def do_predict(args):
if args.use_cuda:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
# define the data generator
processor = reader.DataProcessor(fpattern=args.predict_file,
src_vocab_fpath=args.src_vocab_fpath,
trg_vocab_fpath=args.trg_vocab_fpath,
token_delimiter=args.token_delimiter,
use_token_batch=False,
batch_size=args.batch_size,
device_count=1,
pool_size=args.pool_size,
sort_type=reader.SortType.NONE,
shuffle=False,
shuffle_batch=False,
start_mark=args.special_token[0],
end_mark=args.special_token[1],
unk_mark=args.special_token[2],
max_length=args.max_length,
n_head=args.n_head)
batch_generator = processor.data_generator(phase="predict", place=place)
args.src_vocab_size, args.trg_vocab_size, args.bos_idx, args.eos_idx, \
args.unk_idx = processor.get_vocab_summary()
trg_idx2word = reader.DataProcessor.load_dict(
dict_path=args.trg_vocab_fpath, reverse=True)
args.src_vocab_size, args.trg_vocab_size, args.bos_idx, args.eos_idx, \
args.unk_idx = processor.get_vocab_summary()
with fluid.dygraph.guard(place):
# define data loader
test_loader = fluid.io.DataLoader.from_generator(capacity=10)
test_loader.set_batch_generator(batch_generator, places=place)
# define model
transformer = Transformer(
args.src_vocab_size, args.trg_vocab_size, args.max_length + 1,
args.n_layer, args.n_head, args.d_key, args.d_value, args.d_model,
args.d_inner_hid, args.prepostprocess_dropout,
args.attention_dropout, args.relu_dropout, args.preprocess_cmd,
args.postprocess_cmd, args.weight_sharing, args.bos_idx,
args.eos_idx)
# load the trained model
assert args.init_from_params, (
"Please set init_from_params to load the infer model.")
model_dict, _ = fluid.load_dygraph(
os.path.join(args.init_from_params, "transformer"))
# to avoid a longer length than training, reset the size of position
# encoding to max_length
model_dict["encoder.pos_encoder.weight"] = position_encoding_init(
args.max_length + 1, args.d_model)
model_dict["decoder.pos_encoder.weight"] = position_encoding_init(
args.max_length + 1, args.d_model)
transformer.load_dict(model_dict)
# set evaluate mode
transformer.eval()
f = open(args.output_file, "wb")
for input_data in test_loader():
(src_word, src_pos, src_slf_attn_bias, trg_word,
trg_src_attn_bias) = input_data
finished_seq, finished_scores = transformer.beam_search(
src_word,
src_pos,
src_slf_attn_bias,
trg_word,
trg_src_attn_bias,
bos_id=args.bos_idx,
eos_id=args.eos_idx,
beam_size=args.beam_size,
max_len=args.max_out_len)
finished_seq = finished_seq.numpy()
finished_scores = finished_scores.numpy()
for ins in finished_seq:
for beam_idx, beam in enumerate(ins):
if beam_idx >= args.n_best: break
id_list = post_process_seq(beam, args.bos_idx,
args.eos_idx)
word_list = [trg_idx2word[id] for id in id_list]
sequence = b" ".join(word_list) + b"\n"
f.write(sequence)
def do_predict(args):
if args.use_cuda:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
# define the data generator
'''
# old reader
processor = reader.DataProcessor(fpattern=args.predict_file,
src_vocab_fpath=args.src_vocab_fpath,
trg_vocab_fpath=args.trg_vocab_fpath,
token_delimiter=args.token_delimiter,
use_token_batch=False,
batch_size=args.batch_size,
device_count=1,
pool_size=args.pool_size,
sort_type=reader.SortType.NONE,
shuffle=False,
shuffle_batch=False,
start_mark=args.special_token[0],
end_mark=args.special_token[1],
unk_mark=args.special_token[2],
max_length=args.max_length,
n_head=args.n_head)
'''
processor = reader.DataProcessor(fpattern=args.predict_file,
src_vocab_fpath=args.src_vocab_fpath,
trg_vocab_fpath=args.trg_vocab_fpath,
token_delimiter=args.token_delimiter,
use_token_batch=False,
batch_size=args.batch_size,
device_count=1,
pool_size=args.pool_size,
sort_type=reader.SortType.NONE,
shuffle=False,
shuffle_batch=False,
only_src=args.only_src,
start_mark=args.special_token[0],
end_mark=args.special_token[1],
unk_mark=args.special_token[2],
max_length=args.max_length,
n_head=args.n_head,
stream=args.stream,
src_bpe_dict=args.src_bpe_dict)
batch_generator = processor.data_generator(phase="predict", place=place)
args.src_vocab_size, args.trg_vocab_size, args.bos_idx, args.eos_idx, \
args.unk_idx = processor.get_vocab_summary()
trg_idx2word = reader.DataProcessor.load_dict(
dict_path=args.trg_vocab_fpath, reverse=True)
args.src_vocab_size, args.trg_vocab_size, args.bos_idx, args.eos_idx, \
args.unk_idx = processor.get_vocab_summary()
with fluid.dygraph.guard(place):
# define data loader
test_loader = fluid.io.DataLoader.from_generator(capacity=10)
test_loader.set_batch_generator(batch_generator, places=place)
# define model
transformer = Transformer(
args.src_vocab_size, args.trg_vocab_size, args.max_length + 1,
args.n_layer, args.n_head, args.d_key, args.d_value, args.d_model,
args.d_inner_hid, args.prepostprocess_dropout,
args.attention_dropout, args.relu_dropout, args.preprocess_cmd,
args.postprocess_cmd, args.weight_sharing, args.bos_idx,
args.eos_idx)
# load the trained model
assert args.init_from_params, (
"Please set init_from_params to load the infer model.")
