def __init__(self, engine_path, is_admin):
self.engine_path = engine_path
self.is_admin = is_admin
self.model_path = os.path.join(engine_path, 'model')
self.config_path = os.path.join(engine_path, 'config.yaml')
self.gpu_id = None
# Load parameters from configuration file
config = load_config(self.config_path)
if "load_model" in config['training'].keys():
self.ckpt = os.path.realpath(
os.path.join(app.config['JOEYNMT_FOLDER'],
config['training']["load_model"]))
else:
self.ckpt = get_latest_checkpoint(self.model_path)
self.use_cuda = config["training"].get("use_cuda", False)
self.level = config["data"]["level"]
self.max_output_length = config["training"].get(
"max_output_length", None)
self.lowercase = config["data"].get("lowercase", False)
self.model_data = config["model"]
# load the vocabularies
src_vocab_file = os.path.realpath(
os.path.join(app.config['JOEYNMT_FOLDER'],
config["data"]["src_vocab"]))
trg_vocab_file = os.path.realpath(
os.path.join(app.config['JOEYNMT_FOLDER'],
config["data"]["trg_vocab"]))
self.src_vocab = build_vocab(field="src",
vocab_file=src_vocab_file,
dataset=None,
max_size=-1,
min_freq=0)
self.trg_vocab = build_vocab(field="trg",
vocab_file=trg_vocab_file,
dataset=None,
max_size=-1,
min_freq=0)
# whether to use beam search for decoding, 0: greedy decoding
if "testing" in config.keys():
self.beam_size = config["testing"].get("beam_size", 0)
self.beam_alpha = config["testing"].get("alpha", -1)
else:
self.beam_size = 1
self.beam_alpha = -1
self.logger = logging.getLogger(__name__)
def translate(cfg_file: str,
ckpt: str,
output_path: str = None,
batch_class: Batch = Batch,
n_best: int = 1) -> None:
"""
Interactive translation function.
Loads model from checkpoint and translates either the stdin input or
asks for input to translate interactively.
The input has to be pre-processed according to the data that the model
was trained on, i.e. tokenized or split into subwords.
Translations are printed to stdout.
:param cfg_file: path to configuration file
:param ckpt: path to checkpoint to load
:param output_path: path to output file
:param batch_class: class type of batch
:param n_best: amount of candidates to display
"""
def _load_line_as_data(line):
""" Create a dataset from one line via a temporary file. """
# write src input to temporary file
tmp_name = "tmp"
tmp_suffix = ".src"
tmp_filename = tmp_name + tmp_suffix
with open(tmp_filename, "w") as tmp_file:
tmp_file.write("{}\n".format(line))
test_data = MonoDataset(path=tmp_name, ext=tmp_suffix, field=src_field)
# remove temporary file
if os.path.exists(tmp_filename):
os.remove(tmp_filename)
return test_data
def _translate_data(test_data):
""" Translates given dataset, using parameters from outer scope. """
# pylint: disable=unused-variable
score, loss, ppl, sources, sources_raw, references, hypotheses, \
hypotheses_raw, attention_scores = validate_on_data(
model, data=test_data, batch_size=batch_size,
batch_class=batch_class, batch_type=batch_type, level=level,
max_output_length=max_output_length, eval_metric="",
use_cuda=use_cuda, compute_loss=False, beam_size=beam_size,
beam_alpha=beam_alpha, postprocess=postprocess,
bpe_type=bpe_type, sacrebleu=sacrebleu, n_gpu=n_gpu, n_best=n_best)
return hypotheses
cfg = load_config(cfg_file)
model_dir = cfg["training"]["model_dir"]
_ = make_logger(model_dir, mode="translate")
# version string returned
# when checkpoint is not specified, take oldest from model dir
if ckpt is None:
ckpt = get_latest_checkpoint(model_dir)
# read vocabs
src_vocab_file = cfg["data"].get("src_vocab", model_dir + "/src_vocab.txt")
trg_vocab_file = cfg["data"].get("trg_vocab", model_dir + "/trg_vocab.txt")
src_vocab = Vocabulary(file=src_vocab_file)
trg_vocab = Vocabulary(file=trg_vocab_file)
data_cfg = cfg["data"]
level = data_cfg["level"]
lowercase = data_cfg["lowercase"]
tok_fun = lambda s: list(s) if level == "char" else s.split()
src_field = Field(init_token=None,
eos_token=EOS_TOKEN,
pad_token=PAD_TOKEN,
tokenize=tok_fun,
batch_first=True,
lower=lowercase,
unk_token=UNK_TOKEN,
include_lengths=True)
src_field.vocab = src_vocab
# parse test args
batch_size, batch_type, use_cuda, device, n_gpu, level, _, \
max_output_length, beam_size, beam_alpha, postprocess, \
bpe_type, sacrebleu, _, _ = parse_test_args(cfg, mode="translate")
# load model state from disk
model_checkpoint = load_checkpoint(ckpt, use_cuda=use_cuda)
# build model and load parameters into it
model = build_model(cfg["model"], src_vocab=src_vocab, trg_vocab=trg_vocab)
model.load_state_dict(model_checkpoint["model_state"])
if use_cuda:
model.to(device)
if not sys.stdin.isatty():
# input file given
test_data = MonoDataset(path=sys.stdin, ext="", field=src_field)
all_hypotheses = _translate_data(test_data)
if output_path is not None:
# write to outputfile if given
def write_to_file(output_path_set, hypotheses):
with open(output_path_set, mode="w", encoding="utf-8") \
as out_file:
for hyp in hypotheses:
out_file.write(hyp + "\n")
logger.info("Translations saved to: %s.", output_path_set)
if n_best > 1:
for n in range(n_best):
file_name, file_extension = os.path.splitext(output_path)
write_to_file(
"{}-{}{}".format(
file_name, n,
file_extension if file_extension else ""), [
all_hypotheses[i]
for i in range(n, len(all_hypotheses), n_best)
])
else:
write_to_file("{}".format(output_path), all_hypotheses)
else:
# print to stdout
for hyp in all_hypotheses:
print(hyp)
else:
# enter interactive mode
batch_size = 1
batch_type = "sentence"
while True:
try:
src_input = input("\nPlease enter a source sentence "
"(pre-processed): \n")
if not src_input.strip():
break
# every line has to be made into dataset
test_data = _load_line_as_data(line=src_input)
hypotheses = _translate_data(test_data)
print("JoeyNMT: Hypotheses ranked by score")
for i, hyp in enumerate(hypotheses):
print("JoeyNMT #{}: {}".format(i + 1, hyp))
except (KeyboardInterrupt, EOFError):
print("\nBye.")
break
def test(cfg_file,
ckpt: str,
batch_class: Batch = Batch,
output_path: str = None,
save_attention: bool = False,
datasets: dict = None) -> None:
"""
Main test function. Handles loading a model from checkpoint, generating
translations and storing them and attention plots.
:param cfg_file: path to configuration file
:param ckpt: path to checkpoint to load
:param batch_class: class type of batch
:param output_path: path to output
:param datasets: datasets to predict
:param save_attention: whether to save the computed attention weights
"""
cfg = load_config(cfg_file)
model_dir = cfg["training"]["model_dir"]
if len(logger.handlers) == 0:
_ = make_logger(model_dir, mode="test") # version string returned
# when checkpoint is not specified, take latest (best) from model dir
if ckpt is None:
ckpt = get_latest_checkpoint(model_dir)
try:
step = ckpt.split(model_dir + "/")[1].split(".ckpt")[0]
except IndexError:
step = "best"
# load the data
if datasets is None:
_, dev_data, test_data, src_vocab, trg_vocab = load_data(
data_cfg=cfg["data"], datasets=["dev", "test"])
data_to_predict = {"dev": dev_data, "test": test_data}
else: # avoid to load data again
data_to_predict = {"dev": datasets["dev"], "test": datasets["test"]}
src_vocab = datasets["src_vocab"]
trg_vocab = datasets["trg_vocab"]
# parse test args
batch_size, batch_type, use_cuda, device, n_gpu, level, eval_metric, \
max_output_length, beam_size, beam_alpha, postprocess, \
bpe_type, sacrebleu, decoding_description, tokenizer_info \
= parse_test_args(cfg, mode="test")
# load model state from disk
model_checkpoint = load_checkpoint(ckpt, use_cuda=use_cuda)
# build model and load parameters into it
model = build_model(cfg["model"], src_vocab=src_vocab, trg_vocab=trg_vocab)
model.load_state_dict(model_checkpoint["model_state"])
if use_cuda:
model.to(device)
# multi-gpu eval
if n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
model = _DataParallel(model)
for data_set_name, data_set in data_to_predict.items():
if data_set is None:
continue
dataset_file = cfg["data"][data_set_name] + "." + cfg["data"]["trg"]
logger.info("Decoding on %s set (%s)...", data_set_name, dataset_file)
#pylint: disable=unused-variable
score, loss, ppl, sources, sources_raw, references, hypotheses, \
hypotheses_raw, attention_scores = validate_on_data(
model, data=data_set, batch_size=batch_size,
batch_class=batch_class, batch_type=batch_type, level=level,
max_output_length=max_output_length, eval_metric=eval_metric,
use_cuda=use_cuda, compute_loss=False, beam_size=beam_size,
beam_alpha=beam_alpha, postprocess=postprocess,
bpe_type=bpe_type, sacrebleu=sacrebleu, n_gpu=n_gpu)
#pylint: enable=unused-variable
if "trg" in data_set.fields:
logger.info("%4s %s%s: %6.2f [%s]", data_set_name, eval_metric,
tokenizer_info, score, decoding_description)
else:
logger.info("No references given for %s -> no evaluation.",
data_set_name)
if save_attention:
if attention_scores:
attention_name = "{}.{}.att".format(data_set_name, step)
attention_path = os.path.join(model_dir, attention_name)
logger.info(
"Saving attention plots. This might take a while..")
store_attention_plots(attentions=attention_scores,
targets=hypotheses_raw,
sources=data_set.src,
indices=range(len(hypotheses)),
output_prefix=attention_path)
logger.info("Attention plots saved to: %s", attention_path)
else:
logger.warning("Attention scores could not be saved. "
"Note that attention scores are not available "
"when using beam search. "
"Set beam_size to 1 for greedy decoding.")
if output_path is not None:
output_path_set = "{}.{}".format(output_path, data_set_name)
with open(output_path_set, mode="w", encoding="utf-8") as out_file:
for hyp in hypotheses:
out_file.write(hyp + "\n")
logger.info("Translations saved to: %s", output_path_set)
def translate(cfg_file, ckpt: str, output_path: str = None) -> None:
"""
Interactive translation function.
