PyTorch深度学习：从ResNet34到ResNet18

PyTorch中搭建ResNet34与ResNet18模型的源代码
在深度学习的应用中，图像识别和分类是一个重要的领域。为了解决这个问题，我们通常会使用预训练的深度学习模型，比如ResNet。在本文中，我们将详细解释如何在PyTorch中搭建ResNet34和ResNet18模型。

ResNet34
首先，我们从ResNet34开始。以下是一个简单的源代码示例：

import torch
import torch.nn as nn
class ResNet34(nn.Module):
def __init__(self, num_classes=1000):
super(ResNet34, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
self._block(64, 3, 4),
self._block(128, 4, 6),
self._block(256, 6, 3),
nn.AdaptiveAvgPool2d((1, 1)),
nn.Flatten(),
nn.Linear(256 * 1 * 1, num_classes),
)
def forward(self, x):
x = self.features(x)
return x
def _block(self, channels, num_layers, num_layers_per_residual_group):
layers = []
for i in range(num_layers):
if i % num_layers_per_residual_group == 0:
layers += [nn.Conv2d(channels, channels * 2, kernel_size=3, stride=1, padding=1), nn.BatchNorm2d(channels * 2), nn.ReLU(inplace=True)]
channels *= 2
else:
layers += [nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding=1), nn.BatchNorm2d(channels), nn.ReLU(inplace=True)]
return nn.Sequential(*layers)

此处代码首先定义了一个ResNet34类，它是一个继承自nn.Module的类。在初始化函数__init__中，定义了特征提取的主干网络部分，包括卷积、归一化、激活和最大池化等操作。然后在forward函数中进行前向传播。最后定义了用于构建ResNet34模型的残差块。

ResNet18
接下来，我们来看看如何实现ResNet18模型。以下是一个简单的源代码示例：
```python
import torch
import torch.nn as nn
class ResNet18(nn.Module):
def init(self, numclasses=1000):
super(ResNet18, self)._init()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn._block(64, 2, 2),
nn._block(128, 2, 2),
nn._block(256, 2, 2),
nn._block(512, 2, 2),
nn.AdaptiveAvgPool2d((1, 1)),
nn.Flatten(),
nn.Linear(512 1 1, num_classes),
)
def forward(self, x):
x = self.features(x)
return x
def _block(self, channels, num_layers):
layers = []
for i in range(num_layers):
if i == 0:
layers += [nn.Conv2d(channels , channels 4 , kernel_size = 3 , stride = 1 , padding = 1), nn.BatchNorm2d(channels 4), nn.ReLU()] #conv—>BN—>relu

PyTorch深度学习：从ResNet34到ResNet18

最热文章