat1 = unetUp(filters[1], filters[0], self.is_deconv) # final conv (without any concat) self.final = nn.Conv2d(filters[0], n_classes, 1) # initialise weights for m in self.modules(): if isinstance(m, nn.Conv2d): init_weights(m, init_type='kaiming') elif isinstance(m, nn.BatchNorm2d): init_weights(m, init_type='kaiming')
def __init__(self, in_channels=3, n_classes=2, feature_scale=2, is_deconv=True, is_batchnorm=True, is_ds=True): super(UNet_Nested, self).__init__() self.in_channels = in_channels self.feature_scale = feature_scale self.is_deconv = is_deconv self.is_batchnorm = is_batchnorm self.is_ds = is_ds filters = [64, 128, 256, 512, 1024] filters = [int(x / self.feature_scale) for x in filters] # downsampling self.maxpool = nn.MaxPool2d(kernel_size=2) self.conv00 = unetConv2(self.in_channels, filters[0], self.is_batchnorm) self.conv10 = unetConv2(filters[0], filters[1], self.is_batchnorm) self.conv20 = unetConv2(filters[1], filters[2], self.is_batchnorm) self.conv30 = unetConv2(filters[2], filters[3], self.is_batchnorm) self.conv40 = unetConv2(filters[3], filters[4], self.is_batchnorm) # upsampling self.up_concat01 = unetUp(filters[1], filters[0], self.is_deconv) self.up_concat11 = unetUp(filters[2], filters[1], self.is_deconv) self.up_concat21 = unetUp(filters[3], filters[2], self.is_deconv) self.up_concat31 = unetUp(filters[4], filters[3], self.is_deconv) self.up_concat02 = unetUp(filters[1], filters[0], self.is_deconv, 3) self.up_concat12 = unetUp(filters[2], filters[1], self.is_deconv, 3) self.up_concat22 = unetUp(filters[3], filters[2], self.is_deconv, 3) self.up_concat03 = unetUp(filters[1], filters[0], self.is_deconv, 4) self.up_concat13 = unetUp(filters[2], filters[1], self.is_deconv, 4) self.up_concat04 = unetUp(filters[1], filters[0], self.is_deconv, 5) # final conv (without any concat) self.final_1 = nn.Conv2d(filters[0], n_classes, 1) self.final_2 = nn.Conv2d(filters[0], n_classes, 1) self.final_3 = nn.Conv2d(filters[0], n_classes, 1) self.final_4 = nn.Conv2d(filters[0], n_classes, 1) # initialise weights for m in self.modules(): if isinstance(m, nn.Conv2d): init_weights(m, init_type='kaiming') elif isinstance(m, nn.BatchNorm2d): init_weights(m, init_type='kaiming')
def __init__(self, in_channels, n_classes, channels=64, is_deconv=False, is_batchnorm=True): super(CNN, self).__init__() self.is_deconv = is_deconv self.in_channels = in_channels self.is_batchnorm = is_batchnorm self.channels = channels self.n_classes=n_classes # downsampling self.conv1 = unetConv2(self.in_channels, self.channels, self.is_batchnorm) self.conv2 = unetConv2(self.channels, self.channels, self.is_batchnorm) self.conv3 = unetConv2(self.channels, self.channels, self.is_batchnorm) self.outconv1 = nn.Conv2d(self.channels, self.n_classes, 3, padding=1)
def __init__(self, in_channels, n_classes, channels=128, is_maxpool=True, is_batchnorm=True): super(IMN, self).__init__() self.is_maxpool = is_maxpool self.in_channels = in_channels self.is_batchnorm = is_batchnorm self.channels = channels self.n_classes=n_classes # MNET self.M_conv1 = unetConv2(in_channels, self.channels, self.is_batchnorm) self.M_up1 = nn.ConvTranspose2d(self.channels,self.channels, kernel_size=2, stride=2, padding=0) self.M_conv2 = unetConv2(self.channels, self.channels, self.is_batchnorm) self.M_up2 = nn.ConvTranspose2d(self.channels, self.channels, kernel_size=2, stride=2, padding=0) self.M_center = unetConv2(self.channels, self.channels, self.is_batchnorm) self.M_down2 = mnetDown(self.channels*2, self.channels, 2,self.is_maxpool) self.M_down1 = mnetDown(self.channels*2, self.channels, 2, self.is_maxpool) self.outconv1 = nn.Conv2d(self.channels, self.n_classes, kernel_size=3, padding=1)