Loads model from checkpoint and translates either the stdin input or
asks for input to translate interactively.
The input has to be pre-processed according to the data that the model
was trained on, i.e. tokenized or split into subwords.
Translations are printed to stdout.
:param cfg_file: path to configuration file
:param ckpt: path to checkpoint to load
:param output_path: path to output file
"""
def _load_line_as_data(line):
""" Create a dataset from one line via a temporary file. """
# write src input to temporary file
tmp_name = "tmp"
tmp_suffix = ".src"
tmp_filename = tmp_name + tmp_suffix
with open(tmp_filename, "w") as tmp_file:
tmp_file.write("{}\n".format(line))
test_data = MonoDataset(path=tmp_name, ext=tmp_suffix, field=src_field)
# remove temporary file
if os.path.exists(tmp_filename):
os.remove(tmp_filename)
return test_data
logger = make_logger()
def _translate_data(test_data):
""" Translates given dataset, using parameters from outer scope. """
# pylint: disable=unused-variable
score, loss, ppl, sources, sources_raw, references, hypotheses, \
hypotheses_raw, attention_scores = validate_on_data(
model, data=test_data, batch_size=batch_size,
batch_type=batch_type, level=level,
max_output_length=max_output_length, eval_metric="",
use_cuda=use_cuda, loss_function=None, beam_size=beam_size,
beam_alpha=beam_alpha, logger=logger, postprocess=postprocess)
return hypotheses
cfg = load_config(cfg_file)
# when checkpoint is not specified, take oldest from model dir
if ckpt is None:
model_dir = cfg["training"]["model_dir"]
ckpt = get_latest_checkpoint(model_dir)
batch_size = cfg["training"].get("eval_batch_size",
cfg["training"].get("batch_size", 1))
batch_type = cfg["training"].get(
"eval_batch_type", cfg["training"].get("batch_type", "sentence"))
use_cuda = cfg["training"].get("use_cuda", False)
level = cfg["data"]["level"]
max_output_length = cfg["training"].get("max_output_length", None)
# read vocabs
src_vocab_file = cfg["data"].get(
"src_vocab", cfg["training"]["model_dir"] + "/src_vocab.txt")
trg_vocab_file = cfg["data"].get(
"trg_vocab", cfg["training"]["model_dir"] + "/trg_vocab.txt")
src_vocab = Vocabulary(file=src_vocab_file)
trg_vocab = Vocabulary(file=trg_vocab_file)
data_cfg = cfg["data"]
level = data_cfg["level"]
lowercase = data_cfg["lowercase"]
tok_fun = lambda s: list(s) if level == "char" else s.split()
src_field = Field(init_token=None,
eos_token=EOS_TOKEN,
pad_token=PAD_TOKEN,
tokenize=tok_fun,
batch_first=True,
lower=lowercase,
unk_token=UNK_TOKEN,
include_lengths=True)
src_field.vocab = src_vocab
# load model state from disk
model_checkpoint = load_checkpoint(ckpt, use_cuda=use_cuda)
# build model and load parameters into it
model = build_model(cfg["model"], src_vocab=src_vocab, trg_vocab=trg_vocab)
model.load_state_dict(model_checkpoint["model_state"])
if use_cuda:
model.cuda()
# whether to use beam search for decoding, <2: greedy decoding
if "testing" in cfg.keys():
beam_size = cfg["testing"].get("beam_size", 1)
beam_alpha = cfg["testing"].get("alpha", -1)
postprocess = cfg["testing"].get("postprocess", True)
else:
beam_size = 1
beam_alpha = -1
postprocess = True
if not sys.stdin.isatty():
# input file given
test_data = MonoDataset(path=sys.stdin, ext="", field=src_field)
hypotheses = _translate_data(test_data)
if output_path is not None:
# write to outputfile if given
output_path_set = "{}".format(output_path)
with open(output_path_set, mode="w", encoding="utf-8") as out_file:
for hyp in hypotheses:
out_file.write(hyp + "\n")
logger.info("Translations saved to: %s.", output_path_set)
else:
# print to stdout
for hyp in hypotheses:
print(hyp)
else:
# enter interactive mode
batch_size = 1
batch_type = "sentence"
while True:
try:
src_input = input("\nPlease enter a source sentence "
"(pre-processed): \n")
if not src_input.strip():
break
# every line has to be made into dataset
test_data = _load_line_as_data(line=src_input)
hypotheses = _translate_data(test_data)
print("JoeyNMT: {}".format(hypotheses[0]))
except (KeyboardInterrupt, EOFError):
print("\nBye.")
break
def test(cfg_file,
ckpt: str,
output_path: str = None,
save_attention: bool = False,
logger: Logger = None) -> None:
"""
Main test function. Handles loading a model from checkpoint, generating
translations and storing them and attention plots.
:param cfg_file: path to configuration file
:param ckpt: path to checkpoint to load
:param output_path: path to output
:param save_attention: whether to save the computed attention weights
:param logger: log output to this logger (creates new logger if not set)
"""
if logger is None:
logger = make_logger()
cfg = load_config(cfg_file)
# when checkpoint is not specified, take latest (best) from model dir
step = "best"
model_dir = cfg["training"]["model_dir"]
if ckpt is None:
ckpt = get_latest_checkpoint(model_dir)
if ckpt is None:
raise FileNotFoundError(
"No checkpoint found in directory {}.".format(model_dir))
try:
step = ckpt.split(model_dir + "/")[1].split(".ckpt")[0]
except IndexError:
step = "best"
architecture = cfg["model"].get("architecture", "encoder-decoder")
batch_size = cfg["training"].get("eval_batch_size",
cfg["training"]["batch_size"])
batch_type = cfg["training"].get(
"eval_batch_type", cfg["training"].get("batch_type", "sentence"))
use_cuda = cfg["training"].get("use_cuda", False)
level = cfg["data"]["level"]
eval_metric = cfg["training"]["eval_metric"]
max_output_length = cfg["training"].get("max_output_length", None)
# original encoder-decoder testing
if architecture == "encoder-decoder":
if "test" not in cfg["data"].keys():
raise ValueError("Test data must be specified in config.")
# load the data
_, dev_data, test_data, src_vocab, trg_vocab = load_data(
data_cfg=cfg["data"])
data_to_predict = {"dev": dev_data, "test": test_data}
# load model state from disk
model_checkpoint = load_checkpoint(ckpt, use_cuda=use_cuda)
# build model and load parameters into it
model = build_model(cfg["model"],
src_vocab=src_vocab,
trg_vocab=trg_vocab)
model.load_state_dict(model_checkpoint["model_state"])
if use_cuda:
model.cuda()
# whether to use beam search for decoding, 0: greedy decoding
if "testing" in cfg.keys():
beam_size = cfg["testing"].get("beam_size", 1)
beam_alpha = cfg["testing"].get("alpha", -1)
postprocess = cfg["testing"].get("postprocess", True)
else:
beam_size = 1
beam_alpha = -1
postprocess = True
for data_set_name, data_set in data_to_predict.items():
# pylint: disable=unused-variable
score, loss, ppl, sources, sources_raw, references, hypotheses, \
hypotheses_raw, attention_scores = validate_on_data(
model, data=data_set, batch_size=batch_size,
batch_type=batch_type, level=level,
max_output_length=max_output_length, eval_metric=eval_metric,
use_cuda=use_cuda, loss_function=None, beam_size=beam_size,
beam_alpha=beam_alpha, logger=logger, postprocess=postprocess)
# pylint: enable=unused-variable
if "trg" in data_set.fields:
decoding_description = "Greedy decoding" if beam_size < 2 else \
"Beam search decoding with beam size = {} and alpha = {}". \
format(beam_size, beam_alpha)
logger.info("%4s %s: %6.2f [%s]", data_set_name, eval_metric,
score, decoding_description)
else:
logger.info("No references given for %s -> no evaluation.",
data_set_name)
if save_attention:
if attention_scores:
attention_name = "{}.{}.att".format(data_set_name, step)
attention_path = os.path.join(model_dir, attention_name)
logger.info(
"Saving attention plots. This might take a while..")
store_attention_plots(attentions=attention_scores,
targets=hypotheses_raw,
sources=data_set.src,
indices=range(len(hypotheses)),
output_prefix=attention_path)
logger.info("Attention plots saved to: %s", attention_path)
else:
logger.warning(
"Attention scores could not be saved. "
"Note that attention scores are not available "
"when using beam search. "
"Set beam_size to 1 for greedy decoding.")
if output_path is not None:
output_path_set = "{}.{}".format(output_path, data_set_name)
with open(output_path_set, mode="w",
encoding="utf-8") as out_file:
for hyp in hypotheses:
out_file.write(hyp + "\n")
logger.info("Translations saved to: %s", output_path_set)
else:
# unsupervised NMT testing
if "src2trg_test" not in cfg["data"].keys(
) or "trg2src_test" not in cfg["data"].keys():
raise ValueError("Test data must be specified in config.")
