def attention_sublayers(self, feats, embedding_layers, latent): feats = feats.view((feats.size(0), self.k, -1)) feats = feats.transpose(dim0=1, dim1=2) feats = feats + latent.unsqueeze(1) feats = feats.transpose(dim0=1, dim1=2) feats = embedding_layers(feats).squeeze(-1) p = F.softmax(feats, dim=1) return p def forward(self, x): conv2_2 = self.conv2_2(x) conv3_4 = self.conv3_4(conv2_2) conv4_4 = self.conv4_4(conv3_4) conv5_4 = self.conv5_4(conv4_4) x = F.relu(self.fc4(self.fc_layers(self.tail_layer(conv5_4).view(-1, 25088)))) attr = self.bn1(x[:, :self.k]) latent = self.bn2(x[:, self.k:]) feats_0 = self.extract_0(conv2_2) feats_1 = self.extract_1(conv3_4) feats_2 = self.extract_2(conv4_4) feats_3 = self.extract_3(conv5_4) # N x k x 14 x 14 p_0 = self.attention_sublayers(feats_0, self.fc0, latent) p_1 = self.attention_sublayers(feats_1, self.fc1, latent) p_2 = self.attention_sublayers(feats_2, self.fc2, latent) p_3 = self.attention_sublayers(feats_3, self.fc3, latent) # N x k p = p_0 + p_1 + p_2 + p_3代码中的各个部分的功能是什么

get_feats:vgg16获得壮举

splice-feats解析1

Tencent_Social_Ads2018:2018腾讯社交广告33名；

bag_feats = ['kw1','kw2','kw3','topic1','topic2','topic3', 'appIdAction','appIdInstall', 'interest1', 'interest2', 'interest3', 'interest4', 'interest5'] bag_value_cnts =[5, 5, 5, 5, 5, 5, 50,...

class GraphSAGE(nn.Module): def init(self, in_feats, hidden_feats, out_feats, num_layers, activation): super(GraphSAGE, self).init() self.num_layers = num_layers self.conv1 = SAGEConv(in_feats, hidden_feats, aggregator_type='mean') self.convs = nn.ModuleList() for i in range(num_layers - 2): self.convs.append(SAGEConv(hidden_feats, hidden_feats, aggregator_type='mean')) self.conv_last = SAGEConv(hidden_feats, out_feats, aggregator_type='mean') self.activation = activation def forward(self, blocks, x): h = x for i, block in enumerate(blocks): h_dst = h[:block.number_of_dst_nodes()] h = self.convs[i](block, (h, h_dst)) if i != self.num_layers - 2: h = self.activation(h) h = self.conv_last(blocks[-1], (h, h_dst)) return h改写一下，让它适用于异质图

def __init__(self, in_feats, hidden_feats, out_feats, num_layers, activation): super(GraphSAGE, self).__init__() self.num_layers = num_layers self.conv1 = SAGEConv(in_feats, hidden_feats, ...

class MemoryEncoding(nn.Module): def init(self, in_feats, out_feats, mem_size): #调用 super() 函数来初始化基类 nn.Module，并将输入参数保存为类的属性。 super(MemoryEncoding, self).init() self.in_feats = in_feats self.out_feats = out_feats self.mem_size = mem_size self.linear_coef = nn.Linear(in_feats, mem_size, bias=True) self.act = nn.LeakyReLU(0.2, inplace=True) self.linear_w = nn.Linear(mem_size, out_feats * in_feats, bias=False) def get_weight(self, x): coef = self.linear_coef(x) if self.act is not None: coef = self.act(coef) w = self.linear_w(coef) w = w.view(-1, self.out_feats, self.in_feats) return w def forward(self, h_dst, h_src): w = self.get_weight(h_dst) res = torch.einsum('boi, bi -> bo', w, h_src) return res 举个例子

memory_encoding = MemoryEncoding(in_feats, out_feats, mem_size) # 创建示例输入张量 batch_size = 2 dst_feats = in_feats src_feats = in_feats h_dst = torch.randn(batch_size, dst_feats) h_src = torch....

def init(self, n_feats, ratio=2): super(RRRB, self).init()

