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unsup_net.py
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unsup_net.py
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import torch
from torch import nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import math
from config import global_config as cfg
import copy, random, time, logging
from nltk.corpus import stopwords
import re
from rnn_net import *
sw = set(stopwords.words())
sw = sw.union({',', '.', '?', '!', '"', "'", ':', ';', '(', ')',
'...', '**unknown**', '<unk>', '<go>', '<pad>', '<go2>', '__eot__', 'EOS_M',
'__eou__', '</s>'})
sw_index = set()
def toss_(p):
return random.randint(0, 99) <= p
def orth_gru(gru):
gru.reset_parameters()
for _, hh, _, _ in gru.all_weights:
for i in range(0, hh.size(0), gru.hidden_size):
torch.nn.init.orthogonal_(hh[i:i + gru.hidden_size], gain=1)
return gru
def get_sparse_input_efficient(x_input_np):
ignore_index = sw_index
result = np.zeros((x_input_np.shape[0], x_input_np.shape[1], cfg.vocab_size), dtype=np.float32)
result.fill(0.)
for t in range(x_input_np.shape[0]):
for b in range(x_input_np.shape[1]):
if x_input_np[t][b] not in ignore_index:
result[t][b][x_input_np[t][b]] = 1.0
result_np = result.transpose((1, 0, 2))
result = torch.from_numpy(result_np).float()
return result
def mask_prob(score, prob, aux=None):
"""
:param score: [B,T]
:param prob: [B,T,V]
:param aux:
:return:
"""
score = score.contiguous()
prob = prob.contiguous()
score = cuda_(score, aux)
prob = cuda_(prob, aux)
res = cuda_(score.unsqueeze(1).bmm(prob).squeeze(1)) # [B, V]
freq_mask = cuda_(Variable(torch.Tensor([0] * cfg.freq_thres + [1] * (prob.size(2) - cfg.freq_thres)))) # [V]
return res * freq_mask
def shift(pz_proba):
first_input = np.zeros((pz_proba.size(1), pz_proba.size(2)))
first_input.fill(0.)
first_input = cuda_(Variable(torch.from_numpy(first_input)).float())
pz_proba = list(pz_proba)[:-1]
pz_proba.insert(0, first_input)
pz_proba = torch.stack(pz_proba, 0).contiguous()
return pz_proba
class Encoder(nn.Module):
def __init__(self, input_size, embed_size, hidden_size, n_layers, dropout):
super(Encoder, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.embed_size = embed_size
self.n_layers = n_layers
self.dropout = dropout
self.embedding = nn.Embedding(input_size, embed_size)
self.gru = nn.GRU(embed_size, hidden_size, n_layers, bidirectional=True)
def forward(self, input_seqs, hidden=None):
embedded = self.embedding(input_seqs)
outputs, hidden = self.gru(embedded, hidden)
outputs = outputs[:, :, :self.hidden_size] + outputs[:, :, self.hidden_size:] # Sum bidirectional outputs
return outputs, hidden
class TextSpanDecoder(nn.Module):
def __init__(self, embed_size, hidden_size, vocab_size, dropout_rate):
super().__init__()
self.attn_u = Attn(hidden_size)
self.attn_z = Attn(hidden_size)
self.gru = nn.GRU(embed_size + hidden_size, hidden_size, dropout=dropout_rate)
self.ln1 = LayerNormalization(hidden_size)
self.w1 = nn.Linear(hidden_size, vocab_size)
self.proj_copy1 = nn.Linear(hidden_size * 2, hidden_size)
self.v1 = nn.Linear(hidden_size, 1)
self.proj_copy2 = nn.Linear(hidden_size * 2, hidden_size)
self.v2 = nn.Linear(hidden_size, 1)
self.mu = nn.Linear(vocab_size, embed_size)
self.dropout_rate = dropout_rate
self.gru = orth_gru(self.gru)
self.copy_weight = 1
def forward(self, u_input, u_enc_out, pv_pz_proba, pv_z_dec_out, embed_z, last_hidden, u_input_np,
m_input_np, sparse_u_input):
u_context = self.attn_u(last_hidden, u_enc_out)
embed_z = F.dropout(embed_z, self.dropout_rate)
gru_in = torch.cat([u_context, embed_z], 2)
gru_out, last_hidden = self.gru(gru_in, last_hidden)
gru_out = self.ln1(gru_out)