def __init__(self, in_channels, n_classes, channels=128, is_deconv=False, is_batchnorm=True): super(UNet, self).__init__() self.is_deconv = is_deconv self.in_channels = in_channels self.is_batchnorm = is_batchnorm self.channels = channels self.n_classes=n_classes # downsampling self.conv1 = unetConv2(in_channels, self.channels, self.is_batchnorm) self.maxpool1 = nn.MaxPool2d(kernel_size=2) self.conv2 = unetConv2(self.channels, self.channels, self.is_batchnorm) self.maxpool2 = nn.MaxPool2d(kernel_size=2) self.center = unetConv2(self.channels, self.channels, self.is_batchnorm) # upsampling self.up_concat2 = unetUp(self.channels*2, self.channels, 2, self.is_deconv) self.up_concat1 = unetUp(self.channels*2, self.channels, 2, self.is_deconv) # self.outconv1 = nn.Conv2d(self.channels, self.n_classes, 3, padding=1)
def __init__(self, in_channels, n_classes, channels=128, is_maxpool=True,is_deconv=False, is_batchnorm=True): super(MUNet, self).__init__() self.is_maxpool = is_maxpool self.in_channels = in_channels self.is_batchnorm = is_batchnorm self.is_deconv=is_deconv self.channels = channels self.n_classes=n_classes # MNET self.M_conv1 = unetConv2(in_channels, self.channels, self.is_batchnorm) self.M_up1 = nn.ConvTranspose2d(self.channels,self.channels, kernel_size=2, stride=2, padding=0) self.M_conv2 = unetConv2(self.channels, self.channels, self.is_batchnorm) self.M_up2 = nn.ConvTranspose2d(self.channels, self.channels, kernel_size=2, stride=2, padding=0) self.M_center = unetConv2(self.channels, self.channels, self.is_batchnorm) self.M_down2 = mnetDown(self.channels*2, self.channels, 2,self.is_maxpool) self.M_down1 = mnetDown(self.channels*2, self.channels, 2, self.is_maxpool) # UNET self.U_conv1 = unetConv2(in_channels, self.channels, self.is_batchnorm) self.U_down1 = nn.MaxPool2d(kernel_size=2) self.U_conv2 = unetConv2(self.channels, self.channels, self.is_batchnorm) self.U_down2 = nn.MaxPool2d(kernel_size=2) self.U_center = unetConv2(self.channels, self.channels, self.is_batchnorm) self.U_up2 = unetUp(self.channels*2, self.channels, 2, self.is_deconv) self.U_up1 = unetUp(self.channels*2, self.channels, 2, self.is_deconv) #output self.outconv1 = nn.Conv2d(self.channels*2, self.channels, 3, padding=1) self.outconv2 = nn.Conv2d(self.channels, self.n_classes, 3, padding=1)
def __init__(self,in_channels, n_classes, channels=128): super(DUNet, self).__init__() self.channels = channels self.n_classes=n_classes self.is_batchnorm = False self.in_channels = in_channels # self.inc = deform_inconv(n_channels, 64 // downsize_nb_filters_factor) self.inc = unetConv2(in_channels, self.channels, self.is_batchnorm) self.down1 = deform_down(self.channels, self.channels) self.down2 = deform_down(self.channels, self.channels) self.up3 = deform_up(self.channels*2, self.channels) self.up4 = deform_up(self.channels*2, self.channels) self.outc = nn.Conv2d(self.channels+1, n_classes, 1)
