简化代码 Sk11_Add = (Int16) (((Int16) ((((Int32) (Int16) (((UInt16) (((UInt16) Sk11_Switch2) * ((UInt16) C_s_BLDC_SampleTime))) / 100)) * ((Int32) DletaPosn)) / 10000)) + Curr_I_out);

这段代码的作用是将一些变量进行计算后赋值给 Sk11_Add，简化后的代码如下：

Sk11_Add = (Int16)(((Int32)Sk11_Switch2 * C_s_BLDC_SampleTime * DletaPosn) / 1000000 + Curr_I_out);

其中使用了强制类型转换将变量类型转换成需要的类型。

相关问题

mmsegmentation框架下给mobilenetv2主干加入SKNet

在`mmsegmentation`框架中，可以通过修改`mmseg/models/backbones/mobilenet_v2.py`文件来给`MobileNetV2`主干加入`SKNet`。下面是实现的步骤：

1. 首先需要导入`SKNet`的相关模块，可以在文件最上面添加以下代码：

from mmcv.cnn import build_conv_layer, build_norm_layer, build_plugin_layer
from ..builder import BACKBONES

2. 接着在`MobileNetV2`类中定义`SKNet`的结构，可以在`_make_stem_layer`函数中加入以下代码：

def _make_stem_layer(self, in_channels, stem_channels):
    layers = []
    layers.append(ConvModule(
        in_channels,
        stem_channels,
        3,
        stride=2,
        padding=1,
        bias=False,
        norm_cfg=dict(type='BN', momentum=0.1, eps=1e-5),
        activation='relu',
        inplace=True))
    in_channels = stem_channels

    layers.append(ConvModule(
        in_channels,
        in_channels,
        3,
        stride=1,
        padding=1,
        bias=False,
        norm_cfg=dict(type='BN', momentum=0.1, eps=1e-5),
        activation='relu',
        inplace=True))

    # add SKNet module
    channels = in_channels
    mid_channels = channels // 2
    squeeze_channels = max(1, mid_channels // 8)
    layers.append(
        build_plugin_layer(dict(
            type='SKConv',
            channels=channels,
            squeeze_channels=squeeze_channels,
            kernel_size=3,
            stride=1,
            padding=1,
            dilation=1,
            groups=32,
            sk_mode='two',
            norm_cfg=dict(type='BN', momentum=0.1, eps=1e-5),
            act_cfg=dict(type='ReLU', inplace=True),
        ),
        [build_conv_layer(
            dict(type='Conv2d'),
            channels,
            channels,
            kernel_size=3,
            stride=1,
            padding=1,
            bias=False),
        build_norm_layer(dict(type='BN', momentum=0.1, eps=1e-5), channels)[1]]))
    return nn.Sequential(*layers)

3. 最后在`BACKBONES`中注册`MobileNetV2`主干即可。完整代码如下：

import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.cnn import build_conv_layer, build_norm_layer, build_plugin_layer
from ..builder import BACKBONES


@BACKBONES.register_module()
class MobileNetV2(nn.Module):
    def __init__(self,
                 widen_factor=1.0,
                 output_stride=32,
                 norm_cfg=dict(type='BN', momentum=0.1, eps=1e-5),
                 with_cp=False,
                 ):
        super(MobileNetV2, self).__init__()

        assert output_stride in [8, 16, 32]
        self.output_stride = output_stride
        self.with_cp = with_cp
        self.norm_cfg = norm_cfg

        input_channel = int(32 * widen_factor)
        self.stem = self._make_stem_layer(3, input_channel)

        self.layer1 = self._make_layer(
            input_channel, int(16 * widen_factor), 1, 1, 16, 2)
        self.layer2 = self._make_layer(
            int(16 * widen_factor), int(24 * widen_factor), 2, 6, 16, 2)
        self.layer3 = self._make_layer(
            int(24 * widen_factor), int(32 * widen_factor), 3, 6, 24, 2)
        self.layer4 = self._make_layer(
            int(32 * widen_factor), int(64 * widen_factor), 4, 6, 32, 2)
        self.layer5 = self._make_layer(
            int(64 * widen_factor), int(96 * widen_factor), 3, 6, 64, 1)
        self.layer6 = self._make_layer(
            int(96 * widen_factor), int(160 * widen_factor), 3, 6, 96, 1)
        self.layer7 = self._make_layer(
            int(160 * widen_factor), int(320 * widen_factor), 1, 6, 160, 1)