model_dict, _ = fluid.load_dygraph(
os.path.join(args.init_from_params, "transformer"))
# to avoid a longer length than training, reset the size of position
# encoding to max_length
model_dict["encoder.pos_encoder.weight"] = position_encoding_init(
args.max_length + 1, args.d_model)
model_dict["decoder.pos_encoder.weight"] = position_encoding_init(
args.max_length + 1, args.d_model)
transformer.load_dict(model_dict)
# set evaluate mode
transformer.eval()
f = open(args.output_file, "wb")
detok = MosesDetokenizer(lang='en')
detc = MosesDetruecaser()
for input_data in test_loader():
if args.stream:
(src_word, src_pos, src_slf_attn_bias, trg_word,
trg_src_attn_bias, real_read) = input_data
else:
(src_word, src_pos, src_slf_attn_bias, trg_word,
trg_src_attn_bias) = input_data
finished_seq, finished_scores = transformer.beam_search(
src_word,
src_pos,
src_slf_attn_bias,
trg_word,
trg_src_attn_bias,
bos_id=args.bos_idx,
eos_id=args.eos_idx,
beam_size=args.beam_size,
max_len=args.max_out_len,
waitk=args.waitk,
stream=args.stream)
finished_seq = finished_seq.numpy()
finished_scores = finished_scores.numpy()
for idx, ins in enumerate(finished_seq):
for beam_idx, beam in enumerate(ins):
if beam_idx >= args.n_best: break
id_list = post_process_seq(beam, args.bos_idx,
args.eos_idx)
word_list = [trg_idx2word[id] for id in id_list]
if args.stream:
if args.waitk > 0:
# for wait-k models, wait k words in the beginning
word_list = [b''] * (args.waitk - 1) + word_list
else:
# for full sentence model, wait until the end
word_list = [b''] * (len(real_read[idx].numpy()) -
1) + word_list
final_output = []
real_output = []
_read = real_read[idx].numpy()
sent = ''
bpe_flag = False
for j in range(max(len(_read), len(word_list))):
# append number of reads at step j
r = _read[j] if j < len(_read) else 0
if r > 0:
final_output += [b''] * (r - 1)
# append number of writes at step j
w = word_list[j] if j < len(word_list) else b''
w = w.decode('utf-8')
real_output.append(w)
# if bpe_flag:
# _sent = ('%s@@ %s'%(sent, w)).strip()
# else:
# _sent = ('%s %s'%(sent, w)).strip()
_sent = ' '.join(real_output)
if len(_sent) > 0:
_sent += ' a'
_sent = ' '.join(_sent.split())
# if _sent.endswith('@@ a'):
# bpe_flag = True
# else:
# bpe_flag = False
_sent = _sent.replace('@@ ', '')
_sent = detok.detokenize(_sent.split())
_sent = detc.detruecase(_sent)
_sent = ' '.join(_sent)
_sent = _sent[:-1].strip()
incre = _sent[len(sent):]
#print('_sent0:', _sent)
sent = _sent
#print('sent:', sent)
if r > 0:
# if there is read, append a word to write
# final_output.append(w)
final_output.append(str.encode(incre))
else:
# if there is no read, append word to the final write
if j >= len(word_list):
break
# final_output[-1] += b' '+w
final_output[-1] += str.encode(incre)
#print(final_output)
#print('incre:', incre)
#print('_sent1:', _sent)
# f.write(bytes('part:'+_sent+'\n'))
sequence = b"\n".join(final_output) + b" \n"
f.write(sequence)
# embed()
else:
sequence = b" ".join(word_list) + b"\n"
f.write(sequence)
f.flush()
