def from_module_attr_to_pyobj(module_attr):
if module_attr.type == module_desc_pb2.BOOLEAN:
result = module_attr.b
elif module_attr.type == module_desc_pb2.INT:
result = module_attr.i
elif module_attr.type == module_desc_pb2.STRING:
result = module_attr.s
elif module_attr.type == module_desc_pb2.FLOAT:
result = module_attr.f
elif module_attr.type == module_desc_pb2.LIST:
result = []
for index in range(len(module_attr.list.data)):
result.append(
from_module_attr_to_pyobj(module_attr.list.data[str(index)]))
elif module_attr.type == module_desc_pb2.SET:
result = set()
for index in range(len(module_attr.set.data)):
result.add(
from_module_attr_to_pyobj(module_attr.set.data[str(index)]))
elif module_attr.type == module_desc_pb2.MAP:
result = {}
for key, value in module_attr.map.data.items():
key = get_pykey(key, module_attr.map.key_type[key])
result[key] = from_module_attr_to_pyobj(value)
elif module_attr.type == module_desc_pb2.NONE:
result = None
elif module_attr.type == module_desc_pb2.OBJECT:
result = None
logger.warning("can't tran module attr to python object")
else:
result = None
logger.warning("unknown type of module attr")
return result
def new_eval_end_event(self, run_states):
"""
Paddlehub default handler for eval_end_event, it will complete visualization and metrics calculation
Args:
run_states (object): the results in eval phase
"""
eval_scores, eval_loss, run_speed = self._calculate_metrics(run_states)
if 'train' in self._envs:
self.vdl_writer.add_scalar(tag="Loss_{}".format(self.phase),
value=eval_loss,
step=self._envs['train'].current_step)
log_scores = ""
s = []
for metric in eval_scores:
if 'train' in self._envs:
self.vdl_writer.add_scalar(
tag="{}_{}".format(metric, self.phase),
value=eval_scores[metric],
step=self._envs['train'].current_step)
# dev[metric].add_record(self._envs['train'].current_step, eval_scores[metric])
log_scores += "%s=%.5f " % (metric, eval_scores[metric])
s.append(eval_scores[metric])
# dev[metric].add_record(self.current_step,eval_scores[metric])
logger.eval(
"[%s dataset evaluation result] loss=%.5f %s[step/sec: %.2f]" %
(self.phase, eval_loss, log_scores, run_speed))
s.append(eval_loss)
if 'train' in self._envs:
s = [self._envs['train'].current_step] + s
# dev_loss.add_record(self._envs['train'].current_step,eval_loss)
s = [str(x) for x in s]
with open('./work/log/%s_dev%s.txt' % (args.do_model, id),
'a',
encoding='utf-8') as f:
f.write(','.join(s) + '\n')
eval_scores_items = eval_scores.items()
if len(eval_scores_items):
# The first metric will be chose to eval
if (args.dev_goal == 'f1'):
main_metric, main_value = list(eval_scores_items)[0]
else: #loss
main_metric, main_value = "negative loss", -eval_loss
else:
logger.warning(
"None of metrics has been implemented, loss will be used to evaluate."
)
# The larger, the better
main_metric, main_value = "negative loss", -eval_loss
if self.phase in ["dev", "val"] and main_value > self.best_score:
self.best_score = main_value
model_saved_dir = os.path.join(self.config.checkpoint_dir,
"best_model")
logger.eval("best model saved to %s [best %s=%.5f]" %
(model_saved_dir, main_metric, main_value))
self.save_inference_model(dirname=model_saved_dir)
def __new__(cls,
name=None,
directory=None,
module_dir=None,
version=None,
**kwargs):
if cls.__name__ == "Module":
if name:
module = cls.init_with_name(
name=name, version=version, **kwargs)
elif directory:
module = cls.init_with_directory(directory=directory, **kwargs)
elif module_dir:
logger.warning(
"Parameter module_dir is deprecated, please use directory to specify the path"
)
if isinstance(module_dir, list) or isinstance(
module_dir, tuple):
directory = module_dir[0]
version = module_dir[1]
else:
directory = module_dir
module = cls.init_with_directory(directory=directory, **kwargs)
CacheUpdater("update_cache", module.name, module.version).start()
else:
if not name and not directory:
directory = os.path.dirname(
os.path.abspath(sys.modules[cls.__module__].__file__))
module = Module.init_with_directory(
directory=directory, **kwargs)
else:
module = object.__new__(cls)
return module
def _check_paddle_version(self):
if version_compare(self.paddle_version, paddle.__version__):
logger.warning(
"This Module is generated by the PaddlePaddle with version %s, and the local PaddlePaddle version is %s, which may cause serious incompatible bug. Please upgrade PaddlePaddle to the latest version."