gen_score = self.w1(gru_out).squeeze(0)
max_len = u_enc_out.size(0)
u_copy_score = F.tanh(self.proj_copy1(torch.cat([u_enc_out, gru_out.repeat(max_len, 1, 1)], 2))) # [T,B,H]
u_copy_score = self.v1(u_copy_score).squeeze(2).transpose(0, 1) # [B,T]
if pv_pz_proba is not None:
pv_pz_proba = shift(pv_pz_proba)
pv_z_copy_score = F.tanh(
self.proj_copy2(torch.cat([pv_z_dec_out, gru_out.repeat(pv_z_dec_out.size(0), 1, 1)], 2))) # [T,B,H]
pv_z_copy_score = self.v2(pv_z_copy_score).squeeze(2).transpose(0, 1) # [B,T]
scores = F.softmax(torch.cat([gen_score, u_copy_score, pv_z_copy_score], dim=1), dim=1)
cum_idx = [gen_score.size(1), u_copy_score.size(1), pv_z_copy_score.size(1)]
for i in range(len(cum_idx) - 1):
cum_idx[i + 1] += cum_idx[i]
cum_idx.insert(0, 0)
gen_score, u_copy_score, pv_z_copy_score = tuple(
[scores[:, cum_idx[i]:cum_idx[i + 1]] for i in range(3)])
u_copy_score = mask_prob(u_copy_score, sparse_u_input, aux=cfg.aux_device)
pv_z_copy_score = mask_prob(pv_z_copy_score, pv_pz_proba.transpose(0, 1), aux=cfg.aux_device)
proba = gen_score + self.copy_weight * u_copy_score + self.copy_weight * pv_z_copy_score
else:
scores = F.softmax(torch.cat([gen_score, u_copy_score], dim=1), dim=1)
cum_idx = [gen_score.size(1), u_copy_score.size(1)]
for i in range(len(cum_idx) - 1):
cum_idx[i + 1] += cum_idx[i]
cum_idx.insert(0, 0)
gen_score, u_copy_score = tuple(
[scores[:, cum_idx[i]:cum_idx[i + 1]] for i in range(2)])
u_copy_score = mask_prob(u_copy_score, sparse_u_input, aux=cfg.aux_device)
proba = gen_score + self.copy_weight * u_copy_score
mu_ae = self.mu(proba)
return mu_ae.unsqueeze(0), gru_out, last_hidden, proba, mu_ae
class ResponseDecoder(nn.Module):
"""
Response decoder: P_theta(m_t|s_t, z_t) <- P_theta(m_ti|s_t, z_t, m_t[1..i-1])
This is a deterministic decoder.
"""
def __init__(self, embed_size, hidden_size, vocab_size, dropout_rate, flag_size=5):
super().__init__()
self.emb = nn.Embedding(vocab_size, embed_size)
self.attn_z = Attn(hidden_size)
self.attn_u = Attn(hidden_size)
self.gru = nn.GRU(embed_size + hidden_size * 2, hidden_size, dropout=dropout_rate)
self.gru = orth_gru(self.gru)
self.ln1 = LayerNormalization(hidden_size)
self.proj = nn.Linear(hidden_size, vocab_size)
self.proj_copy1 = nn.Linear(hidden_size * 2, hidden_size)
self.v1 = nn.Linear(hidden_size, 1)
# self.proj_copy2 = nn.Linear(hidden_size,1)
self.dropout_rate = dropout_rate
# orth_gru(self.gru)
self.copy_weight = 1
def forward(self, z_enc_out, pz_proba, u_enc_out, m_t_input, last_hidden, flag=False):
"""
decode the response: P(m|u,z)
:param pz_proba: [Tz,B,V], output of the prior decoder
:param z_enc_out: [Tz,B,H]
:param u_enc_out: [T,B,H]
:param m_t_input: [1,B]
:param last_hidden:
:return: proba: [1,B,V]
"""
batch_size = z_enc_out.size(1)
m_embed = self.emb(m_t_input)
z_context = F.dropout(self.attn_z(last_hidden, z_enc_out), self.dropout_rate)
u_context = F.dropout(self.attn_u(last_hidden, u_enc_out), self.dropout_rate)
# d_control = self.w4(z_context) + torch.mul(F.sigmoid(self.gate_z(z_context)), self.w5(u_context))
gru_out, last_hidden = self.gru(torch.cat([z_context, u_context, m_embed], dim=2),
last_hidden)
gru_out = self.ln1(gru_out)
gen_score = self.proj(gru_out).squeeze(0)
z_copy_score = F.tanh(
self.proj_copy1(torch.cat([z_enc_out, gru_out.repeat(z_enc_out.size(0), 1, 1)], 2))) # [T,B,H]
z_copy_score = self.v1(z_copy_score).squeeze(2).transpose(0, 1) # [B,T]
scores = F.softmax(torch.cat([gen_score, z_copy_score], dim=1), dim=1)
gen_score, z_copy_score = scores[:, :gen_score.size(1)], scores[:, gen_score.size(1):]
z_copy_score = mask_prob(z_copy_score, pz_proba.transpose(0, 1), aux=cfg.aux_device)
proba = gen_score + self.copy_weight * z_copy_score # [B,V]
