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Merge pull request milesial#475 from IshanG97/download-data-bat-script
Fix issue milesial#474: Windows .bat setup script
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,97 @@ | ||
| import torch | ||
| from torch import nn | ||
| from torch.nn import functional as F | ||
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| # 卷积类 | ||
| class Conv_Block(nn.Module): | ||
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| def __init__(self, in_channels, out_channels): | ||
| super(Conv_Block, self).__init__() | ||
| self.layer = nn.Sequential( | ||
| nn.Conv2d(in_channels, out_channels, kernel_size=3,stride=1, padding=1,padding_mode='reflect',bias=False), | ||
| nn.BatchNorm2d(out_channels), | ||
| nn.Dropout(0.3), | ||
| nn.LeakyReLU(), | ||
| # 第一个卷积 | ||
| nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, padding_mode='reflect', | ||
| bias=False), | ||
| nn.BatchNorm2d(out_channels), | ||
| nn.Dropout(0.3), | ||
| nn.LeakyReLU(), | ||
| # 第二个卷积同理 | ||
| ) | ||
| def forward(self, x): | ||
| return self.layer(x) | ||
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| #下采样类 | ||
| class DownSample(nn.Module): | ||
| def __init__(self, channel): | ||
| super(DownSample, self).__init__() | ||
| # 序列构造器 | ||
| self.layer = nn.Sequential( | ||
| nn.Conv2d(channel, channel, kernel_size=3, stride=2, padding=1, padding_mode='reflect', bias=False), | ||
| nn.BatchNorm2d(channel),#可以用也可以不用 | ||
| nn.LeakyReLU() | ||
| ) | ||
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| def forward(self, x): | ||
| return self.layer(x) | ||
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| #上采样 得到特征图，和同层的下采样图进行拼接，然后再进行卷积 | ||
| class UpSample(nn.Module): | ||
| def __init__(self,channel): | ||
| super(UpSample, self).__init__() | ||
| #转置卷积与插值法 | ||
| #用1+1的卷积降通道 | ||
| self.layer = nn.Conv2d(channel, channel//2, kernel_size=1, stride=1) | ||
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| def forward(self, x,feature_map): #feature_map 是同层之前下采样的特征图 | ||
| up = F.interpolate(x,scale_factor=2,mode='nearest')#scale_factor=2 变成原来的2倍 插值法 | ||
| out = self.layer(up) | ||
| return torch.cat([out,feature_map],dim=1) #n c h w 0 1 2 3 | ||
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| 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,kernel_size=1, stride=1, padding=1) | ||
| self.Th=nn.Sigmoid()#激活函数，虽然是彩色图像，只需要对图像进行二分类即可，也就是讲一个像素点分为黑色或白色 | ||
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| def forward(self, x): | ||
| R1=self.c1(x) | ||
| R2=self.c2(self.d1(R1))#先对R1进行下采样，然后再进行第二次的一个卷积 | ||
| 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)) #先将R5上采样，并且和R4进行拼接，然后再进行卷积 | ||
| O2=self.c7(self.u2(O1,R3)) | ||
| O3=self.c8(self.u3(O2,R2)) | ||
| O4=self.c9(self.u4(O3,R1)) | ||
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| return self.Th(self.out(O4)) | ||
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| #测试，输出模型的形状，看看和我们设计的样子是否一样 | ||
| if __name__ == '__main__': | ||
| x = torch.randn(2, 3, 256, 256) | ||
| net = UNet() | ||
| print(net(x).shape) |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,28 @@ | ||
| @echo off | ||
| setlocal enabledelayedexpansion | ||
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| if not exist "%userprofile%\.kaggle\kaggle.json" ( | ||
| set /p USERNAME=Kaggle username: | ||
| echo. | ||
| set /p APIKEY=Kaggle API key: | ||
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| mkdir "%userprofile%\.kaggle" | ||
| echo {"username":"!USERNAME!","key":"!APIKEY!"} > "%userprofile%\.kaggle\kaggle.json" | ||
| attrib +R "%userprofile%\.kaggle\kaggle.json" | ||
| ) | ||
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| pip install kaggle --upgrade | ||
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| kaggle competitions download -c carvana-image-masking-challenge -f train_hq.zip | ||
| powershell Expand-Archive train_hq.zip -DestinationPath data\imgs | ||
| move data\imgs\train_hq\* data\imgs\ | ||
| rmdir /s /q data\imgs\train_hq | ||
| del /q train_hq.zip | ||
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| kaggle competitions download -c carvana-image-masking-challenge -f train_masks.zip | ||
| powershell Expand-Archive train_masks.zip -DestinationPath data\masks | ||
| move data\masks\train_masks\* data\masks\ | ||
| rmdir /s /q data\masks\train_masks | ||
| del /q train_masks.zip | ||
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| exit /b 0 |
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