% (self.paddle_version, paddle.__version__))
return False
return True
def _check_module_proto_version(self):
if self.module_proto_version != module_proto_version:
logger.warning(
"Module description file version cannot be aligned with PaddleHub version"
)
return False
return True
def _postprocessing(self, run_states):
if self._compatible_mode:
id2label = {
val: key
for key, val in self._base_data_reader.label_map.items()
}
else:
if self._label_list:
id2label = {}
for index, label in enumerate(self._label_list):
id2label[index] = label
else:
logger.warning(
"Fail to postprocess the predict output. Please set label_list parameter in predict function or initialize the task with dataset parameter."
)
return run_states
results = []
for batch_states in run_states:
batch_results = batch_states.run_results
batch_infers = batch_results[0].reshape([-1]).astype(
np.int32).tolist()
seq_lens = batch_results[1].reshape([-1]).astype(np.int32).tolist()
current_id = 0
for length in seq_lens:
seq_infers = batch_infers[current_id:current_id + length]
seq_result = list(map(id2label.get, seq_infers[1:-1]))
current_id += length if self.add_crf else self.max_seq_len
results.append(seq_result)
return results
def predict(
self,
data=None,
label_list=None,
load_best_model=True,
return_result=False,
accelerate_mode=True,
):
"""
make prediction for the input data.
Args:
data (list): the data will be predicted. Its element should be a record when the task is initialized without data_reader param,
or a plaintext string list when the task is initialized with data_reader param (deprecated in paddlehub v1.8).
label_list (list): the label list, used to proprocess the output.
load_best_model (bool): load the best model or not
return_result (bool): return a readable result or just the raw run result. Always True when the task is not initialized with data_reader but dataset parameter.
accelerate_mode (bool): use high-performance predictor or not
Returns:
RunState: the running result of predict phase
"""
if accelerate_mode:
if isinstance(self._base_data_reader,
hub.reader.LACClassifyReader):
logger.warning(
"LACClassifyReader does not support predictor, the accelerate_mode is closed now."
)
accelerate_mode = False
elif isinstance(self, hub.TextGenerationTask):
logger.warning(
"TextGenerationTask does not support predictor, the accelerate_mode is closed now."
)
accelerate_mode = False
self.accelerate_mode = accelerate_mode
with self.phase_guard(phase="predict"):
self._predict_data = data
if label_list:
self._label_list = label_list
self._predict_start_event()
if load_best_model:
self.init_if_load_best_model()
else:
self.init_if_necessary()
if not self.accelerate_mode:
run_states = self._run()
else:
if not self._predictor:
self._predictor = self._create_predictor()
run_states = self._run_with_predictor()
self._predict_end_event(run_states)
self._predict_data = None
if return_result or not self._compatible_mode:
return self._postprocessing(run_states)
return run_states
def generate(self, texts, use_gpu=False, beam_width=5):
"""
Get the continuation of the input poetry.
Args:
texts(list): the front part of a poetry.
use_gpu(bool): whether use gpu to predict or not
beam_width(int): the beam search width.
Returns:
results(list): the poetry continuations.
"""
paddle.disable_static()
if texts and isinstance(texts, list) and all(texts) and all(
[isinstance(text, str) for text in texts]):
predicted_data = texts
else:
raise ValueError(
"The input texts should be a list with nonempty string elements."
)
if use_gpu and "CUDA_VISIBLE_DEVICES" not in os.environ:
use_gpu = False
logger.warning(
"use_gpu has been set False as you didn't set the environment variable CUDA_VISIBLE_DEVICES while using use_gpu=True"
)
paddle.set_device('gpu') if use_gpu else paddle.set_device('cpu')
self.model.eval()
results = []
for text in predicted_data:
sample_results = []
encode_text = self.tokenizer.encode(text)
src_ids = paddle.to_tensor(encode_text['input_ids']).unsqueeze(0)
src_sids = paddle.to_tensor(
encode_text['token_type_ids']).unsqueeze(0)
output_ids = beam_search_infilling(
self.model,
src_ids,
src_sids,
eos_id=self.tokenizer.vocab['[SEP]'],
sos_id=self.tokenizer.vocab['[CLS]'],
attn_id=self.tokenizer.vocab['[MASK]'],
pad_id=self.tokenizer.vocab['[PAD]'],
unk_id=self.tokenizer.vocab['[UNK]'],
vocab_size=len(self.tokenizer.vocab),
max_decode_len=80,
max_encode_len=20,
beam_width=beam_width,
tgt_type_id=1)
output_str = self.rev_lookup(output_ids[0])
for ostr in output_str.tolist():
if '[SEP]' in ostr:
ostr = ostr[:ostr.index('[SEP]')]
sample_results.append("".join(ostr))
results.append(sample_results)
return results
def generate(self, texts, use_gpu=False, beam_width=5):
"""
Get the predict result from the input texts.
