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PathFlowAI
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[e9500f]: / deprecated_work / build / lib / pathflowai / unet4.py

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# -*- coding: utf-8 -*-

# https://raw.githubusercontent.com/shreyaspadhy/UNet-Zoo/master/models.py

import torch.nn as nn

import torch





class UNet(nn.Module):

    def __init__(self, num_channels=1, num_classes=2):

        super(UNet, self).__init__()

        num_feat = [64, 128, 256, 512, 1024]



        self.down1 = nn.Sequential(Conv3x3(num_channels, num_feat[0]))



        self.down2 = nn.Sequential(nn.MaxPool2d(kernel_size=2),

                                   Conv3x3(num_feat[0], num_feat[1]))



        self.down3 = nn.Sequential(nn.MaxPool2d(kernel_size=2),

                                   Conv3x3(num_feat[1], num_feat[2]))



        self.down4 = nn.Sequential(nn.MaxPool2d(kernel_size=2),

                                   Conv3x3(num_feat[2], num_feat[3]))



        self.bottom = nn.Sequential(nn.MaxPool2d(kernel_size=2),

                                    Conv3x3(num_feat[3], num_feat[4]))



        self.up1 = UpConcat(num_feat[4], num_feat[3])

        self.upconv1 = Conv3x3(num_feat[4], num_feat[3])



        self.up2 = UpConcat(num_feat[3], num_feat[2])

        self.upconv2 = Conv3x3(num_feat[3], num_feat[2])



        self.up3 = UpConcat(num_feat[2], num_feat[1])

        self.upconv3 = Conv3x3(num_feat[2], num_feat[1])



        self.up4 = UpConcat(num_feat[1], num_feat[0])

        self.upconv4 = Conv3x3(num_feat[1], num_feat[0])



        self.final = nn.Sequential(nn.Conv2d(num_feat[0],

                                             num_classes,

                                             kernel_size=1))

        #""",nn.Softmax2d()"""



    def forward(self, inputs, return_features=False):

        # print(inputs.data.size())

        down1_feat = self.down1(inputs)

        # print(down1_feat.size())

        down2_feat = self.down2(down1_feat)

        # print(down2_feat.size())

        down3_feat = self.down3(down2_feat)

        # print(down3_feat.size())

        down4_feat = self.down4(down3_feat)

        # print(down4_feat.size())

        bottom_feat = self.bottom(down4_feat)



        # print(bottom_feat.size())

        up1_feat = self.up1(bottom_feat, down4_feat)

        # print(up1_feat.size())

        up1_feat = self.upconv1(up1_feat)

        # print(up1_feat.size())

        up2_feat = self.up2(up1_feat, down3_feat)

        # print(up2_feat.size())

        up2_feat = self.upconv2(up2_feat)

        # print(up2_feat.size())

        up3_feat = self.up3(up2_feat, down2_feat)

        # print(up3_feat.size())

        up3_feat = self.upconv3(up3_feat)

        # print(up3_feat.size())

        up4_feat = self.up4(up3_feat, down1_feat)

        # print(up4_feat.size())

        up4_feat = self.upconv4(up4_feat)

        # print(up4_feat.size())



        if return_features:

            outputs = up4_feat

        else:

            outputs = self.final(up4_feat)



        return outputs





class UNetSmall(nn.Module):

    def __init__(self, num_channels=1, num_classes=2):

        super(UNetSmall, self).__init__()

        num_feat = [32, 64, 128, 256]



        self.down1 = nn.Sequential(Conv3x3Small(num_channels, num_feat[0]))



        self.down2 = nn.Sequential(nn.MaxPool2d(kernel_size=2),

                                   nn.BatchNorm2d(num_feat[0]),

                                   Conv3x3Small(num_feat[0], num_feat[1]))



        self.down3 = nn.Sequential(nn.MaxPool2d(kernel_size=2),

                                   nn.BatchNorm2d(num_feat[1]),

                                   Conv3x3Small(num_feat[1], num_feat[2]))



        self.bottom = nn.Sequential(nn.MaxPool2d(kernel_size=2),

                                    nn.BatchNorm2d(num_feat[2]),

                                    Conv3x3Small(num_feat[2], num_feat[3]),

                                    nn.BatchNorm2d(num_feat[3]))



        self.up1 = UpSample(num_feat[3], num_feat[2])

        self.upconv1 = nn.Sequential(Conv3x3Small(num_feat[3] + num_feat[2], num_feat[2]),

                                     nn.BatchNorm2d(num_feat[2]))



        self.up2 = UpSample(num_feat[2], num_feat[1])

        self.upconv2 = nn.Sequential(Conv3x3Small(num_feat[2] + num_feat[1], num_feat[1]),

                                     nn.BatchNorm2d(num_feat[1]))



        self.up3 = UpSample(num_feat[1], num_feat[0])

        self.upconv3 = nn.Sequential(Conv3x3Small(num_feat[1] + num_feat[0], num_feat[0]),

