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utils.py
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utils.py
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# -*- coding:utf-8 -*-
from __future__ import print_function
from pprint import pprint
import tensorflow as tf
import numpy as np
import os
import sys
from collections import defaultdict
import random
random.seed(1994)
PAD = '<pad>'.decode('utf-8')
UNK = '<unk>'.decode('utf-8')
SOS = '<s>'.decode('utf-8')
EOS = '</s>'.decode('utf-8')
PAD_ID = 0
UNK_ID = 1
SOS_ID = 2
EOS_ID = 3
embed_shift = 4
def get_model_config(config):
enc_num_layers = config["encoder"]["num_layers"]
enc_num_units = config["encoder"]["num_units"]
enc_cell_type = config["encoder"]["cell_type"]
enc_bidir = config["encoder"]["bidirectional"]
attn_num_units = config["decoder"]["attn_num_units"]
dec_num_layers = config["decoder"]["num_layers"]
dec_num_units = config["decoder"]["num_units"]
dec_cell_type = config["decoder"]["cell_type"]
state_pass = config["decoder"]["state_pass"]
infer_max_iter = config["decoder"]["infer_max_iter"]
l2_regularize = config["training"]["l2_regularize"]
learning_rate = config["training"]["learning_rate"]
return (enc_num_layers, enc_num_units, enc_cell_type, enc_bidir,
attn_num_units, dec_num_layers, dec_num_units, dec_cell_type,
state_pass, infer_max_iter, l2_regularize, learning_rate)
def get_ecm_model_config(config):
enc_num_layers = config["encoder"]["num_layers"]
enc_num_units = config["encoder"]["num_units"]
enc_cell_type = config["encoder"]["cell_type"]
enc_bidir = config["encoder"]["bidirectional"]
attn_num_units = config["decoder"]["attn_num_units"]
dec_num_layers = config["decoder"]["num_layers"]
dec_num_units = config["decoder"]["num_units"]
dec_cell_type = config["decoder"]["cell_type"]
state_pass = config["decoder"]["state_pass"]
infer_max_iter = config["decoder"]["infer_max_iter"]
emo_cat_emb_size = config["decoder"]["emo_cat_emb_size"]
emo_internal_memory_units = config["decoder"]["emo_internal_memory_units"]
num_emotion = config["decoder"]["num_emotion"]
l2_regularize = config["training"]["l2_regularize"]
learning_rate = config["training"]["learning_rate"]
return (enc_num_layers, enc_num_units, enc_cell_type, enc_bidir,
attn_num_units, dec_num_layers, dec_num_units, dec_cell_type,
state_pass, infer_max_iter,
emo_cat_emb_size, emo_internal_memory_units, num_emotion,
l2_regularize, learning_rate)
def get_training_config(config):
train_s_file = config["training"]["train_source_file"]
train_t_file = config["training"]["train_target_file"]
dev_s_file = config["training"]["dev_source_file"]
dev_t_file = config["training"]["dev_target_file"]
max_length = config["training"]["max_length"]
gpu_fraction = config["training"]["gpu_fraction"]
gpu_id = config["training"]["gpu_id"]
checkpoint_every = config["training"]["checkpoint_every"] # 保存模型的步数
max_checkpoints = config["training"]["max_checkpoints"] # 最大保留模型的个数
print_every = config["training"]["print_every"] # 打印信息
train_steps = config["training"]["train_steps"] # 最大训练步数
is_beam_search = False
batch_size = config["training"]["batch_size"]
beam_size = config["inference"]["beam_size"]
return (train_s_file, train_t_file, dev_s_file, dev_t_file, max_length,
gpu_fraction, gpu_id, checkpoint_every, max_checkpoints, print_every,
train_steps, is_beam_search, batch_size, beam_size)
def get_ecm_training_config(config):
train_s_file = config["training"]["train_source_file"]
train_t_file = config["training"]["train_target_file"]
train_choice_file = config["training"]["train_choice_file"]
train_category_file = config["training"]["train_category_file"]