# load the data
_, _, _, _, dev_src2trg, dev_trg2src, test_src2trg, test_trg2src, src_vocab, trg_vocab, _ = \
load_unsupervised_data(data_cfg=cfg["data"])
data_to_predict = {
"src2trg": {
"dev_src2trg": dev_src2trg,
"test_src2trg": test_src2trg
},
"trg2src": {
"dev_trg2src": dev_trg2src,
"test_trg2src": test_trg2src
}
}
# load model state from disk
model_checkpoint = load_checkpoint(ckpt, use_cuda=use_cuda)
# build model and load parameters into it
model = build_model(cfg["model"],
src_vocab=src_vocab,
trg_vocab=trg_vocab)
assert isinstance(model, UnsupervisedNMTModel)
model.src2src_translator.load_state_dict(
model_checkpoint["src2src_model_state"])
model.trg2trg_translator.load_state_dict(
model_checkpoint["trg2trg_model_state"])
model.src2trg_translator.load_state_dict(
model_checkpoint["src2trg_model_state"])
model.trg2src_translator.load_state_dict(
model_checkpoint["trg2src_model_state"])
if use_cuda:
model.src2trg_translator.cuda()
model.trg2trg_translator.cuda()
model.src2trg_translator.cuda()
model.trg2src_translator.cuda()
# whether to use beam search for decoding, 0: greedy decoding
if "testing" in cfg.keys():
beam_size = cfg["testing"].get("beam_size", 1)
beam_alpha = cfg["testing"].get("alpha", -1)
postprocess = cfg["testing"].get("postprocess", True)
else:
beam_size = 1
beam_alpha = -1
postprocess = True
for translation_direction, dataset_dict in data_to_predict.items():
# choose correct translator
if translation_direction == "src2trg":
model_to_use = model.src2trg_translator
else:
model_to_use = model.trg2src_translator
for dataset_name, dataset in dataset_dict.items():
score, loss, ppl, sources, sources_raw, references, hypotheses, \
hypotheses_raw, attention_scores = validate_on_data(
model_to_use, data=dataset, batch_size=batch_size,
batch_type=batch_type, level=level,
max_output_length=max_output_length, eval_metric=eval_metric,
use_cuda=use_cuda, loss_function=None, beam_size=beam_size,
beam_alpha=beam_alpha, logger=logger, postprocess=postprocess)
if "trg" in dataset.fields:
decoding_description = "Greedy decoding" if beam_size < 2 else \
"Beam search decoding with beam size = {} and alpha = {}". \
format(beam_size, beam_alpha)
logger.info("%4s %s: %6.2f [%s]", dataset_name,
eval_metric, score, decoding_description)
else:
logger.info("No references given for %s -> no evaluation.",
dataset_name)
if save_attention:
if attention_scores:
attention_name = "{}.{}.att".format(dataset_name, step)
attention_path = os.path.join(model_dir,
attention_name)
logger.info(
"Saving attention plots. This might take a while.."
)
store_attention_plots(attentions=attention_scores,
targets=hypotheses_raw,
sources=dataset.src,
indices=list(
range(len(hypotheses))),
output_prefix=attention_path)
logger.info("Attention plots saved to: %s",
attention_path)
else:
logger.warning(
"Attention scores could not be saved. "
"Note that attention scores are not available "
"when using beam search. "
"Set beam_size to 1 for greedy decoding.")
if output_path is not None:
output_path_set = "{}.{}".format(output_path, dataset_name)
with open(output_path_set, mode="w",
encoding="utf-8") as out_file:
for hyp in hypotheses:
out_file.write(hyp + "\n")
logger.info("Translations saved to: %s", output_path_set)
def run_bot(model_dir, bpe_src_code=None, tokenize=None):
"""
Start the bot. This means loading the model according to the config file.
:param model_dir: Model directory of trained Joey NMT model.
:param bpe_src_code: BPE codes for source side processing (optional).
:param tokenize: If True, tokenize inputs with Moses tokenizer.
:return:
"""
cfg_file = model_dir + "/config.yaml"
logger = logging.getLogger(__name__)
# load the Joey configuration
cfg = load_config(cfg_file)
# load the checkpoint
if "load_model" in cfg['training'].keys():
ckpt = cfg['training']["load_model"]
else:
ckpt = get_latest_checkpoint(model_dir)
if ckpt is None:
raise FileNotFoundError(
"No checkpoint found in directory {}.".format(model_dir))
# prediction parameters from config
use_cuda = cfg["training"].get("use_cuda", False)
level = cfg["data"]["level"]
max_output_length = cfg["training"].get("max_output_length", None)
lowercase = cfg["data"].get("lowercase", False)
# load the vocabularies
src_vocab_file = cfg["training"]["model_dir"] + "/src_vocab.txt"
trg_vocab_file = cfg["training"]["model_dir"] + "/trg_vocab.txt"
src_vocab = build_vocab(field="src",
vocab_file=src_vocab_file,
dataset=None,
max_size=-1,
min_freq=0)
trg_vocab = build_vocab(field="trg",
vocab_file=trg_vocab_file,
dataset=None,
max_size=-1,
min_freq=0)
# whether to use beam search for decoding, 0: greedy decoding
if "testing" in cfg.keys():
beam_size = cfg["testing"].get("beam_size", 0)
beam_alpha = cfg["testing"].get("alpha", -1)
else:
beam_size = 1
beam_alpha = -1
# pre-processing
if tokenize is not None:
src_tokenizer = MosesTokenizer(lang=cfg["data"]["src"])
trg_tokenizer = MosesDetokenizer(lang=cfg["data"]["trg"])
# tokenize input
tokenizer = lambda x: src_tokenizer.tokenize(x, return_str=True)
detokenizer = lambda x: trg_tokenizer.detokenize(x.split(),
return_str=True)
else:
tokenizer = lambda x: x
detokenizer = lambda x: x
if bpe_src_code is not None and level == "bpe":
# load bpe merge file
merge_file = open(bpe_src_code, "r")
bpe = apply_bpe.BPE(codes=merge_file)
segmenter = lambda x: bpe.process_line(x.strip())
elif level == "char":
# split to chars
segmenter = lambda x: list(x.strip())
else:
segmenter = lambda x: x.strip()
# build model and load parameters into it
model_checkpoint = load_checkpoint(ckpt, use_cuda)
model = build_model(cfg["model"], src_vocab=src_vocab, trg_vocab=trg_vocab)
model.load_state_dict(model_checkpoint["model_state"])
if use_cuda:
model.cuda()
print("Joey NMT model loaded successfully.")
web_client = slack.WebClient(TOKEN, timeout=30)
# get bot id
bot_id = (web_client.api_call("auth.test")["user_id"].upper())
# find bot channel id
all_channels = web_client.api_call("conversations.list")["channels"]
for c in all_channels:
if c["name"] == BOT_CHANNEL:
bot_channel_id = c["id"]
slack_events_adapter = SlackEventAdapter(BOT_SIGNIN,
endpoint="/slack/events")
@slack_events_adapter.on("message")
def handle_message(event_data):
message = event_data["event"]
if message.get("subtype") is None:
channel = message["channel"]
user = message["user"]
text = message["text"].strip()
if user != bot_id and message.get("subtype") is None:
# translates all messages in its channel and mentions
if channel == bot_channel_id or bot_id in text:
mention = "<@{}>".format(bot_id)
# TODO remove all possible mentions with regex
if mention in text:
parts = text.split(mention)
text = parts[0].strip() + parts[1].strip()
message = translate(text,
beam_size=beam_size,
beam_alpha=beam_alpha,
level=level,
lowercase=lowercase,
max_output_length=max_output_length,
model=model,
postprocess=[detokenizer],
preprocess=[tokenizer, segmenter],
src_vocab=src_vocab,
trg_vocab=trg_vocab,
use_cuda=use_cuda,
logger=logger)
web_client.chat_postMessage(text=message,
token=TOKEN,
channel=channel)
# Error events
@slack_events_adapter.on("error")
def error_handler(err):
print("ERROR: " + str(err))
slack_events_adapter.start(port=3000)
def test(cfg_file,
ckpt,
output_path: str = None,
save_attention: bool = False,
logger: logging.Logger = None,
data_to_test: str = None) -> None:
"""
Main test function. Handles loading a model from checkpoint, generating
translations and storing them and attention plots.
:param cfg_file: path to configuration file
:param ckpt: path to checkpoint to load
:param output_path: path to output
:param save_attention: whether to save the computed attention weights
:param logger: log output to this logger (creates new logger if not set)
"""
if logger is None:
logger = logging.getLogger(__name__)
FORMAT = '%(asctime)-15s - %(message)s'
logging.basicConfig(format=FORMAT)
logger.setLevel(level=logging.DEBUG)
cfg = load_config(cfg_file)
train_cfg = cfg["training"]
data_cfg = cfg["data"]
test_cfg = cfg["testing"]
if "test" not in data_cfg.keys():
raise ValueError("Test data must be specified in config.")
# when checkpoint is not specified, take latest (best) from model dir
model_dir = train_cfg["model_dir"]
if ckpt is None:
ckpt = get_latest_checkpoint(model_dir)
if ckpt is None:
raise FileNotFoundError("No checkpoint at {}.".format(model_dir))
try:
step = ckpt.split(model_dir + "/")[1].split(".ckpt")[0]
except IndexError:
step = "best"
batch_size = train_cfg.get("eval_batch_size", train_cfg["batch_size"])
batch_type = train_cfg.get("batch_type", "sentence")
use_cuda = train_cfg.get("use_cuda", False)
assert "level" in data_cfg or "trg_level" in data_cfg
trg_level = data_cfg.get("level", data_cfg["trg_level"])
eval_metric = train_cfg["eval_metric"]
if isinstance(eval_metric, str):
eval_metric = [eval_metric]
max_output_length = test_cfg.get("max_output_length",
train_cfg.get("max_output_length", None))
# load the data
data = load_data(data_cfg)
dev_data = data["dev_data"]
test_data = data["test_data"]
vocabs = data["vocabs"]
data_to_predict = {"dev": dev_data, "test": test_data}
if data_to_test is not None:
assert data_to_test in data_to_predict
data_to_predict = {data_to_test: data_to_predict[data_to_test]}
# load model state from disk
if isinstance(ckpt, str):
ckpt = [ckpt]
models = []
for c in ckpt:
model_checkpoint = load_checkpoint(c, use_cuda=use_cuda)
# build model and load parameters into it
m = build_model(cfg["model"], vocabs=vocabs)
m.load_state_dict(model_checkpoint["model_state"])
models.append(m)
model = models[0] if len(models) == 1 else EnsembleModel(*models)
if use_cuda:
model.cuda() # should this exist?
