class DnCNN(nn.Module): def __init__(self, channels, num_of_layers=17): super(DnCNN, self).__init__() kernel_size = 3 padding = 1 features = 64 layers = [] layers.append(nn.Conv2d(in_channels=channels, out_channels=features, kernel_size=kernel_size, padding=padding, bias=False)) layers.append(nn.ReLU(inplace=True)) for _ in range(num_of_layers-2): layers.append(nn.Conv2d(in_channels=features, out_channels=features, kernel_size=kernel_size, padding=padding, bias=False)) layers.append(nn.BatchNorm2d(features)) layers.append(nn.ReLU(inplace=True)) layers.append(nn.Conv2d(in_channels=features, out_channels=channels, kernel_size=kernel_size, padding=padding, bias=False)) self.dncnn = nn.Sequential(*layers) def forward(self, x): out = self.dncnn(x) return out怎么改为训练集输出[16,3,50,50],评估集输出[1,3,256,256]
时间: 2023-06-11 13:06:52 浏览: 135
基于matlab-使用深度学习卷积神经网络DnCNN-图像去噪算法设计与实现源码.zip
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你需要将 DnCNN 模型的输入和输出大小调整为适合训练集和评估集的大小。可以使用 torch.nn.functional.interpolate() 函数将输入大小调整为 [16, 3, 50, 50],并在模型输出之前使用一个 nn.Upsample() 层将输出大小调整为 [1, 3, 256, 256]。
以下是修改后的代码:
```python
import torch.nn.functional as F
class DnCNN(nn.Module):
def __init__(self, channels, num_of_layers=17):
super(DnCNN, self).__init__()
kernel_size = 3
padding = 1
features = 64
layers = []
layers.append(nn.Conv2d(in_channels=channels, out_channels=features, kernel_size=kernel_size, padding=padding, bias=False))
layers.append(nn.ReLU(inplace=True))
for _ in range(num_of_layers-2):
layers.append(nn.Conv2d(in_channels=features, out_channels=features, kernel_size=kernel_size, padding=padding, bias=False))
layers.append(nn.BatchNorm2d(features))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.Conv2d(in_channels=features, out_channels=channels, kernel_size=kernel_size, padding=padding, bias=False))
self.dncnn = nn.Sequential(*layers)
def forward(self, x):
# 将输入大小调整为 [16, 3, 50, 50]
x = F.interpolate(x, size=(50, 50), mode='bilinear', align_corners=False)
out = self.dncnn(x)
# 将输出大小调整为 [1, 3, 256, 256]
out = nn.Upsample(size=(256, 256), mode='bilinear', align_corners=False)(out)
return out
```
请注意,这里使用了双线性插值的方法调整了输入和输出的大小。你可以根据实际情况选择其他的插值方法。
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