Args:
texts(list): the input texts.
use_gpu(bool): whether use gpu to predict or not
beam_width(int): the beam search width.
Returns:
results(list): the predict result.
"""
if texts and isinstance(texts, list) and all(texts) and all(
[isinstance(text, str) for text in texts]):
predicted_data = texts
else:
raise ValueError(
"The input texts should be a list with nonempty string elements."
)
if use_gpu and "CUDA_VISIBLE_DEVICES" not in os.environ:
use_gpu = False
logger.warning(
"use_gpu has been set False as you didn't set the environment variable CUDA_VISIBLE_DEVICES while using use_gpu=True"
)
if use_gpu:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
with fluid.dygraph.guard(place):
self.model.eval()
results = []
for text in predicted_data:
sample_results = []
ids, sids = self.tokenizer.encode(text)
src_ids = D.to_variable(np.expand_dims(ids, 0))
src_sids = D.to_variable(np.expand_dims(sids, 0))
output_ids = beam_search_infilling(
self.model,
src_ids,
src_sids,
eos_id=self.tokenizer.sep_id,
sos_id=self.tokenizer.cls_id,
attn_id=self.tokenizer.vocab['[MASK]'],
max_decode_len=50,
max_encode_len=50,
beam_width=beam_width,
tgt_type_id=1)
output_str = self.rev_lookup(output_ids[0].numpy())
for ostr in output_str.tolist():
if '[SEP]' in ostr:
ostr = ostr[:ostr.index('[SEP]')]
sample_results.append("".join(ostr))
results.append(sample_results)
return results
def _read_file(self, input_file, phase=None):
"""Reads a tab separated value file."""
has_warned = False
with io.open(input_file, "r", encoding="UTF-8") as file:
reader = csv.reader(file, delimiter="\t", quotechar=None)
examples = []
for (i, line) in enumerate(reader):
if i == 0:
ncol = len(line)
if self.if_file_with_header[phase]:
continue
if (len(line) != ncol):
print(line)
if phase != "predict":
if ncol == 1:
raise Exception(
"the %s file: %s only has one column but it is not a predict file"
% (phase, input_file))
elif ncol == 2:
example = InputExample(guid=i,
text_a=line[0],
label=line[1])
elif ncol == 3:
example = InputExample(guid=i,
text_a=line[0],
text_b=line[1],
label=line[2])
else:
raise Exception(
"the %s file: %s has too many columns (should <=3_实体识别)"
% (phase, input_file))
else:
if ncol == 1:
example = InputExample(guid=i, text_a=line[0])
elif ncol == 2:
if not has_warned:
logger.warning(
"the predict file: %s has 2 columns, as it is a predict file, the second one will be regarded as text_b"
% (input_file))
has_warned = True
example = InputExample(guid=i,
text_a=line[0],
text_b=line[1])
else:
raise Exception(
"the predict file: %s has too many columns (should <=2)"
% (input_file))
examples.append(example)
# print(example)
return examples
def preprocess(text):
data_dict = {self.feed_key: [text]}
processed = self.lac.lexical_analysis(data=data_dict)
processed = [
self.vocab[word] for word in processed[0]['word']
if word in self.vocab
]
if len(processed) == 0:
if six.PY2:
text = text.encode(sys_stdout_encoding())
logger.warning(
"The words in text %s can't be found in the vocabulary." %
(text))
return processed
def __init__(self,
base_path,
train_file=None,
dev_file=None,
test_file=None,
predict_file=None,
label_file=None,
label_list=None,
train_file_with_header=False,
dev_file_with_header=False,
test_file_with_header=False,
predict_file_with_header=False):
if not (train_file or dev_file or test_file):
raise ValueError("At least one file should be assigned")
self.base_path = base_path
self.train_file = train_file
self.dev_file = dev_file
self.test_file = test_file
self.predict_file = predict_file
self.label_file = label_file
self.label_list = label_list
self.train_examples = []
self.dev_examples = []
self.test_examples = []
self.predict_examples = []
self.if_file_with_header = {
"train": train_file_with_header,
"dev": dev_file_with_header,
"test": test_file_with_header,
"predict": predict_file_with_header
}
if train_file:
self._load_train_examples()
if dev_file:
self._load_dev_examples()
if test_file:
self._load_test_examples()
if predict_file:
self._load_predict_examples()
if self.label_file:
if not self.label_list:
self.label_list = self._load_label_data()
else:
logger.warning(
"As label_list has been assigned, label_file is noneffective"
)
def from_pyobj_to_module_attr(pyobj, module_attr, obj_filter=None):
if obj_filter and obj_filter(pyobj):
return
if isinstance(pyobj, bool):
module_attr.type = module_desc_pb2.BOOLEAN