# whether to use beam search for decoding, 0: greedy decoding
beam_sizes = beam_alpha = 0
if "testing" in cfg.keys():
beam_sizes = test_cfg.get("beam_size", 0)
beam_alpha = test_cfg.get("alpha", 0)
beam_sizes = [beam_sizes] if isinstance(beam_sizes, int) else beam_sizes
assert beam_alpha >= 0, "Use alpha >= 0"
method = test_cfg.get("method", None)
max_hyps = test_cfg.get("max_hyps", 1) # only for the enumerate thing
validate_by_label = test_cfg.get("validate_by_label",
train_cfg.get("validate_by_label", False))
forced_sparsity = test_cfg.get("forced_sparsity",
train_cfg.get("forced_sparsity", False))
for beam_size in beam_sizes:
for data_set_name, data_set in data_to_predict.items():
valid_results = validate_on_data(
model,
data=data_set,
batch_size=batch_size,
batch_type=batch_type,
trg_level=trg_level,
max_output_length=max_output_length,
eval_metrics=eval_metric,
use_cuda=use_cuda,
loss_function=None,
beam_size=beam_size,
beam_alpha=beam_alpha,
save_attention=save_attention,
validate_by_label=validate_by_label,
forced_sparsity=forced_sparsity,
method=method,
max_hyps=max_hyps,
break_at_p=test_cfg.get("break_at_p", 1.0),
break_at_argmax=test_cfg.get("break_at_argmax", False),
short_depth=test_cfg.get("short_depth", 0))
scores = valid_results[0]
hypotheses, hypotheses_raw = valid_results[2:4]
scores_by_label = valid_results[5]
if "trg" in data_set.fields:
log_scores(logger, data_set_name, scores, scores_by_label,
beam_size, beam_alpha)
else:
logger.info("No references given for %s -> no evaluation.",
data_set_name)
attention_scores = valid_results[4]
if save_attention and not attention_scores:
logger.warning("Attention scores could not be saved. "
"Note that attention scores are not "
"available when using beam search. "
"Set beam_size to 0 for greedy decoding.")
if save_attention and attention_scores:
# currently this will break for transformers
logger.info("Saving attention plots. This might be slow.")
store_attention_plots(attentions=attention_scores,
targets=hypotheses_raw,
sources=[s for s in data_set.src],
indices=range(len(hypotheses)),
model_dir=model_dir,
steps=step,
data_set_name=data_set_name)
logger.info("Attention plots saved to: %s", model_dir)
if output_path is not None:
output_path_set = "{}.{}".format(output_path, data_set_name)
with open(output_path_set, mode="w", encoding="utf-8") as outf:
for hyp in hypotheses:
outf.write(hyp + "\n")
logger.info("Translations saved to: %s", output_path_set)
def test(cfg_file,
ckpt: str,
output_path: str = None,
save_attention: bool = False) -> None:
"""
Main test function. Handles loading a model from checkpoint, generating
translations and storing them and attention plots.
:param cfg_file: path to configuration file
:param ckpt: path to checkpoint to load
:param output_path: path to output
:param save_attention: whether to save the computed attention weights
"""
cfg = load_config(cfg_file)
if "test" not in cfg["data"].keys():
raise ValueError("Test data must be specified in config.")
# when checkpoint is not specified, take oldest from model dir
if ckpt is None:
model_dir = cfg["training"]["model_dir"]
ckpt = get_latest_checkpoint(model_dir)
if ckpt is None:
raise FileNotFoundError(
"No checkpoint found in directory {}.".format(model_dir))
try:
step = ckpt.split(model_dir + "/")[1].split(".ckpt")[0]
except IndexError:
step = "best"
batch_size = cfg["training"]["batch_size"]
use_cuda = cfg["training"].get("use_cuda", False)
level = cfg["data"]["level"]
eval_metric = cfg["training"]["eval_metric"]
max_output_length = cfg["training"].get("max_output_length", None)
# load the data
_, dev_data, test_data, src_vocab, trg_vocab = load_data(
data_cfg=cfg["data"])
data_to_predict = {"dev": dev_data, "test": test_data}
# load model state from disk
model_checkpoint = load_checkpoint(ckpt, use_cuda=use_cuda)
# build model and load parameters into it
model = build_model(cfg["model"], src_vocab=src_vocab, trg_vocab=trg_vocab)
model.load_state_dict(model_checkpoint["model_state"])
if use_cuda:
model.cuda()
# whether to use beam search for decoding, 0: greedy decoding
if "testing" in cfg.keys():
beam_size = cfg["testing"].get("beam_size", 0)
beam_alpha = cfg["testing"].get("alpha", -1)
else:
beam_size = 0
beam_alpha = -1
for data_set_name, data_set in data_to_predict.items():
if data_set is None:
# e.g. no valid_data
continue
#pylint: disable=unused-variable
score, loss, ppl, sources, sources_raw, references, hypotheses, \
hypotheses_raw, attention_scores, logprobs = validate_on_data(
model, data=data_set, batch_size=batch_size, level=level,
max_output_length=max_output_length, eval_metric=eval_metric,
use_cuda=use_cuda, loss_function=None, beam_size=beam_size,
beam_alpha=beam_alpha)
#pylint: enable=unused-variable
if "trg" in data_set.fields:
decoding_description = "Greedy decoding" if beam_size == 0 else \
"Beam search decoding with beam size = {} and alpha = {}".\
format(beam_size, beam_alpha)
print("{:4s} {}: {} [{}]".format(data_set_name, eval_metric, score,
decoding_description))
else:
print("No references given for {} -> no evaluation.".format(
data_set_name))
if attention_scores is not None and save_attention:
attention_path = "{}/{}.{}.att".format(model_dir, data_set_name,
step)
print("Attention plots saved to: {}.xx".format(attention_path))
store_attention_plots(attentions=attention_scores,
targets=hypotheses_raw,
sources=[s for s in data_set.src],
indices=range(len(hypotheses)),
output_prefix=attention_path)
if output_path is not None:
output_path_set = "{}.{}".format(output_path, data_set_name)
with open(output_path_set, mode="w", encoding="utf-8") as out_file:
if cfg["data"].get("post_process", True):
for hyp in hypotheses:
out_file.write(hyp + "\n")
else:
for hyp in hypotheses_raw:
out_file.write(" ".join(hyp) + "\n")
print("Translations saved to: {}".format(output_path_set))
def train(cfg_file: str) -> None:
"""
Main training function. After training, also test on test data if given.
:param cfg_file: path to configuration yaml file
"""
cfg = load_config(cfg_file)
# set the random seed
set_seed(seed=cfg["training"].get("random_seed", 42))
# load the data
train_data, dev_data, test_data, src_vocab, trg_vocab = load_data(
data_cfg=cfg["data"])
# build an encoder-decoder model
model = build_model(cfg["model"], src_vocab=src_vocab, trg_vocab=trg_vocab)
# for training management, e.g. early stopping and model selection
trainer = TrainManager(model=model, config=cfg)
# store copy of original training config in model dir
shutil.copy2(cfg_file, trainer.model_dir + "/config.yaml")
# log all entries of config
log_cfg(cfg, trainer.logger)
log_data_info(train_data=train_data,
valid_data=dev_data,
test_data=test_data,
src_vocab=src_vocab,
trg_vocab=trg_vocab,
logging_function=trainer.logger.info)
# store the vocabs
src_vocab_file = "{}/src_vocab.txt".format(cfg["training"]["model_dir"])
src_vocab.to_file(src_vocab_file)
trg_vocab_file = "{}/trg_vocab.txt".format(cfg["training"]["model_dir"])
trg_vocab.to_file(trg_vocab_file)
# train the model
trainer.train_and_validate(train_data=train_data, valid_data=dev_data)
# test the model with the best checkpoint
if test_data is not None:
# load checkpoint
if trainer.best_ckpt_iteration > 0:
checkpoint_path = "{}/{}.ckpt".format(trainer.model_dir,
trainer.best_ckpt_iteration)
else:
## For save_checkpoint by save_freq
checkpoint_path = get_latest_checkpoint(trainer.model_dir)
try:
trainer.init_from_checkpoint(checkpoint_path)
except AssertionError:
trainer.logger.warning(
"Checkpoint %s does not exist. "
"Skipping testing.", checkpoint_path)
if trainer.best_ckpt_iteration == 0 \
and trainer.best_ckpt_score in [np.inf, -np.inf]:
trainer.logger.warning(
"It seems like no checkpoint was written, "
"since no improvement was obtained over the initial model."
)
return
# generate hypotheses for test data
if "testing" in cfg.keys():
beam_size = cfg["testing"].get("beam_size", 0)
beam_alpha = cfg["testing"].get("alpha", -1)
return_logp = cfg["testing"].get("return_logp", False)
else:
beam_size = 0
beam_alpha = -1
return_logp = False
# pylint: disable=unused-variable
score, loss, ppl, sources, sources_raw, references, hypotheses, \
hypotheses_raw, attention_scores, log_probs = validate_on_data(
data=test_data, batch_size=trainer.batch_size,
eval_metric=trainer.eval_metric, level=trainer.level,
max_output_length=trainer.max_output_length,
model=model, use_cuda=trainer.use_cuda, loss_function=None,
beam_size=beam_size, beam_alpha=beam_alpha,
return_logp=return_logp)
if "trg" in test_data.fields:
decoding_description = "Greedy decoding" if beam_size == 0 else \
"Beam search decoding with beam size = {} and alpha = {}"\
.format(beam_size, beam_alpha)
trainer.logger.info("Test data result: %f %s [%s]", score,
trainer.eval_metric, decoding_description)
else:
trainer.logger.info(
"No references given for %s.%s -> no evaluation.",
cfg["data"]["test"], cfg["data"]["src"])
output_path_set = "{}/{}.{}".format(trainer.model_dir, "test",
cfg["data"]["trg"])
with open(output_path_set, mode="w", encoding="utf-8") as f:
for h in hypotheses:
f.write("{}\n".format(h))
trainer.logger.info("Test translations saved to: %s", output_path_set)
if return_logp:
output_path_set_logp = output_path_set + ".logp"
with open(output_path_set_logp, mode="w", encoding="utf-8") as f:
for l in log_probs:
f.write("{}\n".format(l))
trainer.logger.info("Test log probs saved to: %s",
output_path_set_logp)
def test(cfg_file,
ckpt: str = None,
output_path: str = None,
save_attention: bool = False):
"""
Main test function. Handles loading a model from checkpoint, generating
translations and storing them and attention plots.
