kernel.ngroups_max = 65536

`kernel.ngroups_max` 是一个内核参数，用于设置系统中用户组的最大数量。

在 Linux 系统中，每个用户都可以属于一个或多个用户组。`kernel.ngroups_max` 参数限制了系统中用户组的最大数量。

通过设置 `kernel.ngroups_max` 参数，您可以指定系统允许的用户组的最大数量。在这种情况下，`kernel.ngroups_max` 的值被设置为 65536，表示系统中最多可以存在 65536 个用户组。

如果需要修改 `kernel.ngroups_max` 参数，您需要具有超级用户权限。可以通过编辑 `/etc/sysctl.conf` 文件或使用 `sysctl` 命令来修改内核参数。

请注意，在修改参数时，请确保考虑到系统资源和性能方面的限制。设置过大的 `kernel.ngroups_max` 值可能会导致系统资源消耗过多或性能下降。同时，根据应用程序和系统需求，选择合适的最大用户组数量也是很重要的。

相关问题

可变形卷积模块和普通卷积模块有什么区别？

可变形卷积模块和普通卷积模块的区别在于，可变形卷积模块可以对卷积核进行微调，以适应不同形状的目标。在普通卷积中，卷积核的形状是固定的，无法适应目标的形状变化。而在可变形卷积中，卷积核的形状可以根据目标的形状进行微调，从而更好地适应目标的形状变化，提高了模型的准确性。

下面是一个使用PyTorch实现可变形卷积模块的例子：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function

class DeformConv2dFunction(Function):
    @staticmethod
    def forward(ctx, input, offset, weight, bias, stride, padding, dilation, groups):
        # 保存参数，用于反向传播
        ctx.stride = stride
        ctx.padding = padding
        ctx.dilation = dilation
        ctx.groups = groups
        # 计算输出
        output = deform_conv2d(input, weight, bias, offset, stride, padding, dilation, groups)
        # 保存中间结果，用于反向传播
        ctx.save_for_backward(input, offset, weight, bias)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        # 获取中间结果
        input, offset, weight, bias = ctx.saved_tensors
        stride = ctx.stride
        padding = ctx.padding
        dilation = ctx.dilation
        groups = ctx.groups
        # 计算梯度
        grad_input, grad_offset, grad_weight, grad_bias = deform_conv2d_backward(input, weight, bias, offset, grad_output, stride, padding, dilation, groups)
        return grad_input, grad_offset, grad_weight, grad_bias, None, None, None, None

class DeformConv2d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):
        super(DeformConv2d, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.groups = groups
        self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels, kernel_size, kernel_size))
        self.bias = nn.Parameter(torch.Tensor(out_channels)) if bias else None
        self.offset_conv = nn.Conv2d(in_channels, 2 * kernel_size * kernel_size, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=True)
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.kaiming_uniform_(self.weight, a=1)
        if self.bias is not None:
            nn.init.constant_(self.bias, 0)
        nn.init.constant_(self.offset_conv.weight, 0)
        nn.init.constant_(self.offset_conv.bias, 0)

    def forward(self, input):
        offset = self.offset_conv(input)
        output = DeformConv2dFunction.apply(input, offset, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
        return output

def deform_conv2d(input, weight, bias, offset, stride=1, padding=0, dilation=1, groups=1):
    # 计算输出大小
    n, c, h, w = input.size()
    out_h = (h + 2 * padding - dilation * (weight.size(2) - 1) - 1) // stride + 1
    out_w = (w + 2 * padding - dilation * (weight.size(3) - 1) - 1) // stride + 1
    # 生成网格
    grid_h = torch.arange(0, out_h, dtype=torch.float32, device=input.device)
    grid_w = torch.arange(0, out_w, dtype=torch.float32, device=input.device)
    grid_h = (grid_h + 0.5) * stride - padding
    grid_w = (grid_w + 0.5) * stride - padding
    grid_h = grid_h.view(-1, 1).repeat(1, out_w)
    grid_w = grid_w.view(1, -1).repeat(out_h, 1)
    grid = torch.stack([grid_w, grid_h], dim=-1)
    grid = grid.unsqueeze(0).repeat(n, 1, 1, 1)
    # 根据偏移量调整网格
    offset = offset.view(n, 2 * weight.size(2) * weight.size(3), out_h, out_w)
    offset = offset.permute(0, 2, 3, 1).contiguous()
    offset = offset.view(n * out_h * out_w, weight.size(2) * weight.size(3) * 2)
    grid = grid.view(n * out_h * out_w, 2)
    grid = grid + offset
    grid = grid.view(n, out_h, out_w, 2)
    # 双线性插值
    grid[:, :, :, 0] = 2 * grid[:, :, :, 0] / max(w - 1, 1) - 1
    grid[:, :, :, 1] = 2 * grid[:, :, :, 1] / max(h - 1, 1) - 1
    grid = grid.permute(0, 3, 1, 2).contiguous()
    input = F.grid_sample(input, grid, mode='bilinear', padding_mode='zeros')
    # 卷积
    output = F.conv2d(input, weight, bias, stride, padding, dilation, groups)
    return output