module_attr.b = pyobj
elif isinstance(pyobj, six.integer_types):
module_attr.type = module_desc_pb2.INT
module_attr.i = pyobj
elif isinstance(pyobj, six.text_type):
module_attr.type = module_desc_pb2.STRING
module_attr.s = pyobj
elif isinstance(pyobj, six.binary_type):
module_attr.type = module_desc_pb2.STRING
module_attr.s = pyobj
elif isinstance(pyobj, float):
module_attr.type = module_desc_pb2.FLOAT
module_attr.f = pyobj
elif isinstance(pyobj, list) or isinstance(pyobj, tuple):
module_attr.type = module_desc_pb2.LIST
for index, obj in enumerate(pyobj):
from_pyobj_to_module_attr(obj, module_attr.list.data[str(index)],
obj_filter)
elif isinstance(pyobj, set):
module_attr.type = module_desc_pb2.SET
for index, obj in enumerate(list(pyobj)):
from_pyobj_to_module_attr(obj, module_attr.set.data[str(index)],
obj_filter)
elif isinstance(pyobj, dict):
module_attr.type = module_desc_pb2.MAP
for key, value in pyobj.items():
from_pyobj_to_module_attr(value, module_attr.map.data[str(key)],
obj_filter)
module_attr.map.key_type[str(key)] = get_keyed_type_of_pyobj(key)
elif isinstance(pyobj, type(None)):
module_attr.type = module_desc_pb2.NONE
else:
module_attr.type = module_desc_pb2.OBJECT
module_attr.name = str(pyobj.__class__.__name__)
if not hasattr(pyobj, "__dict__"):
logger.warning("python obj %s has not __dict__ attr" %
module_attr.name)
return
for key, value in pyobj.__dict__.items():
from_pyobj_to_module_attr(value, module_attr.object.data[str(key)],
obj_filter)
module_attr.object.key_type[str(key)] = get_keyed_type_of_pyobj(
key)
def step(self):
if self.scheduler["gradual_unfreeze"] > 0:
self.epoch += 1
if self.max_depth > 0 and self.epoch <= self.scheduler[
"gradual_unfreeze"]:
set_gradual_unfreeze(
self.main_program,
unfreeze_depths=self.
sorted_depth[:self.max_depth * self.epoch //
self.scheduler["gradual_unfreeze"]])
else:
logger.warning(
"The max op-depth in the network is %s. That results in that can't use the gradual unfreeze finetune strategy."
% (self.max_depth))
else:
pass
def __init__(self):
logger.warning(
"ImageClassificationDataset is no longer recommended from PaddleHub v1.5.0, "
"please use BaseCVDataset instead of ImageClassificationDataset. "
"It's more easy-to-use with more functions and support evaluating test set "
"in the end of finetune automatically.")
self.base_path = None
self.train_list_file = None
self.test_list_file = None
self.validate_list_file = None
self.label_list_file = None
self.num_labels = 0
self.label_list = []
self.train_examples = []
self.dev_examples = []
self.test_examples = []
def predict(self,
data,
load_best_model=True,
return_result=False,
accelerate_mode=True):
"""
make prediction for the input data.
Args:
data (list): the data will be predicted.
load_best_model (bool): load the best model or not
return_result (bool): return a readable result or just the raw run result
accelerate_mode (bool): use high-performance predictor or not
Returns:
RunState: the running result of predict phase
"""
if not version_compare(paddle.__version__,
"1.6.2") and accelerate_mode:
logger.warning(
"Fail to open predict accelerate mode as it does not support paddle < 1.6.2. Please update PaddlePaddle."
)
accelerate_mode = False
self.accelerate_mode = accelerate_mode
with self.phase_guard(phase="predict"):
self._predict_data = data
self._predict_start_event()
if load_best_model:
self.init_if_load_best_model()
else:
self.init_if_necessary()
if not self.accelerate_mode:
run_states = self._run()
else:
if not self._predictor:
self._predictor = self._create_predictor()
run_states = self._run_with_predictor()
self._predict_end_event(run_states)
self._predict_data = None
if return_result:
return self._postprocessing(run_states)
return run_states
def predict(self,
data,
load_best_model=True,
return_result=False,
accelerate_mode=True):
"""
make prediction for the input data.
Args:
data (list): the data will be predicted.
load_best_model (bool): load the best model or not
return_result (bool): return a readable result or just the raw run result
accelerate_mode (bool): use high-performance predictor or not
Returns:
RunState: the running result of predict phase
"""
if accelerate_mode and isinstance(self._base_data_reader,
hub.reader.LACClassifyReader):
logger.warning(
"LACClassifyReader does not support predictor, the accelerate_mode is closed now."