return proba, last_hidden
class MultinomialKLDivergenceLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, p_proba, q_proba): # [B, T, V]
loss = q_proba * (torch.log(q_proba) - torch.log(p_proba))
loss = torch.sum(loss)
return loss / (p_proba.size(1) * p_proba.size(0))
class UnsupervisedSEDST(nn.Module):
def __init__(self, embed_size, hidden_size, q_hidden_size, vocab_size, layer_num, dropout_rate,
z_length, alpha,
max_ts, beam_search=False, teacher_force=100, **kwargs):
super().__init__()
self.u_encoder = DynamicEncoder(vocab_size, embed_size, hidden_size, layer_num, dropout_rate)
self.p_encoder = DynamicEncoder(vocab_size, embed_size, q_hidden_size, layer_num, dropout_rate)
self.qz_decoder = TextSpanDecoder(embed_size, q_hidden_size, vocab_size, dropout_rate) # posterior
self.pz_decoder = TextSpanDecoder(embed_size, hidden_size, vocab_size, dropout_rate) # prior
self.m_decoder = ResponseDecoder(embed_size, hidden_size, vocab_size, dropout_rate)
self.p_decoder = ResponseDecoder(embed_size, q_hidden_size, vocab_size, dropout_rate)
self.embed_size = embed_size
self.vocab = kwargs['vocab']
self.pr_loss = nn.NLLLoss(ignore_index=0)
self.q_loss = nn.NLLLoss(ignore_index=0)
self.dec_loss = nn.NLLLoss(ignore_index=0)
self.kl_loss = MultinomialKLDivergenceLoss()
self.z_length = z_length
self.alpha = alpha
self.max_ts = max_ts
self.beam_search = beam_search
self.teacher_force = teacher_force
if self.beam_search:
self.beam_size = kwargs['beam_size']
self.eos_token_idx = kwargs['eos_token_idx']
def forward(self, u_input, u_input_np, m_input, m_input_np, z_input, u_len, m_len, turn_states, z_supervised,
mode, p_input, p_len, p_input_np, **kwargs):
if mode == 'train' or mode == 'valid':
if not z_supervised:
z_input = None
if z_supervised:
qz_proba = None
pz_proba, pm_dec_proba, turn_states = \
self.forward_turn(u_input, u_len, m_input=m_input, m_len=m_len, z_input=z_input, is_train=True,
turn_states=turn_states, u_input_np=u_input_np,
m_input_np=m_input_np)
loss, pr_loss, m_loss, q_loss = self.supervised_loss(torch.log(pz_proba), torch.log(qz_proba),
torch.log(pm_dec_proba), z_input, m_input)
return loss, pr_loss, m_loss, q_loss, turn_states, None
else:
# turn states: previous decoded text span S_t for prior network
# turn states_q: previous decoded text span for posterior network
turn_states_q = kwargs['turn_states_q']
pz_proba, pm_dec_proba, turn_states = \
self.forward_turn(u_input, u_len, m_input=m_input, m_len=m_len, z_input=z_input, is_train=True,
turn_states=turn_states, u_input_np=u_input_np,
m_input_np=m_input_np)
qz_proba, qp_dec_proba, turn_states_q = \
self.forward_turn(p_input, p_len, m_input=p_input, m_len=p_len, z_input=z_input, is_train=True,
turn_states=turn_states_q, u_input_np=p_input_np,
m_input_np=p_input_np, flag=True)
for k in turn_states_q:
turn_states_q[k] = cuda_(Variable(turn_states_q[k].data))
loss, m_loss, p_loss, kl_div_loss = self.unsupervised_loss(pz_proba, qz_proba, torch.log(pm_dec_proba),
m_input, torch.log(qp_dec_proba), p_input)
return loss, m_loss, p_loss, kl_div_loss, turn_states, turn_states_q
elif mode == 'test':
m_output_index, pz_index, turn_states = self.forward_turn(u_input, u_len=u_len, is_train=False,
turn_states=turn_states,
u_input_np=u_input_np, m_input_np=m_input_np,
flag=cfg.pretrain,
last_turn=kwargs.get('last_turn', False))
return m_output_index, pz_index, turn_states
def forward_turn(self, u_input, u_len, turn_states, is_train, u_input_np, m_input_np=None,
m_input=None, m_len=None, z_input=None, flag=False, last_turn=False):
"""
compute required outputs for a single dialogue turn. Turn state{Dict} will be updated in each call.