构造函数的参数有两个，n_feats和ratio，默认值为2。在构造函数中没有其他具体的逻辑代码。这个构造函数使用了super函数来调用父类的构造函数，并将n_feats作为参数传递给父类的构造函数。这样做是为了保证在创建...

def forward(self, batch_graph): node_feats = batch_graph.ndata.pop('h') node_feats = self.init_transform(node_feats) node_feats = self.gnn(batch_graph, node_feats) batch_size = batch_graph.batch_size node_feats = node_feats.view(batch_size, -1, self.output_feats) return node_feats什么意思

这是一个PyTorch中的神经网络模型的前向传播函数，输入参数是一个批量的图数据，其中包含节点特征。函数首先从图数据中提取节点特征，然后通过一个初始化变换和一个图神经网络模型对节点特征进行处理。...

class GNNLayer(nn.Module): def init(self, in_feats, out_feats, mem_size, num_rels, bias=True, activation=None, self_loop=True, dropout=0.0, layer_norm=False): super(GNNLayer, self).init() self.in_feats = in_feats self.out_feats = out_feats self.mem_size = mem_size self.num_rels = num_rels self.bias = bias self.activation = activation self.self_loop = self_loop self.layer_norm = layer_norm self.node_ME = MemoryEncoding(in_feats, out_feats, mem_size) self.rel_ME = nn.ModuleList([ MemoryEncoding(in_feats, out_feats, mem_size) for i in range(self.num_rels) ]) if self.bias: self.h_bias = nn.Parameter(torch.empty(out_feats)) nn.init.zeros_(self.h_bias) if self.layer_norm: self.layer_norm_weight = nn.LayerNorm(out_feats) self.dropout = nn.Dropout(dropout)

- self_loop：是否使用自环（self-loop）边。 - dropout：Dropout 的概率。 - layer_norm：是否使用层归一化（layer normalization）。接下来，具体说明 GNNLayer 类的初始化过程： - 调用 super() ...

self.mem_size = mem_size self.linear_coef = nn.Linear(in_feats, mem_size, bias=True) self.act = nn.LeakyReLU(0.2, inplace=True) self.linear_w = nn.Linear(mem_size, out_feats * in_feats, bias=False)

在这段代码中，self.mem_size = mem_size 将输入的 mem_size 参数赋值给模块的 mem_size 属性。 self.linear_coef = nn.Linear(in_feats, mem_size, bias=True) 创建了一个线性层 self.linear_coef，将...

class MolecularGCN(nn.Module): def init(self, in_feats, dim_embedding=128, padding=True, hidden_feats=None, activation=None): super(MolecularGCN, self).init() self.init_transform = nn.Linear(in_feats, dim_embedding, bias=False)是什么

它的输入特征维度为 in_feats，输出特征维度为 dim_embedding，可以选择是否进行 padding，可以设置隐藏层特征维度和激活函数。其中，init_transform 是一个线性变换层，用于将输入特征转换为指定维度的特征表示。

class ARMA4NC(nn.Module): def init(self, in_dim, hid_dim, out_dim, num_stacks, num_layers, activation=None, dropout=0.0): super(ARMA4NC, self).init() self.conv1 = ARMAConv(in_dim=in_dim, out_dim=hid_dim, num_stacks=num_stacks, num_layers=num_layers, activation=activation, dropout=dropout) self.conv2 = ARMAConv(in_dim=hid_dim, out_dim=out_dim, num_stacks=num_stacks, num_layers=num_layers, activation=activation, dropout=dropout) self.dropout = nn.Dropout(p=dropout) def forward(self, g, feats): feats = F.relu(self.conv1(g, feats)) feats = self.dropout(feats) feats = self.conv2(g, feats) return feats。写出对这个进行训练的代码

print('Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(g), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) 在此示例中，train_dataset ...

