def bot_func(bot, update, args):
text = " ".join(args)
words = utils.tokenize(text)
seq_1 = data.encode_words(words, emb_dict)
input_seq = model.pack_input(seq_1, net.emb)
enc = net.encode(input_seq)
if prog_args.sample:
_, tokens = net.decode_chain_sampling(enc,
input_seq.data[0:1],
seq_len=data.MAX_TOKENS,
stop_at_token=end_token)
else:
_, tokens = net.decode_chain_argmax(enc,
input_seq.data[0:1],
seq_len=data.MAX_TOKENS,
stop_at_token=end_token)
if tokens[-1] == end_token:
tokens = tokens[:-1]
reply = data.decode_words(tokens, rev_emb_dict)
if reply:
reply_text = utils.untokenize(reply)
bot.send_message(chat_id=update.message.chat_id, text=reply_text)
def bot_func(bot, update, args):
text = " ".join(args)
words = utils.tokenize(text)
seq_1 = data.encode_words(words, emb_dict)
input_seq = model.pack_input(seq_1, net.emb)
enc = net.encode(input_seq)
if prog_args.sample:
_, tokens = net.decode_chain_sampling(enc, input_seq.data[0:1], seq_len=data.MAX_TOKENS,
stop_at_token=end_token)
else:
_, tokens = net.decode_chain_argmax(enc, input_seq.data[0:1], seq_len=data.MAX_TOKENS,
stop_at_token=end_token)
if tokens[-1] == end_token:
tokens = tokens[:-1]
reply = data.decode_words(tokens, rev_emb_dict)
if reply:
reply_text = utils.untokenize(reply)
bot.send_message(chat_id=update.message.chat_id, text=reply_text)
r_argmax, actions = net.decode_chain_argmax(
item_enc,
beg_embedding,
data.MAX_TOKENS,
stop_at_token=end_token)
argmax_bleu = utils.calc_bleu_many(actions, ref_indices)
bleus_argmax.append(argmax_bleu)
if not args.disable_skip:
if argmax_bleu > 0.99:
skipped_samples += 1
continue
if not dial_shown:
w = data.decode_words(inp_idx, rev_emb_dict)
log.info("Input: %s", utils.untokenize(w))
ref_words = [
utils.untokenize(
data.decode_words(ref, rev_emb_dict))
for ref in ref_indices
]
ref = " ~~|~~ ".join(ref_words)
log.info("Refer: %s", ref)
w = data.decode_words(actions, rev_emb_dict)
log.info("Argmax: %s, bleu=%.4f", utils.untokenize(w),
argmax_bleu)
for _ in range(args.samples):
r_sample, actions = \
net.decode_chain_sampling(
item_enc, beg_embedding,
# Get predicted tokens.
seq = torch.max(r.data, dim=1)[1]
seq = seq.cpu().numpy()
# argmax做训练；
else:
r, seq = net.decode_chain_argmax(enc_item, out_seq.data[0:1],
len(ref_indices))
blue_temp = utils.calc_bleu(seq, ref_indices)
bleu_sum += blue_temp
net_results.append(r)
net_targets.extend(ref_indices)
bleu_count += 1
if not dial_shown:
# data.decode_words transform IDs to tokens.
ref_words = [utils.untokenize(data.decode_words(ref_indices, rev_emb_dict))]
log.info("Reference: %s", " ~~|~~ ".join(ref_words))
log.info("Predicted: %s, bleu=%.4f", utils.untokenize(data.decode_words(seq, rev_emb_dict)), blue_temp)
dial_shown = True
results_v = torch.cat(net_results)
results_v = results_v.cuda()
targets_v = torch.LongTensor(net_targets).to(device)
targets_v = targets_v.cuda()
loss_v = F.cross_entropy(results_v, targets_v)
loss_v = loss_v.cuda()
loss_v.backward()
optimiser.step()
losses.append(loss_v.item())
bleu = bleu_sum / bleu_count
bleu_test = run_test(test_data, net, end_token, device)
r_argmax, actions = net.decode_chain_argmax(
item_enc,
beg_embedding,
data.MAX_TOKENS,
stop_at_token=end_token)
argmax_bleu = utils.calc_bleu_many(actions, ref_indices)
bleus_argmax.append(argmax_bleu)
if not args.disable_skip and argmax_bleu > 0.99:
skipped_samples += 1
continue
if not dial_shown:
log.info(
"Input: %s",
utils.untokenize(
data.decode_words(inp_idx, rev_emb_dict)))
ref_words = [
utils.untokenize(
data.decode_words(ref, rev_emb_dict))
for ref in ref_indices
]
log.info("Refer: %s", " ~~|~~ ".join(ref_words))
log.info(
"Argmax: %s, bleu=%.4f",
utils.untokenize(
data.decode_words(actions, rev_emb_dict)),
argmax_bleu)
for _ in range(args.samples):
r_sample, actions = net.decode_chain_sampling(
item_enc,
logging.basicConfig(format="%(asctime)-15s %(levelname)s %(message)s", level=logging.INFO)
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--model", required=True, help="Model name to load")
parser.add_argument("-s", "--string", help="String to process, otherwise will loop")
parser.add_argument("--sample", default=False, action="store_true", help="Enable sampling generation instead of argmax")
parser.add_argument("--self", type=int, default=1, help="Enable self-loop mode with given amount of phrases.")
