def _get_parser():
parser = ArgumentParser(description='translate.py')
# import ipdb; ipdb.set_trace()
opts.config_opts(parser)
opts.translate_opts(parser)
return parser
def _get_parser():
parser = ArgumentParser(description='translate.py')
opts.config_opts(parser)
opts.translate_opts(parser)
print(parser.parse_args())
return parser
def _get_opt(self, language, method):
parser = ArgumentParser(description='summarizer.py')
if language == 'en' and method == 'bert':
config_file = 'en_bert_transformer.yml'
elif language == 'en' and method == 'conv':
config_file = 'en_conv_transformer.yml'
elif language == 'de' and method == 'bert':
config_file = 'de_bert_transformer.yml'
elif language == 'de' and method == 'conv':
config_file = 'de_conv_transformer.yml'
else:
sys.stderr.write(
f"Method '{method}' for language '{language}' is not supported."
)
#Hack to load parser arguments
prec_argv = sys.argv
sys.argv = [sys.argv[0]]
sys.argv.extend(['-config', 'config/' + config_file])
opts.config_opts(parser)
opts.translate_opts(parser)
opt = parser.parse_args()
sys.argv = prec_argv
return opt
def translate_file(input_filename, output_filename):
parser = ArgumentParser(description='translation')
opts.config_opts(parser)
opts.translate_opts(parser)
# print(opts)
args = f'''-model m16_step_44000.pt
-src source_products_16.txt
-output op_16x_4400_50_10.txt
-batch_size 128
-replace_unk
-max_length 200
-verbose
-beam_size 50
-n_best 10
-min_length 5'''
opt = parser.parse_args(args)
# print(opt.model)
translator = build_translator(opt, report_score=True)
src_shards = split_corpus(opt.src, opt.shard_size)
tgt_shards = repeat(None)
shard_pairs = zip(src_shards, tgt_shards)
for i, (src_shard, tgt_shard) in enumerate(shard_pairs):
scores, predictions = translator.translate(src=src_shard,
tgt=tgt_shard,
src_dir=opt.src_dir,
batch_size=opt.batch_size,
attn_debug=opt.attn_debug)
return scores, predictions
def _get_parser():
parser = ArgumentParser(description='translate.py')
opts.config_opts(parser)
opts.translate_opts(parser)
parser.add(
'--model',
'-model',
dest='models',
metavar='MODEL',
nargs='+',
type=str,
default=[
"F:/Project/Python/selfProject/translate_NMT/transflate_NMT/data_step_100.pt"
],
required=False,
help="模型使用得训练文件")
parser.add(
'--src',
'-src',
required=False,
default=
"F:/Project/Python/selfProject/translate_NMT/transflate_NMT/data/src-test.txt",
help="自己写的测试文件在哪里？？？？")
parser.add(
'--output',
'-output',
default=
'F:/Project/Python/selfProject/translate_NMT/transflate_NMT/data/pred.txt',
help="测试文件输出未知 改成自己得")
return parser
def build_translator(model, fields, model_opt, beam_size=1, n_best=1):
# model, fields, model_opt are all loaded from start of script
model_opt.beta = -0.0
model_opt.coverage_penalty = "none"
model_opt.length_penalty = "none"
scorer = onmt.translate.GNMTGlobalScorer(alpha=0.0,
beta=-0.0,
cov_penalty="none",
length_penalty="none")
dummy_parser = argparse.ArgumentParser(description='translate.py')
opts.translate_opts(dummy_parser)
dummy_translate_opt = dummy_parser.parse_known_args(
"-model dummy -src dummy".split())[0]
dummy_translate_opt.beam_size = beam_size
dummy_translate_opt.cuda = False
translator = Translator(model,
fields,
beam_size,
n_best,
global_scorer=scorer,
out_file=None,
report_score=False,
gpu=False,
replace_unk=True)
# translator.beam_size = beam_size
# translator.n_best = n_best
return translator
def _get_parser():
parser = ArgumentParser(description='translate.py')
opts.config_opts(parser)
opts.translate_opts(parser)
opts.mmod_finetune_translate_opts(parser)
return parser
def _get_parser():
parser = ArgumentParser(description='run_kp_eval.py')
opts.config_opts(parser)
opts.translate_opts(parser)
return parser
def translate_file(input_filename, output_filename):
parser = ArgumentParser(description='translation')
opts.config_opts(parser)
opts.translate_opts(parser)
args = f'''-model Experiments/Checkpoints/retrosynthesis_augmented_medium/retrosynthesis_aug_medium_model_step_100000.pt
-src MCTS_data/{input_filename}.txt
-output MCTS_data/{output_filename}.txt
-batch_size 128
-replace_unk
-max_length 200
-verbose
-beam_size 10
-n_best 10
-min_length 5
-gpu 0'''
opt = parser.parse_args(args)
translator = build_translator(opt, report_score=True)
src_shards = split_corpus(opt.src, opt.shard_size)
tgt_shards = repeat(None)
shard_pairs = zip(src_shards, tgt_shards)
for i, (src_shard, tgt_shard) in enumerate(shard_pairs):
scores, predictions = translator.translate(src=src_shard,
tgt=tgt_shard,
src_dir=opt.src_dir,
batch_size=opt.batch_size,
attn_debug=opt.attn_debug)
return scores, predictions
def _get_parser():
parser = ArgumentParser(description='RL_train.py')
opts.config_opts(parser)
# yida RL
opts.model_opts(parser)
# opts.train_opts(parser)
opts.translate_opts(parser)
return parser
def onmt_parser() -> ArgumentParser:
"""
Create the OpenNMT parser, adapted from OpenNMT-Py repo.