def deform_conv2d_backward(input, weight, bias, offset, grad_output, stride=1, padding=0, dilation=1, groups=1):
    # 计算输出大小
    n, c, h, w = input.size()
    out_h = (h + 2 * padding - dilation * (weight.size(2) - 1) - 1) // stride + 1
    out_w = (w + 2 * padding - dilation * (weight.size(3) - 1) - 1) // stride + 1
    # 生成网格
    grid_h = torch.arange(0, out_h, dtype=torch.float32, device=input.device)
    grid_w = torch.arange(0, out_w, dtype=torch.float32, device=input.device)
    grid_h = (grid_h + 0.5) * stride - padding
    grid_w = (grid_w + 0.5) * stride - padding
    grid_h = grid_h.view(-1, 1).repeat(1, out_w)
    grid_w = grid_w.view(1, -1).repeat(out_h, 1)
    grid = torch.stack([grid_w, grid_h], dim=-1)
    grid = grid.unsqueeze(0).repeat(n, 1, 1, 1)
    # 根据偏移量调整网格
    offset = offset.view(n, 2 * weight.size(2) * weight.size(3), out_h, out_w)
    offset = offset.permute(0, 2, 3, 1).contiguous()
    offset = offset.view(n * out_h * out_w, weight.size(2) * weight.size(3) * 2)
    grid = grid.view(n * out_h * out_w, 2)
    grid = grid + offset
    grid = grid.view(n, out_h, out_w, 2)
    # 双线性插值
    grid[:, :, :, 0] = 2 * grid[:, :, :, 0] / max(w - 1, 1) - 1
    grid[:, :, :, 1] = 2 * grid[:, :, :, 1] / max(h - 1, 1) - 1
    grid = grid.permute(0, 3, 1, 2).contiguous()
    input = F.grid_sample(input, grid, mode='bilinear', padding_mode='zeros')
    # 计算梯度
    grad_input, grad_weight, grad_bias = torch.autograd.grad(outputs=(input, weight, bias), inputs=(input, weight, bias), grad_outputs=(grad_output,), allow_unused=True)
    grad_input = grad_input.contiguous()
    grad_offset = None
    if offset.requires_grad:
        grad_offset = torch.zeros_like(offset)
        grad_grid = torch.autograd.grad(outputs=input, inputs=grid, grad_outputs=grad_output, allow_unused=True)
        grad_grid = grad_grid[0].contiguous()
        grad_grid = grad_grid.view(n * out_h * out_w, 2)
        grad_offset = grad_grid + grad_offset
        grad_offset = grad_offset.view(n, out_h, out_w, weight.size(2) * weight.size(3) * 2)
        grad_offset = grad_offset.permute(0, 3, 1, 2).contiguous()
    return grad_input, grad_offset, grad_weight, grad_bias

ShuffleNet的代码

这里给出ShuffleNet v1和ShuffleNet v2的代码实现，供参考：

**ShuffleNet v1**

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

class ShuffleBlock(nn.Module):
    def __init__(self, groups):
        super(ShuffleBlock, self).__init__()
        self.groups = groups
    
    def forward(self, x):
        n, c, h, w = x.size()
        g = self.groups
        assert c % g == 0, "Error: group number doesn't divide input channels"
        
        x = x.view(n, g, c//g, h, w).permute(0,2,1,3,4).contiguous()
        x = x.view(n, c, h, w)
        return x

class ShuffleNet(nn.Module):
    def __init__(self, groups=3, num_classes=1000):
        super(ShuffleNet, self).__init__()
        
        self.groups = groups
        self.conv1 = nn.Conv2d(3, 24, kernel_size=3, stride=2, padding=1, bias=False)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.stage2 = self._make_stage(24, 144, 3)
        self.stage3 = self._make_stage(144, 288, 7)
        self.stage4 = self._make_stage(288, 576, 3)
        self.conv5 = nn.Conv2d(576, 1024, kernel_size=1, stride=1, padding=0, bias=False)
        self.globalpool = nn.AdaptiveAvgPool2d((1,1))
        self.fc = nn.Linear(1024, num_classes)
        
    def _make_stage(self, inplanes, outplanes, blocks):
        layers = []
        layers.append(ShuffleBlock(self.groups))
        layers.append(nn.Conv2d(inplanes, outplanes//4, kernel_size=1, stride=1, padding=0, bias=False))
        layers.append(nn.BatchNorm2d(outplanes//4))
        layers.append(nn.ReLU(inplace=True))
        layers.append(nn.Conv2d(outplanes//4, outplanes//4, kernel_size=3, stride=1, padding=1, groups=outplanes//4, bias=False))
        layers.append(nn.BatchNorm2d(outplanes//4))
        layers.append(nn.Conv2d(outplanes//4, outplanes, kernel_size=1, stride=1, padding=0, bias=False))
        layers.append(nn.BatchNorm2d(outplanes))
        layers.append(nn.ReLU(inplace=True))
        