:param cfg_file:
:param ckpt:
:param output_path:
:param save_attention:
:return:
"""
cfg = load_config(cfg_file)
if "test" not in cfg["data"].keys():
raise ValueError("Test data must be specified in config.")
# when checkpoint is not specified, take oldest from model dir
if ckpt is None:
dir = cfg["training"]["model_dir"]
ckpt = get_latest_checkpoint(dir)
try:
step = ckpt.split(dir + "/")[1].split(".ckpt")[0]
except IndexError:
step = "best"
batch_size = cfg["training"]["batch_size"]
use_cuda = cfg["training"].get("use_cuda", False)
level = cfg["data"]["level"]
eval_metric = cfg["training"]["eval_metric"]
max_output_length = cfg["training"].get("max_output_length", None)
# load the data
# TODO load only test data
train_data, dev_data, test_data, src_vocab, trg_vocab = \
load_data(cfg=cfg)
# TODO specify this differently
data_to_predict = {"dev": dev_data, "test": test_data}
# load model state from disk
model_checkpoint = load_model_from_checkpoint(ckpt, use_cuda=use_cuda)
# build model and load parameters into it
model = build_model(cfg["model"], src_vocab=src_vocab, trg_vocab=trg_vocab)
model.load_state_dict(model_checkpoint["model_state"])
if use_cuda:
model.cuda()
# whether to use beam search for decoding, 0: greedy decoding
if "testing" in cfg.keys():
beam_size = cfg["testing"].get("beam_size", 0)
beam_alpha = cfg["testing"].get("alpha", -1)
else:
beam_size = 0
beam_alpha = -1
for data_set_name, data_set in data_to_predict.items():
score, loss, ppl, sources, sources_raw, references, hypotheses, \
hypotheses_raw, attention_scores = validate_on_data(
model, data=data_set, batch_size=batch_size, level=level,
max_output_length=max_output_length, eval_metric=eval_metric,
use_cuda=use_cuda, criterion=None, beam_size=beam_size,
beam_alpha=beam_alpha)
if "trg" in data_set.fields:
decoding_description = "Greedy decoding" if beam_size == 0 else \
"Beam search decoding with beam size = {} and alpha = {}".format(
beam_size, beam_alpha)
print("{:4s} {}: {} [{}]".format(data_set_name, eval_metric, score,
decoding_description))
else:
print("No references given for {} -> no evaluation.".format(
data_set_name))
if attention_scores is not None and save_attention:
attention_path = "{}/{}.{}.att".format(dir, data_set_name, step)
print("Attention plots saved to: {}.xx".format(attention_path))
store_attention_plots(attentions=attention_scores,
targets=hypotheses_raw,
sources=[s for s in data_set.src],
idx=range(len(hypotheses)),
output_prefix=attention_path)
if output_path is not None:
output_path_set = "{}.{}".format(output_path, data_set_name)
with open(output_path_set, mode="w", encoding="utf-8") as f:
for h in hypotheses:
f.write(h + "\n")
print("Translations saved to: {}".format(output_path_set))
def translate(cfg_file, ckpt: str, output_path: str = None) -> None:
"""
Interactive translation function.
Loads model from checkpoint and translates either the stdin input or
asks for input to translate interactively.
The input has to be pre-processed according to the data that the model
was trained on, i.e. tokenized or split into subwords.
Translations are printed to stdout.
:param cfg_file: path to configuration file
:param ckpt: path to checkpoint to load
"""
def _load_line_as_data(line):
""" Create a dataset from one line via a temporary file. """
# write src input to temporary file
tmp_name = "tmp"
tmp_suffix = ".src"
tmp_filename = tmp_name+tmp_suffix
with open(tmp_filename, "w") as tmp_file:
tmp_file.write("{}\n".format(line))
test_data = MonoDataset(path=tmp_name, ext=tmp_suffix, field=src_field)
# remove temporary file
if os.path.exists(tmp_filename):
os.remove(tmp_filename)
return test_data
def _translate_data(test_data):
""" Translates given dataset, using parameters from outer scope. """
# pylint: disable=unused-variable
_, _, _, _, hypotheses, _, _, _, _ = validate_on_data(
model, data=test_data, batch_size=batch_size, level=level,
max_output_length=max_output_length, eval_metrics=[],
use_cuda=use_cuda, loss_function=None, beam_size=beam_size,
beam_alpha=beam_alpha)
return hypotheses
cfg = load_config(cfg_file)
# when checkpoint is not specified, take oldest from model dir
if ckpt is None:
model_dir = cfg["training"]["model_dir"]
ckpt = get_latest_checkpoint(model_dir)
data_cfg = cfg["data"]
batch_size = cfg["training"].get("batch_size", 1)
use_cuda = cfg["training"].get("use_cuda", False)
max_output_length = cfg["training"].get("max_output_length", None)
# read vocabs
# This will need to change: currently translate does not support inflection
src_vocab_file = data_cfg.get(
"src_vocab", cfg["training"]["model_dir"] + "/src_vocab.txt")
trg_vocab_file = data_cfg.get(
"trg_vocab", cfg["training"]["model_dir"] + "/trg_vocab.txt")
src_vocab = Vocabulary(file=src_vocab_file)
trg_vocab = Vocabulary(file=trg_vocab_file)
vocabs = {"src": src_vocab, "trg": trg_vocab}
level = data_cfg["level"]
lowercase = data_cfg["lowercase"]
tok_fun = list if level == "char" else str.split
src_field = Field(init_token=None, eos_token=EOS_TOKEN,
pad_token=PAD_TOKEN, tokenize=tok_fun,
batch_first=True, lower=lowercase,
unk_token=UNK_TOKEN,
include_lengths=True)
src_field.vocab = src_vocab
# load model state from disk
model_checkpoint = load_checkpoint(ckpt, use_cuda=use_cuda)
# build model and load parameters into it
model = build_model(cfg["model"], vocabs=vocabs)
model.load_state_dict(model_checkpoint["model_state"])
if use_cuda:
model.cuda()
# whether to use beam search for decoding, 0: greedy decoding
if "testing" in cfg.keys():
beam_size = cfg["testing"].get("beam_size", 0)
beam_alpha = cfg["testing"].get("alpha", 0)
else:
beam_size = 0
beam_alpha = 0
if beam_alpha < 0:
raise ConfigurationError("alpha for length penalty should be >= 0")
if not sys.stdin.isatty():
# file given
test_data = MonoDataset(path=sys.stdin, ext="", field=src_field)
hypotheses = _translate_data(test_data)
if output_path is not None:
output_path_set = "{}".format(output_path)
with open(output_path_set, mode="w", encoding="utf-8") as out_file:
for hyp in hypotheses:
out_file.write(hyp + "\n")
print("Translations saved to: {}".format(output_path_set))
else:
for hyp in hypotheses:
print(hyp)
else:
# enter interactive mode
batch_size = 1
while True:
try:
src_input = input("\nPlease enter a source sentence "
"(pre-processed): \n")
if not src_input.strip():
break
# every line has to be made into dataset
test_data = _load_line_as_data(line=src_input)
hypotheses = _translate_data(test_data)
print("JoeyNMT: {}".format(hypotheses[0]))
except (KeyboardInterrupt, EOFError):
print("\nBye.")
break
def test(cfg_file,
ckpt, # str or list now
output_path: str = None,
save_attention: bool = False,
logger: logging.Logger = None) -> None:
"""
Main test function. Handles loading a model from checkpoint, generating
translations and storing them and attention plots.
:param cfg_file: path to configuration file
:param ckpt: path to checkpoint to load
:param output_path: path to output
:param save_attention: whether to save the computed attention weights
:param logger: log output to this logger (creates new logger if not set)
"""
if logger is None:
logger = logging.getLogger(__name__)
FORMAT = '%(asctime)-15s - %(message)s'
logging.basicConfig(format=FORMAT)
logger.setLevel(level=logging.DEBUG)
cfg = load_config(cfg_file)
train_cfg = cfg["training"]
data_cfg = cfg["data"]
test_cfg = cfg["testing"]
if "test" not in data_cfg.keys():
raise ValueError("Test data must be specified in config.")
# when checkpoint is not specified, take latest (best) from model dir
if ckpt is None:
model_dir = train_cfg["model_dir"]
ckpt = get_latest_checkpoint(model_dir)
if ckpt is None:
raise FileNotFoundError("No checkpoint found in directory {}."
.format(model_dir))
try:
step = ckpt.split(model_dir+"/")[1].split(".ckpt")[0]
except IndexError:
step = "best"
batch_size = train_cfg.get("eval_batch_size", train_cfg["batch_size"])
batch_type = train_cfg.get("eval_batch_type", train_cfg.get("batch_type", "sentence"))
use_cuda = train_cfg.get("use_cuda", False)
src_level = data_cfg.get("src_level", data_cfg.get("level", "word"))
trg_level = data_cfg.get("trg_level", data_cfg.get("level", "word"))
eval_metric = train_cfg["eval_metric"]
if isinstance(eval_metric, str):
eval_metric = [eval_metric]
attn_metric = train_cfg.get("attn_metric", [])
if isinstance(attn_metric, str):
attn_metric = [attn_metric]
max_output_length = train_cfg.get("max_output_length", None)
# load the data
data = load_data(data_cfg)
dev_data = data["dev_data"]
test_data = data["test_data"]
vocabs = data["vocabs"]
data_to_predict = {"dev": dev_data, "test": test_data}
# load model state from disk
if isinstance(ckpt, str):
ckpt = [ckpt]
individual_models = []
for c in ckpt:
model_checkpoint = load_checkpoint(c, use_cuda=use_cuda)
# build model and load parameters into it
m = build_model(cfg["model"], vocabs=vocabs)
m.load_state_dict(model_checkpoint["model_state"])
individual_models.append(m)
if len(individual_models) == 1:
model = individual_models[0]
else:
model = EnsembleModel(*individual_models)
if use_cuda:
model.cuda()
# whether to use beam search for decoding, 0: greedy decoding
if "testing" in cfg.keys():
beam_sizes = test_cfg.get("beam_size", 0)
beam_alpha = test_cfg.get("alpha", 0)
else:
beam_sizes = 0
beam_alpha = 0
if isinstance(beam_sizes, int):
beam_sizes = [beam_sizes]
assert beam_alpha >= 0, "Use alpha >= 0"
for beam_size in beam_sizes:
for data_set_name, data_set in data_to_predict.items():
#pylint: disable=unused-variable
scores, sources, sources_raw, references, hypotheses, \
hypotheses_raw, attention_scores, scores_by_lang, by_lang = validate_on_data(
model, data=data_set, batch_size=batch_size,
batch_type=batch_type,
src_level=src_level, trg_level=trg_level,
max_output_length=max_output_length, eval_metrics=eval_metric,
attn_metrics=attn_metric,
use_cuda=use_cuda, loss_function=None, beam_size=beam_size,
beam_alpha=beam_alpha, save_attention=save_attention)
#pylint: enable=unused-variable
if "trg" in data_set.fields:
labeled_scores = sorted(scores.items())
eval_report = ", ".join("{}: {:.5f}".format(n, v)
for n, v in labeled_scores)
decoding_description = "Greedy decoding" if beam_size == 0 else \
"Beam search decoding with beam size = {} and alpha = {}".\
format(beam_size, beam_alpha)
logger.info("%4s %s: [%s]",
data_set_name, eval_report, decoding_description)
if scores_by_lang is not None:
for metric, scores in scores_by_lang.items():
# make a report
lang_report = [metric]
numbers = sorted(scores.items())
lang_report.extend(["{}: {:.5f}".format(k, v)
for k, v in numbers])
logger.info("\n\t".join(lang_report))
else:
logger.info("No references given for %s -> no evaluation.",
data_set_name)
if save_attention:
# currently this will break for transformers
if attention_scores:
#attention_name = "{}.{}.att".format(data_set_name, step)
#attention_path = os.path.join(model_dir, attention_name)
logger.info("Saving attention plots. This might take a while..")
