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Loc sens attention

This commit is contained in:
Eren G 2018-07-17 17:01:40 +02:00
parent ddaf414434
commit 4e6596a8e1
2 changed files with 7 additions and 6 deletions
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10
layers/attention.py
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@ -69,24 +69,25 @@ class LocationSensitiveAttention(nn.Module):
class AttentionRNNCell(nn.Module): class AttentionRNNCell(nn.Module):
def __init__(self, out_dim, annot_dim, memory_dim, align_model): def __init__(self, out_dim, rnn_dim, annot_dim, memory_dim, align_model):
r""" r"""
General Attention RNN wrapper General Attention RNN wrapper
Args: Args:
out_dim (int): context vector feature dimension. out_dim (int): context vector feature dimension.
rnn_dim (int): rnn hidden state dimension.
annot_dim (int): annotation vector feature dimension. annot_dim (int): annotation vector feature dimension.
memory_dim (int): memory vector (decoder autogression) feature dimension. memory_dim (int): memory vector (decoder autogression) feature dimension.
align_model (str): 'b' for Bahdanau, 'ls' Location Sensitive alignment. align_model (str): 'b' for Bahdanau, 'ls' Location Sensitive alignment.
""" """
super(AttentionRNNCell, self).__init__() super(AttentionRNNCell, self).__init__()
self.align_model = align_model self.align_model = align_model
self.rnn_cell = nn.GRUCell(out_dim + memory_dim, out_dim) self.rnn_cell = nn.GRUCell(out_dim + memory_dim, rnn_dim)
# pick bahdanau or location sensitive attention # pick bahdanau or location sensitive attention
if align_model == 'b': if align_model == 'b':
self.alignment_model = BahdanauAttention(annot_dim, out_dim, out_dim) self.alignment_model = BahdanauAttention(annot_dim, out_dim, out_dim)
if align_model == 'ls': if align_model == 'ls':
self.alignment_model = LocationSensitiveAttention(annot_dim, out_dim, out_dim) self.alignment_model = LocationSensitiveAttention(annot_dim, rnn_dim, out_dim)
else: else:
raise RuntimeError(" Wrong alignment model name: {}. Use\ raise RuntimeError(" Wrong alignment model name: {}. Use\
'b' (Bahdanau) or 'ls' (Location Sensitive).".format(align_model)) 'b' (Bahdanau) or 'ls' (Location Sensitive).".format(align_model))
@ -100,11 +101,10 @@ class AttentionRNNCell(nn.Module):
- context: (batch, dim) - context: (batch, dim)
- rnn_state: (batch, out_dim) - rnn_state: (batch, out_dim)
- annots: (batch, max_time, annot_dim) - annots: (batch, max_time, annot_dim)
- atten: (batch, max_time) - atten: (batch, 2, max_time)
- annot_lens: (batch,) - annot_lens: (batch,)
""" """
# Concat input query and previous context context # Concat input query and previous context context
print(context.shape)
rnn_input = torch.cat((memory, context), -1) rnn_input = torch.cat((memory, context), -1)
# Feed it to RNN # Feed it to RNN
# s_i = f(y_{i-1}, c_{i}, s_{i-1}) # s_i = f(y_{i-1}, c_{i}, s_{i-1})

3
layers/tacotron.py
View File

@ -203,7 +203,8 @@ class Decoder(nn.Module):
# memory -> |Prenet| -> processed_memory # memory -> |Prenet| -> processed_memory
self.prenet = Prenet(memory_dim * r, out_features=[256, 128]) self.prenet = Prenet(memory_dim * r, out_features=[256, 128])
# processed_inputs, processed_memory -> |Attention| -> Attention, attention, RNN_State # processed_inputs, processed_memory -> |Attention| -> Attention, attention, RNN_State
self.attention_rnn = AttentionRNNCell(128, in_features, 128, align_model='ls') self.attention_rnn = AttentionRNNCell(out_dim=128, rnn_dim=256, annot_dim=in_features,
memory_dim=128, align_model='ls')
# (processed_memory | attention context) -> |Linear| -> decoder_RNN_input # (processed_memory | attention context) -> |Linear| -> decoder_RNN_input
self.project_to_decoder_in = nn.Linear(256+in_features, 256) self.project_to_decoder_in = nn.Linear(256+in_features, 256)
# decoder_RNN_input -> |RNN| -> RNN_state # decoder_RNN_input -> |RNN| -> RNN_state