args = parser.parse_args()
emb_dict = data.load_emb_dict(os.path.dirname(args.model))
net = model.PhraseModel(emb_size=model.EMBEDDING_DIM, dict_size=len(emb_dict), hid_size=model.HIDDEN_STATE_SIZE)
net.load_state_dict(torch.load(args.model))
rev_emb_dict = {idx: word for word, idx in emb_dict.items()}
while True:
if args.string:
input_string = args.string
else:
input_string = input(">>> ")
if not input_string:
break
words = utils.tokenize(input_string)
for _ in range(args.self):
words = words_to_words(words, emb_dict, rev_emb_dict, net, use_sampling=args.sample)
print(utils.untokenize(words))
if args.string:
break
pass
seq = seq.cpu().numpy()
# argmax做训练；
else:
r, seq = net.decode_chain_argmax(enc_item,
out_seq.data[0:1],
len(ref_indices))
blue_temp = utils.calc_bleu(seq, ref_indices)
bleu_sum += blue_temp
net_results.append(r)
net_targets.extend(ref_indices)
bleu_count += 1
if not dial_shown:
# data.decode_words transform IDs to tokens.
ref_words = [
utils.untokenize(
data.decode_words(ref_indices, rev_emb_dict))
]
log.info("Reference: %s", " ~~|~~ ".join(ref_words))
log.info(
"Predicted: %s, bleu=%.4f",
utils.untokenize(data.decode_words(seq, rev_emb_dict)),
blue_temp)
dial_shown = True
results_v = torch.cat(net_results)
results_v = results_v.cuda()
targets_v = torch.LongTensor(net_targets).to(device)
targets_v = targets_v.cuda()
loss_v = F.cross_entropy(results_v, targets_v)
loss_v = loss_v.cuda()
loss_v.backward()
optimiser.step()
args = parser.parse_args()
emb_dict = data.load_emb_dict(os.path.dirname(args.model))
net = model.PhraseModel(emb_size=model.EMBEDDING_DIM,
dict_size=len(emb_dict),
hid_size=model.HIDDEN_STATE_SIZE)
net.load_state_dict(torch.load(args.model))
rev_emb_dict = {idx: word for word, idx in emb_dict.items()}
while True:
if args.string:
input_string = args.string
else:
input_string = input(">>> ")
if not input_string:
break
words = utils.tokenize(input_string)
for _ in range(args.self):
words = words_to_words(words,
emb_dict,
rev_emb_dict,
net,
use_sampling=args.sample)
print(utils.untokenize(words))
if args.string:
break
pass
ref_indices = [
indices[1:]
for indices in output_batch[idx]
]
item_enc = net.get_encoded_item(enc, idx)
r_argmax, actions = net.decode_chain_argmax(item_enc, beg_embedding, data.MAX_TOKENS,
stop_at_token=end_token)
argmax_bleu = utils.calc_bleu_many(actions, ref_indices)
bleus_argmax.append(argmax_bleu)
if not args.disable_skip and argmax_bleu > 0.99:
skipped_samples += 1
continue
if not dial_shown:
log.info("Input: %s", utils.untokenize(data.decode_words(inp_idx, rev_emb_dict)))
ref_words = [utils.untokenize(data.decode_words(ref, rev_emb_dict)) for ref in ref_indices]
log.info("Refer: %s", " ~~|~~ ".join(ref_words))
log.info("Argmax: %s, bleu=%.4f", utils.untokenize(data.decode_words(actions, rev_emb_dict)),
argmax_bleu)
for _ in range(args.samples):
r_sample, actions = net.decode_chain_sampling(item_enc, beg_embedding,
data.MAX_TOKENS, stop_at_token=end_token)
sample_bleu = utils.calc_bleu_many(actions, ref_indices)
if not dial_shown:
log.info("Sample: %s, bleu=%.4f", utils.untokenize(data.decode_words(actions, rev_emb_dict)),
sample_bleu)
net_policies.append(r_sample)
