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model.py: Adding DecoderState class to enable simultaneously call decoder.inference #176

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model.py: Adding DecoderState class to enable simultaneously call decoder.inference #176

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model.py: adding DecoderState to enable simultaneously access to infe…
delgerdalai Apr 2, 2019
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Merge branch 'master' into master
delgerdalai Apr 4, 2019
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Merge remote-tracking branch 'upstream/master'
delgerdalai Jun 19, 2019
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sync upstream
delgerdalai Jun 19, 2019
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Revert "sync upstream"
delgerdalai Jun 19, 2019
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119 changes: 72 additions & 47 deletions model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -200,6 +200,41 @@ def inference(self, x):

return outputs

class DecoderState():
""" Initializes attention rnn states, decoder rnn states, attention
weights, attention cumulative weights, attention context, stores memory
and stores processed memory
PARAMS
------
memory: Encoder outputs
mask: Mask for padded data if training, expects None for inference
"""
def __init__(self, memory, mask,
attention_rnn_dim, decoder_rnn_dim,
encoder_embedding_dim, attention_layer):
B = memory.size(0)
MAX_TIME = memory.size(1)

self.attention_hidden = Variable(memory.data.new(
B, attention_rnn_dim).zero_())
self.attention_cell = Variable(memory.data.new(
B, attention_rnn_dim).zero_())

self.decoder_hidden = Variable(memory.data.new(
B, decoder_rnn_dim).zero_())
self.decoder_cell = Variable(memory.data.new(
B, decoder_rnn_dim).zero_())

self.attention_weights = Variable(memory.data.new(
B, MAX_TIME).zero_())
self.attention_weights_cum = Variable(memory.data.new(
B, MAX_TIME).zero_())
self.attention_context = Variable(memory.data.new(
B, encoder_embedding_dim).zero_())

self.memory = memory
self.processed_memory = attention_layer.memory_layer(memory)
self.mask = mask

class Decoder(nn.Module):
def __init__(self, hparams):
Expand Down Expand Up @@ -263,30 +298,15 @@ def initialize_decoder_states(self, memory, mask):
------
memory: Encoder outputs
mask: Mask for padded data if training, expects None for inference
"""
B = memory.size(0)
MAX_TIME = memory.size(1)

self.attention_hidden = Variable(memory.data.new(
B, self.attention_rnn_dim).zero_())
self.attention_cell = Variable(memory.data.new(
B, self.attention_rnn_dim).zero_())

self.decoder_hidden = Variable(memory.data.new(
B, self.decoder_rnn_dim).zero_())
self.decoder_cell = Variable(memory.data.new(
B, self.decoder_rnn_dim).zero_())

self.attention_weights = Variable(memory.data.new(
B, MAX_TIME).zero_())
self.attention_weights_cum = Variable(memory.data.new(
B, MAX_TIME).zero_())
self.attention_context = Variable(memory.data.new(
B, self.encoder_embedding_dim).zero_())

self.memory = memory
self.processed_memory = self.attention_layer.memory_layer(memory)
self.mask = mask
RETURNS
-------
decoder_state
"""
decoder_state = DecoderState(memory, mask,
self.attention_rnn_dim, self.decoder_rnn_dim,
self.encoder_embedding_dim, self.attention_layer)
return decoder_state

def parse_decoder_inputs(self, decoder_inputs):
""" Prepares decoder inputs, i.e. mel outputs
Expand Down Expand Up @@ -337,46 +357,51 @@ def parse_decoder_outputs(self, mel_outputs, gate_outputs, alignments):

return mel_outputs, gate_outputs, alignments

def decode(self, decoder_input):
def decode(self, decoder_input, decoder_state):
""" Decoder step using stored states, attention and memory
PARAMS
------
decoder_input: previous mel output