        for i in range(1, blocks):
            layers.append(ShuffleBlock(self.groups))
            layers.append(nn.Conv2d(outplanes, outplanes//4, kernel_size=1, stride=1, padding=0, bias=False))
            layers.append(nn.BatchNorm2d(outplanes//4))
            layers.append(nn.ReLU(inplace=True))
            layers.append(nn.Conv2d(outplanes//4, outplanes//4, kernel_size=3, stride=1, padding=1, groups=outplanes//4, bias=False))
            layers.append(nn.BatchNorm2d(outplanes//4))
            layers.append(nn.Conv2d(outplanes//4, outplanes, kernel_size=1, stride=1, padding=0, bias=False))
            layers.append(nn.BatchNorm2d(outplanes))
            layers.append(nn.ReLU(inplace=True))
        
        return nn.Sequential(*layers)
    
    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool(x)
        x = self.stage2(x)
        x = self.stage3(x)
        x = self.stage4(x)
        x = self.conv5(x)
        x = self.globalpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

**ShuffleNet v2**

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

class ShuffleBlock(nn.Module):
    def __init__(self, inp, oup, mid_channels, ksize, stride):
        super(ShuffleBlock, self).__init__()
        
        self.stride = stride
        self.ksize = ksize
        self.mid_channels = mid_channels
        
        self.conv1 = nn.Conv2d(inp, mid_channels, kernel_size=1, stride=1, padding=0, bias=False)
        self.bn1 = nn.BatchNorm2d(mid_channels)
        self.conv2 = nn.Conv2d(mid_channels, mid_channels, kernel_size=ksize, stride=stride, padding=(ksize-1)//2, groups=mid_channels, bias=False)
        self.bn2 = nn.BatchNorm2d(mid_channels)
        self.conv3 = nn.Conv2d(mid_channels, oup, kernel_size=1, stride=1, padding=0, bias=False)
        self.bn3 = nn.BatchNorm2d(oup)
        
        self.shortcut = nn.Sequential()
        if stride == 2:
            self.shortcut = nn.Sequential(
                nn.Conv2d(inp, inp, kernel_size=ksize, stride=stride, padding=(ksize-1)//2, groups=inp, bias=False),
                nn.BatchNorm2d(inp),
                nn.Conv2d(inp, oup, kernel_size=1, stride=1, padding=0, bias=False),
                nn.BatchNorm2d(oup)
            )
        
    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))
        
        if self.stride == 2:
            shortcut = self.shortcut(x)
        else:
            shortcut = x
        
        out = F.relu(torch.cat([out, shortcut], 1))
        out = self.channel_shuffle(out, 2)
        return out
    
    def channel_shuffle(self, x, groups):
        n, c, h, w = x.size()
        x = x.view(n, groups, c//groups, h, w).permute(0,2,1,3,4).contiguous()
        x = x.view(n, c, h, w)
        return x

class ShuffleNetV2(nn.Module):
    def __init__(self, num_classes=1000, input_size=224, width_mult=1.):
        super(ShuffleNetV2, self).__init__()
        
        self.stage_repeats = [4, 8, 4]
        self.stage_out_channels = [24, 116, 232, 464, 1024]
        if width_mult == 0.5:
            self.stage_out_channels = [int(x * width_mult) for x in self.stage_out_channels]
        
        self.conv1 = nn.Conv2d(3, self.stage_out_channels[0], kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(self.stage_out_channels[0])
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        
        self.stage2 = self._make_stage(1)
        self.stage3 = self._make_stage(2)
        self.stage4 = self._make_stage(3)
        self.conv5 = nn.Conv2d(self.stage_out_channels[3], self.stage_out_channels[4], kernel_size=1, stride=1, padding=0, bias=False)
        self.bn5 = nn.BatchNorm2d(self.stage_out_channels[4])
        self.avgpool = nn.AvgPool2d(kernel_size=7, stride=1)
        self.fc = nn.Linear(self.stage_out_channels[4], num_classes)
        
    def _make_stage(self, stage):
        modules = []
        
        stage_repeats = self.stage_repeats[stage-1]
        stage_out_channels = self.stage_out_channels[stage]
        for i in range(stage_repeats):
            if i == 0:
                modules.append(ShuffleBlock(self.stage_out_channels[stage-1], stage_out_channels, stage_out_channels//2, ksize=3, stride=2))
            else:
                modules.append(ShuffleBlock(stage_out_channels, stage_out_channels, stage_out_channels//2, ksize=3, stride=1))
        
        return nn.Sequential(*modules)
    
    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = self.maxpool(x)
        x = self.stage2(x)
        x = self.stage3(x)
        x = self.stage4(x)
        x = F.relu(self.bn5(self.conv5(x)))
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

以上给出的是PyTorch的实现代码，其中包括ShuffleBlock和ShuffleNet/ShuffleNetV2两个模型的实现。具体实现细节可以参考论文中的介绍。