.gitignore

@@ -1,5 +1 @@
 ./flowers/*
-.DS_Store
-./UNet/train_image/*
-./UNet/params/*
-./UNet/__pycache__/*

UNet/data.py

@@ -1,31 +0,0 @@
-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

@@ -1,87 +0,0 @@
-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

@@ -1,53 +0,0 @@
-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

@@ -1,10 +0,0 @@
-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
-