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Runtime error
| from torch import nn | |
| from .constants import * | |
| from .deepunet import DeepUnet0 | |
| from .seq import BiGRU | |
| class E2E0(nn.Module): | |
| def __init__(self, n_blocks, n_gru, kernel_size, en_de_layers=5, inter_layers=4, in_channels=1, | |
| en_out_channels=16): | |
| super(E2E0, self).__init__() | |
| self.unet = DeepUnet0(kernel_size, n_blocks, en_de_layers, inter_layers, in_channels, en_out_channels) | |
| self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1)) | |
| if n_gru: | |
| self.fc = nn.Sequential( | |
| BiGRU(3 * N_MELS, 256, n_gru), | |
| nn.Linear(512, N_CLASS), | |
| nn.Dropout(0.25), | |
| nn.Sigmoid() | |
| ) | |
| else: | |
| self.fc = nn.Sequential( | |
| nn.Linear(3 * N_MELS, N_CLASS), | |
| nn.Dropout(0.25), | |
| nn.Sigmoid() | |
| ) | |
| def forward(self, mel): | |
| mel = mel.transpose(-1, -2).unsqueeze(1) | |
| x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2) | |
| x = self.fc(x) | |
| return x | |