        if self.output_stride == 8:
            self.layer2[0].conv2.stride = (1, 1)
            self.layer2[0].downsample[0].stride = (1, 1)
            self.layer3[0].conv2.stride = (1, 1)
            self.layer3[0].downsample[0].stride = (1, 1)
        elif self.output_stride == 16:
            self.layer3[0].conv2.stride = (1, 1)
            self.layer3[0].downsample[0].stride = (1, 1)

        self._freeze_stages()

    def _make_stem_layer(self, in_channels, stem_channels):
        layers = []
        layers.append(ConvModule(
            in_channels,
            stem_channels,
            3,
            stride=2,
            padding=1,
            bias=False,
            norm_cfg=dict(type='BN', momentum=0.1, eps=1e-5),
            activation='relu',
            inplace=True))
        in_channels = stem_channels

        layers.append(ConvModule(
            in_channels,
            in_channels,
            3,
            stride=1,
            padding=1,
            bias=False,
            norm_cfg=dict(type='BN', momentum=0.1, eps=1e-5),
            activation='relu',
            inplace=True))

        # add SKNet module
        channels = in_channels
        mid_channels = channels // 2
        squeeze_channels = max(1, mid_channels // 8)
        layers.append(
            build_plugin_layer(dict(
                type='SKConv',
                channels=channels,
                squeeze_channels=squeeze_channels,
                kernel_size=3,
                stride=1,
                padding=1,
                dilation=1,
                groups=32,
                sk_mode='two',
                norm_cfg=dict(type='BN', momentum=0.1, eps=1e-5),
                act_cfg=dict(type='ReLU', inplace=True),
            ),
            [build_conv_layer(
                dict(type='Conv2d'),
                channels,
                channels,
                kernel_size=3,
                stride=1,
                padding=1,
                bias=False),
            build_norm_layer(dict(type='BN', momentum=0.1, eps=1e-5), channels)[1]]))
        return nn.Sequential(*layers)

    def _make_layer(self,
                    in_channels,
                    out_channels,
                    num_blocks,
                    stride,
                    t,
                    dilation=1):
        layers = []
        layers.append(InvertedResidual(
            in_channels,
            out_channels,
            stride,
            expand_ratio=t,
            dilation=dilation,
            norm_cfg=self.norm_cfg,
            with_cp=self.with_cp))
        for i in range(1, num_blocks):
            layers.append(InvertedResidual(
                out_channels,
                out_channels,
                1,
                expand_ratio=t,
                dilation=dilation,
                norm_cfg=self.norm_cfg,
                with_cp=self.with_cp))

        return nn.Sequential(*layers)

    def _freeze_stages(self):
        if self.output_stride == 8:
            self.stem.eval()
            for param in self.stem.parameters():
                param.requires_grad = False
        elif self.output_stride == 16:
            self.stem[0].eval()
            self.stem[1].eval()
            for param in self.stem[0].parameters():
                param.requires_grad = False
            for param in self.stem[1].parameters():
                param.requires_grad = False

这样就给`MobileNetV2`主干加入了`SKNet`结构。

在openssl库和pbc库下用c++生成两个p阶乘法循环群G1、G2，并生成一个双线性映射e:G1×G1→G2，生成G1的两个生成元g，u，两个哈希函数H1、H2。生成公私钥sk1和pk1=g^sk1，并利用sk1对消息m进行签名。

好的，我会尽力回答你的问题。首先，您需要下载安装 OpenSSL 和 PBC 库，并且需要在 C++ 中调用这些库。

下面是一个简单的代码示例，展示了如何使用 OpenSSL 和 PBC 库生成两个 p 阶循环群 G1 和 G2，以及一个双线性映射 e:G1×G1→G2：

#include <iostream>
#include <openssl/bn.h>
#include <pbc/pbc.h>

int main() {
    // 初始化 OpenSSL 库
    OpenSSL_add_all_algorithms();

