在 ResNet 中实现多尺度的特征融合,类似于特征金字塔网络(Feature Pyramid Network,FPN)的思想。下面是一个简单的示例,演示如何在 ResNet 中添加多尺度的特征融合:文章来源:https://www.toymoban.com/news/detail-774534.html
import torch
import torch.nn as nn
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(self.expansion * planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes)
)
def forward(self, x):
out = nn.ReLU()(self.bn1(self.conv1(x)))
out = nn.ReLU()(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
out += self.shortcut(x)
out = nn.ReLU()(out)
return out
class ResNetWithFeaturePyramid(nn.Module):
def __init__(self, block, num_blocks, num_classes=1000):
super(ResNetWithFeaturePyramid, self).__init__()
self.in_planes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
# 添加额外的卷积层用于构建特征金字塔
self.extra_conv = nn.Conv2d(2048, 256, kernel_size=1, stride=1, bias=False)
self.pyramid_conv1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, bias=False)
self.pyramid_conv2 = nn.Conv2d(512, 256, kernel_size=1, stride=1, bias=False)
self.pyramid_conv3 = nn.Conv2d(256, 256, kernel_size=1, stride=1, bias=False)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(256, num_classes)
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
out = nn.ReLU()(self.bn1(self.conv1(x)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
# 获取不同层次的特征
c4 = out
c3 = self.layer3(out)
c2 = self.layer2(c3)
c1 = self.layer1(c2)
# 构建特征金字塔
p4 = self.pyramid_conv1(c4)
p3 = self.pyramid_conv2(c3)
p2 = self.pyramid_conv3(c2)
# 从高层到低层进行上采样和融合
p3 = p3 + nn.functional.interpolate(p4, scale_factor=2, mode='nearest')
p2 = p2 + nn.functional.interpolate(p3, scale_factor=2, mode='nearest')
# 降采样
p2 = nn.functional.interpolate(p2, scale_factor=0.5, mode='nearest')
# 使用额外的卷积层
p1 = self.extra_conv(c1)
# 融合所有尺度的特征
fused_feature = p1 + p2 + p3
# 全局平均池化和全连接层
out = self.avgpool(fused_feature)
out = out.view(out.size(0), -1)
out = self.fc(out)
return out
def ResNet50WithFeaturePyramid():
return ResNetWithFeaturePyramid(Bottleneck, [3, 4, 6, 3])
# 创建 ResNet-50 模型
resnet50_with_fpn = ResNet50WithFeaturePyramid()
# 打印模型结构
print(resnet50_with_fpn)
这个代码示例中,我添加了额外的卷积层和三个特征金字塔层,以便从不同的卷积层获得特征并进行融合。大家可以根据任务需求进行更改和优化。特征金字塔的思想能够提供更好的多尺度信息,有助于提高模型对不同目标大小的适应性。文章来源地址https://www.toymoban.com/news/detail-774534.html
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