dev_s_file = config["training"]["dev_source_file"]
dev_t_file = config["training"]["dev_target_file"]
dev_choice_file = config["training"]["dev_choice_file"]
dev_category_file = config["training"]["dev_category_file"]
max_length = config["training"]["max_length"]
gpu_fraction = config["training"]["gpu_fraction"]
gpu_id = config["training"]["gpu_id"]
checkpoint_every = config["training"]["checkpoint_every"] # 保存模型的步数
max_checkpoints = config["training"]["max_checkpoints"] # 最大保留模型的个数
print_every = config["training"]["print_every"] # 打印信息
train_steps = config["training"]["train_steps"] # 最大训练步数
is_beam_search = False
batch_size = config["training"]["batch_size"]
beam_size = config["inference"]["beam_size"]
return (train_s_file, train_t_file, train_choice_file, train_category_file,
dev_s_file, dev_t_file, dev_choice_file, dev_category_file,
max_length,
gpu_fraction, gpu_id, checkpoint_every, max_checkpoints, print_every,
train_steps, is_beam_search, batch_size, beam_size)
def get_infer_config(config):
is_beam_search = config["inference"]["is_beam_search"]
beam_size = config["inference"]["beam_size"]
batch_size = config["inference"]["infer_batch_size"]
infer_source_file = config["inference"]["infer_source_file"]
infer_source_max_length = config["inference"]["infer_source_max_length"]
output_path = config["inference"]["output_path"]
gpu_fraction = config["training"]["gpu_fraction"]
gpu_id = config["training"]["gpu_id"]
return (is_beam_search, beam_size, batch_size,
infer_source_file, infer_source_max_length,
output_path, gpu_fraction, gpu_id)
def get_ecm_infer_config(config):
is_beam_search = config["inference"]["is_beam_search"]
beam_size = config["inference"]["beam_size"]
batch_size = config["inference"]["infer_batch_size"]
infer_source_file = config["inference"]["infer_source_file"]
infer_target_file = config["inference"]["infer_target_file"]
infer_emotion_category_file = config["inference"]["infer_emotion_category_file"]
infer_source_max_length = config["inference"]["infer_source_max_length"]
output_path = config["inference"]["output_path"]
output_choice_path = config["inference"]["output_choice_path"]
gpu_fraction = config["training"]["gpu_fraction"]
gpu_id = config["training"]["gpu_id"]
return (is_beam_search, beam_size, batch_size,
infer_source_file, infer_target_file, infer_emotion_category_file,infer_source_max_length,
output_path,output_choice_path, gpu_fraction, gpu_id)
def load(saver, sess, logdir, model_path=None):
"""
Load the latest checkpoint
"""
print("Trying to restore saved checkpoints from {} ...".format(logdir),
end="")
if model_path:
global_step = int(model_path.split("-")[-1])
print("Global step was: {}".format(global_step))
print("Restoring...",end="")
saver.restore(sess, model_path)
print("\tDone")
return global_step
else:
ckpt = tf.train.get_checkpoint_state(logdir)
if ckpt:
print(" Checkpoint found: {}".format(ckpt.model_checkpoint_path))
global_step = int(ckpt.model_checkpoint_path
.split('/')[-1]
.split('-')[-1])
print(" Global step was: {}".format(global_step))
print(" Restoring...", end="")
# vars = tf.train.list_variables(ckpt.model_checkpoint_path)
# print('checkpoint_variables:')
# pprint(vars)
saver.restore(sess, ckpt.model_checkpoint_path)
print(" Done.")
return global_step
else:
print(" No checkpoint found.")
return None
def save(saver, sess, logdir, step):
"""
Save the checkpoint
"""
model_name = 'model.ckpt'
checkpoint_path = os.path.join(logdir, model_name)
print('Storing checkpoint to {} ...'.format(logdir), end="")
sys.stdout.flush()
if not os.path.exists(logdir):
os.makedirs(logdir)
saver.save(sess, checkpoint_path, global_step=step)
print(' Done.')