class MLPs(nn.Module): def init(self, W_sizes_ope, hidden_size_ope, out_size_ope, num_head, dropout): super(MLPs, self).init() self.in_sizes_ope = W_sizes_ope self.hidden_size_ope = hidden_size_ope self.out_size_ope = out_size_ope self.num_head = num_head self.dropout = dropout self.gnn_layers = nn.ModuleList() for i in range(len(self.in_sizes_ope)): self.gnn_layers.append(MLPsim(self.in_sizes_ope[i],self.out_size_ope, self.hidden_size_ope, self.num_head, self.dropout, self.dropout)) self.project = nn.Sequential( nn.ELU(), nn.Linear(self.out_size_ope * len(self.in_sizes_ope), self.hidden_size_ope), nn.ELU(), nn.Linear(self.hidden_size_ope, self.hidden_size_ope), nn.ELU(), nn.Linear(self.hidden_size_ope, self.out_size_ope), ) def forward(self, ope_ma_adj_batch, ope_pre_adj_batch, ope_sub_adj_batch, batch_idxes, feats): h = (feats[1], feats[0], feats[0], feats[0]) self_adj = torch.eye(feats[0].size(-2),dtype=torch.int64).unsqueeze(0).expand_as(ope_pre_adj_batch[batch_idxes]) adj = (ope_ma_adj_batch[batch_idxes], ope_pre_adj_batch[batch_idxes], ope_sub_adj_batch[batch_idxes], self_adj) MLP_embeddings = [] for i in range(len(adj)): MLP_embeddings.append(self.gnn_layers[i](h[i], adj[i])) MLP_embedding_in = torch.cat(MLP_embeddings, dim=-1) mu_ij_prime = self.project(MLP_embedding_in) return mu_ij_prime

它包含了两个主要的部分：gnn_layers 和 project。 gnn_layers 是一个 nn.ModuleList，其中包含了多个 MLPsim 模块，每个 MLPsim 模块都对应一个输入张量，用于对输入进行处理。MLPsim 模块的定义可能在其他地方，...

class PositionalEncoding(nn.Module): def init(self, num_pos_feats_x=64, num_pos_feats_y=64, num_pos_feats_z=128, temperature=10000, normalize=True, scale=None):、

其中，num_pos_feats_x、num_pos_feats_y和num_pos_feats_z分别表示在x、y和z方向上的位置特征数量；temperature是位置编码中的温度参数；normalize表示是否对位置编码进行归一化；scale表示位置编码的缩放因子。

nn.Linear(in_feats, dim_embedding, bias=False)举例说明

其中，in_feats 表示输入特征向量的维度，dim_embedding 表示嵌入空间的维度，bias=False 表示不使用偏置项。举个例子，如果输入特征向量的维度为 100，嵌入空间的维度为 50，那么这个模块就会将输入的 100 维特征...

nn.Linear(in_feats, dim_embedding, bias=False)怎么理解

这是一个 PyTorch 中的神经网络层，它将输入的特征向量进行线性变换，将其映射到一个指定维度的嵌入空间...其中，in_feats 表示输入特征向量的维度，dim_embedding 表示嵌入空间的维度，bias=False 表示不使用偏置项。

class PointnetFPModule(nn.Module): r"""Propigates the features of one set to another""" def init(self, , mlp: List[int], bn: bool = True): """ :param mlp: list of int :param bn: whether to use batchnorm """ super().init() self.mlp = pt_utils.SharedMLP(mlp, bn=bn) def forward( self, unknown: torch.Tensor, known: torch.Tensor, unknow_feats: torch.Tensor, known_feats: torch.Tensor ) -> torch.Tensor: """ :param unknown: (B, n, 3) tensor of the xyz positions of the unknown features :param known: (B, m, 3) tensor of the xyz positions of the known features :param unknow_feats: (B, C1, n) tensor of the features to be propigated to :param known_feats: (B, C2, m) tensor of features to be propigated :return: new_features: (B, mlp[-1], n) tensor of the features of the unknown features """ if known is not None: dist, idx = pointnet2_utils.three_nn(unknown, known) dist_recip = 1.0 / (dist + 1e-8) norm = torch.sum(dist_recip, dim=2, keepdim=True) weight = dist_recip / norm interpolated_feats = pointnet2_utils.three_interpolate(known_feats, idx, weight) else: interpolated_feats = known_feats.expand(known_feats.size()[0:2], unknown.size(1)) if unknow_feats is not None: new_features = torch.cat([interpolated_feats, unknow_feats], dim=1) # (B, C2 + C1, n) else: new_features = interpolated_feats new_features = new_features.unsqueeze(-1) new_features = self.mlp(new_features) return new_features.squeeze(-1)你可以为我详细讲解一下这个代码吗？

