class STHSL(nn.Module): def __init__(self): super(STHSL, self).__init__() self.dimConv_in = nn.Conv3d(1, args.latdim, kernel_size=1, padding=0, bias=True) self.dimConv_local = nn.Conv2d(args.latdim, 1, kernel_size=1, padding=0, bias=True) self.dimConv_global = nn.Conv2d(args.latdim, 1, kernel_size=1, padding=0, bias=True) self.spa_cnn_local1 = spa_cnn_local(args.latdim, args.latdim) self.spa_cnn_local2 = spa_cnn_local(args.latdim, args.latdim) self.tem_cnn_local1 = tem_cnn_local(args.latdim, args.latdim) self.tem_cnn_local2 = tem_cnn_local(args.latdim, args.latdim) self.Hypergraph_Infomax = Hypergraph_Infomax() self.tem_cnn_global1 = tem_cnn_global(args.latdim, args.latdim, 9) self.tem_cnn_global2 = tem_cnn_global(args.latdim, args.latdim, 9) self.tem_cnn_global3 = tem_cnn_global(args.latdim, args.latdim, 9) self.tem_cnn_global4 = tem_cnn_global(args.latdim, args.latdim, 6) self.local_tra = Transform_3d() self.global_tra = Transform_3d() def forward(self, embeds_true, neg): embeds_in_global = self.dimConv_in(embeds_true.unsqueeze(1)) DGI_neg = self.dimConv_in(neg.unsqueeze(1)) embeds_in_local = embeds_in_global.permute(0, 3, 1, 2, 4).contiguous().view(-1, args.latdim, args.row, args.col, 4) spa_local1 = self.spa_cnn_local1(embeds_in_local) spa_local2 = self.spa_cnn_local2(spa_local1) spa_local2 = spa_local2.view(-1, args.temporalRange, args.latdim, args.areaNum, args.cateNum).permute(0, 2, 3, 1, 4) tem_local1 = self.tem_cnn_local1(spa_local2) tem_local2 = self.tem_cnn_local2(tem_local1) eb_local = tem_local2.mean(3) eb_tra_local = self.local_tra(tem_local2) out_local = self.dimConv_local(eb_local).squeeze(1) hy_embeds, Infomax_pred = self.Hypergraph_Infomax(embeds_in_global, DGI_neg) tem_global1 = self.tem_cnn_global1(hy_embeds) tem_global2 = self.tem_cnn_global2(tem_global1) tem_global3 = self.tem_cnn_global3(tem_global2) tem_global4 = self.tem_cnn_global4(tem_global3) eb_global = tem_global4.squeeze(3) eb_tra_global = self.global_tra(tem_global4) out_global = self.dimConv_global(eb_global).squeeze(1) return out_local, eb_tra_local, eb_tra_global, Infomax_pred, out_global

class A_cSE(nn.Module): def init(self, in_ch): super(A_cSE, self).init()中super(A_cSE, self).init()什么意思

cSE 继承了 nn.Module 类，super(A_cSE, self) 表示调用父类（即 nn.Module）的初始化方法，__init__() 是 nn.Module 类的初始化方法，因此 super(A_cSE, self).__init__() 就是调用父类 nn.Module ...

class Block1(nn.Module): def init(self): super(Block1, self).init() self.block = self.build_block()

这是一个 PyTorch 中的自定义模块类 Block1，它继承了 nn.Module 类。在初始化方法中，它调用了父类的初始化方法，并定义了一个名为 block 的成员变量，它是通过调用 build_block 方法构建而来的。build_block 方法...

class LPRNet(nn.Module): def init(self, lpr_max_len, phase, class_num, dropout_rate): super(LPRNet, self).init() self.phase = phase self.lpr_max_len = lpr_max_len self.class_num = class_num

这是一个 Python 代码段，定义了一个名为 LPRNet 的类，它是 nn.Module 的子类。它有四个参数：lpr_max_len，phase，class_num 和 dropout_rate。其中，lpr_max_len 是车牌号码的最大长度，class_num 是车牌字符的...

class A_cSE(nn.Module): def init(self, in_ch): super(A_cSE, self).init()

这是一个 PyTorch 模型的定义，其中 A_cSE 是一个继承了 nn.Module 的类。in_ch 是输入通道数。在 __init__ 方法中，首先调用父类的构造函数初始化模型，并且定义了一些模型参数和模块。这个模型可能是一...

class R_NN(nn.Module): def init(self, in_dim, n_class): super(R_NN, self).init() self.logstic = nn.Linear(in_dim, n_class) self.predict=nn.Sigmoid() def forward(self, x): out = self.logstic(x) out = out.squeeze(1) #降维 return out

在初始化函数中，调用了super()函数来获得父类nn.Module的属性和方法，然后定义了一个线性层（logstic）和一个Sigmoid层（predict）。在前向传播函数forward()中，对输入数据x进行线性变换，再使用squeeze()函数将一...

