Xception代码
时间: 2024-08-15 22:09:11 浏览: 34
Xception是一个深度学习模型,源自于Google在2016年的ImageNet图像识别竞赛中提出的Inception-v4网络结构。Xception全称为“Extreme Inception”,它是Inception系列的一个变种,但是采用了更简单且计算效率更高的深度 separable 卷积(也称作分离卷积或空间分块卷积)代替了传统的Inception模块中的全连接层。
Xception的核心特点是其深度可分离卷积层,这种结构将卷积分为两个步骤:首先在一个方向上进行点卷积操作(深度卷积),然后再做另一个方向的一维卷积(宽度卷积)。这减少了计算量和参数数量,有助于避免过拟合,并提高了模型的运行速度。
在计算机视觉任务中,尤其是图像分类,Xception因其高效的特征提取能力和良好的性能而受到欢迎。它的架构通常包括一系列瓶颈残差块(Bottleneck Residual Blocks),以及一些全局平均池化层来获取整个输入的上下文信息。
相关问题
xception pytorch代码
Xception是一种深度卷积神经网络模型,它在ImageNet数据集上取得了很好的性能。下面是一个简单的Xception模型的PyTorch代码示例:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, bias=False):
super(SeparableConv2d, self).__init__()
self.depthwise = nn.Conv2d(in_channels, in_channels, kernel_size, stride, padding, dilation, groups=in_channels, bias=bias)
self.pointwise = nn.Conv2d(in_channels, out_channels, 1, 1, 0, 1, 1, bias=bias)
def forward(self, x):
x = self.depthwise(x)
x = self.pointwise(x)
return x
class Block(nn.Module):
def __init__(self, in_channels, out_channels, reps, stride=1, start_with_relu=True, grow_first=True):
super(Block, self).__init__()
if out_channels != in_channels or stride != 1:
self.skip = nn.Conv2d(in_channels, out_channels, 1, stride=stride, bias=False)
self.skipbn = nn.BatchNorm2d(out_channels)
else:
self.skip = None
self.relu = nn.ReLU(inplace=True)
rep = []
filters = in_channels
if grow_first:
rep.append(self.relu)
rep.append(SeparableConv2d(in_channels, out_channels, 3, stride=1, padding=1, bias=False))
rep.append(nn.BatchNorm2d(out_channels))
filters = out_channels
for i in range(reps - 1):
rep.append(self.relu)
rep.append(SeparableConv2d(filters, filters, 3, stride=1, padding=1, bias=False))
rep.append(nn.BatchNorm2d(filters))
if not grow_first:
rep.append(self.relu)
rep.append(SeparableConv2d(in_channels, out_channels, 3, stride=1, padding=1, bias=False))
rep.append(nn.BatchNorm2d(out_channels))
if stride != 1:
rep.append(self.relu)
rep.append(SeparableConv2d(out_channels, out_channels, 3, stride=2, padding=1, bias=False))
rep.append(nn.BatchNorm2d(out_channels))
self.rep = nn.Sequential(*rep)
def forward(self, inp):
x = self.rep(inp)
if self.skip is not None:
skip = self.skip(inp)
skip = self.skipbn(skip)
else:
skip = inp
x += skip
return x
class Xception(nn.Module):
def __init__(self, num_classes=1000):
super(Xception, self).__init__()
self.conv1 = nn.Conv2d(3, 32, 3, 2, 0, bias=False)
self.bn1 = nn.BatchNorm2d(32)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(32, 64, 3, bias=False)
self.bn2 = nn.BatchNorm2d(64)
self.block1 = Block(64, 128, 2, 2, start_with_relu=False, grow_first=True)
self.block2 = Block(128, 256, 2, 2, start_with_relu=True, grow_first=True)
self.block3 = Block(256, 728, 2, 2, start_with_relu=True, grow_first=True)
self.block4 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
self.block5 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
self.block6 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
self.block7 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
self.block8 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
self.block9 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
self.block10 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
self.block11 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
self.block12 = Block(728, 1024, 2, 2, start_with_relu=True, grow_first=False)
self.conv3 = SeparableConv2d(1024, 1536, 3, stride=1, padding=1)
self.bn3 = nn.BatchNorm2d(1536)
self.conv4 = SeparableConv2d(1536, 2048, 3, stride=1, padding=1)
self.bn4 = nn.BatchNorm2d(2048)
self.fc = nn.Linear(2048, num_classes)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
x = self.block1(x)
x = self.block2(x)
x = self.block3(x)
x = self.block4(x)
x = self.block5(x)
x = self.block6(x)
x = self.block7(x)
x = self.block8(x)
x = self.block9(x)
x = self.block10(x)
x = self.block11(x)
x = self.block12(x)
x = self.conv3(x)
x = self.bn3(x)
x = self.relu(x)
x = self.conv4(x)
x = self.bn4(x)
x = self.relu(x)
x = F.adaptive_avg_pool2d(x, (1, 1))
x = torch.flatten(x, 1)
x = self.fc(x)
return x
model = Xception()
```
这段代码定义了一个Xception模型,包括了各个模块和层的定义。你可以根据自己的需求进行修改和使用。
pytorch Xception
Xception是一种卷积神经网络模型,用于图像分类和目标检测任务。它是基于Inception架构的改进版本,通过引入depthwise separable convolution(深度可分离卷积)的思想,减少了参数数量和计算量,同时提高了模型的准确性。
Xception的代码示例如下:
```python
from torch import nn
from torch.nn import Conv2d,BatchNorm2d
class Xception(nn.Module):
def __init__(self, inp, oup):
super(Xception, self).__init__()
# depthwise
self.conv1 = Conv2d(inp, inp, kernel_size=(3, 3), stride=(1, 1), padding=1, groups=inp)
self.bn1 = BatchNorm2d(inp)
# pointwise
self.conv2 = Conv2d(inp, oup, (1, 1))
self.bn2 = BatchNorm2d(oup)
self.relu = nn.ReLU()
def forward(self, input):
output = self.conv1(input)
output = self.bn1(output)
output = self.relu(output)
output = self.conv2(output)
output = self.bn2(output)
output = self.relu(output)
return output
```