)
accelerate_mode = False
self.accelerate_mode = accelerate_mode
with self.phase_guard(phase="predict"):
self._predict_data = data
self._predict_start_event()
if load_best_model:
self.init_if_load_best_model()
else:
self.init_if_necessary()
if not self.accelerate_mode:
run_states = self._run()
else:
if not self._predictor:
self._predictor = self._create_predictor()
run_states = self._run_with_predictor()
self._predict_end_event(run_states)
self._predict_data = None
if return_result:
return self._postprocessing(run_states)
return run_states
def check_module_valid(self, module_path):
try:
desc_pb_path = os.path.join(module_path, 'module_desc.pb')
if os.path.exists(desc_pb_path) and os.path.isfile(desc_pb_path):
info = {}
desc = module_desc_pb2.ModuleDesc()
with open(desc_pb_path, "rb") as fp:
desc.ParseFromString(fp.read())
info['version'] = desc.attr.map.data["module_info"].map.data[
"version"].s
return True, info
else:
logger.warning(
"%s does not exist, the module will be reinstalled" %
desc_pb_path)
except:
pass
return False, None
def _default_eval_end_event(self, run_states):
"""
Paddlehub default handler for eval_end_event, it will complete visualization and metrics calculation
Args:
run_states (object): the results in eval phase
"""
eval_scores, eval_loss, run_speed = self._calculate_metrics(run_states)
if 'train' in self._envs:
self.tb_writer.add_scalar(
tag="Loss_{}".format(self.phase),
scalar_value=eval_loss,
global_step=self._envs['train'].current_step)
log_scores = ""
for metric in eval_scores:
if 'train' in self._envs:
self.tb_writer.add_scalar(
tag="{}_{}".format(metric, self.phase),
scalar_value=eval_scores[metric],
global_step=self._envs['train'].current_step)
log_scores += "%s=%.5f " % (metric, eval_scores[metric])
logger.eval(
"[%s dataset evaluation result] loss=%.5f %s[step/sec: %.2f]" %
(self.phase, eval_loss, log_scores, run_speed))
eval_scores_items = eval_scores.items()
if len(eval_scores_items):
# The first metric will be chose to eval
main_metric, main_value = list(eval_scores_items)[0]
else:
logger.warning(
"None of metrics has been implemented, loss will be used to evaluate."
)
# The larger, the better
main_metric, main_value = "negative loss", -eval_loss
if self.phase in ["dev", "val"] and main_value > self.best_score:
self.best_score = main_value
model_saved_dir = os.path.join(self.config.checkpoint_dir,
"best_model")
logger.eval("best model saved to %s [best %s=%.5f]" %
(model_saved_dir, main_metric, main_value))
self.save_inference_model(dirname=model_saved_dir)
def __init__(self,
log_interval=10,
eval_interval=100,
use_pyreader=True,
use_data_parallel=True,
save_ckpt_interval=None,
use_cuda=True,
checkpoint_dir=None,
num_epoch=1,
batch_size=32,
enable_memory_optim=False,
strategy=None):
""" Construct finetune Config """
self._log_interval = log_interval
self._eval_interval = eval_interval
self._save_ckpt_interval = save_ckpt_interval
self._use_cuda = use_cuda
self._checkpoint_dir = checkpoint_dir
self._num_epoch = num_epoch
self._batch_size = batch_size
if not use_pyreader:
logger.warning(
"The parameter use_pyreader has been dropped! PaddleHub over v1.8.0 will use pyreader by default."
)
self._use_pyreader = True
self._use_data_parallel = use_data_parallel
if strategy is None:
self._strategy = DefaultStrategy()
else:
self._strategy = strategy
if enable_memory_optim:
logger.warning(
"The memory optimization feature has been dropped! PaddleHub now doesn't optimize the memory of the program."
)
self._enable_memory_optim = False
if checkpoint_dir is None:
now = int(time.time())
time_str = time.strftime("%Y%m%d%H%M%S", time.localtime(now))
self._checkpoint_dir = "ckpt_" + time_str
else:
self._checkpoint_dir = checkpoint_dir
logger.info("Checkpoint dir: {}".format(self._checkpoint_dir))
def _eval_end_event(self, run_states):
eval_scores, eval_loss, run_speed = self._calculate_metrics(run_states)
if 'train' in self._envs:
self.tb_writer.add_scalar(
tag="Loss_{}".format(self.phase),
scalar_value=eval_loss,
global_step=self._envs['train'].current_step)
log_scores = ""
for metric in eval_scores:
if 'train' in self._envs:
self.tb_writer.add_scalar(
tag="{}_{}".format(metric, self.phase),
scalar_value=eval_scores[metric],
global_step=self._envs['train'].current_step)
log_scores += "%s=%.5f " % (metric, eval_scores[metric])
logger.info(
"[%s dataset evaluation result] loss=%.5f %s[step/sec: %.2f]" %
(self.phase, eval_loss, log_scores, run_speed))
eval_scores_items = eval_scores.items()
if len(eval_scores_items):
# The first metric will be chose to eval
main_metric, main_value = list(eval_scores_items)[0]
else:
logger.warning(
"None of metrics has been implemented, loss will be used to evaluate."