"""
parser = ArgumentParser(description='translate.py')
opts.config_opts(parser)
opts.translate_opts(parser)
return parser
def configure_opt(self, description):
# Uses OpenNMT's ArgumentParser class to create an object
# That holds all the parameters needed to load the model
parser = ArgumentParser(description='translation')
opts.config_opts(parser)
opts.translate_opts(parser)
opt = {a.dest: a.default for a in parser._actions}
opt.update(description['opt'])
opt['models'] = [description['model']]
opt = types.SimpleNamespace(**opt)
return opt
def __init__(self):
# おまじない
parser = ArgumentParser()
opts.config_opts(parser)
opts.translate_opts(parser)
self.opt = parser.parse_args()
ArgumentParser.validate_translate_opts(self.opt)
self.translator = build_translator(self.opt, report_score=True)
# 単語分割用にMeCabを使用
self.mecab = MeCab.Tagger("-Owakati")
self.mecab.parse("")
def load_model(self, path=None):
if path is None or not os.path.exists(
os.path.abspath(os.path.join(os.getcwd(), path))):
print("No model present at the specified path : {}".format(path))
parser = ArgumentParser(description='translate.py')
opts.config_opts(parser)
opts.translate_opts(parser)
opt = parser.parse_args(["--model", path])
self.model = build_translator(opt, report_score=True)
return
def __init__(self):
# コマンドラインで指定したオプションをもとにモデルを読み込む
parser = ArgumentParser()
opts.config_opts(parser)
opts.translate_opts(parser)
self.opt = parser.parse_args()
ArgumentParser.validate_translate_opts(self.opt)
self.translator = build_translator(self.opt, report_score=True)
# 分かち書きのためにMeCabを使用
self.mecab = MeCab.Tagger("-Owakati")
self.mecab.parse("")
# 前回の応答を保存しておく辞書
self.prev_uttr_dict = {}
def __init__(self):
# おまじない
parser = ArgumentParser()
opts.config_opts(parser)
opts.translate_opts(parser)
self.opt = parser.parse_args(args=[
"-model", "../models/model.pt", "-src", "None", "-replace_unk",
"--beam_size", "10", "--min_length", "7", "--block_ngram_repeat",
"2"
])
ArgumentParser.validate_translate_opts(self.opt)
self.translator = build_translator(self.opt, report_score=True)
# 単語分割用にMeCabを使用
self.mecab = MeCab.Tagger("-Owakati")
self.mecab.parse("")
def _build_translator(args):
"""
Initializes a seq2seq translator model
"""
from onmt.utils.parse import ArgumentParser
parser = ArgumentParser()
import onmt.opts as opts
opts.config_opts(parser)
opts.translate_opts(parser)
opt = parser.parse_args(args=args)
ArgumentParser.validate_translate_opts(opt)
from onmt.translate.translator import build_translator
translator = build_translator(opt, report_score=False)
return translator, opt
def trans(n_bset,content_src):
parser = argparse.ArgumentParser()
translate_opts(parser)
opt = parser.parse_args()
model_dir = os.path.join(os.path.dirname(__file__), 'available_models')
opt.model=os.path.join(model_dir, 'zh2zh_model_2000000.pt')
opt.n_best=n_bset
opt.gpu=-1
opt.data_type='text'
translator = build_translator(opt, report_score=True)
result = translator.translate(src_path=opt.src,
src_data_iter=content_src,
tgt_path=opt.tgt,
src_dir=opt.src_dir,
batch_size=opt.batch_size,
attn_debug=opt.attn_debug)
return result
def __init__(self, gpu):
parser = configargparse.ArgumentParser(
description='translate.py',
config_file_parser_class=configargparse.YAMLConfigFileParser,
formatter_class=configargparse.ArgumentDefaultsHelpFormatter)
opts.config_opts(parser)
opts.add_md_help_argument(parser)
opts.translate_opts(parser)
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
if len(gpu) > 1:
print('do not try hacking')
exit()
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu)
sys.argv = ["python"]
opt = parser.parse_args()
opt.models = [
'./modules/multi_summ/dataset_m2s2/korean_bert_8_single_new_economy_add_cls_sep_segment_eos_penalty_new_bloom_step_25000.pt'
]
opt.segment = True