decoder_state: decoder states

RETURNS
-------
mel_output:
gate_output: gate output energies
attention_weights:
"""
cell_input = torch.cat((decoder_input, self.attention_context), -1)
self.attention_hidden, self.attention_cell = self.attention_rnn(
cell_input, (self.attention_hidden, self.attention_cell))
self.attention_hidden = F.dropout(
self.attention_hidden, self.p_attention_dropout, self.training)
cell_input = torch.cat((decoder_input, decoder_state.attention_context), -1)
decoder_state.attention_hidden, decoder_state.attention_cell = self.attention_rnn(
cell_input, (decoder_state.attention_hidden, decoder_state.attention_cell))
decoder_state.attention_hidden = F.dropout(
decoder_state.attention_hidden, self.p_attention_dropout, self.training)
decoder_state.attention_cell = F.dropout(
decoder_state.attention_cell, self.p_attention_dropout, self.training)

attention_weights_cat = torch.cat(
(self.attention_weights.unsqueeze(1),
self.attention_weights_cum.unsqueeze(1)), dim=1)
self.attention_context, self.attention_weights = self.attention_layer(
self.attention_hidden, self.memory, self.processed_memory,
attention_weights_cat, self.mask)
(decoder_state.attention_weights.unsqueeze(1),
decoder_state.attention_weights_cum.unsqueeze(1)), dim=1)
decoder_state.attention_context, decoder_state.attention_weights = self.attention_layer(
decoder_state.attention_hidden, decoder_state.memory, decoder_state.processed_memory,
attention_weights_cat, decoder_state.mask)

self.attention_weights_cum += self.attention_weights
decoder_state.attention_weights_cum += decoder_state.attention_weights
decoder_input = torch.cat(
(self.attention_hidden, self.attention_context), -1)
self.decoder_hidden, self.decoder_cell = self.decoder_rnn(
decoder_input, (self.decoder_hidden, self.decoder_cell))
self.decoder_hidden = F.dropout(
self.decoder_hidden, self.p_decoder_dropout, self.training)
(decoder_state.attention_hidden, decoder_state.attention_context), -1)
decoder_state.decoder_hidden, decoder_state.decoder_cell = self.decoder_rnn(
decoder_input, (decoder_state.decoder_hidden, decoder_state.decoder_cell))
decoder_state.decoder_hidden = F.dropout(
decoder_state.decoder_hidden, self.p_decoder_dropout, self.training)
decoder_state.decoder_cell = F.dropout(
decoder_state.decoder_cell, self.p_decoder_dropout, self.training)

decoder_hidden_attention_context = torch.cat(
(self.decoder_hidden, self.attention_context), dim=1)
(decoder_state.decoder_hidden, decoder_state.attention_context), dim=1)
decoder_output = self.linear_projection(
decoder_hidden_attention_context)

gate_prediction = self.gate_layer(decoder_hidden_attention_context)
return decoder_output, gate_prediction, self.attention_weights
return decoder_output, gate_prediction, decoder_state.attention_weights

def forward(self, memory, decoder_inputs, memory_lengths):
""" Decoder forward pass for training
Expand All @@ -398,14 +423,14 @@ def forward(self, memory, decoder_inputs, memory_lengths):
decoder_inputs = torch.cat((decoder_input, decoder_inputs), dim=0)
decoder_inputs = self.prenet(decoder_inputs)

self.initialize_decoder_states(
decoder_state = self.initialize_decoder_states(
memory, mask=~get_mask_from_lengths(memory_lengths))

mel_outputs, gate_outputs, alignments = [], [], []
while len(mel_outputs) < decoder_inputs.size(0) - 1:
decoder_input = decoder_inputs[len(mel_outputs)]
mel_output, gate_output, attention_weights = self.decode(
decoder_input)
decoder_input, decoder_state)
mel_outputs += [mel_output.squeeze(1)]
gate_outputs += [gate_output.squeeze()]
alignments += [attention_weights]
Expand All @@ -429,12 +454,12 @@ def inference(self, memory):
"""
decoder_input = self.get_go_frame(memory)

self.initialize_decoder_states(memory, mask=None)
decoder_state = self.initialize_decoder_states(memory, mask=None)

mel_outputs, gate_outputs, alignments = [], [], []
while True:
decoder_input = self.prenet(decoder_input)
mel_output, gate_output, alignment = self.decode(decoder_input)
mel_output, gate_output, alignment = self.decode(decoder_input, decoder_state)

mel_outputs += [mel_output.squeeze(1)]
gate_outputs += [gate_output]
Expand Down