Class Encoder

model.py:251–281

        return x + self.pe[:, :, 0:x.shape[2]]

class Encoder(nn.Module):
    def __init__(self, num_layers, r1, r2, num_f_maps, input_dim, num_classes, channel_masking_rate, att_type, alpha):
        super(Encoder, self).__init__()
        self.conv_1x1 = nn.Conv1d(input_dim, num_f_maps, 1) # fc layer
        self.layers = nn.ModuleList(
            [AttModule(2 ** i, num_f_maps, num_f_maps, r1, r2, att_type, 'encoder', alpha) for i in # 2**i
             range(num_layers)])
        
        self.conv_out = nn.Conv1d(num_f_maps, num_classes, 1)
        self.dropout = nn.Dropout2d(p=channel_masking_rate)
        self.channel_masking_rate = channel_masking_rate

    def forward(self, x, mask):
        '''
        :param x: (N, C, L)
        :param mask:
        :return:
        '''

        if self.channel_masking_rate > 0:
            x = x.unsqueeze(2)
            x = self.dropout(x)
            x = x.squeeze(2)

        feature = self.conv_1x1(x)
        for layer in self.layers:
            feature = layer(feature, None, mask)
        
        out = self.conv_out(feature) * mask[:, 0:1, :]

        return out, feature


class Decoder(nn.Module):