opt.batch_size = 8
opt.beam_size = 10
opt.src = '.1'
opt.output = '.1'
opt.verbose = True
opt.stepwise_penalty = True
opt.coverage_penalty = 'sumarry'
opt.beta = 5
opt.length_penalty = 'wu'
opt.alpha = 0.9
opt.block_ngram_repeat = 3
opt.ignore_when_blocking = [".", "</t", "<t>", ",_", "%"]
opt.max_length = 300
opt.min_length = 35
opt.gpu = 0
opt.segment = True
logger = init_logger(opt.log_file)
self.translator = build_translator(opt, report_score=True)
def onmt_translator_builder(my_opts, s0_model_path, logger=None):
if logger is not None:
logger.info('Building ONMT translator with model from ' + s0_model_path)
else:
print('Building ONMT translator with model from ' + s0_model_path)
parser = ArgumentParser()
opts.config_opts(parser)
opts.translate_opts(parser)
arglist = ['-model', s0_model_path] + opts_to_list(my_opts)
print(arglist)
opt = parser.parse_args(arglist)
ArgumentParser.validate_translate_opts(opt)
translator = build_translator(opt, report_score=True, logger=logger)
if logger is not None:
logger.info('Finished building ONMT translator.')
else:
print('Finished building ONMT translator.')
return translator
def translate_trained_model(n_latent,
data_path,
output_dir,
trained_model_path,
use_segments=False,
max_segments=10):
parser = ArgumentParser()
opts.config_opts(parser)
opts.translate_opts(parser)
opt = parser.parse_args('')
for k, v in translate.DEFAULT_TRANSLATE_PARAMS.items():
vars(opt)[k] = v
vars(opt)['models'] = [trained_model_path]
src_path = '/'.join(data_path.split('/')[:-1]) + '/src-test.txt'
vars(opt)['src'] = src_path
output_path = '/'.join(output_dir.split('/')[:-2]) + '/preds'
vars(opt)['output_dir'] = output_path
vars(opt)['n_latent'] = n_latent
vars(opt)['use_segments'] = use_segments
vars(opt)['max_segments'] = max_segments
translate.main(opt)
def translate_esan(input_eng, model):
input_file = "corpus/input.txt"
output_file = "corpus/output.txt"
f = open(input_file, "w", encoding="UTF-8")
f.write(input_eng)
f.close()
open(output_file, "w", encoding="UTF-8").close()
parser = configargparse.ArgumentParser(
description='translate.py',
config_file_parser_class=configargparse.DefaultConfigFileParser,
formatter_class=configargparse.ArgumentDefaultsHelpFormatter,
add_env_var_help=True)
opts.config_opts(parser)
opts.translate_opts(parser)
s = "-model " + model + " -src " + input_file + " -output " + output_file
opt = parser.parse_args(s)
main(opt)
f = open(output_file, "r", encoding="Utf-8")
content = ""
if f.mode == 'r':
content = f.read()
f.close()
return content
# else:
# tgt_iter = None
translator.translate(src_data_iter=src_iter,
tgt_data_iter=tgt_iter,
batch_size=opt.batch_size,
out_file=out_file)
out_file.close()
if __name__ == "__main__":
parser = configargparse.ArgumentParser(
description='translate.py',
config_file_parser_class=configargparse.YAMLConfigFileParser,
formatter_class=configargparse.ArgumentDefaultsHelpFormatter)
opts.config_opts(parser)
opts.translate_opts(parser)
opt = parser.parse_args()
logger = init_logger(opt.log_file)
logger.info("Input args: %r", opt)
path = 'rein_model/rein_model_step'
for i in range(0, 25000, 10):
current_path = path + '_' + str(i) + '.pt'
if os.path.exists(current_path):
model_path = current_path
opt.output = 'rein_data/rein.tran' + '_' + str(i)
main(opt, model_path)
else:
continue
def __build_translator(self, model_addr, src_addr):
parser = ArgumentParser()
opts.config_opts(parser)
opts.translate_opts(parser)
opt = parser.parse_args(['-model', model_addr, '-src', src_addr])
return build_translator(opt, report_score=False)
def _get_parser():
parser = ArgumentParser(description='translate.py')
parser.add_argument('--pivot_vocab', action='store', dest='pivot_vocab')
opts.config_opts(parser)
opts.translate_opts(parser)
return parser
