def forward(self, inputs, input_lens=None, noise=False):
if self.embedding is not None:
inputs = self.embedding(inputs)
batch_size, seq_len, emb_size = inputs.size()
inputs = F.dropout(inputs, 0.5, self.training)
if input_lens is not None:
input_lens_sorted, indices = input_lens.sort(descending=True)
inputs_sorted = inputs.index_select(0, indices)
inputs = pack_padded_sequence(inputs_sorted,
input_lens_sorted.data.tolist(),
batch_first=True)
init_hidden = gVar(
torch.zeros(self.n_layers * (1 + self.bidirectional), batch_size,
self.hidden_size))
hids, h_n = self.rnn(inputs, init_hidden)
if input_lens is not None:
_, inv_indices = indices.sort()
hids, lens = pad_packed_sequence(hids, batch_first=True)
hids = hids.index_select(0, inv_indices)
h_n = h_n.index_select(1, inv_indices)
h_n = h_n.view(self.n_layers, (1 + self.bidirectional), batch_size,
self.hidden_size)
h_n = h_n[-1]
enc = h_n.transpose(1, 0).contiguous().view(batch_size, -1)
if noise and self.noise_radius > 0:
gauss_noise = gVar(
torch.normal(means=torch.zeros(enc.size()),
std=self.noise_radius))
enc = enc + gauss_noise
return enc, hids
def forward(self, context, context_lens, utt_lens, floors, noise=False):
batch_size, max_context_len, max_utt_len = context.size()
utts = context.view(-1, max_utt_len)
utt_lens = utt_lens.view(-1)
utt_encs, _ = self.utt_encoder(utts, utt_lens)
utt_encs = utt_encs.view(batch_size, max_context_len, -1)
floor_one_hot = gVar(torch.zeros(floors.numel(), 2))
floor_one_hot.data.scatter_(1, floors.view(-1, 1), 1)
floor_one_hot = floor_one_hot.view(-1, max_context_len, 2)
utt_floor_encs = torch.cat([utt_encs, floor_one_hot], 2)
utt_floor_encs = F.dropout(utt_floor_encs, 0.25, self.training)
context_lens_sorted, indices = context_lens.sort(descending=True)
utt_floor_encs = utt_floor_encs.index_select(0, indices)
utt_floor_encs = pack_padded_sequence(
utt_floor_encs,
context_lens_sorted.data.tolist(),
batch_first=True)
init_hidden = gVar(torch.zeros(1, batch_size, self.hidden_size))
hids, h_n = self.rnn(utt_floor_encs, init_hidden)
_, inv_indices = indices.sort()
h_n = h_n.index_select(1, inv_indices)
enc = h_n.transpose(1, 0).contiguous().view(batch_size, -1)
if noise and self.noise_radius > 0:
gauss_noise = gVar(
torch.normal(means=torch.zeros(enc.size()),
std=self.noise_radius))
enc = enc + gauss_noise
return enc
def sampling(self, init_hidden, context, maxlen, SOS_tok, EOS_tok, mode='greedy'):
batch_size = init_hidden.size(0)
decoded_words = np.zeros((batch_size, maxlen), dtype=np.int)
sample_lens = np.zeros(batch_size, dtype=np.int)
decoder_input = gVar(torch.LongTensor([[SOS_tok] * batch_size]).view(batch_size, 1))
decoder_input = self.embedding(decoder_input) if self.embedding is not None else decoder_input
decoder_input = torch.cat([decoder_input, context.unsqueeze(1)], 2) if context is not None else decoder_input
decoder_hidden = init_hidden.unsqueeze(0)
for di in range(maxlen):
decoder_output, decoder_hidden = self.rnn(decoder_input, decoder_hidden)
decoder_output = self.out(decoder_output)
if mode == 'greedy':
topi = decoder_output[:, -1].max(1, keepdim=True)[1]
elif mode == 'sample':
topi = torch.multinomial(F.softmax(decoder_output[:, -1], dim=1), 1)
decoder_input = self.embedding(topi) if self.embedding is not None else topi
decoder_input = torch.cat([decoder_input, context.unsqueeze(1)],
2) if context is not None else decoder_input
ni = topi.squeeze().data.cpu().numpy()
decoded_words[:, di] = ni
for i in range(batch_size):
for word in decoded_words[i]:
if word == EOS_tok:
break
sample_lens[i] = sample_lens[i] + 1
return decoded_words, sample_lens
def forward(self, context):
batch_size, _ = context.size()
context = self.fc(context)
mu = self.context_to_mu(context)
logsigma = self.context_to_logsigma(context)
std = torch.exp(0.5 * logsigma)
epsilon = gVar(torch.randn([batch_size, self.z_size]))
z = epsilon * std + mu
return z, mu, logsigma
def evaluate(model, metrics, test_loader, ivocab, vocab, repeat, PAD_token=0):
recall_bleus, prec_bleus, bows_extrema, bows_avg, bows_greedy, intra_dist1s, intra_dist2s, \
avg_lens, inter_dist1s, inter_dist2s = [], [], [], [], [], [], [], [], [], []