def create_vocab_file(train_s_file, train_t_file,
dev_s_file, dev_t_file,
vocab_file, vocab_size):
vocab = defaultdict(lambda: 0)
for file_path in [train_s_file, train_t_file, dev_s_file, dev_t_file]:
for _, line in enumerate(open(file_path, 'r')):
words = line.strip().decode('utf-8').split(' ')
for word in words:
vocab[word] += 1
vocab = list(sorted(vocab.items(), key=lambda x: x[1], reverse=True))
vocab = [item[0] for item in vocab if item[0] != "<unk>"]
vocab = vocab[:vocab_size - embed_shift]
vocab = [PAD, UNK, SOS, EOS] + vocab
with open(vocab_file, 'w') as f:
for word in vocab:
f.write("%s\n" % word.encode('utf-8'))
def create_vocab_tables(vocab_file):
def load_vocab(vocab_file):
vocab = []
for _, word in enumerate(open(vocab_file, 'r')):
vocab.append(word.strip().decode('utf-8'))
return vocab
vocab = load_vocab(vocab_file)
vocab_table = {k: v for v, k in enumerate(vocab)}
reverse_vocab_table = {k: v for k, v in enumerate(vocab)}
return vocab_table, reverse_vocab_table
def prepare_train_dev_data(train_s_file, train_t_file,
dev_s_file, dev_t_file,
vocab_table, max_length, ):
def readfile(file_path):
data = []
for _, line in enumerate(open(file_path, 'r')):
word_list = line.strip().decode('utf-8').split(' ')
data.append([vocab_table.get(word, UNK_ID) for word in word_list])
return data
train_s_dataset = readfile(train_s_file)
train_t_dataset = readfile(train_t_file)
dev_s_dataset = readfile(dev_s_file)
dev_t_dataset = readfile(dev_t_file)
train_dataset = []
dev_dataset = []
for idx, i in enumerate(train_s_dataset):
if len(train_s_dataset[idx]) >= max_length or len(train_t_dataset[idx]) >= max_length:
continue
train_dataset.append((train_s_dataset[idx], train_t_dataset[idx]))
for idx, i in enumerate(dev_s_dataset):
if len(dev_s_dataset[idx]) >= max_length or len(dev_t_dataset[idx]) >= max_length:
continue
dev_dataset.append((dev_s_dataset[idx], dev_t_dataset[idx]))
return train_dataset, dev_dataset
def prepare_ecm_train_dev_data(train_s_file, train_t_file,
train_choice_file, train_category_file,
dev_s_file, dev_t_file,
dev_choice_file, dev_category_file,
vocab_table, max_length, ):
def readfile(file_path):
data = []
for _, line in enumerate(open(file_path, 'r')):
word_list = line.strip().decode('utf-8').split(' ')
data.append([vocab_table.get(word, UNK_ID) for word in word_list])
return data
def read_ecm_file(file_path):
data = []
for _, line in enumerate(open(file_path, 'r')):
l_list = line.strip().decode('utf-8').split(' ')
data.append(l_list)
return data
train_s_dataset = readfile(train_s_file)
train_t_dataset = readfile(train_t_file)
train_choice_dataset = read_ecm_file(train_choice_file)
train_category_dataset = read_ecm_file(train_category_file)
dev_s_dataset = readfile(dev_s_file)
dev_t_dataset = readfile(dev_t_file)
dev_choice_dataset = read_ecm_file(dev_choice_file)
dev_category_dataset = read_ecm_file(dev_category_file)
train_dataset = []
dev_dataset = []
for idx, i in enumerate(train_s_dataset):
if len(train_s_dataset[idx]) >= max_length or len(train_t_dataset[idx]) >= max_length:
continue
train_dataset.append((train_s_dataset[idx], train_t_dataset[idx], train_choice_dataset[idx], train_category_dataset[idx]))
for idx, i in enumerate(dev_s_dataset):
if len(dev_s_dataset[idx]) >= max_length or len(dev_t_dataset[idx]) >= max_length:
continue
dev_dataset.append((dev_s_dataset[idx], dev_t_dataset[idx], dev_choice_dataset[idx], dev_category_dataset[idx]))
return train_dataset, dev_dataset
def prepare_infer_data(infer_data_file, vocab_table, max_length):
src_dataset = []
for _, line in enumerate(open(infer_data_file, 'r')):
word_list = line.strip().decode('utf-8').split(' ')
if len(word_list) >= max_length:
continue
id_list = [vocab_table.get(word, UNK_ID) for word in word_list]
src_dataset.append(id_list)
return np.array(src_dataset)
def prepare_ecm_infer_data(infer_s_file, infer_emo_cat_file, vocab_table, max_length):
src_dataset = []
for _, line in enumerate(open(infer_s_file, 'r')):
word_list = line.strip().decode('utf-8').split(' ')
id_list = [vocab_table.get(word, UNK_ID) for word in word_list]
src_dataset.append(id_list)
category_list = []
for _, line in enumerate(open(infer_emo_cat_file, 'r')):
cat = line.strip().decode('utf-8')
category_list.append(cat)
infer_data_set = []
for idx, i in enumerate(src_dataset):
if len(src_dataset[idx]) <= max_length:
infer_data_set.append((src_dataset[idx], category_list[idx]))
return infer_data_set
def get_train_batch(dataset, max_length, batch_size):
encoder_inputs, decoder_inputs, decoder_targets, decoder_targets_masks,\
encoder_length, decoder_length = [], [], [], [], [], []
