informer恶意代码

Informer恶意代码是一种计算机病毒，它属于信息窃取恶意软件的一种。它的主要目标是窃取用户的敏感信息，如登录凭据、银行账户信息、个人身份信息等。Informer恶意代码通常通过电子邮件附件、恶意下载链接、感染的网站或者携带恶意代码的软件安装程序等方式传播。

一旦用户的设备感染了Informer恶意代码，它会在后台运行并开始收集用户的敏感信息。它可以监视用户的键盘输入、截取屏幕截图、访问浏览器历史记录等。收集到的信息会被发送给攻击者，用于非法活动，如身份盗窃、金融欺诈等。

为了保护自己免受Informer恶意代码的攻击，建议用户采取以下措施：
1. 保持操作系统和应用程序的更新，及时安装安全补丁。
2. 使用可靠的安全软件，并定期进行病毒扫描。
3. 谨慎打开电子邮件附件和点击不明来源的链接。
4. 避免下载和安装来自不可信来源的软件。
5. 使用强密码，并定期更改密码。
6. 定期备份重要的文件和数据。

相关问题

informer模型代码

以下是一个简单的informer模型的PyTorch代码实现：

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

class Informer(nn.Module):
    def __init__(self, enc_in, dec_in, out_seq_len, n_heads, e_layers, d_layers, d_ff, dropout, activation='relu'):
        super(Informer, self).__init__()
        
        self.encoder = Encoder(enc_in, n_heads, e_layers, d_ff, dropout, activation)
        self.decoder = Decoder(dec_in, out_seq_len, n_heads, d_layers, d_ff, dropout, activation)
        self.out = nn.Linear(dec_in, out_seq_len)
        
    def forward(self, x):
        enc_out, attn = self.encoder(x)
        dec_out = self.decoder(enc_out, attn)
        out = self.out(dec_out)
        return out

class Encoder(nn.Module):
    def __init__(self, input_dim, n_heads, n_layers, d_ff, dropout, activation):
        super(Encoder, self).__init__()

        self.layers = nn.ModuleList()
        for i in range(n_layers):
            self.layers.append(EncoderLayer(input_dim, n_heads, d_ff, dropout, activation))

    def forward(self, x):
        attn_weights = []
        for layer in self.layers:
            x, attn_weight = layer(x)
            attn_weights.append(attn_weight)
        return x, attn_weights

class EncoderLayer(nn.Module):
    def __init__(self, input_dim, n_heads, d_ff, dropout, activation):
        super(EncoderLayer, self).__init__()

        self.self_attn = MultiHeadAttention(n_heads, input_dim, input_dim, dropout)
        self.feed_forward = FeedForward(input_dim, d_ff, activation, dropout)
        self.norm1 = nn.LayerNorm(input_dim)
        self.norm2 = nn.LayerNorm(input_dim)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)

    def forward(self, x):
        # self-attention
        residual = x
        x, attn_weight = self.self_attn(x, x, x)
        x = self.norm1(residual + self.dropout1(x))

        # feed forward
        residual = x
        x = self.feed_forward(x)
        x = self.norm2(residual + self.dropout2(x))

        return x, attn_weight

class Decoder(nn.Module):
    def __init__(self, input_dim, out_seq_len, n_heads, n_layers, d_ff, dropout, activation):
        super(Decoder, self).__init__()

        self.layers = nn.ModuleList()
        for i in range(n_layers):
            self.layers.append(DecoderLayer(input_dim, n_heads, d_ff, dropout, activation))

        self.out_seq_len = out_seq_len
        self.linear = nn.Linear(input_dim, out_seq_len)

    def forward(self, enc_out, attn_weights):
        # mask future positions
        mask = torch.triu(torch.ones(self.out_seq_len, self.out_seq_len), diagonal=1)
        mask = mask.unsqueeze(0).bool().to(enc_out.device)