def _get_parser():
parser = ArgumentParser(description='kp_generate.py')
opts.config_opts(parser)
opts.translate_opts(parser)
return parser
def _get_parser():
parser = ArgumentParser(description='translate_dynamic.py')
opts.config_opts(parser)
opts.translate_opts(parser, dynamic=True)
return parser
def __init__(self, model_filename, cmdline_args):
parser = argparse.ArgumentParser(
description='translate.py',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
opts.add_md_help_argument(parser)
opts.translate_opts(parser)
opt = parser.parse_args(['-model', model_filename, '-src', ''] +
(cmdline_args or []))
translator = make_translator(opt)
model = translator.model
fields = translator.fields
tgt_vocab = fields["tgt"].vocab
def encode_from_src(src):
enc_states, memory_bank = model.encoder(src)
return dict(enc_states=enc_states,
memory_bank=memory_bank,
src=src)
@lru_cache(maxsize=32)
def encode_text(in_text):
text_preproc = fields['src'].preprocess(in_text)
src, src_len = fields['src'].process([text_preproc],
device=-1,
train=False)
src = src.unsqueeze(2) # not sure why
return encode_from_src(src)
@lru_cache(maxsize=32)
def encode_img(image_idx):
if isinstance(image_idx, str):
image_idx = int(image_idx)
src = Variable(torch.IntTensor([image_idx]), volatile=True)
return encode_from_src(src)
def encode(inp):
if model.encoder.__class__.__name__ == 'VecsEncoder':
return encode_img(inp)
else:
return encode_text(inp)
@lru_cache(maxsize=128)
def get_decoder_state(in_text, tokens_so_far):
encoder_out = encode(in_text)
enc_states = encoder_out['enc_states']
memory_bank = encoder_out['memory_bank']
src = encoder_out['src']
if len(tokens_so_far) == 0:
return None, translator.model.decoder.init_decoder_state(
src, memory_bank, enc_states)
prev_out, prev_state = get_decoder_state(in_text,
tokens_so_far[:-1])
tgt_in = Variable(torch.LongTensor(
[tgt_vocab.stoi[tokens_so_far[-1]]]),
volatile=True) # [tgt_len]
tgt_in = tgt_in.unsqueeze(1) # [tgt_len x batch=1]
tgt_in = tgt_in.unsqueeze(1) # [tgt_len x batch=1 x nfeats=1]
# Prepare to call the decoder. Unfortunately the decoder mutates the state passed in!
memory_bank = copy.deepcopy(memory_bank)
assert isinstance(prev_state.hidden, tuple)
prev_state.hidden = tuple(v.detach() for v in prev_state.hidden)
prev_state = copy.deepcopy(prev_state)
assert memory_bank.size()[1] == 1
dec_out, dec_states, attn = translator.model.decoder(
tgt_in, memory_bank, prev_state)
assert dec_out.shape[0] == 1
return dec_out[0], dec_states
def generate_completions(in_text, tokens_so_far):
tokens_so_far = [onmt.io.BOS_WORD] + tokens_so_far
tokens_so_far = tuple(tokens_so_far) # Make it hashable
dec_out, dec_states = get_decoder_state(in_text, tokens_so_far)
logits = model.generator.forward(dec_out).data
vocab = tgt_vocab.itos
assert logits.shape[0] == 1
logits = logits[0]
return logits, vocab
def eval_logprobs(in_text, tokens, *, use_eos):
encoder_out = encode(in_text)
enc_states = encoder_out['enc_states']
memory_bank = encoder_out['memory_bank']
src = encoder_out['src']
tokens = [onmt.io.BOS_WORD] + tokens
if use_eos:
tokens = tokens + [onmt.io.EOS_WORD]
decoder_state = model.decoder.init_decoder_state(
src, memory_bank=memory_bank, encoder_final=enc_states)
tgt = Variable(
torch.LongTensor([tgt_vocab.stoi[tok] for tok in tokens
]).unsqueeze(1).unsqueeze(1))
dec_out, dec_states, attn = model.decoder(tgt[:-1], memory_bank,
decoder_state)
logits = model.generator(dec_out)
return F.nll_loss(logits.squeeze(1),
tgt[1:].squeeze(1).squeeze(1),
reduce=False,
size_average=False).data.numpy()
self.model = model
self.fields = fields
self.translator = translator
self.encode = encode
self.get_decoder_state = get_decoder_state
self.generate_completions = generate_completions
self.eval_logprobs = eval_logprobs