:param u_input_np:
:param m_input_np:
:param u_len:
:param turn_states:
:param is_train:
:param u_input: [T,B]
:param m_input: [T,B]
:param z_input: [T,B]
:return:
"""
decoder = self.m_decoder if not flag else self.p_decoder
z_decoder = self.pz_decoder if not flag else self.qz_decoder
encoder = self.u_encoder if not flag else self.p_encoder
pv_pz_proba = turn_states.get('pv_pz_proba', None)
pv_z_outs = turn_states.get('pv_z_dec_outs', None)
batch_size = u_input.size(1)
u_enc_out, u_enc_hidden = encoder(u_input, u_len)
last_hidden = u_enc_hidden[:-1]
# initial approximate embedding: SOS token initialized with all zero
# Pi(z|u)
pz_ae = cuda_(Variable(torch.zeros(1, batch_size, self.embed_size)))
pz_proba, pz_mu = [], []
pz_dec_outs = []
z_length = z_input.size(0) if z_input is not None else self.z_length
sparse_u_input = Variable(get_sparse_input_efficient(u_input_np), requires_grad=False)
for t in range(z_length):
pz_ae, last_hidden, pz_dec_out, proba, mu_ae = \
z_decoder(u_input=u_input, u_enc_out=u_enc_out, pv_pz_proba=pv_pz_proba, pv_z_dec_out=pv_z_outs,
embed_z=pz_ae, last_hidden=last_hidden, u_input_np=u_input_np,
m_input_np=m_input_np, sparse_u_input=sparse_u_input)
pz_proba.append(proba)
pz_mu.append(mu_ae)
pz_dec_outs.append(pz_dec_out)
pz_dec_outs = torch.cat(pz_dec_outs, dim=0) # [Tz,B,H]
pz_proba, pz_mu = torch.stack(pz_proba, dim=0), torch.stack(pz_mu, dim=0)
shift_pz_proba = shift(pz_proba)
# P(m|z,u)
m_tm1 = cuda_(Variable(torch.ones(1, batch_size).long())) # GO token
pm_dec_proba = []
turn_states = {
'pv_z_dec_outs': pz_dec_outs,
'pv_pz_proba': pz_proba,
}
if flag:
last_hidden = u_enc_hidden[-1:] # backward pass
else:
last_hidden = u_enc_hidden[:-1] # forward pass
if is_train:
m_length = m_input.size(0) # Tm
for t in range(m_length):
teacher_forcing = toss_(self.teacher_force)
proba, last_hidden = decoder(pz_dec_outs, shift_pz_proba, u_enc_out, m_tm1, last_hidden,
flag)
if teacher_forcing:
m_tm1 = m_input[t].view(1, -1)
else:
_, m_tm1 = torch.topk(proba, 1)
m_tm1 = m_tm1.view(1, -1)
pm_dec_proba.append(proba)
pm_dec_proba = torch.stack(pm_dec_proba, dim=0) # [T,B,V]
return pz_proba, pm_dec_proba, turn_states
else:
if last_turn or not cfg.last_turn_only:
if not self.beam_search:
m_output_index = self.greedy_decode(pz_dec_outs, shift_pz_proba, u_enc_out, m_tm1, last_hidden,
flag)
else:
m_output_index = self.beam_search_decode(pz_dec_outs, shift_pz_proba, u_enc_out, m_tm1, last_hidden,
self.eos_token_idx, flag)
else:
m_output_index = None
pz_index = self.pz_selective_sampling(pz_proba)
return m_output_index, pz_index, turn_states
def greedy_decode(self, pz_dec_outs, pz_proba, u_enc_out, m_tm1, last_hidden, flag):
"""
greedy decoding of the response
:param pz_dec_outs:
:param u_enc_out:
:param m_tm1:
:param last_hidden:
:return: nested-list
"""
decoded = []
decoder = self.m_decoder if not flag else self.p_decoder
for t in range(self.max_ts):
proba, last_hidden = decoder(pz_dec_outs, pz_proba, u_enc_out, m_tm1, last_hidden)
mt_proba, mt_index = torch.topk(proba, 1) # [B,1]
mt_index = mt_index.data.view(-1)
decoded.append(mt_index)
m_tm1 = cuda_(Variable(mt_index).view(1, -1))
decoded = torch.stack(decoded, dim=0).transpose(0, 1)
decoded = list(decoded)
return [list(_) for _ in decoded]
def pz_max_sampling(self, pz_proba):
"""
Max-sampling procedure of pz during testing.
