def forward(self, input): # input has shape [batch_size, num_features, time] # discriminator requires shape [batchSize, 1, num_features, time] input = input.unsqueeze(1) # print("Discriminator forward input: ", input.shape) conv_layer_1 = self.convLayer1(input) # print("Discriminator forward conv_layer_1: ", conv_layer_1.shape) downsample1 = self.downSample1(conv_layer_1) # print("Discriminator forward downsample1: ", downsample1.shape) downsample2 = self.downSample2(downsample1) # print("Discriminator forward downsample2: ", downsample2.shape) downsample3 = self.downSample3(downsample2) # print("Discriminator forward downsample3: ", downsample3.shape) # downsample3 = downsample3.contiguous().permute(0, 2, 3, 1).contiguous() # print("Discriminator forward downsample3: ", downsample3.shape) output = torch.sigmoid(self.outputConvLayer(downsample3)) # print("Discriminator forward output: ", output.shape) return output

这段代码是一个神经网络模型的前向传播函数实现，该模型是一个鉴别器（discriminator），用于判断输入的数据是否真实（real）或假的（fake）。输入数据的形状是 [batch_size, num_features, time]，其中 batch_size 表示输入的数据有多少个样本，num_features 表示每个样本的特征维度，time 表示输入数据的时间步数。为了符合鉴别器的输入要求，需要将输入数据的形状变为 [batchSize, 1, num_features, time]，这可以通过 unsqueeze 函数实现。接下来，通过卷积层和下采样层对数据进行特征提取和降维处理，最后通过输出层得到判断结果。这里使用了 sigmoid 函数将输出值压缩到 [0,1] 范围内，表示输出结果为真实样本的概率。