        # self-attention
        x = torch.zeros(enc_out.shape[0], self.out_seq_len, enc_out.shape[-1]).to(enc_out.device)
        for i in range(self.out_seq_len):
            residual = x[:, i, :]
            x[:, i, :], attn_weight = self.layers[i](x[:, :i+1, :], enc_out, mask, attn_weights)
            x[:, i, :] = residual + x[:, i, :]

        # linear
        out = self.linear(x)
        return out

class DecoderLayer(nn.Module):
    def __init__(self, input_dim, n_heads, d_ff, dropout, activation):
        super(DecoderLayer, self).__init__()

        self.self_attn = MultiHeadAttention(n_heads, input_dim, input_dim, dropout)
        self.enc_attn = MultiHeadAttention(n_heads, input_dim, input_dim, dropout)
        self.feed_forward = FeedForward(input_dim, d_ff, activation, dropout)
        self.norm1 = nn.LayerNorm(input_dim)
        self.norm2 = nn.LayerNorm(input_dim)
        self.norm3 = nn.LayerNorm(input_dim)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.dropout3 = nn.Dropout(dropout)

    def forward(self, x, enc_out, mask, attn_weights):
        # self-attention
        residual = x[:, -1, :]
        x[:, -1, :], attn_weight1 = self.self_attn(x[:, -1:, :], x[:, -1:, :], x[:, -1:, :], mask)
        x[:, -1, :] = residual + self.dropout1(x[:, -1, :])

        # encoder-decoder attention
        residual = x[:, -1, :]
        x[:, -1, :], attn_weight2 = self.enc_attn(x[:, -1:, :], enc_out, enc_out)
        x[:, -1, :] = residual + self.dropout2(x[:, -1, :])

        # feed forward
        residual = x[:, -1, :]
        x[:, -1, :] = self.feed_forward(x[:, -1, :])
        x[:, -1, :] = residual + self.dropout3(x[:, -1, :])

        attn_weights.append(torch.cat([attn_weight1, attn_weight2], dim=1))
        return x, attn_weights

class MultiHeadAttention(nn.Module):
    def __init__(self, n_heads, q_dim, k_dim, dropout):
        super(MultiHeadAttention, self).__init__()

        self.n_heads = n_heads
        self.q_dim = q_dim
        self.k_dim = k_dim

        self.query = nn.Linear(q_dim, q_dim * n_heads)
        self.key = nn.Linear(k_dim, k_dim * n_heads)
        self.value = nn.Linear(k_dim, k_dim * n_heads)
        self.out = nn.Linear(k_dim * n_heads, q_dim)
        self.dropout = nn.Dropout(dropout)

    def forward(self, query, key, value, mask=None):
        batch_size = query.shape[0]

        # linear
        query = self.query(query).view(batch_size, -1, self.n_heads, self.q_dim // self.n_heads).transpose(1, 2)
        key = self.key(key).view(batch_size, -1, self.n_heads, self.k_dim // self.n_heads).transpose(1, 2)
        value = self.value(value).view(batch_size, -1, self.n_heads, self.k_dim // self.n_heads).transpose(1, 2)

        # dot product attention
        attn_weight = torch.matmul(query, key.transpose(-2, -1)) / torch.sqrt(torch.tensor(self.k_dim // self.n_heads).float().to(query.device))
        if mask is not None:
            attn_weight = attn_weight.masked_fill(mask == False, -1e9)
        attn_weight = F.softmax(attn_weight, dim=-1)
        attn_weight = self.dropout(attn_weight)

        # linear
        output = torch.matmul(attn_weight, value).transpose(1, 2).contiguous().view(batch_size, -1, self.q_dim)
        output = self.out(output)

        return output, attn_weight

class FeedForward(nn.Module):
    def __init__(self, input_dim, hidden_dim, activation, dropout):
        super(FeedForward, self).__init__()

        self.linear1 = nn.Linear(input_dim, hidden_dim)
        self.linear2 = nn.Linear(hidden_dim, input_dim)
        self.activation = getattr(F, activation)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        x = self.linear1(x)
        x = self.activation(x)
        x = self.dropout(x)
        x = self.linear2(x)
        return x

