| import torch | |
| import torch.nn as nn | |
| from feature_extractor import FeatureExtractor | |
| from encoder import Encoder | |
| from decoder import Decoder | |
| class SELDModel(nn.Module): | |
| def __init__(self, | |
| input_ch=2, | |
| n_fft=1024, | |
| hop_length=512, | |
| num_classes=10): | |
| super(SELDModel, self).__init__() | |
| self.feature_extractor = FeatureExtractor(n_fft=n_fft, hop_length=hop_length) | |
| self.encoder = Encoder(input_channels=self.feature_extractor.input_ch*2) | |
| self.decoder = Decoder(input_features=self.encoder.output_features, num_classes=num_classes) | |
| def forward(self, x): | |
| """ | |
| x: (batch, channels=2, time) | |
| returns: | |
| - sed_output: (batch, time, num_classes) | |
| - doa_output: (batch, time, num_classes*3) | |
| """ | |
| features = self.feature_extractor(x) # (batch, time, freq, ch) | |
| encoded = self.encoder(features) # (batch, encoder_output_size) | |
| sed_output, doa_output = self.decoder(encoded) | |
| return sed_output, doa_output | |