store_attention_plots(attentions=attention_scores,
targets=hypotheses_raw,
sources=[s for s in data_set.src],
indices=range(len(hypotheses)),
model_dir=model_dir,
steps=step,
data_set_name=data_set_name)
logger.info("Attention plots saved to: %s", model_dir)
else:
logger.warning("Attention scores could not be saved. "
"Note that attention scores are not available "
"when using beam search. "
"Set beam_size to 0 for greedy decoding.")
if output_path is not None:
for lang, ref_and_hyp in by_lang.items():
if lang is None:
# monolingual case
output_path_set = "{}.{}".format(output_path, data_set_name)
else:
output_path_set = "{}.{}.{}".format(output_path, lang, data_set_name)
if isinstance(ref_and_hyp[0], str):
hyps = ref_and_hyp
else:
hyps = [hyp for (ref, hyp) in ref_and_hyp]
with open(output_path_set, mode="w", encoding="utf-8") as out_file:
for hyp in hyps:
out_file.write(hyp + "\n")
logger.info("Translations saved to: %s", output_path_set)
def __init__(self,
cfg_file,
ckpt: str,
output_path: str = None,
logger: Logger = None) -> None:
"""
Recover the saved model, specified as in configuration.
:param cfg_file: path to configuration file
:param ckpt: path to checkpoint to load
:param output_path: path to output
:param logger: log output to this logger (creates new logger if not set)
"""
if logger is None:
logger = make_logger()
cfg = load_config(cfg_file)
if "test" not in cfg["data"].keys():
raise ValueError("Test data must be specified in config.")
#print(cfg.keys())
if "dqn" not in cfg.keys():
raise ValueError("dqn data must be specified in config.")
self.model_dir = cfg["training"]["model_dir"]
# when checkpoint is not specified, take latest (best) from model dir
if ckpt is None:
model_dir = cfg["training"]["model_dir"]
ckpt = get_latest_checkpoint(model_dir)
if ckpt is None:
raise FileNotFoundError(
"No checkpoint found in directory {}.".format(model_dir))
try:
step = ckpt.split(model_dir + "/")[1].split(".ckpt")[0]
except IndexError:
step = "best"
self.batch_size = 1 #**
self.batch_type = cfg["training"].get(
"eval_batch_type", cfg["training"].get("batch_type", "sentence"))
self.use_cuda = cfg["training"].get("use_cuda", False)
self.level = cfg["data"]["level"]
self.eval_metric = cfg["training"]["eval_metric"]
self.max_output_length = cfg["training"].get("max_output_length", None)
# load the data
train_data, dev_data, test_data, src_vocab, trg_vocab = load_data(
data_cfg=cfg["data"])
#Loading the DQN parameters:
self.sample_size = cfg["dqn"]["sample_size"]
self.lr = cfg["dqn"].get("lr", 0.01)
self.egreed_max = cfg["dqn"].get("egreed_max", 0.9)
self.egreed_min = cfg["dqn"].get("egreed_min", 0.01)
self.gamma_max = cfg["dqn"].get("gamma_max", 0.9)
self.gamma_min = cfg["dqn"].get("gamma_min", 0.5)
self.nu_iter = cfg["dqn"]["nu_iter"]
self.mem_cap = cfg["dqn"]["mem_cap"]
self.beam_min = cfg["dqn"]["beam_min"]
self.beam_max = cfg["dqn"]["beam_max"]
self.state_type = cfg["dqn"]["state_type"]
if self.state_type == 'hidden':
self.state_size = cfg["model"]["encoder"]["hidden_size"] * 2
else:
self.state_size = cfg["model"]["encoder"]["hidden_size"]
self.actions_size = len(src_vocab)
self.gamma = None
print("Sample size: ", self.sample_size)
print("State size: ", self.state_size)
print("Action size: ", self.actions_size)
self.epochs = cfg["dqn"]["epochs"]
# Inii the Qnet and Qnet2
self.eval_net = Net(self.state_size, self.actions_size)
self.target_net = Net(self.state_size, self.actions_size)
#Following the algorithm
self.target_net.load_state_dict(self.eval_net.state_dict())
self.learn_step_counter = 0
self.memory_counter = 0
self.size_memory1 = self.state_size * 2 + 2 + 1
self.memory = np.zeros((self.mem_cap, self.size_memory1))
self.optimizer = torch.optim.Adam(self.eval_net.parameters(),
lr=self.lr)
self.loss_func = nn.MSELoss()
#others parameters
self.bos_index = trg_vocab.stoi[BOS_TOKEN]
self.eos_index = trg_vocab.stoi[EOS_TOKEN]
self.pad_index = trg_vocab.stoi[PAD_TOKEN]
self.data_to_train_dqn = {"train": train_data}
#self.data_to_train_dqn = {"test": test_data}
#self.data_to_dev = {"dev": dev_data}
self.data_to_dev = {"dev": dev_data}
#self.data_to_train_dqn = {"train": train_data
# ,"dev": dev_data, "test": test_data}
# load model state from disk
model_checkpoint = load_checkpoint(ckpt, use_cuda=self.use_cuda)
# build model and load parameters into it
self.model = build_model(cfg["model"],
src_vocab=src_vocab,
trg_vocab=trg_vocab)
self.model.load_state_dict(model_checkpoint["model_state"])
if self.use_cuda:
self.model.cuda()
# whether to use beam search for decoding, 0: greedy decoding
beam_size = 1
beam_alpha = -1
#others not important parameters
self.index_fin = None
path_tensroboard = self.model_dir + "/tensorboard_DQN/"
self.tb_writer = SummaryWriter(log_dir=path_tensroboard, purge_step=0)
self.dev_network_count = 0
print(cfg["dqn"]["reward_type"])
#Reward funtion related:
if cfg["dqn"]["reward_type"] == "bleu_diff":
print("You select the reward based on the Bleu score differences")
self.Reward = self.Reward_bleu_diff
elif cfg["dqn"]["reward_type"] == "bleu_lin":
print(
"You select the reward based on the linear Bleu socres, and several punishments"
)
self.Reward = self.Reward_lin
else:
print(
"You select the reward based on the final score on the last state "
)
self.Reward = self.Reward_bleu_fin
def test(cfg_file,
ckpt: str,
output_path: str = None,
save_attention: bool = False,
logger: Logger = None) -> None:
"""
Main test function. Handles loading a model from checkpoint, generating
translations and storing them and attention plots.
:param cfg_file: path to configuration file
:param ckpt: path to checkpoint to load
:param output_path: path to output
:param save_attention: whether to save the computed attention weights
:param logger: log output to this logger (creates new logger if not set)
"""
if logger is None:
logger = make_logger()
cfg = load_config(cfg_file)
if "test" not in cfg["data"].keys():
raise ValueError("Test data must be specified in config.")
# when checkpoint is not specified, take latest (best) from model dir
if ckpt is None:
model_dir = cfg["training"]["model_dir"]
ckpt = get_latest_checkpoint(model_dir)
if ckpt is None:
raise FileNotFoundError(
"No checkpoint found in directory {}.".format(model_dir))
try:
step = ckpt.split(model_dir + "/")[1].split(".ckpt")[0]
except IndexError:
step = "best"
batch_size = cfg["training"].get("eval_batch_size",
cfg["training"]["batch_size"])
batch_type = cfg["training"].get(
"eval_batch_type", cfg["training"].get("batch_type", "sentence"))
use_cuda = cfg["training"].get("use_cuda", False)
level = cfg["data"]["level"]
eval_metric = cfg["training"]["eval_metric"]
max_output_length = cfg["training"].get("max_output_length", None)
# load the data
_, dev_data, test_data, src_vocab, trg_vocab = load_data(
data_cfg=cfg["data"])
data_to_predict = {"dev": dev_data, "test": test_data}
# load model state from disk
model_checkpoint = load_checkpoint(ckpt, use_cuda=use_cuda)
# build model and load parameters into it
model = build_model(cfg["model"], src_vocab=src_vocab, trg_vocab=trg_vocab)
model.load_state_dict(model_checkpoint["model_state"])
if use_cuda:
model.cuda()
# whether to use beam search for decoding, 0: greedy decoding
if "testing" in cfg.keys():
beam_size = cfg["testing"].get("beam_size", 1)
beam_alpha = cfg["testing"].get("alpha", -1)
else:
beam_size = 1
beam_alpha = -1
for data_set_name, data_set in data_to_predict.items():
#pylint: disable=unused-variable
score, loss, ppl, sources, sources_raw, references, hypotheses, \
hypotheses_raw, attention_scores,valid_hypotheses_full_n_best,scores = validate_on_data(
model, data=data_set, batch_size=batch_size,
batch_type=batch_type, level=level,
max_output_length=max_output_length, eval_metric=eval_metric,
use_cuda=use_cuda, loss_function=None, beam_size=beam_size,
beam_alpha=beam_alpha, logger=logger)
#pylint: enable=unused-variable
if "trg" in data_set.fields:
decoding_description = "Greedy decoding" if beam_size < 2 else \
"Beam search decoding with beam size = {} and alpha = {}".\
format(beam_size, beam_alpha)
logger.info("%4s %s: %6.2f [%s]", data_set_name, eval_metric,
score, decoding_description)
else:
logger.info("No references given for %s -> no evaluation.",
data_set_name)
if save_attention:
if attention_scores:
attention_name = "{}.{}.att".format(data_set_name, step)
attention_path = os.path.join(model_dir, attention_name)
logger.info(
"Saving attention plots. This might take a while..")