这里实现了一个简单的Informer模型，包括Encoder、Decoder和MultiHeadAttention等模块。你可以根据具体的任务和数据来调整模型的参数和结构，以获得更好的性能。

informer完整代码

Informer是一种用于时间序列预测的神经网络模型，其主要特点是使用了Transformer架构。以下是Informer的完整代码实现。

首先，我们需要导入所需的库：

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

接下来，我们定义Informer的主体模型类：

class Informer(nn.Module):
    def __init__(self, enc_in, dec_in, c_out=1, seq_len=96, label_len=48, 
                 attn='prob', embed='fixed', freq='h', d_model=512, n_heads=8, e_layers=2, 
                 d_layers=1, d_ff=2048, factor=5, activation='gelu', 
                 dropout=0.05, attn_dropout=0.0, embed_dropout=0.0):
        super(Informer, self).__init__()
        
        # Encoder and Decoder Input Embeddings
        self.embed_in = nn.Linear(enc_in, d_model)
        self.embed_out = nn.Linear(dec_in, d_model)
        
        # Positional Encoding
        self.pos_enc = PositionalEncoding(d_model, seq_len)
        
        # Encoder and Decoder Stacks
        self.encoder = Encoder(d_model, n_heads, e_layers, d_ff, attn, dropout, attn_dropout, activation)
        self.decoder = Decoder(d_model, n_heads, d_layers, d_ff, attn, dropout, attn_dropout, activation, factor)
        
        # Prediction Head
        self.prediction_head = PredictionHead(label_len, c_out, d_model, freq, embed, dropout, embed_dropout)
        
    def forward(self, x_enc, x_dec, x_mask=None, x_dec_mask=None, x_pos=None, x_dec_pos=None):
        # Input Embedding
        enc_inp = self.embed_in(x_enc)
        dec_inp = self.embed_out(x_dec)
        
        # Positional Encoding
        enc_inp = self.pos_enc(enc_inp, x_pos)
        dec_inp = self.pos_enc(dec_inp, x_dec_pos)
        
        # Encoder
        enc_out = self.encoder(enc_inp, x_mask)
        
        # Decoder
        dec_out = self.decoder(dec_inp, enc_out, x_mask, x_dec_mask)
        
        # Prediction Head
        pred = self.prediction_head(dec_out)
        
        return pred

其中，`enc_in`是Encoder输入的维度，`dec_in`是Decoder输入的维度，`c_out`是输出的维度，`seq_len`是序列长度，`label_len`是预测的长度，`attn`是Attention机制的类型，`embed`是Embedding的类型，`freq`是时间序列的采样频率，`d_model`是Transformer中的Hidden Size，`n_heads`是Multi-Head Attention中的Head数，`e_layers`是Encoder中的Encoder Layer数，`d_layers`是Decoder中的Decoder Layer数，`d_ff`是Feed Forward网络的维度，`factor`是Decoder中的Attention Mask的因子，`activation`是激活函数，`dropout`是Dropout概率，`attn_dropout`是Attention Dropout概率，`embed_dropout`是Embedding Dropout概率。

