def forward(self, x): out = self.conv1(x) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = F.avg_pool2d(out, 4) out = out.view(out.size(0), -1) out = self.fc(out) return out
时间: 2024-04-28 18:27:21 浏览: 9
这段代码是一个 PyTorch 模型的前向传播函数,该模型包含几个卷积层和池化层(通过 self.conv1 和 F.avg_pool2d 实现),以及几个残差块(通过 self.layer1 到 self.layer4 实现),最后是一个全连接层(通过 self.fc 实现)。输入 x 是模型的输入数据,经过一系列的卷积、池化和残差块操作后,最后通过全连接层输出模型的预测结果。
相关问题
def forward(self, x): out = self.conv1(x) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = self.avgpool(out) out = out.reshape(x.shape[0], -1) out = self.fc(out) return out ———————————————— 逐行解释
这段代码是RestNet18类中的forward方法,用于定义模型的前向传播过程。以下是对代码逐行的解释:
1. `out = self.conv1(x)`
将输入x通过卷积层self.conv1进行卷积操作,得到输出out。
2. `out = self.layer1(out)`
将上一层的输出out作为输入,通过self.layer1进行前向传播,得到更新后的输出out。
3. `out = self.layer2(out)`
将上一层的输出out作为输入,通过self.layer2进行前向传播,得到更新后的输出out。
4. `out = self.layer3(out)`
将上一层的输出out作为输入,通过self.layer3进行前向传播,得到更新后的输出out。
5. `out = self.layer4(out)`
将上一层的输出out作为输入,通过self.layer4进行前向传播,得到更新后的输出out。
6. `out = self.avgpool(out)`
将上一层的输出out通过自适应平均池化层self.avgpool进行池化操作,得到输出out。
7. `out = out.reshape(x.shape[0], -1)`
将输出out进行形状重塑,保持batch的维度不变,将其转换成二维张量。
8. `out = self.fc(out)`
将重塑后的输出out通过全连接层self.fc进行线性变换和非线性激活操作,得到最终的输出out。
9. `return out`
返回最终的输出out作为模型的前向传播结果。
这样,forward方法定义了模型的前向传播过程,将输入x经过一系列的卷积、池化、线性变换和非线性激活操作后,得到最终的输出结果。
更改import torch import torchvision.models as models import torch.nn as nn import torch.nn.functional as F class eca_Resnet50(nn.Module): def init(self): super().init() self.model = models.resnet50(pretrained=True) self.model.avgpool = nn.AdaptiveAvgPool2d((1,1)) self.model.fc = nn.Linear(2048, 1000) self.eca = ECA_Module(2048, 8) def forward(self, x): x = self.model.conv1(x) x = self.model.bn1(x) x = self.model.relu(x) x = self.model.maxpool(x) x = self.model.layer1(x) x = self.model.layer2(x) x = self.model.layer3(x) x = self.model.layer4(x) x = self.eca(x) x = self.model.avgpool(x) x = torch.flatten(x, 1) x = self.model.fc(x) return x class ECA_Module(nn.Module): def init(self, channel, k_size=3): super(ECA_Module, self).init() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): b, c, _, _ = x.size() y = self.avg_pool(x) y = self.conv(y.squeeze(-1).transpose(-1,-2)).transpose(-1,-2).unsqueeze(-1) y = self.sigmoid(y) return x * y.expand_as(x) class ImageDenoising(nn.Module): def init(self): super().init() self.model = eca_Resnet50() self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1) self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1) self.conv3 = nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = self.conv3(x) x = F.relu(x) return x,使最后输出为[16,1,50,50,]。
更改后的代码如下:
```
import torch
import torchvision.models as models
import torch.nn as nn
import torch.nn.functional as F
class eca_Resnet50(nn.Module):
def __init__(self):
super().__init__()
self.model = models.resnet50(pretrained=True)
self.model.avgpool = nn.AdaptiveAvgPool2d((1,1))
self.model.fc = nn.Linear(2048, 1000)
self.eca = ECA_Module(2048, 8)
def forward(self, x):
x = self.model.conv1(x)
x = self.model.bn1(x)
x = self.model.relu(x)
x = self.model.maxpool(x)
x = self.model.layer1(x)
x = self.model.layer2(x)
x = self.model.layer3(x)
x = self.model.layer4(x)
x = self.eca(x)
x = self.model.avgpool(x)
x = torch.flatten(x, 1)
x = self.model.fc(x)
return x
class ECA_Module(nn.Module):
def __init__(self, channel, k_size=3):
super().__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x)
y = self.conv(y.squeeze(-1).transpose(-1,-2)).transpose(-1,-2).unsqueeze(-1)
y = self.sigmoid(y)
return x * y.expand_as(x)
class ImageDenoising(nn.Module):
def __init__(self):
super().__init__()
self.model = eca_Resnet50()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(64, 1, kernel_size=3, stride=1, padding=1)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
x = self.conv3(x)
x = F.relu(x)
x = x.view(-1, 1, 50, 50)
return x
```
在修改的过程中,主要是将最后一层卷积层的输出通道数改为 1,并在 forward 方法中添加了 x.view(-1, 1, 50, 50),将输出的大小修改为 [16,1,50,50]。