如何将1280*10*10的图像通过多次反卷积操作变成4*300*300的图像
时间: 2023-07-23 21:09:55 浏览: 155
可以通过多次使用`nn.ConvTranspose2d()`函数来实现多次反卷积操作。具体实现步骤如下:
```python
import torch.nn as nn
class Deconv(nn.Module):
def __init__(self):
super(Deconv, self).__init__()
self.deconv1 = nn.ConvTranspose2d(1280, 512, kernel_size=4, stride=3, padding=0, output_padding=0)
self.deconv2 = nn.ConvTranspose2d(512, 256, kernel_size=4, stride=3, padding=0, output_padding=0)
self.deconv3 = nn.ConvTranspose2d(256, 128, kernel_size=4, stride=3, padding=0, output_padding=0)
self.deconv4 = nn.ConvTranspose2d(128, 4, kernel_size=4, stride=3, padding=0, output_padding=0)
def forward(self, x):
x = self.deconv1(x)
x = self.deconv2(x)
x = self.deconv3(x)
x = self.deconv4(x)
return x
# 创建模型
model = Deconv()
# 创建输入张量
x = torch.randn(1, 1280, 10, 10)
# 输出反卷积后的尺寸
output = model(x)
print(output.shape)
```
这里使用了四个`nn.ConvTranspose2d()`函数,分别进行了四次反卷积操作。每次反卷积操作的输入通道数和输出通道数不同,kernel size为4,stride为3,padding为0,output_padding为0。最终输出的图像尺寸为4*300*300。
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