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：

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