def __init__(self, in_channels=32, n_classes=32, feature_scale=4, is_deconv=False, is_batchnorm=True): super(UNet_2Plus, self).__init__() self.is_deconv = is_deconv self.in_channels = in_channels self.is_batchnorm = is_batchnorm self.feature_scale = feature_scale filters = [8, 16, 32, 64, 128] # downsampling self.conv00 = unetConv2(self.in_channels, filters[0], self.is_batchnorm) self.maxpool0 = nn.MaxPool2d(kernel_size=2) self.conv10 = unetConv2(filters[0], filters[1], self.is_batchnorm) self.maxpool1 = nn.MaxPool2d(kernel_size=2) self.conv20 = unetConv2(filters[1], filters[2], self.is_batchnorm) self.maxpool2 = nn.MaxPool2d(kernel_size=2) self.conv30 = unetConv2(filters[2], filters[3], self.is_batchnorm) self.maxpool3 = nn.MaxPool2d(kernel_size=2) self.conv40 = unetConv2(filters[3], filters[4], self.is_batchnorm) self.getfeature = unetConv2(filters[4], filters[3], self.is_batchnorm) # upsampling self.up_concat01 = unetUp_origin(filters[1], filters[0], self.is_deconv) self.up_concat11 = unetUp_origin(filters[2], filters[1], self.is_deconv) self.up_concat21 = unetUp_origin(filters[3], filters[2], self.is_deconv) self.up_concat31 = unetUp_origin(filters[4], filters[3], self.is_deconv) self.up_concat02 = unetUp_origin(filters[1], filters[0], self.is_deconv, 3) self.up_concat12 = unetUp_origin(filters[2], filters[1], self.is_deconv, 3) self.up_concat22 = unetUp_origin(filters[3], filters[2], self.is_deconv, 3) self.up_concat03 = unetUp_origin(filters[1], filters[0], self.is_deconv, 4) self.up_concat13 = unetUp_origin(filters[2], filters[1], self.is_deconv, 4) self.up_concat04 = unetUp_origin(filters[1], filters[0], self.is_deconv, 5) # final conv (without any concat) self.final_1 = nn.Conv2d(filters[0], n_classes, 1) self.final_2 = nn.Conv2d(filters[0], n_classes, 1) self.final_3 = nn.Conv2d(filters[0], n_classes, 1) self.final_4 = nn.Conv2d(filters[0], n_classes, 1) self.skip_add = nn.quantized.FloatFunctional() # initialise weights for m in self.modules(): if isinstance(m, nn.Conv2d): init_weights(m, init_type='kaiming') elif isinstance(m, nn.BatchNorm2d): init_weights(m, init_type='kaiming')
def __init__(self, in_channels=3, n_classes=1, feature_scale=4, is_deconv=True, is_batchnorm=True): super(UNet_3Plus_DeepSup_CGM, self).__init__() self.is_deconv = is_deconv self.in_channels = in_channels self.is_batchnorm = is_batchnorm self.feature_scale = feature_scale filters = [64, 128, 256, 512, 1024] ## -------------Encoder-------------- self.conv1 = unetConv2(self.in_channels, filters[0], self.is_batchnorm) self.maxpool1 = nn.MaxPool2d(kernel_size=2) self.conv2 = unetConv2(filters[0], filters[1], self.is_batchnorm) self.maxpool2 = nn.MaxPool2d(kernel_size=2) self.conv3 = unetConv2(filters[1], filters[2], self.is_batchnorm) self.maxpool3 = nn.MaxPool2d(kernel_size=2) self.conv4 = unetConv2(filters[2], filters[3], self.is_batchnorm) self.maxpool4 = nn.MaxPool2d(kernel_size=2) self.conv5 = unetConv2(filters[3], filters[4], self.is_batchnorm) ## -------------Decoder-------------- self.CatChannels = filters[0] self.CatBlocks = 5 self.UpChannels = self.CatChannels * self.CatBlocks '''stage 4d''' # h1->320*320, hd4->40*40, Pooling 8 times self.h1_PT_hd4 = nn.MaxPool2d(8, 8, ceil_mode=True) self.h1_PT_hd4_conv = nn.Conv2d(filters[0], self.CatChannels, 3, padding=1) self.h1_PT_hd4_bn = nn.BatchNorm2d(self.CatChannels) self.h1_PT_hd4_relu = nn.ReLU(inplace=True) # h2->160*160, hd4->40*40, Pooling 4 times self.h2_PT_hd4 = nn.MaxPool2d(4, 4, ceil_mode=True) self.h2_PT_hd4_conv = nn.Conv2d(filters[1], self.CatChannels, 3, padding=1) self.h2_PT_hd4_bn = nn.BatchNorm2d(self.CatChannels) self.h2_PT_hd4_relu = nn.ReLU(inplace=True) # h3->80*80, hd4->40*40, Pooling 2 times self.h3_PT_hd4 = nn.MaxPool2d(2, 2, ceil_mode=True) self.h3_PT_hd4_conv = nn.Conv2d(filters[2], self.CatChannels, 3, padding=1) self.h3_PT_hd4_bn = nn.BatchNorm2d(self.CatChannels) self.h3_PT_hd4_relu = nn.ReLU(inplace=True) # h4->40*40, hd4->40*40, Concatenation self.h4_Cat_hd4_conv = nn.Conv2d(filters[3], self.CatChannels, 3, padding=1) self.h4_Cat_hd4_bn = nn.BatchNorm2d(self.CatChannels) self.h4_Cat_hd4_relu = nn.ReLU(inplace=True) # hd5->20*20, hd4->40*40, Upsample 2 times self.hd5_UT_hd4 = nn.Upsample(scale_factor=2, mode='bilinear') # 14*14 self.hd5_UT_hd4_conv = nn.Conv2d(filters[4], self.CatChannels, 3, padding=1) self.hd5_UT_hd4_bn = nn.BatchNorm2d(self.CatChannels) self.hd5_UT_hd4_relu = nn.ReLU(inplace=True) # fusion(h1_PT_hd4, h2_PT_hd4, h3_PT_hd4, h4_Cat_hd4, hd5_UT_hd4) self.conv4d_1 = nn.Conv2d(self.UpChannels, self.UpChannels, 3, padding=1) # 16 self.bn4d_1 = nn.BatchNorm2d(self.UpChannels) self.relu4d_1 = nn.ReLU(inplace=True) '''stage 3d''' # h1->320*320, hd3->80*80, Pooling 4 times self.h1_PT_hd3 = nn.MaxPool2d(4, 4, ceil_mode=True) self.h1_PT_hd3_conv = nn.Conv2d(filters[0], self.CatChannels, 3, padding=1) self.h1_PT_hd3_bn = nn.BatchNorm2d(self.CatChannels) self.h1_PT_hd3_relu = nn.ReLU(inplace=True) # h2->160*160, hd3->80*80, Pooling 2 times self.h2_PT_hd3 = nn.MaxPool2d(2, 2, ceil_mode=True) self.h2_PT_hd3_conv = nn.Conv2d(filters[1], self.CatChannels, 3, padding=1) self.h2_PT_hd3_bn = nn.BatchNorm2d(self.CatChannels) self.h2_PT_hd3_relu = nn.ReLU(inplace=True) # h3->80*80, hd3->80*80, Concatenation self.h3_Cat_hd3_conv = nn.Conv2d(filters[2], self.CatChannels, 3, padding=1) self.h3_Cat_hd3_bn = nn.BatchNorm2d(self.CatChannels) self.h3_Cat_hd3_relu = nn.ReLU(inplace=True) # hd4->40*40, hd4->80*80, Upsample 2 times self.hd4_UT_hd3 = nn.Upsample(scale_factor=2, mode='bilinear') # 14*14 self.hd4_UT_hd3_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1) self.hd4_UT_hd3_bn = nn.BatchNorm2d(self.CatChannels) self.hd4_UT_hd3_relu = nn.ReLU(inplace=True) # hd5->20*20, hd4->80*80, Upsample 4 times self.hd5_UT_hd3 = nn.Upsample(scale_factor=4, mode='bilinear') # 14*14 self.hd5_UT_hd3_conv = nn.Conv2d(filters[4], self.CatChannels, 3, padding=1) self.hd5_UT_hd3_bn = nn.BatchNorm2d(self.CatChannels) self.hd5_UT_hd3_relu = nn.ReLU(inplace=True) # fusion(h1_PT_hd3, h2_PT_hd3, h3_Cat_hd3, hd4_UT_hd3, hd5_UT_hd3) self.conv3d_1 = nn.Conv2d(self.UpChannels, self.UpChannels, 3, padding=1) # 16 self.bn3d_1 = nn.BatchNorm2d(self.UpChannels) self.relu3d_1 = nn.ReLU(inplace=True) '''stage 2d ''' # h1->320*320, hd2->160*160, Pooling 2 times self.h1_PT_hd2 = nn.MaxPool2d(2, 2, ceil_mode=True) self.h1_PT_hd2_conv = nn.Conv2d(filters[0], self.CatChannels, 3, padding=1) self.h1_PT_hd2_bn = nn.BatchNorm2d(self.CatChannels) self.h1_PT_hd2_relu = nn.ReLU(inplace=True) # h2->160*160, hd2->160*160, Concatenation self.h2_Cat_hd2_conv = nn.Conv2d(filters[1], self.CatChannels, 3, padding=1) self.h2_Cat_hd2_bn = nn.BatchNorm2d(self.CatChannels) self.h2_Cat_hd2_relu = nn.ReLU(inplace=True) # hd3->80*80, hd2->160*160, Upsample 2 times self.hd3_UT_hd2 = nn.Upsample(scale_factor=2, mode='bilinear') # 14*14 self.hd3_UT_hd2_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1) self.hd3_UT_hd2_bn = nn.BatchNorm2d(self.CatChannels) self.hd3_UT_hd2_relu = nn.ReLU(inplace=True) # hd4->40*40, hd2->160*160, Upsample 