add UNet code

UNet/data.py

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+import os
+
+from torch.utils.data import Dataset
+from utils import *
+from torchvision import transforms
+transform = transforms.Compose([
+    transforms.ToTensor()
+])
+
+
+#use VOC2007 Dataset
+class MyDataset(Dataset):
+    def __init__(self, path):
+        self.path = path
+        self.name = os.listdir(os.path.join(path, 'SegmentationClass'))
+
+    def __len__(self):
+        return len(self.name)
+
+    def __getitem__(self, index):
+        segment_name = self.name[index] #xx.png
+        segment_path = os.path.join(self.path, 'SegmentationClass',segment_name)
+        image_path = os.path.join(self.path,'JPEGImages', segment_name.replace('png','jpg'))
+        segment_image = keep_image_size_open(segment_path) 
+        image = keep_image_size_open(image_path)
+        return transform(image), transform(segment_image)
+
+if __name__ == '__main__':
+    data = MyDataset('/Users/hanzhangma/Document/DataSet/VOC2007')
+    print(data[0][0].shape) # print the size of image(0,0)
+    print(data[0][1].shape) # print the size of image(0,1)

UNet/net.py

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+from torch import nn
+from torch.nn import functional as F
+from torch import randn
+import torch
+
+class Conv_Block(nn.Module):
+    def __init__(self, in_channel, out_channel):
+        super(Conv_Block, self).__init__()
+        self.layer = nn.Sequential(
+            nn.Conv2d(in_channels=in_channel, out_channels=out_channel, kernel_size=3, stride=1, padding=1, padding_mode='reflect', bias=False),
+            nn.BatchNorm2d(out_channel),
+            nn.Dropout2d(0.3),
+            nn.LeakyReLU(),
+            nn.Conv2d(in_channels=out_channel, out_channels=out_channel, kernel_size=3,stride=1,padding=1,padding_mode='reflect', bias=False),
+            nn.BatchNorm2d(out_channel),
+            nn.Dropout2d(0.3),
+            nn.LeakyReLU()
+        )
+
+    def forward(self, x):
+        return self.layer(x)
+
+class DownSample(nn.Module):
+    def __init__(self, channel):
+        super(DownSample, self).__init__()
+        self.layer = nn.Sequential(
+            nn.Conv2d(channel, channel, 3, 2, 1, padding_mode='reflect', bias=False),
+            nn.BatchNorm2d(channel),
+            nn.LeakyReLU()
+        )
+    def forward(self, x):
+        return self.layer(x)
+
+class UpSample(nn.Module):
+    def __init__(self, channel):
+        super(UpSample, self).__init__()
+        self.layer = nn.Sequential(
+            nn.Conv2d(channel, channel//2, 1, 1)
+        )
+    def forward(self, x, feature_map):
+        up = F.interpolate(x, scale_factor=2, mode='nearest')
+        out = self.layer(up)
+        return torch.cat((out, feature_map), dim=1)
+
+class UNet(nn.Module):
+    def __init__(self):
+        super(UNet, self).__init__()
+        self.c1 = Conv_Block(3,64)
+        self.d1 = DownSample(64)
+        self.c2 = Conv_Block(64, 128)
+        self.d2 = DownSample(128)
+        self.c3 = Conv_Block(128, 256)
+        self.d3 = DownSample(256)
+        self.c4 = Conv_Block(256, 512)
+        self.d4 = DownSample(512)
+        self.c5 = Conv_Block(512, 1024)
+
+        self.u1 = UpSample(1024)
+        self.c6 = Conv_Block(1024, 512)
+        self.u2 = UpSample(512)
+        self.c7 = Conv_Block(512, 256)
+        self.u3 = UpSample(256)
+        self.c8 = Conv_Block(256, 128)
+        self.u4 = UpSample(128)
+        self.c9 = Conv_Block(128, 64)
+
+        self.out = nn.Conv2d(64, 3, 3, 1, 1)
+        self.Th = nn.Sigmoid()
+
+    def forward(self, x):
+        R1 = self.c1(x)
+        R2 = self.c2(self.d1(R1))
+        R3 = self.c3(self.d2(R2))
+        R4 = self.c4(self.d3(R3))
+        R5 = self.c5(self.d4(R4))
+
+        O1 = self.c6(self.u1(R5, R4))
+        O2 = self.c7(self.u2(O1, R3))
+        O3 = self.c8(self.u3(O2, R2))
+        O4 = self.c9(self.u4(O3, R1))
+
+        return self.Th(self.out(O4))
+
+if __name__ == '__main__':
+    x = randn(2, 3, 256, 256)
+    net = UNet()
+    print(net(x).shape)

UNet/train.py

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+import torch
+from torch import optim
+from torch.utils.data import DataLoader
+from data import *
+from net import *
+
+from torchvision.utils import save_image
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+weight_path = r'/Users/hanzhangma/Nextcloud/mhz/Study/SS24/MasterThesis/UNet/params/unet.pth'
+data_path = r'/Users/hanzhangma/Document/DataSet/VOC2007'
+save_path = r'/Users/hanzhangma/Nextcloud/mhz/Study/SS24/MasterThesis/Unet/train_image'
+
+if __name__ == '__main__':
+    data_loader = DataLoader(MyDataset(data_path), batch_size= 4, shuffle=True)
+
+    net = UNet().to(device)
+    if os.path.exists(weight_path):
+        net.load_state_dict(torch.load(weight_path))
+        print('successful load weight!')
+    else:
+        print('Failed on load weight!')
+
+    opt = optim.Adam(net.parameters())
+    loss_fun = nn.BCELoss()
+
+    epoch=1
+
+    while True:
+        for i,(image,segment_image) in enumerate(data_loader):
+            image, segment_image = image.to(device), segment_image.to(device)
+
+            out_image = net(image)
+            train_loss = loss_fun(out_image, segment_image)
+
+            opt.zero_grad()
+            train_loss.backward()
+            opt.step() # 更新梯度
+
+            if i%5 ==0 :
+                print(f'{epoch} -- {i} -- train loss ===>> {train_loss.item()}')
+
+            if i % 50 == 0:
+                torch.save(net.state_dict(), weight_path)
+
+            _image = image[0]
+            _segment_image = segment_image[0]
+            _out_image = out_image[0]
+
+            img = torch.stack([_image, _segment_image, _out_image], dim=0)
+            save_image(img, f'{save_path}/{i}.png')
+
+        epoch += 1

UNet/utils.py

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+from PIL import Image
+
+def keep_image_size_open(path,size=(256,256)):
+    img = Image.open(path)
+    tmp = max(img.size)
+    mask = Image.new('RGB', (tmp, tmp),(0,0,0))
+    mask.paste(img,(0,0))
+    mask = mask.resize(size)
+    return mask
+