:param pz_proba: # [Tz, B, Vz]
:return: nested-list: B * [T]
"""
pz_proba = pz_proba.data
z_proba, z_token = torch.topk(pz_proba, 1, dim=2) # [Tz, B, 1]
z_token = list(z_token.squeeze(2).transpose(0, 1))
return [list(_) for _ in z_token]
def pz_selective_sampling(self, pz_proba):
"""
Selective sampling of pz(do max-sampling but prevent repeated words)
"""
pz_proba = pz_proba.data
z_proba, z_token = torch.topk(pz_proba, pz_proba.size(0), dim=2)
z_token = z_token.transpose(0, 1) # [B,Tz,top_Tz]
all_sampled_z = []
for b in range(z_token.size(0)):
sampled_z = []
for t in range(z_token.size(1)):
for i in range(z_token.size(2)):
if z_token[b][t][i] not in sampled_z:
sampled_z.append(z_token[b][t][i])
break
all_sampled_z.append(sampled_z)
return all_sampled_z
def beam_search_decode_single(self, pz_dec_outs, pz_proba, u_enc_out, m_tm1, last_hidden,
eos_token_id, flag):
"""
Single beam search decoding. Batch size have to be 1.
:param eos_token_id:
:param last_hidden:
:param m_tm1:
:param pz_dec_outs: [T,1,H]
:param pz_proba: [T,1,V]
:param u_enc_out: [T,1,H]
:return:
"""
eos_token_id = self.vocab.encode(cfg.eos_m_token)
batch_size = pz_dec_outs.size(1)
if batch_size != 1:
raise ValueError('"Beam search single" requires batch size to be 1')
class BeamState:
def __init__(self, score, last_hidden, decoded, length):
"""
Beam state in beam decoding
:param score: sum of log-probabilities
:param last_hidden: last hidden
:param decoded: list of *Variable[1*1]* of all decoded words
:param length: current decoded sentence length
"""
self.score = score
self.last_hidden = last_hidden
self.decoded = decoded
self.length = length
def update_clone(self, score_incre, last_hidden, decoded_t):
decoded = copy.copy(self.decoded)
decoded.append(decoded_t)
clone = BeamState(self.score + score_incre, last_hidden, decoded, self.length + 1)
return clone
def beam_result_valid(decoded_t):
decoded_t = [_.item() for _ in decoded_t]
decoded_sentence = self.vocab.sentence_decode(decoded_t, cfg.eos_m_token)
# return True
return len(decoded_sentence.split(' ')) >= 5 and '[' not in decoded_sentence
def score_bonus(state, decoded, dead_k, t):
"""
bonus scheme: bonus per token, or per new decoded slot.
