我在终端中收到此错误：
Traceback (most recent call last):
File "inference.py", line 277, in <module>
answers = process_questions(question)[0]
File "inference.py", line 238, in process_questions
answers_list = inference_helper(prepared_questions)
File "inference.py", line 163, in start_inference
inference_object = do_start_inference(out_dir, hparams)
File "inference.py", line 37, in do_start_inference
nmt.add_arguments(nmt_parser)
AttributeError: module 'nmt' has no attribute 'add_arguments'
我正在尝试将文件nmt.py从nmt_chatbot / nmt / nmt导入到文件inference.py中。如嵌入图像所示，inference.py和nmt.py文件位于同一文件夹中。
我在inference.py文件中得到了这一行： import nmt
This image shows the how my folders and files are organized
这是下面的inference.py文件的完整代码：
导入系统 进口口 sys.path.append（os.path.realpath（os.path.dirname（的文件））） sys.path.append（os.path.realpath（os.path.dirname（ file ））+＆＃34; / nmt＆＃34;） import argparse 来自setup.settings import hparams，out_dir，preprocessing 导入nmt 导入张量流为tf 来自core.tokenizer import tokenize，detokenize，apply_bpe，apply_bpe_load 来自core.sentence import score_answers，replace_in_answers 进口colorama
current_stdout =无
def do_start_inference（out_dir，hparams）：
# Silence all outputs
#os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
global current_stdout
current_stdout = sys.stdout
sys.stdout = open(os.devnull, "w")
# Modified autorun from nmt.py (bottom of the file)
# We want to use original argument parser (for validation, etc)
nmt_parser = argparse.ArgumentParser()
nmt.add_arguments(nmt_parser)
# But we have to hack settings from our config in there instead of commandline options
flags, unparsed = nmt_parser.parse_known_args(['--'+k+'='+str(v) for k,v in hparams.items()])
# And now we can run TF with modified arguments
#tf.app.run(main=nmt.main, argv=[os.getcwd() + '\nmt\nmt\nmt.py'] + unparsed)
# Add output (model) folder to flags
flags.out_dir = out_dir
# Make hparams
hparams = nmt.create_hparams(flags)
## Train / Decode
if not tf.gfile.Exists(flags.out_dir):
nmt.utils.print_out("# Model folder (out_dir) doesn't exist")
sys.exit()
# Load hparams from model folder
hparams = nmt.create_or_load_hparams(flags.out_dir, hparams, flags.hparams_path, save_hparams=True)
# Choose checkpoint (provided with hparams or last one)
if not flags.ckpt:
flags.ckpt = tf.train.latest_checkpoint(flags.out_dir)
# Create model
if not hparams.attention:
model_creator = nmt.inference.nmt_model.Model
elif hparams.attention_architecture == "standard":
model_creator = nmt.inference.attention_model.AttentionModel
elif hparams.attention_architecture in ["gnmt", "gnmt_v2"]:
model_creator = nmt.inference.gnmt_model.GNMTModel
else:
raise ValueError("Unknown model architecture")
infer_model = nmt.inference.model_helper.create_infer_model(model_creator, hparams, None)
return (infer_model, flags, hparams)
def do_inference（infer_data，infer_model，flags，hparams）：
# Disable TF logs for a while
# Workaround for bug: https://github.com/tensorflow/tensorflow/issues/12414
# Already fixed, available in nightly builds, but not in stable version
# Maybe that will stay here to silence any outputs
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
global current_stdout
if not current_stdout:
current_stdout = sys.stdout
sys.stdout = open(os.devnull, "w")
# Spawn new session
with tf.Session(graph=infer_model.graph, config=nmt.utils.get_config_proto()) as sess:
# Load model
loaded_infer_model = nmt.inference.model_helper.load_model(infer_model.model, flags.ckpt, sess, "infer")
# Run model (translate)
sess.run(
infer_model.iterator.initializer,
feed_dict={
infer_model.src_placeholder: infer_data,
infer_model.batch_size_placeholder: hparams.infer_batch_size
})
# calculate number of translations to be returned
num_translations_per_input = max(min(hparams.num_translations_per_input, hparams.beam_width), 1)
answers = []
while True:
try:
nmt_outputs, _ = loaded_infer_model.decode(sess)
if hparams.beam_width == 0:
nmt_outputs = nmt.inference.nmt_model.np.expand_dims(nmt_outputs, 0)
batch_size = nmt_outputs.shape[1]