count = 0
while count < batch_size:
encoder, decoder = random.choice(dataset)
enc_leng, dec_leng = len(encoder), len(decoder)
if enc_leng >= max_length or dec_leng >= max_length:
continue
encoder_input = encoder
e_length = len(encoder_input)
pads = PAD_ID * np.ones(max_length - e_length, dtype=np.int32)
encoder_input = np.concatenate([encoder_input, pads])
encoder_inputs.append(encoder_input)
encoder_length.append(e_length)
decoder_input = [SOS_ID] + decoder
d_length = len(decoder_input)
pads = PAD_ID * np.ones(max_length - d_length, dtype=np.int32)
decoder_input = np.concatenate([decoder_input, pads])
decoder_inputs.append(decoder_input)
decoder_target = decoder + [EOS_ID]
pads = PAD_ID * np.ones(max_length - d_length, dtype=np.int32)
decoder_target = np.concatenate([decoder_target, pads])
decoder_targets.append(decoder_target)
decoder_length.append(d_length)
count += 1
target_max_length = max(decoder_length)
encoder_inputs = np.array(encoder_inputs)
decoder_inputs = np.array(decoder_inputs)
decoder_targets = np.array(decoder_targets)
decoder_targets = decoder_targets[:, :target_max_length]
decoder_targets_masks = decoder_targets >= 3 # eos字符算在loss里
decoder_targets_masks = decoder_targets_masks.astype(np.float32)
encoder_length = np.array(encoder_length)
decoder_length = np.array(decoder_length)
return encoder_inputs, decoder_inputs, decoder_targets, decoder_targets_masks, encoder_length, decoder_length
def get_infer_batch(src_dataset, start, end, max_length):
encoder_inputs = []
encoder_length = []
batch_data = src_dataset[start: end]
for id_list in batch_data:
length = len(id_list)
# padding with EOS token
pads = PAD_ID * np.ones(max_length - length, dtype=np.int32)
encoder_input = np.concatenate([id_list, pads])
encoder_inputs.append(encoder_input)
encoder_length.append(length)
encoder_inputs = np.array(encoder_inputs)
encoder_length = np.array(encoder_length)
return encoder_inputs, encoder_length
def get_ecm_train_batch(dataset, max_length, batch_size):
encoder_inputs, decoder_inputs, decoder_targets, decoder_targets_masks, \
encoder_length, decoder_length, choice_qs, emo_cat = [], [], [], [], [], [], [], []
count = 0
while count < batch_size:
encoder, decoder, choice, emotion = random.choice(dataset)
enc_leng, dec_leng = len(encoder), len(decoder)
if enc_leng >= max_length or dec_leng >= max_length:
continue
encoder_input = encoder
e_length = len(encoder_input)
pads = PAD_ID * np.ones(max_length - e_length, dtype=np.int32)
encoder_input = np.concatenate([encoder_input, pads])
encoder_inputs.append(encoder_input)
encoder_length.append(e_length)
decoder_input = [SOS_ID] + decoder
d_length = len(decoder_input)
pads = PAD_ID * np.ones(max_length - d_length, dtype=np.int32)
decoder_input = np.concatenate([decoder_input, pads])
decoder_inputs.append(decoder_input)
decoder_target = decoder + [EOS_ID]
pads = PAD_ID * np.ones(max_length - d_length, dtype=np.int32)
decoder_target = np.concatenate([decoder_target, pads])
decoder_targets.append(decoder_target)
decoder_length.append(d_length)
choice = [int(i) for i in choice] + [0] # + [0] represents for eos
pads = PAD_ID * np.ones(max_length - d_length, dtype=np.int32)
choice = np.concatenate([choice, pads])
choice_qs.append(choice)
emotion = int(emotion[0])
emo_cat.append(emotion)
count += 1
target_max_length = max(decoder_length)
encoder_inputs = np.array(encoder_inputs)
decoder_inputs = np.array(decoder_inputs)
decoder_targets = np.array(decoder_targets)
decoder_targets = decoder_targets[:, :target_max_length]
decoder_targets_masks = decoder_targets >= 3 # eos字符算在loss里
# decoder_targets_masks = decoder_targets_masks.astype(np.float32)
encoder_length = np.array(encoder_length)
decoder_length = np.array(decoder_length)
choice_qs = np.array(choice_qs)
choice_qs = choice_qs[:, :target_max_length]
emo_cat = np.array(emo_cat)
return (encoder_inputs, decoder_inputs, decoder_targets, decoder_targets_masks, encoder_length, decoder_length,
choice_qs, emo_cat)
def get_ecm_infer_batch(dataset, start, end, max_length):
encoder_inputs = []
encoder_length = []
emotion_category = []
batch_data = dataset[start: end]
for (id_list, emo_cat) in batch_data:
length = len(id_list)
pads = PAD_ID * np.ones(max_length - length, dtype=np.int32)
encoder_input = np.concatenate([id_list, pads])
encoder_inputs.append(encoder_input)
encoder_length.append(length)
emotion_category.append(int(emo_cat))
encoder_inputs = np.array(encoder_inputs)
encoder_length = np.array(encoder_length)
emotion_category = np.array(emotion_category)
return encoder_inputs, encoder_length, emotion_category