local_t = 0
model.eval()
pbar = tqdm(range(test_loader.num_batch))
for bat in pbar:
batch = test_loader.next_batch()
if bat == test_loader.num_batch: break # end of epoch
local_t += 1
context, context_lens, utt_lens, floors, _, _, _, response, res_lens, _ = batch
# remove the sos token in the context and reduce the context length
context, utt_lens = context[:, :, 1:], utt_lens - 1
if local_t % 2000 == 0:
logging.info("Batch %d \n" % (local_t)) # print the context
start = np.maximum(0, context_lens[0] - 5)
for t_id in range(start, context.shape[1], 1):
context_str = indexes2sent(context[0, t_id], ivocab,
ivocab["</s>"], PAD_token)
if local_t % 2000 == 0:
logging.info("Context %d-%d: %s\n" %
(t_id, floors[0, t_id], context_str))
# print the true outputs
ref_str, _ = indexes2sent(response[0], ivocab, ivocab["</s>"],
ivocab["<s>"])
ref_tokens = ref_str.split(' ')
if local_t % 2000 == 0:
logging.info("Target >> %s\n" % (ref_str.replace(" ' ", "'")))
context, context_lens, utt_lens, floors = gVar(context), gVar(
context_lens), gVar(utt_lens), gData(floors)
sample_words, sample_lens = model.sample(context, context_lens,
utt_lens, floors, repeat,
ivocab["<s>"], ivocab["</s>"])
# nparray: [repeat x seq_len]
pred_sents, _ = indexes2sent(sample_words, ivocab, ivocab["</s>"],
PAD_token)
pred_tokens = [sent.split(' ') for sent in pred_sents]
for r_id, pred_sent in enumerate(pred_sents):
if local_t % 2000 == 0:
logging.info("Sample %d >> %s\n" %
(r_id, pred_sent.replace(" ' ", "'")))
max_bleu, avg_bleu = metrics.sim_bleu(pred_tokens, ref_tokens)
recall_bleus.append(max_bleu)
prec_bleus.append(avg_bleu)
bow_extrema, bow_avg, bow_greedy = metrics.sim_bow(
sample_words, sample_lens, response[:, 1:], res_lens - 2)
bows_extrema.append(bow_extrema)
bows_avg.append(bow_avg)
bows_greedy.append(bow_greedy)
intra_dist1, intra_dist2, inter_dist1, inter_dist2 = metrics.div_distinct(
sample_words, sample_lens)
intra_dist1s.append(intra_dist1)
intra_dist2s.append(intra_dist2)
avg_lens.append(np.mean(sample_lens))
inter_dist1s.append(inter_dist1)
inter_dist2s.append(inter_dist2)
recall_bleu = float(np.mean(recall_bleus))
prec_bleu = float(np.mean(prec_bleus))
f1 = 2 * (prec_bleu * recall_bleu) / (prec_bleu + recall_bleu + 10e-12)
bow_extrema = float(np.mean(bows_extrema))
bow_avg = float(np.mean(bows_avg))
bow_greedy = float(np.mean(bows_greedy))
intra_dist1 = float(np.mean(intra_dist1s))
intra_dist2 = float(np.mean(intra_dist2s))
avg_len = float(np.mean(avg_lens))
inter_dist1 = float(np.mean(inter_dist1s))
inter_dist2 = float(np.mean(inter_dist2s))
report = "Avg recall BLEU %f, avg precision BLEU %f, F1 %f, \nbow_avg %f, bow_extrema %f, bow_greedy %f, \n" \
"intra_dist1 %f, intra_dist2 %f, inter_dist1 %f, inter_dist2 %f, \navg_len %f" \
% (recall_bleu, prec_bleu, f1, bow_avg, bow_extrema, bow_greedy, intra_dist1, intra_dist2,
inter_dist1, inter_dist2, avg_len)
print(report)
logging.info(report + "\n")
print("Done testing")
model.train()
return recall_bleu, prec_bleu, bow_extrema, bow_avg, bow_greedy, intra_dist1, intra_dist2, avg_len, inter_dist1, inter_dist2
n_iters = train_loader.num_batch / max(1, config['n_iters_d'])
itr = 1
pbar = tqdm(range(train_loader.num_batch))
for bat in pbar:
model.train()
loss_records = []
batch = train_loader.next_batch()
if bat == train_loader.num_batch: break # end of epoch
context, context_lens, utt_lens, floors, _, _, _, response, res_lens, _ = batch
# remove the sos token in the context and reduce the context length
context, utt_lens = context[:, :, 1:], utt_lens - 1
context, context_lens, utt_lens, floors, response, res_lens \
= gVar(context), gVar(context_lens), gVar(utt_lens), gData(floors), gVar(response), gVar(res_lens)
loss_AE = model.train_AE(context, context_lens, utt_lens, floors,
response, res_lens)
loss_records.extend(loss_AE)
loss_G = model.train_G(context, context_lens, utt_lens, floors,
response, res_lens)
loss_records.extend(loss_G)
for i in range(config['n_iters_d']): # train discriminator/critic
loss_D = model.train_D(context, context_lens, utt_lens, floors,
response, res_lens)
if i == 0:
loss_records.extend(loss_D)
if i == config['n_iters_d'] - 1:
