def main(params):
# initialize the experiment
logger = initialize_exp(params)
# generate parser / parse parameters
parser = get_parser()
params = parser.parse_args()
torch.manual_seed(
params.seed
) # Set random seed. NB: Multi-GPU also needs torch.cuda.manual_seed_all(params.seed)
assert (params.sample_temperature
== 0) or (params.beam_size == 1), 'Cannot sample with beam search.'
assert params.amp <= 1, f'params.amp == {params.amp} not yet supported.'
reloaded = torch.load(params.model_path)
model_params = AttrDict(reloaded['params'])
logger.info("Supported languages: %s" %
", ".join(model_params.lang2id.keys()))
# update dictionary parameters
for name in [
'n_words', 'bos_index', 'eos_index', 'pad_index', 'unk_index',
'mask_index'
]:
setattr(params, name, getattr(model_params, name))
# build dictionary / build encoder / build decoder / reload weights
dico = Dictionary(reloaded['dico_id2word'], reloaded['dico_word2id'],
reloaded['dico_counts'])
encoder = TransformerModel(model_params,
dico,
is_encoder=True,
with_output=False).cuda().eval()
decoder = TransformerModel(model_params,
dico,
is_encoder=False,
with_output=True).cuda().eval()
if all([k.startswith('module.') for k in reloaded['encoder'].keys()]):
reloaded['encoder'] = {
k[len('module.'):]: v
for k, v in reloaded['encoder'].items()
}
encoder.load_state_dict(reloaded['encoder'])
if all([k.startswith('module.') for k in reloaded['decoder'].keys()]):
reloaded['decoder'] = {
k[len('module.'):]: v
for k, v in reloaded['decoder'].items()
}
decoder.load_state_dict(reloaded['decoder'])
if params.amp != 0:
models = apex.amp.initialize([encoder, decoder],
opt_level=('O%i' % params.amp))
encoder, decoder = models
params.src_id = model_params.lang2id[params.src_lang]
params.tgt_id = model_params.lang2id[params.tgt_lang]
# read sentences from stdin
src_sent = []
for line in sys.stdin.readlines():
assert len(line.strip().split()) > 0
src_sent.append(line)
logger.info("Read %i sentences from stdin. Translating ..." %
len(src_sent))
# f = io.open(params.output_path, 'w', encoding='utf-8')
hypothesis = [[] for _ in range(params.beam_size)]
for i in range(0, len(src_sent), params.batch_size):
# prepare batch
word_ids = [
torch.LongTensor([dico.index(w) for w in s.strip().split()])
for s in src_sent[i:i + params.batch_size]
]
lengths = torch.LongTensor([len(s) + 2 for s in word_ids])
batch = torch.LongTensor(lengths.max().item(),
lengths.size(0)).fill_(params.pad_index)
batch[0] = params.eos_index
for j, s in enumerate(word_ids):
if lengths[j] > 2: # if sentence not empty
batch[1:lengths[j] - 1, j].copy_(s)
batch[lengths[j] - 1, j] = params.eos_index
langs = batch.clone().fill_(params.src_id)
# encode source batch and translate it
encoded = encoder('fwd',
x=batch.cuda(),
lengths=lengths.cuda(),
langs=langs.cuda(),
causal=False)
encoded = encoded.transpose(0, 1)
max_len = int(1.5 * lengths.max().item() + 10)
if params.beam_size == 1:
decoded, dec_lengths = decoder.generate(
encoded,
lengths.cuda(),
params.tgt_id,
max_len=max_len,
sample_temperature=(None if params.sample_temperature == 0 else
params.sample_temperature))
else:
decoded, dec_lengths, all_hyp_strs = decoder.generate_beam(
encoded,
lengths.cuda(),
params.tgt_id,
beam_size=params.beam_size,
length_penalty=params.length_penalty,
early_stopping=params.early_stopping,
max_len=max_len,
output_all_hyps=True)
# hypothesis.extend(convert_to_text(decoded, dec_lengths, dico, params))
# convert sentences to words
for j in range(decoded.size(1)):
# remove delimiters
sent = decoded[:, j]
delimiters = (sent == params.eos_index).nonzero().view(-1)
assert len(delimiters) >= 1 and delimiters[0].item() == 0
sent = sent[1:] if len(delimiters) == 1 else sent[1:delimiters[1]]
# output translation
source = src_sent[i + j].strip().replace('<unk>', '<<unk>>')
target = " ".join([dico[sent[k].item()] for k in range(len(sent))
]).replace('<unk>', '<<unk>>')
if params.beam_size == 1:
hypothesis[0].append(target)
else:
for hyp_rank in range(params.beam_size):
print(
all_hyp_strs[j]
[hyp_rank if hyp_rank < len(all_hyp_strs[j]) else -1])
hypothesis[hyp_rank].append(
all_hyp_strs[j]
[hyp_rank if hyp_rank < len(all_hyp_strs[j]) else -1])
sys.stderr.write("%i / %i: %s -> %s\n" %
(i + j, len(src_sent), source.replace(
'@@ ', ''), target.replace('@@ ', '')))
# f.write(target + "\n")
# f.close()
# export sentences to reference and hypothesis files / restore BPE segmentation
save_dir, split = params.output_path.rsplit('/', 1)
for hyp_rank in range(len(hypothesis)):
hyp_name = f'hyp.st={params.sample_temperature}.bs={params.beam_size}.lp={params.length_penalty}.es={params.early_stopping}.seed={params.seed if (len(hypothesis) == 1) else str(hyp_rank)}.{params.src_lang}-{params.tgt_lang}.{split}.txt'
hyp_path = os.path.join(save_dir, hyp_name)
with open(hyp_path, 'w', encoding='utf-8') as f:
f.write('\n'.join(hypothesis[hyp_rank]) + '\n')
restore_segmentation(hyp_path)
# evaluate BLEU score
if params.ref_path:
bleu = eval_moses_bleu(params.ref_path, hyp_path)
logger.info("BLEU %s %s : %f" % (hyp_path, params.ref_path, bleu))
def main(params):
# initialize the experiment
logger = initialize_exp(params)
# generate parser / parse parameters
parser = get_parser()
params = parser.parse_args()
reloaded = torch.load(params.model_path)
model_params = AttrDict(reloaded['params'])
model_params.add_pred = ""
logger.info("Supported languages: %s" %
", ".join(model_params.lang2id.keys()))
# update dictionary parameters
for name in [
'n_words', 'bos_index', 'eos_index', 'pad_index', 'unk_index',
'mask_index'
]:
setattr(params, name, getattr(model_params, name))
# build dictionary / build encoder / build decoder / reload weights
src_dico = load_binarized(params.src_data)
tgt_dico = load_binarized(params.tgt_data)
encoder = TransformerModel(model_params,
src_dico,
is_encoder=True,
with_output=False).cuda().eval()
decoder = TransformerModel(model_params,
tgt_dico,
is_encoder=False,
with_output=True).cuda().eval()
if all([k.startswith('module.') for k in reloaded['encoder'].keys()]):
reloaded['encoder'] = {
k[len('module.'):]: v
for k, v in reloaded['encoder'].items()
}
reloaded['decoder'] = {
k[len('module.'):]: v
for k, v in reloaded['decoder'].items()
}
encoder.load_state_dict(reloaded['encoder'], strict=False)
decoder.load_state_dict(reloaded['decoder'], strict=False)
params.src_id = model_params.lang2id[params.src_lang]
params.tgt_id = model_params.lang2id[params.tgt_lang]
# # float16
# # read sentences from stdin
src_sent = []
input_f = open(params.input_path, 'r')
for line in input_f:
line = line.strip()
assert len(line.strip().split()) > 0
src_sent.append(line)
logger.info("Read %i sentences from stdin. Translating ..." %
len(src_sent))
f = io.open(params.output_path, 'w', encoding='utf-8')
for i in range(0, len(src_sent), params.batch_size):
# prepare batch
word_ids = [
torch.LongTensor([src_dico.index(w) for w in s.strip().split()])
for s in src_sent[i:i + params.batch_size]
]
lengths = torch.LongTensor([len(s) + 2 for s in word_ids])
batch = torch.LongTensor(lengths.max().item(),
lengths.size(0)).fill_(params.pad_index)
batch[0] = params.eos_index
for j, s in enumerate(word_ids):
if lengths[j] > 2: # if sentence not empty
batch[1:lengths[j] - 1, j].copy_(s)
batch[lengths[j] - 1, j] = params.eos_index
langs = batch.clone().fill_(params.src_id)
# encode source batch and translate it
encoded = encoder('fwd',
x=batch.cuda(),
lengths=lengths.cuda(),
langs=langs.cuda(),
causal=False)
encoded = [enc.transpose(0, 1) for enc in encoded]
decoded, dec_lengths = decoder.generate(
encoded,
lengths.cuda(),
params.tgt_id,
max_len=int(1.5 * lengths.max().item() + 10))
# convert sentences to words
for j in range(decoded.size(1)):
# remove delimiters
sent = decoded[:, j]
delimiters = (sent == params.eos_index).nonzero().view(-1)
assert len(delimiters) >= 1 and delimiters[0].item() == 0
sent = sent[1:] if len(delimiters) == 1 else sent[1:delimiters[1]]
# output translation
source = src_sent[i + j].strip()
target = " ".join(
[tgt_dico[sent[k].item()] for k in range(len(sent))])
#sys.stderr.write("%i / %i: %s -> %s\n" % (i + j, len(src_sent), source, target))
f.write(target + "\n")
f.close()
class Translate():
def __init__(self,
model_path,
tgt_lang,
src_lang,
dump_path="./dumped/",
exp_name="translate",
exp_id="test",
batch_size=32):
# parse parameters
parser = argparse.ArgumentParser(description="Translate sentences")
# main parameters
parser.add_argument("--dump_path",
type=str,
default=dump_path,
help="Experiment dump path")
parser.add_argument("--exp_name",
type=str,
default=exp_name,
help="Experiment name")
parser.add_argument("--exp_id",
type=str,
default=exp_id,
help="Experiment ID")
parser.add_argument("--batch_size",
type=int,
default=batch_size,
help="Number of sentences per batch")
# model / output paths
parser.add_argument("--model_path",
type=str,
default=model_path,
help="Model path")
# parser.add_argument("--max_vocab", type=int, default=-1, help="Maximum vocabulary size (-1 to disable)")
# parser.add_argument("--min_count", type=int, default=0, help="Minimum vocabulary count")
# source language / target language
parser.add_argument("--src_lang",
type=str,
default=src_lang,
help="Source language")
parser.add_argument("--tgt_lang",
type=str,
default=tgt_lang,
help="Target language")
parser.add_argument('-d',
"--text",
type=str,
default="",
nargs='+',
help="Text to be translated")
params = parser.parse_args()
assert params.src_lang != '' and params.tgt_lang != '' and params.src_lang != params.tgt_lang
# initialize the experiment
logger = initialize_exp(params)
# On a pas de GPU
#reloaded = torch.load(params.model_path)
reloaded = torch.load(params.model_path,
map_location=torch.device('cpu'))
model_params = AttrDict(reloaded['params'])
self.supported_languages = model_params.lang2id.keys()
logger.info("Supported languages: %s" %
", ".join(self.supported_languages))
# update dictionary parameters
for name in [
'n_words', 'bos_index', 'eos_index', 'pad_index', 'unk_index',
'mask_index'
]:
try:
setattr(params, name, getattr(model_params, name))
except AttributeError:
key = list(model_params.meta_params.keys())[0]
attr = getattr(model_params.meta_params[key], name)
setattr(params, name, attr)
setattr(model_params, name, attr)
# build dictionary / build encoder / build decoder / reload weights
self.dico = Dictionary(reloaded['dico_id2word'],
reloaded['dico_word2id'],
reloaded['dico_counts'])
#self.encoder = TransformerModel(model_params, dico, is_encoder=True, with_output=True).cuda().eval()
self.encoder = TransformerModel(model_params,
self.dico,
is_encoder=True,
with_output=True).eval()
#self.decoder = TransformerModel(model_params, dico, is_encoder=False, with_output=True).cuda().eval()
self.decoder = TransformerModel(model_params,
self.dico,
is_encoder=False,
with_output=True).eval()
self.encoder.load_state_dict(reloaded['encoder'])
self.decoder.load_state_dict(reloaded['decoder'])
params.src_id = model_params.lang2id[params.src_lang]
params.tgt_id = model_params.lang2id[params.tgt_lang]
self.model_params = model_params
self.params = params
def translate(self, src_sent=[]):
flag = False
if type(src_sent) == str:
src_sent = [src_sent]
flag = True
tgt_sent = []
for i in range(0, len(src_sent), self.params.batch_size):
# prepare batch
word_ids = [
torch.LongTensor(
[self.dico.index(w) for w in s.strip().split()])
for s in src_sent[i:i + self.params.batch_size]
]
lengths = torch.LongTensor([len(s) + 2 for s in word_ids])
batch = torch.LongTensor(lengths.max().item(),
lengths.size(0)).fill_(
self.params.pad_index)
batch[0] = self.params.eos_index
for j, s in enumerate(word_ids):
if lengths[j] > 2: # if sentence not empty
batch[1:lengths[j] - 1, j].copy_(s)
batch[lengths[j] - 1, j] = self.params.eos_index
langs = batch.clone().fill_(self.params.src_id)
# encode source batch and translate it
#encoded = self.encoder('fwd', x=batch.cuda(), lengths=lengths.cuda(), langs=langs.cuda(), causal=False)
encoded = self.encoder('fwd',
x=batch,
lengths=lengths,
langs=langs,
causal=False)
encoded = encoded.transpose(0, 1)
#decoded, dec_lengths = self.decoder.generate(encoded, lengths.cuda(), self.params.tgt_id, max_len=int(1.5 * lengths.max().item() + 10))
decoded, dec_lengths = self.decoder.generate(
encoded,
lengths,
self.params.tgt_id,
max_len=int(1.5 * lengths.max().item() + 10))
# convert sentences to words
for j in range(decoded.size(1)):
# remove delimiters
sent = decoded[:, j]
delimiters = (sent == self.params.eos_index).nonzero().view(-1)
assert len(delimiters) >= 1 and delimiters[0].item() == 0
sent = sent[1:] if len(
delimiters) == 1 else sent[1:delimiters[1]]
# output translation
source = src_sent[i + j].strip()
target = " ".join(
[self.dico[sent[k].item()] for k in range(len(sent))])
sys.stderr.write("%i / %i: %s -> %s\n" %
(i + j, len(src_sent), source, target))
tgt_sent.append(target)
if flag:
return tgt_sent[0]
return tgt_sent
def main(params):
# initialize the experiment
logger = initialize_exp(params)
# generate parser / parse parameters
parser = get_parser()
params = parser.parse_args()
reloaded = torch.load(params.model_path)
model_params = AttrDict(reloaded['params'])
logger.info("Supported languages: %s" %
", ".join(model_params.lang2id.keys()))
# update dictionary parameters
for name in [
'n_words', 'bos_index', 'eos_index', 'pad_index', 'unk_index',
'mask_index'
]:
setattr(params, name, getattr(model_params, name))
# build dictionary / build encoder / build decoder / reload weights
dico = Dictionary(reloaded['dico_id2word'], reloaded['dico_word2id'],
reloaded['dico_counts'])
encoder = TransformerModel(model_params,
dico,
is_encoder=True,
with_output=True).cuda().eval()
decoder = TransformerModel(model_params,
dico,
is_encoder=False,
with_output=True).cuda().eval()
encoder.load_state_dict(package_module(reloaded['encoder']))
decoder.load_state_dict(package_module(reloaded['decoder']))
params.src_id = model_params.lang2id[params.src_lang]
params.tgt_id = model_params.lang2id[params.tgt_lang]
# float16
if params.fp16:
assert torch.backends.cudnn.enabled
encoder = network_to_half(encoder)
decoder = network_to_half(decoder)
# read sentences from stdin
src_sent = []
for line in sys.stdin.readlines():
assert len(line.strip().split()) > 0
src_sent.append(line)
logger.info("Read %i sentences from stdin. Translating ..." %
len(src_sent))
f = io.open(params.output_path, 'w', encoding='utf-8')
for i in range(0, len(src_sent), params.batch_size):
# prepare batch
word_ids = [
torch.LongTensor([dico.index(w) for w in s.strip().split()])
for s in src_sent[i:i + params.batch_size]
]
lengths = torch.LongTensor([len(s) + 2 for s in word_ids])
batch = torch.LongTensor(lengths.max().item(),
lengths.size(0)).fill_(params.pad_index)
batch[0] = params.eos_index
for j, s in enumerate(word_ids):
if lengths[j] > 2: # if sentence not empty
batch[1:lengths[j] - 1, j].copy_(s)
batch[lengths[j] - 1, j] = params.eos_index
langs = batch.clone().fill_(params.src_id)
# encode source batch and translate it
encoded = encoder('fwd',
x=batch.cuda(),
lengths=lengths.cuda(),
langs=langs.cuda(),
causal=False)
encoded = encoded.transpose(0, 1)
if params.beam == 1:
decoded, dec_lengths = decoder.generate(
encoded,
lengths.cuda(),
params.tgt_id,
max_len=int(1.5 * lengths.max().item() + 10))
else:
decoded, dec_lengths = decoder.generate_beam(
encoded,
lengths.cuda(),
params.tgt_id,
beam_size=params.beam,
length_penalty=params.length_penalty,
early_stopping=False,
max_len=int(1.5 * lengths.max().item() + 10))
# convert sentences to words
for j in range(decoded.size(1)):
# remove delimiters
sent = decoded[:, j]
delimiters = (sent == params.eos_index).nonzero().view(-1)
assert len(delimiters) >= 1 and delimiters[0].item() == 0
sent = sent[1:] if len(delimiters) == 1 else sent[1:delimiters[1]]
# output translation
source = src_sent[i + j].strip()
target = " ".join([dico[sent[k].item()] for k in range(len(sent))])
sys.stderr.write("%i / %i: %s -> %s\n" %
(i + j, len(src_sent), source, target))
f.write(target + "\n")
f.close()
def main(params):
# initialize the experiment
logger = initialize_exp(params)
# generate parser / parse parameters
parser = get_parser()
params = parser.parse_args()
reloaded = torch.load(params.model_path)
model_params = AttrDict(reloaded['params'])
logger.info("Supported languages: %s" % ", ".join(model_params.lang2id.keys()))
# update dictionary parameters
for name in ['n_words', 'bos_index', 'eos_index', 'pad_index', 'unk_index', 'mask_index']:
setattr(params, name, getattr(model_params, name))
# build dictionary / build encoder / build decoder / reload weights
dico = Dictionary(reloaded['dico_id2word'], reloaded['dico_word2id'], reloaded['dico_counts'])
encoder = TransformerModel(model_params, dico, is_encoder=True, with_output=True).cuda().eval()
decoder = TransformerModel(model_params, dico, is_encoder=False, with_output=True).cuda().eval()
encoder.load_state_dict(reloaded['encoder'])
decoder.load_state_dict(reloaded['decoder'])
params.src_id = model_params.lang2id[params.src_lang]
params.tgt_id = model_params.lang2id[params.tgt_lang]
# float16
if params.fp16:
assert torch.backends.cudnn.enabled
encoder = network_to_half(encoder)
decoder = network_to_half(decoder)
input_data = torch.load(params.input)
eval_dataset = Dataset(input_data["sentences"], input_data["positions"], params)
if params.subset_start is not None:
assert params.subset_end
eval_dataset.select_data(params.subset_start, params.subset_end)
eval_dataset.remove_empty_sentences()
eval_dataset.remove_long_sentences(params.max_len)
n_batch = 0
out = io.open(params.output_path, "w", encoding="utf-8")
inp_dump = io.open(os.path.join(params.dump_path, "input.txt"), "w", encoding="utf-8")
logger.info("logging to {}".format(os.path.join(params.dump_path, 'input.txt')))
with open(params.output_path, "w", encoding="utf-8") as out:
for batch in eval_dataset.get_iterator(shuffle=False):
n_batch += 1
(x1, len1) = batch
input_text = convert_to_text(x1, len1, input_data["dico"], params)
inp_dump.write("\n".join(input_text))
inp_dump.write("\n")
langs1 = x1.clone().fill_(params.src_id)
# cuda
x1, len1, langs1 = to_cuda(x1, len1, langs1)
# encode source sentence
enc1 = encoder("fwd", x=x1, lengths=len1, langs=langs1, causal=False)
enc1 = enc1.transpose(0, 1)
# generate translation - translate / convert to text
max_len = int(1.5 * len1.max().item() + 10)
if params.beam_size == 1:
generated, lengths = decoder.generate(enc1, len1, params.tgt_id, max_len=max_len)
else:
generated, lengths = decoder.generate_beam(
enc1, len1, params.tgt_id, beam_size=params.beam_size,
length_penalty=params.length_penalty,
early_stopping=params.early_stopping,
max_len=max_len)
hypotheses_batch = convert_to_text(generated, lengths, input_data["dico"], params)
out.write("\n".join(hypotheses_batch))
out.write("\n")
if n_batch % 100 == 0:
logger.info("{} batches processed".format(n_batch))
out.close()
inp_dump.close()