def initialize_state(self,
hidden,
feature=None,
attn_memory=None,
attn_mask=None,
memory_lengths=None,
knowledge=None):
if self.feature_size is not None:
assert feature is not None
if self.attn_mode is not None:
assert attn_memory is not None
if memory_lengths is not None and attn_mask is None:
max_len = attn_memory.size(1)
attn_mask = sequence_mask(memory_lengths, max_len).eq(0)
init_state = DecoderState(
hidden=hidden,
feature=feature,
attn_memory=attn_memory,
attn_mask=attn_mask,
knowledge=knowledge,
)
return init_state
def initialize_state(
self,
hidden,
attn_memory=None,
attn_mask=None,
memory_lengths=None,
guide_score=None,
topic_feature=None,
):
"""
initialize_state
"""
if self.attn_mode is not None:
assert attn_memory is not None
if memory_lengths is not None and attn_mask is None:
max_len = attn_memory.size(1)
attn_mask = sequence_mask(memory_lengths, max_len).eq(0)
init_state = DecoderState(
hidden=hidden,
attn_memory=attn_memory,
attn_mask=attn_mask,
topic_feature=topic_feature.unsqueeze(1),
# bridge_memory=bridge_memory,
guide_score=guide_score,
state_vector=None,
)
return init_state
def initialize_state(self,
hidden,
fact,
hist,
attn_fact=None,
attn_hist=None,
hist_mask=None,
fact_mask=None,
fact_lengths=None,
hist_lengths=None):
"""
initialize_state
"""
if self.attn_mode is not None:
assert attn_fact is not None
assert attn_hist is not None
if hist_lengths is not None and hist_mask is None:
max_len = attn_hist.size(1)
hist_mask = sequence_mask(hist_lengths, max_len).eq(0)
if fact_lengths is not None and fact_mask is None:
sent_len = torch.max(fact_lengths)
fact_mask = sequence_mask(fact_lengths, sent_len).eq(0).view(
fact_lengths.size(0), -1)
init_state = DecoderState(hidden=hidden,
fact=fact,
hist=hist,
attn_hist=attn_hist,
attn_fact=attn_fact,
hist_mask=hist_mask,
fact_mask=fact_mask)
return init_state
def initialize_state(
self,
hidden,
feature=None,
attn_memory=None,
attn_mask=None,
memory_lengths=None,
cue_attn_mask=None,
cue_lengths=None,
cue_enc_outputs=None,
task_id=1,
):
"""
initialize_state
"""
if self.feature_size is not None:
assert feature is not None
if self.attn_mode is not None:
assert attn_memory is not None
if memory_lengths is not None and attn_mask is None:
if task_id == 1:
max_len = attn_memory.size(
1
) # 第二阶段attn_memory(batch_size, src_len, 2*rnn_hidden_size)
else:
max_len = attn_memory.size(
2
) # 第一阶段attn_memory(batch_size, sent_num, num_enc_inputs, 2*rnn_hidden_size)
attn_mask = sequence_mask(memory_lengths, max_len).eq(
0
) # 第一阶段attn_mask(batch_size, sent_num, num_enc_inputs) 第二阶段attn_mask(batch_size, num_enc_inputs)
if cue_lengths is not None and cue_attn_mask is None:
cue_max_len = cue_enc_outputs.size(
1) # cue_enc_outputs(batch_size, cue_len, 2*rnn_hidden_size)
cue_attn_mask = sequence_mask(cue_lengths, cue_max_len).eq(
0) # cue_attn_mask(batch_size,max_len-2)
init_state = DecoderState(hidden=hidden,
feature=feature,
attn_memory=attn_memory,
attn_mask=attn_mask,
cue_attn_mask=cue_attn_mask,
cue_enc_outputs=cue_enc_outputs,
cue_lengths=cue_lengths,
memory_lengths=memory_lengths,
task_id=task_id)
return init_state
def initialize_state(self,
hidden,
feature=None,
src_enc_outputs=None,
src_inputs=None,
src_lengths=None,
src_mask=None,
cue_enc_outputs=None,
cue_inputs=None,
cue_lengths=None,
cue_mask=None,
selected_cue_memory=None,
selected_cue_length=None,
selected_cue_mask=None,
knowledge=None):
"""
initialize_state
"""
if self.feature_size is not None:
assert feature is not None
if self.attn_mode is not None:
assert src_enc_outputs is not None
if src_lengths is not None and src_mask is None:
max_len = src_enc_outputs.size(1)
src_mask = sequence_mask(src_lengths, max_len).eq(0)
if selected_cue_length is not None and selected_cue_mask is None:
max_len = selected_cue_memory.size(1)
selected_cue_mask = sequence_mask(selected_cue_length,
max_len).eq(0)
if cue_lengths is not None and cue_mask is None:
max_len = cue_enc_outputs.size(2)
cue_mask = sequence_mask(cue_lengths, max_len).eq(0)
init_state = DecoderState(hidden=hidden,
feature=feature,
src_enc_outputs=src_enc_outputs,
src_inputs=src_inputs,
src_mask=src_mask,
cue_enc_outputs=cue_enc_outputs,
cue_inputs=cue_inputs,
cue_mask=cue_mask,
selected_cue_memory=selected_cue_memory,
selected_cue_mask=selected_cue_mask,
knowledge=knowledge)
return init_state
def initialize_state(self,
hidden,
attn_memory=None,
input_feed=None,
mask=None):
"""
initialize_state
"""
if self.attn_mode is not None:
assert attn_memory is not None
init_state = DecoderState(hidden=hidden,
attn_memory=attn_memory,
input_feed=input_feed,
mask=mask)
return init_state
def initialize_state(self,
hidden,
cue_inputs=None,
attn_memory=None,
attn_mask=None,
memory_lengths=None):
"""
initialize_state
"""
if self.attn_mode is not None:
assert attn_memory is not None
if memory_lengths is not None and attn_mask is None:
max_len = attn_memory.size(1)
attn_mask = sequence_mask(memory_lengths, max_len).eq(0)
init_state = DecoderState(
hidden=hidden,
cue_inputs=cue_inputs,
attn_memory=attn_memory,
attn_mask=attn_mask,
)
return init_state