其中，B 表示 batch size，n 表示未知点云的点数，m 表示已知点云的点数，C1 和 C2 分别表示未知点云和已知点云的特征维度。接下来的代码实现主要目的是将未知点云的特征传递给已知点云。具体步骤如下：...

def getitem(self, index): while(self.index+self.TreePoint>self.datalen): filename = self.dataNames[self.fileIndx] # print(filename) if self.dataBuffer: a = [self.dataBuffer[0][self.index:].copy()] else: a=[] cell,mat = self.loader(filename) for i in range(cell.shape[1]): data = np.transpose(mat[cell[0,i]]) #shape[ptNum,Kparent, Seq[1],Level[1],Octant[1],Pos[3] ] e.g 12345676 data[:,:,0] = data[:,:,0] - 1 a.append(data[:,-levelNumK:,:])# only take levelNumK level feats self.dataBuffer = [] self.dataBuffer.append(np.vstack(tuple(a))) self.datalen = self.dataBuffer[0].shape[0] self.fileIndx+=1 # shuffle step = 1, will load continuous mat self.index = 0 if(self.fileIndx>=self.fileLen): self.fileIndx=index%self.fileLen # try read img = [] img.append(self.dataBuffer[0][self.index:self.index+self.TreePoint]) self.index+=self.TreePoint if self.transform is not None: img = self.transform(img) return img

9. 将索引 self.index 重置为0。 10. 如果文件索引 self.fileIndx 超过了文件数量 self.fileLen，则将文件索引设置为 index 对文件数量取模的结果，以确保循环使用文件。 11. 尝试读取数据缓冲区中从 ...

解释下这段代码 def transcribe(self, wav_file): """语音转文本的推理调用接口""" feats_pad, feats_lengths = self.preprocess(wav_file) output = self.model.infer([feats_pad, feats_lengths]) txt = self.post_process(output) return txt

这段代码定义了一个名为transcribe的方法，接受一个WAV文件作为输入，将其转换为文本。具体流程如下： 1. preprocess方法被调用，将WAV文件转换为模型可接受的输入格式，即对音频进行特征提取并进行填充，返回...

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get_feats:vgg16获得壮举

splice-feats解析1

Tencent_Social_Ads2018:2018腾讯社交广告33名；

def __init__(self, n_feats, ratio=2): super(RRRB, self).__init__()

def forward(self, batch_graph): node_feats = batch_graph.ndata.pop('h') node_feats = self.init_transform(node_feats) node_feats = self.gnn(batch_graph, node_feats) batch_size = batch_graph.batch_size node_feats = node_feats.view(batch_size, -1, self.output_feats) return node_feats什么意思

self.mem_size = mem_size self.linear_coef = nn.Linear(in_feats, mem_size, bias=True) self.act = nn.LeakyReLU(0.2, inplace=True) self.linear_w = nn.Linear(mem_size, out_feats * in_feats, bias=False)

class MolecularGCN(nn.Module): def __init__(self, in_feats, dim_embedding=128, padding=True, hidden_feats=None, activation=None): super(MolecularGCN, self).__init__() self.init_transform = nn.Linear(in_feats, dim_embedding, bias=False)是什么

class PositionalEncoding(nn.Module): def __init__(self, num_pos_feats_x=64, num_pos_feats_y=64, num_pos_feats_z=128, temperature=10000, normalize=True, scale=None):、

nn.Linear(in_feats, dim_embedding, bias=False)举例说明

nn.Linear(in_feats, dim_embedding, bias=False)怎么理解

解释下这段代码 def transcribe(self, wav_file): """语音转文本的推理调用接口""" feats_pad, feats_lengths = self.preprocess(wav_file) output = self.model.infer([feats_pad, feats_lengths]) txt = self.post_process(output) return txt

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def init(self, n_feats, ratio=2): super(RRRB, self).init()

class MolecularGCN(nn.Module): def init(self, in_feats, dim_embedding=128, padding=True, hidden_feats=None, activation=None): super(MolecularGCN, self).init() self.init_transform = nn.Linear(in_feats, dim_embedding, bias=False)是什么

class PositionalEncoding(nn.Module): def init(self, num_pos_feats_x=64, num_pos_feats_y=64, num_pos_feats_z=128, temperature=10000, normalize=True, scale=None):、