4 times self.hd4_UT_hd2 = nn.Upsample(scale_factor=4, mode='bilinear') # 14*14 self.hd4_UT_hd2_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1) self.hd4_UT_hd2_bn = nn.BatchNorm2d(self.CatChannels) self.hd4_UT_hd2_relu = nn.ReLU(inplace=True) # hd5->20*20, hd2->160*160, Upsample 8 times self.hd5_UT_hd2 = nn.Upsample(scale_factor=8, mode='bilinear') # 14*14 self.hd5_UT_hd2_conv = nn.Conv2d(filters[4], self.CatChannels, 3, padding=1) self.hd5_UT_hd2_bn = nn.BatchNorm2d(self.CatChannels) self.hd5_UT_hd2_relu = nn.ReLU(inplace=True) # fusion(h1_PT_hd2, h2_Cat_hd2, hd3_UT_hd2, hd4_UT_hd2, hd5_UT_hd2) self.conv2d_1 = nn.Conv2d(self.UpChannels, self.UpChannels, 3, padding=1) # 16 self.bn2d_1 = nn.BatchNorm2d(self.UpChannels) self.relu2d_1 = nn.ReLU(inplace=True) '''stage 1d''' # h1->320*320, hd1->320*320, Concatenation self.h1_Cat_hd1_conv = nn.Conv2d(filters[0], self.CatChannels, 3, padding=1) self.h1_Cat_hd1_bn = nn.BatchNorm2d(self.CatChannels) self.h1_Cat_hd1_relu = nn.ReLU(inplace=True) # hd2->160*160, hd1->320*320, Upsample 2 times self.hd2_UT_hd1 = nn.Upsample(scale_factor=2, mode='bilinear') # 14*14 self.hd2_UT_hd1_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1) self.hd2_UT_hd1_bn = nn.BatchNorm2d(self.CatChannels) self.hd2_UT_hd1_relu = nn.ReLU(inplace=True) # hd3->80*80, hd1->320*320, Upsample 4 times self.hd3_UT_hd1 = nn.Upsample(scale_factor=4, mode='bilinear') # 14*14 self.hd3_UT_hd1_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1) self.hd3_UT_hd1_bn = nn.BatchNorm2d(self.CatChannels) self.hd3_UT_hd1_relu = nn.ReLU(inplace=True) # hd4->40*40, hd1->320*320, Upsample 8 times self.hd4_UT_hd1 = nn.Upsample(scale_factor=8, mode='bilinear') # 14*14 self.hd4_UT_hd1_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1) self.hd4_UT_hd1_bn = nn.BatchNorm2d(self.CatChannels) self.hd4_UT_hd1_relu = nn.ReLU(inplace=True) # hd5->20*20, hd1->320*320, Upsample 16 times self.hd5_UT_hd1 = nn.Upsample(scale_factor=16, mode='bilinear') # 14*14 self.hd5_UT_hd1_conv = nn.Conv2d(filters[4], self.CatChannels, 3, padding=1) self.hd5_UT_hd1_bn = nn.BatchNorm2d(self.CatChannels) self.hd5_UT_hd1_relu = nn.ReLU(inplace=True) # fusion(h1_Cat_hd1, hd2_UT_hd1, hd3_UT_hd1, hd4_UT_hd1, hd5_UT_hd1) self.conv1d_1 = nn.Conv2d(self.UpChannels, self.UpChannels, 3, padding=1) # 16 self.bn1d_1 = nn.BatchNorm2d(self.UpChannels) self.relu1d_1 = nn.ReLU(inplace=True) # -------------Bilinear Upsampling-------------- self.upscore6 = nn.Upsample(scale_factor=32, mode='bilinear') ### self.upscore5 = nn.Upsample(scale_factor=16, mode='bilinear') self.upscore4 = nn.Upsample(scale_factor=8, mode='bilinear') self.upscore3 = nn.Upsample(scale_factor=4, mode='bilinear') self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear') # DeepSup self.outconv1 = nn.Conv2d(self.UpChannels, n_classes, 3, padding=1) self.outconv2 = nn.Conv2d(self.UpChannels, n_classes, 3, padding=1) self.outconv3 = nn.Conv2d(self.UpChannels, n_classes, 3, padding=1) self.outconv4 = nn.Conv2d(self.UpChannels, n_classes, 3, padding=1) self.outconv5 = nn.Conv2d(filters[4], n_classes, 3, padding=1) self.cls = nn.Sequential(nn.Dropout(p=0.5), nn.Conv2d(filters[4], 2, 1), nn.AdaptiveMaxPool2d(1), nn.Sigmoid()) # initialise weights for m in self.modules(): if isinstance(m, nn.Conv2d): init_weights(m, init_type='kaiming') elif isinstance(m, nn.BatchNorm2d): init_weights(m, init_type='kaiming')