resnet50引入自注意力机制
时间: 2024-06-18 14:01:39 浏览: 20
ResNet50 是一种经典的深度残差网络,由 Kaiming He 和他的团队在 2015 年提出的,用于图像识别任务,特别是大规模的 ImageNet 数据集。原始的 ResNet 模型并没有直接引入自注意力机制,自注意力(Self-Attention)机制通常源自Transformer架构,在自然语言处理领域非常流行,能够捕捉输入序列中的长期依赖关系。
如果要将自注意力应用到视觉任务中,比如将它融合到 ResNet50 中,通常会涉及到设计一种称为“视觉注意力模块”(Visual Attention Module)或“空间注意力模块”(Spatial Attention Module)。这通常包括以下几个步骤:
1. **位置编码**:由于 CNN 在处理空间位置信息上不如自注意力机制直观,可能会加入位置编码信息,如Sin-Cos编码,以便模型了解像素间的相对关系。
2. **注意力映射**:通过卷积层生成一组注意力权重,这些权重表示每个位置对全局特征的重要性。
3. **加权聚合**:将输入特征与注意力权重相乘,然后求和,得到注意力加权的特征表示。
4. **融合回原网络**:将注意力模块的输出与原始特征进行融合,可能是通过逐通道加权、残差连接等方式。
这种改造并非官方的 ResNet50 更新,而是研究者为了提升模型性能而进行的一些创新尝试,如 Non-local Networks 或 Convolutional Block Attention Module (CBAM)。
相关问题
在resnet50中引入通道注意力机制(SE)代码
在ResNet50中引入通道注意力机制(SE)的代码如下所示:
```python
import tensorflow as tf
from tensorflow.keras import layers
def se_block(input_tensor, reduction_ratio=16):
channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1
channel = input_tensor.shape[channel_axis]
se = layers.GlobalAveragePooling2D()(input_tensor)
se = layers.Reshape((1, 1, channel))(se)
se = layers.Dense(channel // reduction_ratio, activation='relu', kernel_initializer='he_normal', use_bias=False)(se)
se = layers.Dense(channel, activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(se)
if tf.keras.backend.image_data_format() == 'channels_first':
se = layers.Permute((3, 1, 2))(se)
x = layers.multiply([input_tensor, se])
return x
def resnet_block(input_tensor, filters, kernel_size, strides, reduction_ratio=16):
x = layers.Conv2D(filters, kernel_size=kernel_size, strides=strides, padding='same')(input_tensor)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(filters, kernel_size=kernel_size, padding='same')(x)
x = layers.BatchNormalization()(x)
x = se_block(x, reduction_ratio) # 添加SE模块
x = layers.add([x, input_tensor])
x = layers.Activation('relu')(x)
return x
def build_resnet50(input_shape=(224, 224, 3), num_classes=1000):
input_tensor = layers.Input(shape=input_shape)
x = layers.Conv2D(64, kernel_size=7, strides=2, padding='same')(input_tensor)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
x = layers.MaxPooling2D(pool_size=3, strides=2, padding='same')(x)
x = resnet_block(x, filters=64, kernel_size=3, strides=1)
x = resnet_block(x, filters=64, kernel_size=3, strides=1)
x = resnet_block(x, filters=64, kernel_size=3, strides=1)
x = resnet_block(x, filters=128, kernel_size=3, strides=2)
x = resnet_block(x, filters=128, kernel_size=3, strides=1)
x = resnet_block(x, filters=128, kernel_size=3, strides=1)
x = resnet_block(x, filters=128, kernel_size=3, strides=1)
x = resnet_block(x, filters=256, kernel_size=3, strides=2)
for _ in range(5):
x = resnet_block(x, filters=256, kernel_size=3, strides=1)
x = resnet_block(x, filters=512, kernel_size=3, strides=2)
for _ in range(2):
x = resnet_block(x, filters=512, kernel_size=3, strides=1)
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dense(num_classes, activation='softmax')(x)
model = tf.keras.Model(inputs=input_tensor, outputs=x)
return model
```
这段代码定义了一个ResNet50模型,并在每个残差块中引入了通道注意力机制(SE)。通道注意力机制通过全局平均池化、全连接层和Sigmoid激活函数来计算通道权重,然后将通道权重与输入特征图相乘,实现通道的自适应加权。这样可以增强模型对重要通道的关注,提升模型性能。
resnet-18添加注意力机制
在ResNet-18中添加注意力机制可以通过引入SE(Squeeze-and-Excitation)注意力模块来实现。SE注意力模块是一种传统的通道注意力机制,可以有效地改进网络的分类精度。
以下是在ResNet-18中添加SE注意力模块的示例代码:
```python
import torch
import torch.nn as nn
# 定义SE注意力模块
class SELayer(nn.Module):
def __init__(self, channel, reduction=16):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction, bias=False),
nn.ReLU(inplace=True),
nn.Linear(channel // reduction, channel, bias=False),
nn.Sigmoid()
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y
# 定义ResNet-18网络结构
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000):
super(ResNet, self).__init__()
self.in_channels = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
# 添加SE注意力模块
self.se = SELayer(512 * block.expansion)
def _make_layer(self, block, channels, blocks, stride=1):
layers = []
layers.append(block(self.in_channels, channels, stride))
self.in_channels = channels * block.expansion
for _ in range(1, blocks):
layers.append(block(self.in_channels, channels))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
# 应用SE注意力模块
x = self.se(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x
# 创建ResNet-18模型并添加SE注意力模块
model = ResNet(block, [2, 2, 2, 2])
```
在上述代码中,我们定义了一个SE注意力模块`SELayer`,并在ResNet-18的每个残差块的输出上应用了该注意力模块。通过这种方式,我们可以在ResNet-18中添加注意力机制,从而改进网络的分类精度。
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