)
# The larger, the better
main_metric, main_value = "negative loss", -eval_loss
if self.phase in ["dev", "val"] and main_value > self.best_score:
self.best_score = main_value
model_saved_dir = os.path.join(self.config.checkpoint_dir,
"best_model")
logger.info("best model saved to %s [best %s=%.5f]" %
(model_saved_dir, main_metric, main_value))
save_result = fluid.io.save_persistables(
executor=self.exe,
dirname=model_saved_dir,
main_program=self.main_program)
def __init__(self,
vocab_path,
dataset=None,
label_map_config=None,
max_seq_len=512,
do_lower_case=True,
random_seed=None,
use_task_id=False,
sp_model_path=None,
word_dict_path=None,
in_tokens=False):
super(BaseNLPReader, self).__init__(dataset, random_seed)
self.max_seq_len = max_seq_len
if sp_model_path and word_dict_path:
self.tokenizer = tokenization.WSSPTokenizer(vocab_path,
sp_model_path,
word_dict_path,
ws=True,
lower=True)
else:
self.tokenizer = tokenization.FullTokenizer(
vocab_file=vocab_path, do_lower_case=do_lower_case)
self.vocab = self.tokenizer.vocab
self.pad_id = self.vocab["[PAD]"]
self.cls_id = self.vocab["[CLS]"]
self.sep_id = self.vocab["[SEP]"]
self.mask_id = self.vocab["[MASK]"]
self.in_tokens = in_tokens
self.use_task_id = use_task_id
if self.use_task_id:
logger.warning(
"use_task_id has been de discarded since PaddleHub v1.4.0, it's no necessary to feed task_ids now."
)
self.task_id = 0
self.Record_With_Label_Id = namedtuple(
'Record',
['token_ids', 'text_type_ids', 'position_ids', 'label_id'])
self.Record_Wo_Label_Id = namedtuple(
'Record', ['token_ids', 'text_type_ids', 'position_ids'])
def check_module_valid(self, module_path):
try:
desc_pb_path = os.path.join(module_path, 'module_desc.pb')
if os.path.exists(desc_pb_path) and os.path.isfile(desc_pb_path):
info = {}
desc = module_desc_pb2.ModuleDesc()
with open(desc_pb_path, "rb") as fp:
desc.ParseFromString(fp.read())
info['version'] = desc.attr.map.data["module_info"].map.data[
"version"].s
info['name'] = desc.attr.map.data["module_info"].map.data[
"name"].s
return True, info
else:
module_file = os.path.realpath(
os.path.join(module_path, 'module.py'))
if os.path.exists(module_file):
basename = os.path.split(module_path)[-1]
dirname = os.path.join(
*list(os.path.split(module_path)[:-1]))
sys.path.insert(0, dirname)
_module = importlib.import_module(
"{}.module".format(basename))
for _item, _cls in inspect.getmembers(
_module, inspect.isclass):
_item = _module.__dict__[_item]
_file = os.path.realpath(
sys.modules[_item.__module__].__file__)
if issubclass(
_item,
hub.Module) and _file.startswith(module_file):
version = _item._version
break
sys.path.pop(0)
return True, {'version': version, 'name': _item._name}
logger.warning(
"%s does not exist, the module will be reinstalled" %
desc_pb_path)
except:
pass
return False, None
def __new__(cls, name=None, directory=None, module_dir=None, version=None):
if cls.__name__ == "Module":
if name:
module = cls.init_with_name(name=name, version=version)
elif directory:
module = cls.init_with_directory(directory=directory)
elif module_dir:
logger.warning(
"Parameter module_dir is deprecated, please use directory to specify the path"
)
if isinstance(module_dir, list) or isinstance(
module_dir, tuple):
directory = module_dir[0]
version = module_dir[1]
else:
directory = module_dir
module = cls.init_with_directory(directory=directory)
CacheUpdater("update_cache", module.name, module.version).start()
else:
module = object.__new__(cls)
return module
def _postprocessing(self, run_states):
if self._compatible_mode:
try:
label_list = list(self._base_data_reader.label_map.keys())
except:
raise Exception(
"ImageClassificationDataset does not support postprocessing, please use BaseCVDataset instead"
)
else:
if self._label_list:
label_list = self._label_list
else:
logger.warning(
"Fail to postprocess the predict output. Please set label_list parameter in predict function or initialize the task with dataset parameter."
