def _get_encoded_h(self, xt, BS):
# Compute encoder RNN hidden states
h_prime = torch.zeros(1, BS, self.rnn_hidden_dim).to(device)
hidden_seq, _ = self.rnn_module(xt, h_prime) # (T, BS, rnn_hidden_dim)
augmented_hidden_seq = BaseModel.augment_hidden_sequence(
h_prime, hidden_seq)
return augmented_hidden_seq
def _get_encoded_h(self, xt: torch.Tensor, mask: torch.Tensor, T: int,
BS: int):
"""
Encoder for Forward and Reverse RNN hidden states for Model 2
:param xt:
:param mask:
:return: augmented_hidden_seq, reverse_hidden_seq
augmented_hidden_seq = [h_prime, h_0, ..., h_{T-1}]
reverse_hidden_seq = [a_0, a_1, ..., a_{T-1}]
"""
h_prime = torch.zeros(1, BS, self.rnn_hidden_dim).to(device)
hidden_seq, _ = self.rnn_module(xt, h_prime) # (T, BS, rnn_hidden_dim)
augmented_hidden_seq = BaseModel.augment_hidden_sequence(
h_prime, hidden_seq)
reverse_hidden_seq = self._get_reversed_hidden_seq(
xt, augmented_hidden_seq, mask, T, BS)
return augmented_hidden_seq, reverse_hidden_seq
