--- a
+++ b/opengait/modeling/backbones/u_net.py
@@ -0,0 +1,105 @@
+import torch.nn as nn
+import torch
+
+
+class ConvBlock(nn.Module):
+    def __init__(self, ch_in, ch_out):
+        super(ConvBlock, self).__init__()
+        self.conv = nn.Sequential(
+            nn.Conv2d(ch_in, ch_out, kernel_size=3,
+                      stride=1, padding=1, bias=True),
+            nn.BatchNorm2d(ch_out),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(ch_out, ch_out, kernel_size=3,
+                      stride=1, padding=1, bias=True),
+            nn.BatchNorm2d(ch_out),
+            nn.ReLU(inplace=True)
+        )
+
+    def forward(self, x):
+        x = self.conv(x)
+        return x
+
+
+class UpConv(nn.Module):
+    def __init__(self, ch_in, ch_out):
+        super(UpConv, self).__init__()
+        self.up = nn.Sequential(
+            nn.Upsample(scale_factor=2),
+            nn.Conv2d(ch_in, ch_out, kernel_size=3,
+                      stride=1, padding=1, bias=True),
+            nn.BatchNorm2d(ch_out),
+            nn.ReLU(inplace=True)
+        )
+
+    def forward(self, x):
+        x = self.up(x)
+        return x
+
+
+class U_Net(nn.Module):
+    def __init__(self, in_channels=3, freeze_half=True):
+        super(U_Net, self).__init__()
+
+        self.Maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        self.Conv1 = ConvBlock(ch_in=in_channels, ch_out=16)
+        self.Conv2 = ConvBlock(ch_in=16, ch_out=32)
+        self.Conv3 = ConvBlock(ch_in=32, ch_out=64)
+        self.Conv4 = ConvBlock(ch_in=64, ch_out=128)
+        self.freeze = freeze_half
+        # Begin Fine-tuning
+        if freeze_half:
+            self.Conv1.requires_grad_(False)
+            self.Conv2.requires_grad_(False)
+            self.Conv3.requires_grad_(False)
+            self.Conv4.requires_grad_(False)
+        # End Fine-tuning
+
+        self.Up4 = UpConv(ch_in=128, ch_out=64)
+        self.Up_conv4 = ConvBlock(ch_in=128, ch_out=64)
+
+        self.Up3 = UpConv(ch_in=64, ch_out=32)
+        self.Up_conv3 = ConvBlock(ch_in=64, ch_out=32)
+
+        self.Up2 = UpConv(ch_in=32, ch_out=16)
+        self.Up_conv2 = ConvBlock(ch_in=32, ch_out=16)
+
+        self.Conv_1x1 = nn.Conv2d(
+            16, 1, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, x):
+        if self.freeze:
+            with torch.no_grad():
+                # encoding path
+                # Begin Fine-tuning
+
+                x1 = self.Conv1(x)
+                x2 = self.Maxpool(x1)
+                x2 = self.Conv2(x2)
+                x3 = self.Maxpool(x2)
+                x3 = self.Conv3(x3)
+                x4 = self.Maxpool(x3)
+                x4 = self.Conv4(x4)
+        # End Fine-tuning
+        else:
+            x1 = self.Conv1(x)
+            x2 = self.Maxpool(x1)
+            x2 = self.Conv2(x2)
+            x3 = self.Maxpool(x2)
+            x3 = self.Conv3(x3)
+            x4 = self.Maxpool(x3)
+            x4 = self.Conv4(x4)
+
+        d4 = self.Up4(x4)
+        d4 = torch.cat((x3, d4), dim=1)
+        d4 = self.Up_conv4(d4)
+        d3 = self.Up3(d4)
+        d3 = torch.cat((x2, d3), dim=1)
+        d3 = self.Up_conv3(d3)
+
+        d2 = self.Up2(d3)
+        d2 = torch.cat((x1, d2), dim=1)
+        d2 = self.Up_conv2(d2)
+        d1 = self.Conv_1x1(d2)
+        return d1