for sent_id in range(batch_size):
# Iterate through responses
translations = []
for beam_id in range(num_translations_per_input):
if hparams.eos: tgt_eos = hparams.eos.encode("utf-8")
# Select a sentence
output = nmt_outputs[beam_id][sent_id, :].tolist()
# If there is an eos symbol in outputs, cut them at that point
if tgt_eos and tgt_eos in output:
output = output[:output.index(tgt_eos)]
print(output)
# Format response
if hparams.subword_option == "bpe": # BPE
translation = nmt.utils.format_bpe_text(output)
elif hparams.subword_option == "spm": # SPM
translation = nmt.utils.format_spm_text(output)
else:
translation = nmt.utils.format_text(output)
# Add response to the list
translations.append(translation.decode('utf-8'))
answers.append(translations)
except tf.errors.OutOfRangeError:
print("end")
break
# bug workaround end
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '0'
sys.stdout.close()
sys.stdout = current_stdout
current_stdout = None
return answers
def start_inference（问题）：
global inference_helper, inference_object
# Start inference, set global tuple with model, flags and hparams
inference_object = do_start_inference(out_dir, hparams)
# First inference() call calls that method
# Now we have everything running, so replace inference() with actual function call
inference_helper = lambda question: do_inference(question, *inference_object)
# Load BPE join pairs
if preprocessing['use_bpe']:
apply_bpe_load()
# Rerun inference() call
return inference_helper(question)
inference_object =无
inference_helper = start_inference
def推理（问题，include_blacklisted = True）：
# Process questions
answers_list = process_questions(questions, include_blacklisted)
# Return (one or more?)
if len(answers_list) == 1:
return answers_list[0]
else:
return answers_list
def inference_internal（questions）：
# Process questions and return
return process_questions(questions)
def get_best_score（answers_score，include_blacklisted = True）：
try:
index = answers_score.index(1)
score = 1
except:
index = None
if index is None and include_blacklisted:
try:
index = answers_score.index(0)
score = 0
except:
index = 0
score = -1
if index is None:
index = 0
score = -1
return (index, score)
def process_questions（questions，include_blacklisted = True）：
# Make a list
if not isinstance(questions, list):
questions = [questions]
# Clean and tokenize
prepared_questions = []
for question in questions:
question = question.strip()
prepared_questions.append(apply_bpe(tokenize(question)) if question else '##emptyquestion##')
# Run inference
answers_list = inference_helper(prepared_questions)
# Process answers
prepared_answers_list = []
for index, answers in enumerate(answers_list):
answers = detokenize(answers)
answers = replace_in_answers(answers)
answers_score = score_answers(answers)
best_index, best_score = get_best_score(answers_score, include_blacklisted)
if prepared_questions[index] == '##emptyquestion##':
prepared_answers_list.append(None)
else:
prepared_answers_list.append({'answers': answers, 'scores': answers_score, 'best_index': best_index, 'best_score': best_score})
return prepared_answers_list
如果名称 ==＆＃34; 主要＆＃34;：
# Input file
if sys.stdin.isatty() == False:
# Process questions
answers_list = process_questions(sys.stdin.readlines())
# Print answers
for answers in answers_list:
print(answers['answers'][answers['best_index']])
sys.exit()
# Interactive mode
print("\n\nStarting interactive mode (first response will take a while):")
colorama.init()
# QAs
while True:
question = input("\n> ")
answers = process_questions(question)[0]
if answers is None:
print(colorama.Fore.RED + "! Question can't be empty")
else:
for i, _ in enumerate(answers['scores']):
print("{}- {}{}".format(colorama.Fore.GREEN if answers['scores'][i] == 1 else colorama.Fore.YELLOW if answers['scores'][i] == 0 else colorama.Fore.RED, answers['answers'][i], colorama.Fore.RESET))
答案 0 :(得分：0)
这里有大量代码与您的问题无关，但问题出在此处：
nmt.add_arguments(nmt_parser)
您的消息说模块nmt实际上并不包含函数add_arguments。首先查看模块或其文档，找出为什么这不正确，即使你的消息来源说它是。