From c3908abc416bc8721d84aeff8fa49271eeb751ce Mon Sep 17 00:00:00 2001
From: pfo49kjfx <2512671328@qq.com>
Date: Mon, 4 Dec 2023 16:57:40 +0800
Subject: [PATCH] ADD file via upload

---
 Unet_parts.py | 79 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 79 insertions(+)
 create mode 100644 Unet_parts.py

diff --git a/Unet_parts.py b/Unet_parts.py
new file mode 100644
index 0000000..8619b33
--- /dev/null
+++ b/Unet_parts.py
@@ -0,0 +1,79 @@
+""" Parts of the U-Net model """
+"""https://github.com/milesial/Pytorch-UNet/blob/master/unet/unet_parts.py"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class DoubleConv(nn.Module):
+    """(convolution => [BN] => ReLU) * 2
+    +
+    (element-wise add)"""
+
+    def __init__(self, in_channels, out_channels):
+        super().__init__()
+        self.double_conv = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True)
+        )
+        self.element_wise_add = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=1),
+            nn.BatchNorm2d(out_channels)
+        )
+
+    def forward(self, x):
+        return self.double_conv(x)
+
+
+class Down(nn.Module):
+    """Downscaling with maxpool then double conv"""
+
+    def __init__(self, in_channels, out_channels):
+        super().__init__()
+        self.maxpool_conv = nn.Sequential(
+            nn.MaxPool2d(2),
+            DoubleConv(in_channels, out_channels)
+        )
+
+    def forward(self, x):
+        return self.maxpool_conv(x)
+
+
+class Up(nn.Module):
+    """Upscaling then double conv"""
+
+    def __init__(self, in_channels, out_channels, bilinear=True):
+        super().__init__()
+
+        # if bilinear, use the normal convolutions to reduce the number of channels
+        if bilinear:
+            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
+        else:
+            self.up = nn.ConvTranspose2d(in_channels // 2, in_channels // 2, kernel_size=2, stride=2)# //为整数除法
+
+        self.conv = DoubleConv(in_channels, out_channels)
+
+    def forward(self, x1, x2):
+        x1 = self.up(x1)
+        diffY = torch.tensor([x2.size()[2] - x1.size()[2]])
+        diffX = torch.tensor([x2.size()[3] - x1.size()[3]])
+
+        x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
+                        diffY // 2, diffY - diffY // 2])
+
+        x = torch.cat([x2, x1], dim=1)
+        return self.conv(x)
+
+
+class OutConv(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super(OutConv, self).__init__()
+        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
+
+    def forward(self, x):
+        return self.conv(x)