)
return run_states
results = []
for batch_state in run_states:
batch_result = batch_state.run_results
batch_infer = np.argmax(batch_result[0], axis=1)
results += [
label_list[sample_infer] for sample_infer in batch_infer
]
return results
def _read_file(self, input_file, phase=False):
"""
读入json格式数据集
"""
examples = []
drop = 0
with open(input_file, "r") as reader:
input_data = json.load(reader)["data"]
for entry in input_data:
for paragraph in entry["paragraphs"]:
paragraph_text = paragraph["context"]
guid = []
labels = [0] * len(self.label_list)
for qa in paragraph["qas"]:
guid.append(qa["id"])
labels[self.label_list.index(qa["question"].replace(
'的主体是什么？', ''))] = 1
guid = str(list(set(guid)))
example = InputExample(guid=guid,
label=labels,
text_a=paragraph_text)
examples.append(example)
logger.warning("%i bad examples has been dropped" % drop)
return examples
def _postprocessing(self, run_states):
results = []
if self._compatible_mode:
label_list = list(self._base_data_reader.label_map.keys())
else:
if self._label_list:
label_list = self._label_list
else:
logger.warning(
"Fail to postprocess the predict output. Please set label_list parameter in predict function or initialize the task with dataset parameter."
)
return run_states
for batch_state in run_states:
batch_result = batch_state.run_results
for sample_id in range(len(batch_result[0])):
sample_result = []
for category_id in range(len(label_list)):
sample_category_prob = batch_result[category_id][sample_id]
sample_category_value = np.argmax(sample_category_prob)
sample_result.append(
{label_list[category_id]: sample_category_value})
results.append(sample_result)
return results
def _check_module_integrity(self):
result = True
for file_info in self.file_infos:
file_type = file_info.type
file_path = file_info.file_name.replace(FILE_SEP, os.sep)
file_path = os.path.join(self.module_path, file_path)
if not os.path.exists(file_path):
if file_info.is_need:
logger.warning(
"Module integrity check failed! Missing file [%s]" %
file_path)
result = False
else:
if file_type == check_info_pb2.FILE:
if not os.path.isfile(file_path):
logger.warning("File type check error %s" % file_path)
result = False
if file_type == check_info_pb2.DIR:
if not os.path.isdir(file_path):
logger.warning("File type check error %s" % file_path)
result = False
return result
def get_predictions(all_examples, all_features, all_results, n_best_size,
max_answer_length, do_lower_case, version_2_with_negative,
null_score_diff_threshold, is_english):
_PrelimPrediction = collections.namedtuple("PrelimPrediction", [
"feature_index", "start_index", "end_index", "start_logit", "end_logit"
])
_NbestPrediction = collections.namedtuple(
"NbestPrediction", ["text", "start_logit", "end_logit"])
example_index_to_features = collections.defaultdict(list)
for feature in all_features:
example_index_to_features[feature.example_index].append(feature)
unique_id_to_result = {}
for result in all_results:
unique_id_to_result[result.unique_id] = result
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict()
for (example_index, example) in enumerate(all_examples):
features = example_index_to_features[example_index]
prelim_predictions = []
# keep track of the minimum score of null start+end of position 0
score_null = 1000000 # large and positive
min_null_feature_index = 0 # the paragraph slice with min mull score
null_start_logit = 0 # the start logit at the slice with min null score
null_end_logit = 0 # the end logit at the slice with min null score
for (feature_index, feature) in enumerate(features):
if feature.unique_id not in unique_id_to_result:
logger.info(
"As using multidevice, the last one batch is so small that the feature %s in the last batch is discarded "
% feature.unique_id)
continue
result = unique_id_to_result[feature.unique_id]
start_indexes = _get_best_indexes(result.start_logits, n_best_size)
end_indexes = _get_best_indexes(result.end_logits, n_best_size)
# if we could have irrelevant answers, get the min score of irrelevant
if version_2_with_negative:
feature_null_score = result.start_logits[
0] + result.end_logits[0]
if feature_null_score < score_null:
score_null = feature_null_score
min_null_feature_index = feature_index
null_start_logit = result.start_logits[0]
null_end_logit = result.end_logits[0]
for start_index in start_indexes:
for end_index in end_indexes:
# We could hypothetically create invalid predictions, e.g., predict
# that the start of the span is in the question. We throw out all
# invalid predictions.
if start_index >= len(feature.tokens):
continue
if end_index >= len(feature.tokens):
continue
if start_index not in feature.token_to_orig_map:
continue
if end_index not in feature.token_to_orig_map:
continue
if not feature.token_is_max_context.get(
start_index, False):
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > max_answer_length:
continue
prelim_predictions.append(
_PrelimPrediction(
feature_index=feature_index,
start_index=start_index,
end_index=end_index,
start_logit=result.start_logits[start_index],
end_logit=result.end_logits[end_index]))
if version_2_with_negative:
prelim_predictions.append(
_PrelimPrediction(feature_index=min_null_feature_index,
start_index=0,
end_index=0,
start_logit=null_start_logit,
end_logit=null_end_logit))
prelim_predictions = sorted(prelim_predictions,
key=lambda x:
(x.start_logit + x.end_logit),
reverse=True)
seen_predictions = {}
nbest = []
if not prelim_predictions:
logger.warning(("not prelim_predictions:", example.qas_id))
for pred in prelim_predictions:
if len(nbest) >= n_best_size:
break
feature = features[pred.feature_index]
if pred.start_index > 0: # this is a non-null prediction
tok_tokens = feature.tokens[pred.start_index:(pred.end_index +
1)]
orig_doc_start = feature.token_to_orig_map[pred.start_index]
orig_doc_end = feature.token_to_orig_map[pred.end_index]
orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end +
1)]
if is_english:
tok_text = " ".join(tok_tokens)
else:
tok_text = "".join(tok_tokens)
# De-tokenize WordPieces that have been split off.
tok_text = tok_text.replace(" ##", "")
tok_text = tok_text.replace("##", "")
# Clean whitespace
tok_text = tok_text.strip()
tok_text = " ".join(tok_text.split())
if is_english:
orig_text = " ".join(orig_tokens)
else:
orig_text = "".join(orig_tokens)
final_text = get_final_text(tok_text, orig_text, do_lower_case,
is_english)
if final_text in seen_predictions:
continue
seen_predictions[final_text] = True
else:
final_text = ""
seen_predictions[final_text] = True
nbest.append(
_NbestPrediction(text=final_text,
start_logit=pred.start_logit,
end_logit=pred.end_logit))
# if we didn't include the empty option in the n-best, include it
if version_2_with_negative:
if "" not in seen_predictions:
nbest.append(
_NbestPrediction(text="",
start_logit=null_start_logit,
end_logit=null_end_logit))
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(
_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
assert len(nbest) >= 1
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_logit + entry.end_logit)
if not best_non_null_entry:
if entry.text:
best_non_null_entry = entry
probs = _compute_softmax(total_scores)
nbest_json = []
for (i, entry) in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["probability"] = probs[i]
output["start_logit"] = entry.start_logit
output["end_logit"] = entry.end_logit
nbest_json.append(output)
assert len(nbest_json) >= 1
if not version_2_with_negative:
all_predictions[example.qas_id] = nbest_json[0]["text"]
else:
# predict "" iff the null score - the score of best non-null > threshold
score_diff = score_null
if best_non_null_entry:
score_diff -= best_non_null_entry.start_logit + best_non_null_entry.end_logit
scores_diff_json[example.qas_id] = score_diff
if score_diff > null_score_diff_threshold:
all_predictions[example.qas_id] = ""
else:
all_predictions[example.qas_id] = best_non_null_entry.text
all_nbest_json[example.qas_id] = nbest_json
return all_predictions, all_nbest_json, scores_diff_json
def generate(self, texts, use_gpu=False, beam_width=5):
"""
Get the continuation of the input poetry.
Args:
texts(list): the front part of a poetry.
use_gpu(bool): whether use gpu to predict or not
beam_width(int): the beam search width.
Returns:
results(list): the poetry continuations.
"""
if texts and isinstance(texts, list) and all(texts) and all(
[isinstance(text, str) for text in texts]):
predicted_data = texts
else:
raise ValueError(
"The input texts should be a list with nonempty string elements."
)
for i, text in enumerate(texts):
if len(text) > self.line:
logger.warning(
'The input text: %s, contains more than %i characters, which will be cut off'
% (text, self.line))
texts[i] = text[:self.line]
for char in text:
if not '\u4e00' <= char <= '\u9fff':
logger.warning(
'The input text: %s, contains non-Chinese characters, which may result in magic output'
% text)
break
if use_gpu and "CUDA_VISIBLE_DEVICES" not in os.environ:
use_gpu = False
logger.warning(
"use_gpu has been set False as you didn't set the environment variable CUDA_VISIBLE_DEVICES while using use_gpu=True"
)
if use_gpu:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
with fluid.dygraph.guard(place):
self.model.eval()
results = []
for text in predicted_data:
sample_results = []
ids, sids = self.tokenizer.encode(text)
src_ids = D.to_variable(np.expand_dims(ids, 0))
src_sids = D.to_variable(np.expand_dims(sids, 0))
output_ids = beam_search_infilling(
self.model,
src_ids,
src_sids,
eos_id=self.tokenizer.sep_id,
sos_id=self.tokenizer.cls_id,
attn_id=self.tokenizer.vocab['[MASK]'],
max_decode_len=80,
max_encode_len=20,
beam_width=beam_width,
tgt_type_id=1)
output_str = self.rev_lookup(output_ids[0].numpy())
for ostr in output_str.tolist():
if '[SEP]' in ostr:
ostr = ostr[:ostr.index('[SEP]')]
sample_results.append("".join(ostr))
results.append(sample_results)
return results