class NormedLinear(nn.Module): def init(self, feat_dim, num_classes): super().init() self.weight = nn.Parameter(torch.Tensor(feat_dim, num_classes)) self.weight.data.uniform_(-1, 1).renorm_(2, 1, 1e-5).mul_(1e5) def forward(self, x): return F.normalize(x, dim=1).mm(F.normalize(self.weight, dim=0)) class LearnableWeightScalingLinear(nn.Module): def init(self, feat_dim, num_classes, use_norm=False): super().init() self.classifier = NormedLinear(feat_dim, num_classes) if use_norm else nn.Linear(feat_dim, num_classes) self.learned_norm = nn.Parameter(torch.ones(1, num_classes)) def forward(self, x): return self.classifier(x) * self.learned_norm class DisAlignLinear(nn.Module): def init(self, feat_dim, num_classes, use_norm=False): super().init() self.classifier = NormedLinear(feat_dim, num_classes) if use_norm else nn.Linear(feat_dim, num_classes) self.learned_magnitude = nn.Parameter(torch.ones(1, num_classes)) self.learned_margin = nn.Parameter(torch.zeros(1, num_classes)) self.confidence_layer = nn.Linear(feat_dim, 1) torch.nn.init.constant_(self.confidence_layer.weight, 0.1) def forward(self, x): output = self.classifier(x) confidence = self.confidence_layer(x).sigmoid() return (1 + confidence * self.learned_magnitude) * output + confidence * self.learned_margin class MLP_ConClassfier(nn.Module): def init(self): super(MLP_ConClassfier, self).init() self.num_inputs, self.num_hiddens_1, self.num_hiddens_2, self.num_hiddens_3, self.num_outputs \ = 41, 512, 128, 32, 5 self.num_proj_hidden = 32 self.mlp_conclassfier = nn.Sequential( nn.Linear(self.num_inputs, self.num_hiddens_1), nn.ReLU(), nn.Linear(self.num_hiddens_1, self.num_hiddens_2), nn.ReLU(), nn.Linear(self.num_hiddens_2, self.num_hiddens_3), ) self.fc1 = torch.nn.Linear(self.num_hiddens_3, self.num_proj_hidden) self.fc2 = torch.nn.Linear(self.num_proj_hidden, self.num_hiddens_3) self.linearclassfier = nn.Linear(self.num_hiddens_3, self.num_outputs) self.NormedLinearclassfier = NormedLinear(feat_dim=self.num_hiddens_3, num_classes=self.num_outputs) self.DisAlignLinearclassfier = DisAlignLinear(feat_dim=self.num_hiddens_3, num_classes=self.num_outputs, use_norm=True) self.LearnableWeightScalingLinearclassfier = LearnableWeightScalingLinear(feat_dim=self.num_hiddens_3, num_classes=self.num_outputs, use_norm=True)

这段代码定义了一个名为MLP_ConClassfier的神经网络模型，它包含了多个子模块，包括三个不同的分类器：NormedLinearclassfier、DisAlignLinearclassfier和LearnableWeightScalingLinearclassfier。...

class Chomp1d(nn.Module): def init(self, chomp_size): super(Chomp1d, self).init() self.chomp_size = chomp_size

这段代码定义了一个名为 "Chomp1d" 的类，该类继承了 PyTorch 中的 nn.Module 类。该类的构造函数 "__init__" 接受一个 "chomp_size" 参数，并将其保存在 "self.chomp_size" 变量中。 "Chomp" 是一种用于调整卷...

class Identity(nn.Module): def init(self): super().init() def forward(self, x): return x是什么意思

1. Identity 类继承自 nn.Module，表示这是一个 PyTorch 模块。 2. 在 __init__ 方法中，调用父类的 __init__ 方法进行初始化。 3. forward 方法定义了模块的前向传播逻辑。它接收一个输入张量 x，并...

class View(nn.Module): def init(self, shape): super().init() self.shape = shape, def forward(self, x): return x.view(*self.shape)

在 __init__ 方法中，它继承了 nn.Module 类，并且定义了一个 shape 属性。在 forward 方法中，它对输入的 x 应用了 view 方法，并将 shape 属性作为参数传入。这个模型的作用是将输入的张量的形状调整为 shape 属性...

class LinearModel(torch.nn.Module): def init(self): super(LinearModel, self).init() def forward(self, x): y_pred = self.linear(x) return y_pred

在__init__方法中，你首先调用了父类的构造函数super(LinearModel, self).__init__()来初始化继承自torch.nn.Module的基类。接下来，你可以在这个方法中定义模型的结构和参数。在forward方法中，你通过...

import torch from torch import nn class MLP(nn.Module): def init(self, input_dim, num_class, hidden_dim) -> None: super().init() self.hidden_dim = hidden_dim self.mlp = nn.Sequential(*[ nn.Linear(input_dim, self.hidden_dim), nn.ReLU(), nn.Linear(self.hidden_dim, self.hidden_dim), nn.ReLU(), nn.Linear(self.hidden_dim, self.hidden_dim), nn.ReLU(), nn.Linear(self.hidden_dim, num_class) ]) def forward(self, x): return self.mlp(x)

其中，nn.Linear 表示全连接层，nn.ReLU 表示激活函数 ReLU。forward() 方法定义了模型的前向传播过程，即输入特征经过多个全连接层和激活函数后得到输出。在模型训练时，可以通过调用 forward() 方法来计算模型的...

class Net1d(nn.Module): def init(self, modes, width): super(Net1d, self).init()

这是一个用 PyTorch 构建神经网络的代码，定义了一个名为 Net1d 的类，继承自 nn.Module 类。该类包含两个参数：modes 和 width，分别表示该网络输入数据的模式和宽度。在 __init__ 方法中，首先调用父类的构造...

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