我们还需要定义Positional Encoding的类：

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, seq_len):
        super(PositionalEncoding, self).__init__()
        
        pe = torch.zeros(seq_len, d_model)
        position = torch.arange(0, seq_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0)
        
        self.register_buffer('pe', pe)
        
    def forward(self, x, pos):
        x = x + self.pe[:, pos, :]
        return x

其中，`d_model`是Transformer中的Hidden Size，`seq_len`是序列长度。

接下来，我们定义Encoder和Decoder的类：

class Encoder(nn.Module):
    def __init__(self, d_model, n_heads, e_layers, d_ff, attn='prob', dropout=0.05, attn_dropout=0.0, activation='gelu'):
        super(Encoder, self).__init__()
        
        self.layers = nn.ModuleList([EncoderLayer(d_model, n_heads, d_ff, attn, dropout, attn_dropout, activation) for i in range(e_layers)])
        self.norm = nn.LayerNorm(d_model)
        
    def forward(self, x, mask=None):
        for layer in self.layers:
            x = layer(x, mask)
        x = self.norm(x)
        return x
    
class Decoder(nn.Module):
    def __init__(self, d_model, n_heads, d_layers, d_ff, attn='prob', dropout=0.05, attn_dropout=0.0, activation='gelu', factor=5):
        super(Decoder, self).__init__()
        
        self.layers = nn.ModuleList([DecoderLayer(d_model, n_heads, d_ff, attn, dropout, attn_dropout, activation, factor) for i in range(d_layers)])
        self.norm = nn.LayerNorm(d_model)
        
    def forward(self, x, enc_out, mask=None, dec_mask=None):
        for layer in self.layers:
            x = layer(x, enc_out, mask, dec_mask)
        x = self.norm(x)
        return x

其中，`d_model`是Transformer中的Hidden Size，`n_heads`是Multi-Head Attention中的Head数，`e_layers`是Encoder中的Encoder Layer数，`d_layers`是Decoder中的Decoder Layer数，`d_ff`是Feed Forward网络的维度，`attn`是Attention机制的类型，`dropout`是Dropout概率，`attn_dropout`是Attention Dropout概率，`activation`是激活函数，`factor`是Decoder中的Attention Mask的因子。

接下来，我们定义Encoder Layer和Decoder Layer的类：

class EncoderLayer(nn.Module):
    def __init__(self, d_model, n_heads, d_ff, attn='prob', dropout=0.05, attn_dropout=0.0, activation='gelu'):
        super(EncoderLayer, self).__init__()
        
        self.self_attn = MultiHeadAttention(d_model, n_heads, attn, dropout, attn_dropout)
        self.feed_forward = FeedForward(d_model, d_ff, activation, dropout)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, x, mask=None):
        x = x + self.dropout(self.self_attn(x, x, x, mask))
        x = self.norm1(x)
        x = x + self.dropout(self.feed_forward(x))
        x = self.norm2(x)
        return x
    
class DecoderLayer(nn.Module):
    def __init__(self, d_model, n_heads, d_ff, attn='prob', dropout=0.05, attn_dropout=0.0, activation='gelu', factor=5):
        super(DecoderLayer, self).__init__()
        
        self.self_attn = MultiHeadAttention(d_model, n_heads, attn, dropout, attn_dropout)
        self.enc_dec_attn = MultiHeadAttention(d_model, n_heads, attn, dropout, attn_dropout)
        self.feed_forward = FeedForward(d_model, d_ff, activation, dropout)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.norm3 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)
        self.factor = factor
        
    def forward(self, x, enc_out, mask=None, dec_mask=None):
        x = x + self.dropout(self.self_attn(x, x, x, dec_mask))
        x = self.norm1(x)
        x = x + self.dropout(self.enc_dec_attn(x, enc_out, enc_out, mask))
        x = self.norm2(x)
        x = x + self.dropout(self.feed_forward(x))
        x = self.norm3(x)
        return x

其中，`d_model`是Transformer中的Hidden Size，`n_heads`是Multi-Head Attention中的Head数，`d_ff`是Feed Forward网络的维度，`attn`是Attention机制的类型，`dropout`是Dropout概率，`attn_dropout`是Attention Dropout概率，`activation`是激活函数，`factor`是Decoder中的Attention Mask的因子。

