| | |
|---|
| | | self.n_units = n_units |
|---|
| | | |
|---|
| | | def forward_core(self, xs, zeros): |
|---|
| | | ilens = torch.from_numpy(np.array([x.shape[0] for x in xs])).to(torch.float32).to(xs[0].device) |
|---|
| | | ilens = torch.from_numpy(np.array([x.shape[0] for x in xs])).to(torch.int64) |
|---|
| | | xs = [self.enc0_dropout(x) for x in xs] |
|---|
| | | xs = nn.utils.rnn.pad_sequence(xs, batch_first=True, padding_value=-1) |
|---|
| | | xs = nn.utils.rnn.pack_padded_sequence(xs, ilens, batch_first=True, enforce_sorted=False) |
|---|
| | | _, (hx, cx) = self.encoder(xs) |
|---|
| | | zlens = torch.from_numpy(np.array([z.shape[0] for z in zeros])).to(torch.float32).to(zeros[0].device) |
|---|
| | | zlens = torch.from_numpy(np.array([z.shape[0] for z in zeros])).to(torch.int64) |
|---|
| | | max_zlen = torch.max(zlens).to(torch.int).item() |
|---|
| | | zeros = [self.enc0_dropout(z) for z in zeros] |
|---|
| | | zeros = nn.utils.rnn.pad_sequence(zeros, batch_first=True, padding_value=-1) |
|---|
| | |
|---|
| | | zeros = [torch.zeros(max_n_speakers, self.n_units).to(torch.float32).to(xs[0].device) for _ in xs] |
|---|
| | | attractors = self.forward_core(xs, zeros) |
|---|
| | | probs = [torch.sigmoid(torch.flatten(self.counter(att))) for att in attractors] |
|---|
| | | return attractors, probs |
|---|
| | | return attractors, probs |
|---|