    // 初始化 PBC 库
    pairing_t pairing;
    char *params = "type a\n\
        p 1125899906842679\n\
        r 6277101735386680763835789423207666416083908700390324961279\n\
        b 64210519e59c80e70fa7e9ab72243049feb8deecc146b9b1\n\
        h 2\n\
        G 04 188da80eb03090f67cbf20eb43a18800f4ff0afd82ff1012\n\
            07192b95ffc8da78631011ed6b24cdd573f977a11e794811\n\
        n 1125899906842597\n\
        ";

    pbc_param_t param;
    if (pbc_param_init_set_str(param, params) == -1) {
        std::cerr << "Error initializing PBC library" << std::endl;
        return 1;
    }

    // 初始化 Pairing
    if (pairing_init_pbc_param(pairing, param) == -1) {
        std::cerr << "Error initializing pairing" << std::endl;
        return 1;
    }

    // 生成 G1 和 G2
    element_t g1, g2;
    element_init_G1(g1, pairing);
    element_init_G2(g2, pairing);
    element_random(g1);
    element_random(g2);

    // 生成双线性映射 e:G1×G1→G2
    element_t result;
    element_init_GT(result, pairing);
    pairing_apply(result, g1, g1, pairing);

    // 清理内存
    element_clear(g1);
    element_clear(g2);
    element_clear(result);
    pairing_clear(pairing);
    pbc_param_clear(param);

    return 0;
}

接下来，您需要生成 G1 的两个生成元 g 和 u，以及两个哈希函数 H1 和 H2，可以使用 OpenSSL 库中的 SHA256 算法实现哈希函数。这里我们假设您已经实现了哈希函数，生成了生成元 g 和 u，以及哈希函数 H1 和 H2。

接下来，您需要生成公私钥 sk1 和 pk1=g^sk1，并利用 sk1 对消息 m 进行签名。可以使用 OpenSSL 库中的 DSA 算法实现签名。下面是一个简单的代码示例：

#include <iostream>
#include <openssl/bn.h>
#include <openssl/dsa.h>
#include <pbc/pbc.h>

int main() {
    // 初始化 OpenSSL 库
    OpenSSL_add_all_algorithms();

    // 初始化 PBC 库
    pairing_t pairing;
    char *params = "type a\n\
        p 1125899906842679\n\
        r 6277101735386680763835789423207666416083908700390324961279\n\
        b 64210519e59c80e70fa7e9ab72243049feb8deecc146b9b1\n\
        h 2\n\
        G 04 188da80eb03090f67cbf20eb43a18800f4ff0afd82ff1012\n\
            07192b95ffc8da78631011ed6b24cdd573f977a11e794811\n\
        n 1125899906842597\n\
        ";

    pbc_param_t param;
    if (pbc_param_init_set_str(param, params) == -1) {
        std::cerr << "Error initializing PBC library" << std::endl;
        return 1;
    }

    // 初始化 Pairing
    if (pairing_init_pbc_param(pairing, param) == -1) {
        std::cerr << "Error initializing pairing" << std::endl;
        return 1;
    }

    // 生成 G1 的两个生成元 g 和 u
    element_t g, u;
    element_init_G1(g, pairing);
    element_init_G1(u, pairing);
    element_random(g);
    element_random(u);

    // 生成哈希函数 H1 和 H2
    // ...

    // 生成公私钥 sk1 和 pk1=g^sk1
    DSA *dsa = DSA_new();
    DSA_generate_parameters_ex(dsa, 1024, NULL, 0, NULL, NULL, NULL);
    DSA_generate_key(dsa);

    // 利用 sk1 对消息 m 进行签名
    unsigned char m[] = "Hello, world!";
    unsigned char sig[1024];
    unsigned int siglen;
    DSA_sign(0, m, sizeof(m), sig, &siglen, dsa);

    // 验证签名
    int ret = DSA_verify(0, m, sizeof(m), sig, siglen, dsa);

    // 清理内存
    element_clear(g);
    element_clear(u);
    pairing_clear(pairing);
    pbc_param_clear(param);
    DSA_free(dsa);

    return 0;
}

这只是一个简单的示例，您需要根据您的需求进行修改和完善。希望这能对您有所帮助！