请写出人体行为识别中slowfast架构的核心代码

SlowFast架构是一种在视频行为识别中广泛使用的架构，它结合了慢速和快速两种不同的卷积神经网络。以下是SlowFast架构的核心代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class Bottleneck(nn.Module):
    def __init__(self, in_planes, planes, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv3d(in_planes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm3d(planes)
        self.conv2 = nn.Conv3d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm3d(planes)
        self.conv3 = nn.Conv3d(planes, planes*4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm3d(planes*4)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != planes*4:
            self.shortcut = nn.Sequential(
                nn.Conv3d(in_planes, planes*4, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm3d(planes*4)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out

class SlowFast(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(SlowFast, self).__init__()
        self.in_planes = 64

        self.fast = nn.Sequential(
            nn.Conv3d(3, 8, kernel_size=(1, 5, 5), stride=(1, 2, 2), padding=(0, 2, 2), bias=False),
            nn.BatchNorm3d(8),
            nn.ReLU(inplace=True),

            nn.Conv3d(8, 16, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False),
            nn.BatchNorm3d(16),
            nn.ReLU(inplace=True),

            nn.Conv3d(16, 32, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False),
            nn.BatchNorm3d(32),
            nn.ReLU(inplace=True),

            nn.Conv3d(32, 64, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False),
            nn.BatchNorm3d(64),
            nn.ReLU(inplace=True)
        )

        self.slow = nn.Sequential(
            nn.Conv3d(3, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=False),
            nn.BatchNorm3d(64),
            nn.ReLU(inplace=True),

            nn.Conv3d(64, 64, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False),
            nn.BatchNorm3d(64),
            nn.ReLU(inplace=True),

            nn.Conv3d(64, 64, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False),
            nn.BatchNorm3d(64),
            nn.ReLU(inplace=True),

            nn.Conv3d(64, 128, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False),
            nn.BatchNorm3d(128),
            nn.ReLU(inplace=True)
        )

        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)

        self.avgpool = nn.AdaptiveAvgPool3d((1, 1, 1))
        self.fc = nn.Linear(512 * block.expansion, 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):
        fast = self.fast(x[:, :, ::2])
        slow = self.slow(x[:, :, ::16])
        x = torch.cat([slow, fast], dim=2)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

该代码定义了SlowFast架构中的Bottleneck块和SlowFast类，用于构建整个网络。其中，Bottleneck块是SlowFast中的基本块，用于构建各个层；SlowFast类则是整个网络的主体部分，定义了各个层的结构和前向传播的过程。在构建网络时，可以根据需要调整Bottleneck块和SlowFast类的超参数，以满足不同的视频行为识别任务需求。