store_attention_plots(attentions=attention_scores,
targets=hypotheses_raw,
sources=data_set.src,
indices=range(len(hypotheses)),
output_prefix=attention_path)
logger.info("Attention plots saved to: %s", attention_path)
else:
logger.warning("Attention scores could not be saved. "
"Note that attention scores are not available "
"when using beam search. "
"Set beam_size to 1 for greedy decoding.")
if output_path is not None:
'''
output_path_set = "{}.{}".format(output_path, data_set_name)
with open(output_path_set, mode="w", encoding="utf-8") as out_file:
for hyp in hypotheses:
out_file.write(hyp + "\n")
#sy_debug
alt_output = "{}.n_best.{}".format(output_path, data_set_name)
with open(alt_output, mode="w", encoding="utf-8") as out_file:
for n in valid_hypotheses_full_n_best:
out_file.write(n + "\n")
'''
#@Shiya: exporting hypothesis and associated score to .csv file
#TODO: write_to_csv(hyps,scores)
def write_to_csv(hyps: list, scores: list):
import csv
output_file = "{}.n_csv.{}".format(output_path, data_set_name)
with open(output_file, mode="w", newline='',
encoding="utf-8") as out_file:
fieldnames = ['Predictions', 'Scores']
writer = csv.DictWriter(out_file, fieldnames=fieldnames)
writer.writeheader()
for prediction, score in zip(hyps, scores):
writer.writerow({
fieldnames[0]: prediction,
fieldnames[1]: score
})
write_to_csv(valid_hypotheses_full_n_best, scores)
def test(cfg_file,
ckpt: str,
output_path: str = None,
save_attention: bool = False,
logger: logging.Logger = None) -> None:
"""
Main test function. Handles loading a model from checkpoint, generating
translations and storing them and attention plots.
:param cfg_file: path to configuration file
:param ckpt: path to checkpoint to load
:param output_path: path to output
:param save_attention: whether to save the computed attention weights
:param logger: log output to this logger (creates new logger if not set)
"""
if logger is None:
logger = logging.getLogger(__name__)
FORMAT = '%(asctime)-15s - %(message)s'
logging.basicConfig(format=FORMAT)
logger.setLevel(level=logging.DEBUG)
cfg = load_config(cfg_file)
if "test" not in cfg["data"].keys():
raise ValueError("Test data must be specified in config.")
# when checkpoint is not specified, take latest (best) from model dir
if ckpt is None:
model_dir = cfg["training"]["model_dir"]
ckpt = get_latest_checkpoint(model_dir)
if ckpt is None:
raise FileNotFoundError(
"No checkpoint found in directory {}.".format(model_dir))
try:
step = ckpt.split(model_dir + "/")[1].split(".ckpt")[0]
except IndexError:
step = "best"
batch_size = cfg["training"]["batch_size"]
batch_type = cfg["training"].get("batch_type", "sentence")
use_cuda = cfg["training"].get("use_cuda", False)
level = cfg["data"]["level"]
eval_metric = cfg["training"]["eval_metric"]
max_output_length = cfg["training"].get("max_output_length", None)
# load the data
_, dev_data, test_data, src_vocab, trg_vocab = load_data(
data_cfg=cfg["data"])
data_to_predict = {"dev": dev_data, "test": test_data}
# load model state from disk
model_checkpoint = load_checkpoint(ckpt, use_cuda=use_cuda)
# build model and load parameters into it
model = build_model(cfg["model"], src_vocab=src_vocab, trg_vocab=trg_vocab)
model.load_state_dict(model_checkpoint["model_state"])
if use_cuda:
model.cuda()
# whether to use beam search for decoding, 0: greedy decoding
if "testing" in cfg.keys():
beam_size = cfg["testing"].get("beam_size", 0)
beam_alpha = cfg["testing"].get("alpha", -1)
else:
beam_size = 0
beam_alpha = -1
for data_set_name, data_set in data_to_predict.items():
#pylint: disable=unused-variable
score, loss, ppl, sources, sources_raw, references, hypotheses, \
hypotheses_raw, attention_scores = validate_on_data(
model, data=data_set, batch_size=batch_size,
batch_type=batch_type, level=level,
max_output_length=max_output_length, eval_metric=eval_metric,
use_cuda=use_cuda, loss_function=None, beam_size=beam_size,
beam_alpha=beam_alpha)
#pylint: enable=unused-variable
if "trg" in data_set.fields:
decoding_description = "Greedy decoding" if beam_size == 0 else \
"Beam search decoding with beam size = {} and alpha = {}".\
format(beam_size, beam_alpha)
logger.info("%4s %s: %6.2f [%s]", data_set_name, eval_metric,
score, decoding_description)
else:
logger.info("No references given for %s -> no evaluation.",
data_set_name)
if save_attention:
if attention_scores:
attention_name = "{}.{}.att".format(data_set_name, step)
attention_path = os.path.join(model_dir, attention_name)
logger.info(
"Saving attention plots. This might take a while..")
store_attention_plots(attentions=attention_scores,
targets=hypotheses_raw,
sources=[s for s in data_set.src],
indices=range(len(hypotheses)),
output_prefix=attention_path)
logger.info("Attention plots saved to: %s", attention_path)
else:
logger.warning("Attention scores could not be saved. "
"Note that attention scores are not available "
"when using beam search. "
"Set beam_size to 0 for greedy decoding.")
if output_path is not None:
output_path_set = "{}.{}".format(output_path, data_set_name)
with open(output_path_set, mode="w", encoding="utf-8") as out_file:
for hyp in hypotheses:
out_file.write(hyp + "\n")
logger.info("Translations saved to: %s", output_path_set)
def load_model(model_dir, bpe_src_code=None, tokenize=None):
"""
Start the bot. This means loading the model according to the config file.
:param model_dir: Model directory of trained Joey NMT model.
:param bpe_src_code: BPE codes for source side processing (optional).
:param tokenize: If True, tokenize inputs with Moses tokenizer.
:return:
"""
conf = {}
cfg_file = model_dir+"/config.yaml"
logger = logging.getLogger(__name__)
conf["logger"] = logger
# load the Joey configuration
cfg = load_config(cfg_file)
# load the checkpoint
if "load_model" in cfg['training'].keys():
ckpt = cfg['training']["load_model"]
else:
ckpt = get_latest_checkpoint(model_dir)
if ckpt is None:
raise FileNotFoundError("No checkpoint found in directory {}."
.format(model_dir))
# prediction parameters from config
conf["use_cuda"] = cfg["training"].get("use_cuda", False)
conf["level"] = cfg["data"]["level"]
conf["max_output_length"] = cfg["training"].get("max_output_length", None)
conf["lowercase"] = cfg["data"].get("lowercase", False)
# load the vocabularies
src_vocab_file = cfg["training"]["model_dir"] + "/src_vocab.txt"
trg_vocab_file = cfg["training"]["model_dir"] + "/trg_vocab.txt"
conf["src_vocab"] = build_vocab(field="src", vocab_file=src_vocab_file,
dataset=None, max_size=-1, min_freq=0)
conf["trg_vocab"] = build_vocab(field="trg", vocab_file=trg_vocab_file,
dataset=None, max_size=-1, min_freq=0)
# whether to use beam search for decoding, 0: greedy decoding
if "testing" in cfg.keys():
conf["beam_size"] = cfg["testing"].get("beam_size", 0)
conf["beam_alpha"] = cfg["testing"].get("alpha", -1)
else:
conf["beam_size"] = 1
conf["beam_alpha"] = -1
# pre-processing
if tokenize is not None:
src_tokenizer = MosesTokenizer(lang=cfg["data"]["src"])
trg_tokenizer = MosesDetokenizer(lang=cfg["data"]["trg"])
# tokenize input
tokenizer = lambda x: src_tokenizer.tokenize(x, return_str=True)
detokenizer = lambda x: trg_tokenizer.detokenize(
x.split(), return_str=True)
else:
tokenizer = lambda x: x
detokenizer = lambda x: x
if bpe_src_code is not None and level == "bpe":
# load bpe merge file
merge_file = open(bpe_src_code, "r")
bpe = apply_bpe.BPE(codes=merge_file)
segmenter = lambda x: bpe.process_line(x.strip())
elif conf["level"] == "char":
# split to chars
segmenter = lambda x: list(x.strip())
else:
segmenter = lambda x: x.strip()
conf["preprocess"] = [tokenizer, segmenter]
conf["postprocess"] = [detokenizer]
# build model and load parameters into it
model_checkpoint = load_checkpoint(ckpt, conf["use_cuda"])
model = build_model(cfg["model"], src_vocab=conf["src_vocab"], trg_vocab=conf["trg_vocab"])
model.load_state_dict(model_checkpoint["model_state"])
if conf["use_cuda"]:
model.cuda()
conf["model"] = model
print("Joey NMT model loaded successfully.")
return conf
def load_model(self, src_language, trg_language, domain, bpe_src_code=None, tokenize=None):
""" Load model for given trg language. """
# model_dir = "{}-{}".format(self._model_dir_prefix, trg_language)
model_dir = f"{self._model_dir_prefix}{src_language}-{trg_language}-{domain}"
# Load the checkpoint.
ckpt_path = os.path.join(model_dir, 'model.ckpt')
# Load the vocabularies.
src_vocab_path = os.path.join(model_dir, 'src_vocab.txt')
trg_vocab_path = os.path.join(model_dir, 'trg_vocab.txt')
# Load the config.
config_path = os.path.join(model_dir, 'config_orig.yaml')
# Adjust config.
config = load_config(config_path)
new_config_file = os.path.join(model_dir, 'config.yaml')
config = self._update_config(config, src_vocab_path, trg_vocab_path,
model_dir, ckpt_path)
with open(new_config_file, 'w') as cfile:
yaml.dump(config, cfile)
# print('Loaded model for {}-{}.'.format(self._src_language, trg_language))
print('Loaded model for {}-{}.'.format(src_language, trg_language))
conf = {}
logger = logging.getLogger(__name__)
conf["logger"] = logger
# load the Joey configuration
cfg = load_config(new_config_file)
# load the checkpoint
if "load_model" in cfg['training'].keys():
ckpt = cfg['training']["load_model"]
else:
ckpt = get_latest_checkpoint(model_dir)
if ckpt is None:
raise FileNotFoundError("No checkpoint found in directory {}."