:param state:
:return:
"""
bonus = cfg.beam_len_bonus
decoded = self.vocab.decode(decoded)
decoded_t = [_.item() for _ in state.decoded]
decoded_sentence = self.vocab.sentence_decode(decoded_t, cfg.eos_m_token)
decoded_sentence = decoded_sentence.split()
if len(decoded_sentence) >= 1 and decoded_sentence[-1] == decoded:
bonus -= 10000
if decoded == '**unknown**' or decoded == '<unk>':
bonus -= 3.0
bonus -= self.repeat_penalty(decoded_sentence)
return bonus
def soft_score_incre(score, turn):
return score
decoder = self.m_decoder if not flag else self.p_decoder
finished, failed = [], []
states = [] # sorted by score decreasingly
dead_k = 0
states.append(BeamState(0, last_hidden, [m_tm1], 0))
for t in range(self.max_ts):
new_states = []
k = 0
while k < len(states) and k < self.beam_size - dead_k:
state = states[k]
last_hidden, m_tm1 = state.last_hidden, state.decoded[-1]
proba, last_hidden = decoder(pz_dec_outs, pz_proba, u_enc_out, m_tm1, last_hidden)
proba = torch.log(proba)
mt_proba, mt_index = torch.topk(proba, self.beam_size - dead_k) # [1,K]
for new_k in range(self.beam_size - dead_k):
score_incre = soft_score_incre(mt_proba[0][new_k].item(), t) + \
score_bonus(state, mt_index[0][new_k].item(), dead_k, t)
if len(new_states) >= self.beam_size - dead_k and state.score + score_incre < new_states[-1].score:
break
decoded_t = mt_index[0][new_k]
if self.vocab.decode(decoded_t.item()) == cfg.eos_m_token: # and k == 0:
if new_k == 0:
if beam_result_valid(state.decoded):
finished.append(state)
dead_k += 1
else:
failed.append(state)
else:
decoded_t = decoded_t.view(1, -1)
new_state = state.update_clone(score_incre, last_hidden, decoded_t)
new_states.append(new_state)
k += 1
if self.beam_size - dead_k < 0:
break
new_states = new_states[:self.beam_size - dead_k]
new_states.sort(key=lambda x: -x.score)
states = new_states
if t == self.max_ts - 1 and not finished:
finished = failed
if not finished:
finished.append(states[0])
finished.sort(key=lambda x: -x.score)
decoded_t = finished[0].decoded
decoded_t = [_.item() for _ in decoded_t]
decoded_sentence = self.vocab.sentence_decode(decoded_t, cfg.eos_m_token)
print(decoded_sentence)
generated = torch.cat(finished[0].decoded, dim=1).data # [B=1, T]
return generated
def beam_search_decode(self, pz_dec_outs, pz_proba, u_enc_out, m_tm1, last_hidden, eos_token_id,
flag=False):
vars = torch.split(pz_dec_outs, 1, dim=1), torch.split(pz_proba, 1, dim=1), torch.split(u_enc_out, 1,
dim=1), torch.split(
m_tm1, 1, dim=1), torch.split(last_hidden, 1, dim=1)
decoded = []
for pz_dec_out_s, pz_proba_s, u_enc_out_s, m_tm1_s, last_hidden_s in zip(*vars):
decoded_s = self.beam_search_decode_single(pz_dec_out_s, pz_proba_s, u_enc_out_s, m_tm1_s, last_hidden_s,
eos_token_id, flag)
decoded.append(decoded_s)
return [list(_.view(-1)) for _ in decoded]
def supervised_loss(self, pz_proba, qz_proba, pm_dec_proba, z_input, m_input):
pr_loss = self.pr_loss(pz_proba.view(-1, pz_proba.size(2)), z_input.view(-1))
m_loss = self.dec_loss(pm_dec_proba.view(-1, pm_dec_proba.size(2)), m_input.view(-1))
q_loss = self.q_loss(qz_proba.view(-1, pz_proba.size(2)), z_input.view(-1))
loss = pr_loss + m_loss + q_loss
return loss, pr_loss, m_loss, q_loss
def unsupervised_loss(self, pz_proba, qz_proba, log_pm_dec_proba, m_input, log_qp_dec_proba, p_input):
m_loss = self.dec_loss(log_pm_dec_proba.view(-1, log_pm_dec_proba.size(2)), m_input.view(-1))
m_loss = cuda_(m_loss)
p_loss = self.dec_loss(log_qp_dec_proba.view(-1, log_qp_dec_proba.size(2)), p_input.view(-1))
p_loss = cuda_(p_loss)
qz_proba = cuda_(Variable(qz_proba.data)) # qz_proba is detached for loss computation
kl_div_loss = self.kl_loss(pz_proba, qz_proba)
loss = m_loss + self.alpha * kl_div_loss + p_loss
return loss, m_loss, p_loss, self.alpha * kl_div_loss
def basic_loss(self, log_pm_dec_proba, m_input):
m_loss = self.dec_loss(log_pm_dec_proba.view(-1, log_pm_dec_proba.size(2)), m_input.view(-1))
return m_loss
def self_adjust(self, epoch_num, iter_num):
pass
def repeat_penalty(self, seq):
"""
brute force n^2
:param seq:
:return:
"""
def overlap(s, i, j):
res = 0
while i < len(s) and j < len(s):
if s[i] == s[j]:
res += 1
else:
break
i += 1
j += 1
return res
res = -1
for i in range(len(seq)):
for j in range(i + 1, len(seq)):
res = max(res, overlap(seq, i, j))
if res <= 4:
return 0
else:
return 1.0 * res