                                     nn.BatchNorm2d(num_feat[0]))



        self.final = nn.Sequential(nn.Conv2d(num_feat[0],

                                             num_classes,

                                             kernel_size=1))

        #,nn.Sigmoid()



    def forward(self, inputs, return_features=False):

        # print(inputs.data.size())

        down1_feat = self.down1(inputs)

        # print(down1_feat.size())

        down2_feat = self.down2(down1_feat)

        # print(down2_feat.size())

        down3_feat = self.down3(down2_feat)

        # print(down3_feat.size())

        bottom_feat = self.bottom(down3_feat)



        # print(bottom_feat.size())

        up1_feat = self.up1(bottom_feat, down3_feat)

        # print(up1_feat.size())

        up1_feat = self.upconv1(up1_feat)

        # print(up1_feat.size())

        up2_feat = self.up2(up1_feat, down2_feat)

        # print(up2_feat.size())

        up2_feat = self.upconv2(up2_feat)

        # print(up2_feat.size())

        up3_feat = self.up3(up2_feat, down1_feat)

        # print(up3_feat.size())

        up3_feat = self.upconv3(up3_feat)

        # print(up3_feat.size())



        if return_features:

            outputs = up3_feat

        else:

            outputs = self.final(up3_feat)



        return outputs





class Conv3x3(nn.Module):

    def __init__(self, in_feat, out_feat):

        super(Conv3x3, self).__init__()



        self.conv1 = nn.Sequential(nn.Conv2d(in_feat, out_feat,

                                             kernel_size=3,

                                             stride=1,

                                             padding=1),

                                   nn.BatchNorm2d(out_feat),

                                   nn.ReLU())



        self.conv2 = nn.Sequential(nn.Conv2d(out_feat, out_feat,

                                             kernel_size=3,

                                             stride=1,

                                             padding=1),

                                   nn.BatchNorm2d(out_feat),

                                   nn.ReLU())



    def forward(self, inputs):

        outputs = self.conv1(inputs)

        outputs = self.conv2(outputs)

        return outputs





class Conv3x3Drop(nn.Module):

    def __init__(self, in_feat, out_feat):

        super(Conv3x3Drop, self).__init__()



        self.conv1 = nn.Sequential(nn.Conv2d(in_feat, out_feat,

                                             kernel_size=3,

                                             stride=1,

                                             padding=1),

                                   nn.Dropout(p=0.2),

                                   nn.ReLU())



        self.conv2 = nn.Sequential(nn.Conv2d(out_feat, out_feat,

                                             kernel_size=3,

                                             stride=1,

                                             padding=1),

                                   nn.BatchNorm2d(out_feat),

                                   nn.ReLU())



    def forward(self, inputs):

        outputs = self.conv1(inputs)

        outputs = self.conv2(outputs)

        return outputs





class Conv3x3Small(nn.Module):

    def __init__(self, in_feat, out_feat):

        super(Conv3x3Small, self).__init__()



        self.conv1 = nn.Sequential(nn.Conv2d(in_feat, out_feat,

                                             kernel_size=3,

                                             stride=1,

                                             padding=1),

                                   nn.ELU(),

                                   nn.Dropout(p=0.2))



        self.conv2 = nn.Sequential(nn.Conv2d(out_feat, out_feat,

                                             kernel_size=3,

                                             stride=1,

                                             padding=1),

                                   nn.ELU())



    def forward(self, inputs):

        outputs = self.conv1(inputs)

        outputs = self.conv2(outputs)

        return outputs





class UpConcat(nn.Module):

    def __init__(self, in_feat, out_feat):

        super(UpConcat, self).__init__()



        self.up = nn.UpsamplingBilinear2d(scale_factor=2)



        # self.deconv = nn.ConvTranspose2d(in_feat, out_feat,

        #                                  kernel_size=3,

        #                                  stride=1,

        #                                  dilation=1)



        self.deconv = nn.ConvTranspose2d(in_feat,

                                         out_feat,

                                         kernel_size=2,

                                         stride=2)



    def forward(self, inputs, down_outputs):

        # TODO: Upsampling required after deconv?

        # outputs = self.up(inputs)

        outputs = self.deconv(inputs)

        out = torch.cat([down_outputs, outputs], 1)

        return out





class UpSample(nn.Module):

    def __init__(self, in_feat, out_feat):

        super(UpSample, self).__init__()



        self.up = nn.Upsample(scale_factor=2, mode='nearest')



        self.deconv = nn.ConvTranspose2d(in_feat,

                                         out_feat,

                                         kernel_size=2,

                                         stride=2)



    def forward(self, inputs, down_outputs):

        # TODO: Upsampling required after deconv?

        outputs = self.up(inputs)

        # outputs = self.deconv(inputs)

        out = torch.cat([outputs, down_outputs], 1)

        return out