.format(model_dir))
# prediction parameters from config
conf["use_cuda"] = cfg["training"].get("use_cuda", False) if torch.cuda.is_available() else False
conf["level"] = cfg["data"]["level"]
conf["max_output_length"] = cfg["training"].get("max_output_length", None)
conf["lowercase"] = cfg["data"].get("lowercase", False)
# load the vocabularies
src_vocab_file = cfg["training"]["model_dir"] + "/src_vocab.txt"
trg_vocab_file = cfg["training"]["model_dir"] + "/trg_vocab.txt"
conf["src_vocab"] = build_vocab(field="src", vocab_file=src_vocab_file,
dataset=None, max_size=-1, min_freq=0)
conf["trg_vocab"] = build_vocab(field="trg", vocab_file=trg_vocab_file,
dataset=None, max_size=-1, min_freq=0)
# whether to use beam search for decoding, 0: greedy decoding
if "testing" in cfg.keys():
conf["beam_size"] = cfg["testing"].get("beam_size", 0)
conf["beam_alpha"] = cfg["testing"].get("alpha", -1)
else:
conf["beam_size"] = 1
conf["beam_alpha"] = -1
# pre-processing
if tokenize is not None:
src_tokenizer = MosesTokenizer(lang=cfg["data"]["src"])
trg_tokenizer = MosesDetokenizer(lang=cfg["data"]["trg"])
# tokenize input
tokenizer = lambda x: src_tokenizer.tokenize(x, return_str=True)
detokenizer = lambda x: trg_tokenizer.detokenize(
x.split(), return_str=True)
else:
tokenizer = lambda x: x
detokenizer = lambda x: x
if bpe_src_code is not None and level == "bpe":
# load bpe merge file
merge_file = open(bpe_src_code, "r")
bpe = apply_bpe.BPE(codes=merge_file)
segmenter = lambda x: bpe.process_line(x.strip())
elif conf["level"] == "char":
# split to chars
segmenter = lambda x: list(x.strip())
else:
segmenter = lambda x: x.strip()
conf["preprocess"] = [tokenizer, segmenter]
conf["postprocess"] = [detokenizer]
# build model and load parameters into it
model_checkpoint = load_checkpoint(ckpt, conf["use_cuda"])
model = build_model(cfg["model"], src_vocab=conf["src_vocab"], trg_vocab=conf["trg_vocab"])
model.load_state_dict(model_checkpoint["model_state"])
# ipdb.set_trace()
if conf["use_cuda"]:
model.cuda()
conf["model"] = model
print("Joey NMT model loaded successfully.")
return conf
def test(cfg_file,
ckpt: str,
output_path: str = None,
save_attention: bool = False,
logger: logging.Logger = None) -> None:
"""
Main test function. Handles loading a model from checkpoint, generating
translations and storing them and attention plots.
:param cfg_file: path to configuration file
:param ckpt: path to checkpoint to load
:param output_path: path to output
:param save_attention: whether to save the computed attention weights
:param logger: log output to this logger (creates new logger if not set)
"""
if logger is None:
logger = logging.getLogger(__name__)
FORMAT = '%(asctime)-15s - %(message)s'
logging.basicConfig(format=FORMAT)
logger.setLevel(level=logging.DEBUG)
cfg = load_config(cfg_file)
if "test" not in cfg["data"].keys():
raise ValueError("Test data must be specified in config.")
# when checkpoint is not specified, take latest (best) from model dir
if ckpt is None:
model_dir = cfg["training"]["model_dir"]
ckpt = get_latest_checkpoint(model_dir)
if ckpt is None:
raise FileNotFoundError(
"No checkpoint found in directory {}.".format(model_dir))
try:
step = ckpt.split(model_dir + "/")[1].split(".ckpt")[0]
except IndexError:
step = "best"
batch_size = cfg["training"].get("eval_batch_size",
cfg["training"]["batch_size"])
batch_type = cfg["training"].get(
"eval_batch_type", cfg["training"].get("batch_type", "sentence"))
use_cuda = cfg["training"].get("use_cuda", False)
level = cfg["data"]["level"]
eval_metric = cfg["training"]["eval_metric"]
max_output_length = cfg["training"].get("max_output_length", None)
# load the data
_, dev_data, test_data,\
src_vocab, trg_vocab,\
_, dev_kb, test_kb,\
_, dev_kb_lookup, test_kb_lookup, \
_, dev_kb_lengths, test_kb_lengths,\
_, dev_kb_truvals, test_kb_truvals, \
trv_vocab, canon_fun,\
dev_data_canon, test_data_canon \
= load_data(
data_cfg=cfg["data"]
)
report_entf1_on_canonicals = cfg["training"].get(
"report_entf1_on_canonicals", False)
kb_task = (test_kb != None)
data_to_predict = {"dev": dev_data, "test": test_data}
# load model state from disk
model_checkpoint = load_checkpoint(ckpt, use_cuda=use_cuda)
# build model and load parameters into it
model = build_model(cfg["model"],
src_vocab=src_vocab,
trg_vocab=trg_vocab,
trv_vocab=trv_vocab,
canonizer=canon_fun)
model.load_state_dict(model_checkpoint["model_state"])
# FIXME for the moment, for testing, try overriding model.canonize with canon_fun from test functions loaded data
# should hopefully not be an issue with gridsearch results...
if use_cuda:
model.cuda() # move to GPU
# whether to use beam search for decoding, 0: greedy decoding
if "testing" in cfg.keys():
beam_size = cfg["testing"].get("beam_size", 0)
beam_alpha = cfg["testing"].get("alpha", -1)
else:
beam_size = 0
beam_alpha = -1
for data_set_name, data_set in data_to_predict.items():
if data_set_name == "dev":
kb_info = [
dev_kb, dev_kb_lookup, dev_kb_lengths, dev_kb_truvals,
dev_data_canon
]
elif data_set_name == "test":
kb_info = [
test_kb, test_kb_lookup, test_kb_lengths, test_kb_truvals,
test_data_canon
]
else:
raise ValueError((data_set_name, data_set))
#pylint: disable=unused-variable
score, loss, ppl, sources, sources_raw, references, hypotheses, \
hypotheses_raw, attention_scores, kb_att_scores, ent_f1, ent_mcc = validate_on_data(
model,
data=data_set,
batch_size=batch_size,
batch_type=batch_type,
level=level,
max_output_length=max_output_length,
eval_metric=eval_metric,
use_cuda=use_cuda,
loss_function=None,
beam_size=beam_size,
beam_alpha=beam_alpha,
kb_task = kb_task,
valid_kb=kb_info[0],
valid_kb_lkp=kb_info[1],
valid_kb_lens=kb_info[2],
valid_kb_truvals=kb_info[3],
valid_data_canon=kb_info[4],
report_on_canonicals=report_entf1_on_canonicals
)
"""
batch_size=self.eval_batch_size,
data=valid_data,
eval_metric=self.eval_metric,
level=self.level,
model=self.model,
use_cuda=self.use_cuda,
max_output_length=self.max_output_length,
loss_function=self.loss,
beam_size=0,
batch_type=self.eval_batch_type,
kb_task=kb_task,
valid_kb=valid_kb,
valid_kb_lkp=valid_kb_lkp,
valid_kb_lens=valid_kb_lens,
valid_kb_truvals=valid_kb_truvals
"""
#pylint: enable=unused-variable
if "trg" in data_set.fields:
decoding_description = "Greedy decoding" if beam_size == 0 else \
"Beam search decoding with beam size = {} and alpha = {}".\
format(beam_size, beam_alpha)
logger.info("%4s %s: %6.2f f1: %6.2f mcc: %6.2f [%s]",
data_set_name, eval_metric, score, ent_f1, ent_mcc,
decoding_description)
else:
logger.info("No references given for %s -> no evaluation.",
data_set_name)
if save_attention:
if attention_scores:
attention_name = "{}.{}.att".format(data_set_name, step)
attention_path = os.path.join(model_dir, attention_name)
logger.info(
"Saving attention plots. This might take a while..")
store_attention_plots(attentions=attention_scores,
targets=hypotheses_raw,
sources=data_set.src,
indices=range(len(hypotheses)),
output_prefix=attention_path)
logger.info("Attention plots saved to: %s", attention_path)
if kb_att_scores:
kb_att_name = "{}.{}.kbatt".format(data_set_name, step)
kb_att_path = os.path.join(model_dir, kb_att_name)
store_attention_plots(
attentions=kb_att_scores,
targets=hypotheses_raw,
sources=list(data_set.kbsrc), #TODO
indices=range(len(hypotheses)),
output_prefix=kb_att_path,
kb_info=(dev_kb_lookup, dev_kb_lengths,
list(data_set.kbtrg)))
logger.info("KB Attention plots saved to: %s", attention_path)
else:
logger.warning("Attention scores could not be saved. "
"Note that attention scores are not available "
"when using beam search. "
"Set beam_size to 0 for greedy decoding.")
if output_path is not None:
output_path_set = "{}.{}".format(output_path, data_set_name)
with open(output_path_set, mode="w", encoding="utf-8") as out_file:
for hyp in hypotheses:
out_file.write(hyp + "\n")
logger.info("Translations saved to: %s", output_path_set)