接下来，我们定义Multi-Head Attention、Feed Forward和Prediction Head的类：

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, n_heads, attn='prob', dropout=0.05, attn_dropout=0.0):
        super(MultiHeadAttention, self).__init__()
        
        self.n_heads = n_heads
        self.d_head = d_model // n_heads
        
        self.qkv = nn.Linear(d_model, 3*d_model)
        self.attn = Attention(attn, dropout, attn_dropout)
        self.proj = nn.Linear(d_model, d_model)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, query, key, value, mask=None):
        batch_size = query.size(0)
        qkv = self.qkv(query).view(batch_size, -1, self.n_heads, self.d_head*3).transpose(1, 2)
        q, k, v = qkv.chunk(3, dim=-1)
        q = q.view(batch_size*self.n_heads, -1, self.d_head)
        k = k.view(batch_size*self.n_heads, -1, self.d_head)
        v = v.view(batch_size*self.n_heads, -1, self.d_head)
        if mask is not None:
            mask = mask.unsqueeze(1).repeat(1, self.n_heads, 1, 1).view(batch_size*self.n_heads, 1, -1, query.size(-2))
        out = self.attn(q, k, v, mask)
        out = out.view(batch_size, self.n_heads, -1, self.d_head).transpose(1, 2).contiguous().view(batch_size, -1, self.n_heads*self.d_head)
        out = self.proj(out)
        out = self.dropout(out)
        return out
    
class Attention(nn.Module):
    def __init__(self, attn='prob', dropout=0.05, attn_dropout=0.0):
        super(Attention, self).__init__()
        
        self.attn = attn
        self.dropout = nn.Dropout(attn_dropout)
        
        if self.attn == 'prob':
            self.softmax = nn.Softmax(dim=-1)
        elif self.attn == 'full':
            self.softmax = nn.Softmax(dim=-1)
            
    def forward(self, q, k, v, mask=None):
        attn = torch.matmul(q, k.transpose(-2, -1))
        if mask is not None:
            attn = attn.masked_fill(mask == 0, -1e9)
        attn = self.softmax(attn)
        attn = self.dropout(attn)
        out = torch.matmul(attn, v)
        return out
    
class FeedForward(nn.Module):
    def __init__(self, d_model, d_ff, activation='gelu', dropout=0.05):
        super(FeedForward, self).__init__()
        
        self.linear1 = nn.Linear(d_model, d_ff)
        self.activation = getattr(nn, activation)()
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(d_ff, d_model)
        
    def forward(self, x):
        x = self.linear1(x)
        x = self.activation(x)
        x = self.dropout(x)
        x = self.linear2(x)
        return x
    
class PredictionHead(nn.Module):
    def __init__(self, label_len, c_out, d_model, freq='h', embed='fixed', dropout=0.05, embed_dropout=0.0):
        super(PredictionHead, self).__init__()
        
        self.label_len = label_len
        self.c_out = c_out
        self.freq = freq
        
        if embed == 'fixed':
            self.embed = nn.Linear(1, d_model)
        elif embed == 'learned':
            self.embed = nn.Parameter(torch.randn(label_len, d_model))
        self.dropout = nn.Dropout(embed_dropout)
        self.proj = nn.Linear(d_model, c_out)
        
    def forward(self, x):
        x = x[:, -self.label_len:, :]
        if self.freq == 'h':
            x = x[:, ::int(24/self.label_len), :]
        if hasattr(self, 'embed'):
            x = self.embed(x)
        x = self.dropout(x)
        x = self.proj(x)
        return x

其中，`d_model`是Transformer中的Hidden Size，`n_heads`是Multi-Head Attention中的Head数，`attn`是Attention机制的类型，`dropout`是Dropout概率，`attn_dropout`是Attention Dropout概率，`activation`是激活函数，`label_len`是预测的长度，`c_out`是输出的维度，`freq`是时间序列的采样频率，`embed`是Embedding的类型，`embed_dropout`是Embedding Dropout概率。

现在，我们已经定义了Informer的完整代码实现。