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reader.py
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reader.py
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import os
import _pickle as pickle
import numpy as np
import tensorflow as tf
from args import read_args
from common import Common
from config import Config
TARGET_INDEX_KEY = 'TARGET_INDEX_KEY'
TARGET_STRING_KEY = 'TARGET_STRING_KEY'
TARGET_LENGTH_KEY = 'TARGET_LENGTH_KEY'
PATH_SOURCE_INDICES_KEY = 'PATH_SOURCE_INDICES_KEY'
NODE_INDICES_KEY = 'NODES_INDICES_KEY'
PATH_TARGET_INDICES_KEY = 'PATH_TARGET_INDICES_KEY'
VALID_CONTEXT_MASK_KEY = 'VALID_CONTEXT_MASK_KEY'
PATH_SOURCE_LENGTHS_KEY = 'PATH_SOURCE_LENGTHS_KEY'
PATH_LENGTHS_KEY = 'PATH_LENGTHS_KEY'
PATH_TARGET_LENGTHS_KEY = 'PATH_TARGET_LENGTHS_KEY'
PATH_SOURCE_STRINGS_KEY = 'PATH_SOURCE_STRINGS_KEY'
PATH_STRINGS_KEY = 'PATH_STRINGS_KEY'
PATH_TARGET_STRINGS_KEY = 'PATH_TARGET_STRINGS_KEY'
class Reader:
class_subtoken_table = None
class_target_table = None
class_node_table = None
def __init__(self, subtoken_to_index, target_to_index, node_to_index, config, is_evaluating=False):
self.config = config
self.file_path = config.TEST_PATH if is_evaluating else (config.TRAIN_PATH + '.train.c2s')
if self.file_path is not None and not os.path.exists(self.file_path):
print(
'%s cannot find file: %s' % ('Evaluation reader' if is_evaluating else 'Train reader', self.file_path))
self.batch_size = config.BATCH_SIZE
self.is_evaluating = is_evaluating
self.context_pad = '{},{},{}'.format(Common.PAD, Common.PAD, Common.PAD)
self.record_defaults = [[self.context_pad]] * (self.config.DATA_NUM_CONTEXTS + 1)
self.subtoken_table = Reader.get_subtoken_table(subtoken_to_index)
self.target_table = Reader.get_target_table(target_to_index)
self.node_table = Reader.get_node_table(node_to_index)
self.dataset = None
@classmethod
def get_subtoken_table(cls, subtoken_to_index):
if cls.class_subtoken_table is None:
cls.class_subtoken_table = cls.initialize_hash_map(subtoken_to_index, subtoken_to_index[Common.UNK])
return cls.class_subtoken_table
@classmethod
def get_target_table(cls, target_to_index):
if cls.class_target_table is None:
cls.class_target_table = cls.initialize_hash_map(target_to_index, target_to_index[Common.UNK])
return cls.class_target_table
@classmethod
def get_node_table(cls, node_to_index):
if cls.class_node_table is None:
cls.class_node_table = cls.initialize_hash_map(node_to_index, node_to_index[Common.UNK])
return cls.class_node_table
@classmethod
def initialize_hash_map(cls, word_to_index, default_value):
return tf.lookup.StaticHashTable(
tf.lookup.KeyValueTensorInitializer(list(word_to_index.keys()), list(word_to_index.values()),
key_dtype=tf.string,
value_dtype=tf.int32), default_value)
def process_from_placeholder(self, row):
parts = tf.io.decode_csv(row, record_defaults=self.record_defaults, field_delim=' ', use_quote_delim=False)
res_dict = self.process_dataset(*parts)
# add batch size dimension
for key, value in res_dict.items():
res_dict[key] = tf.expand_dims(value, 0)
return res_dict
def process_dataset(self, *row_parts):
row_parts = list(row_parts)
word = row_parts[0] # (, )
if not self.is_evaluating and self.config.RANDOM_CONTEXTS:
#print('we are not evaluating')
all_contexts = tf.stack(row_parts[1:])
all_contexts_padded = tf.concat([all_contexts, [self.context_pad]], axis=-1)
index_of_blank_context = tf.where(tf.equal(all_contexts_padded, self.context_pad))
num_contexts_per_example = tf.reduce_min(index_of_blank_context)
# if there are less than self.max_contexts valid contexts, still sample self.max_contexts
safe_limit = tf.cast(tf.maximum(num_contexts_per_example, self.config.MAX_CONTEXTS), tf.int32)
rand_indices = tf.random.shuffle(tf.range(safe_limit))[:self.config.MAX_CONTEXTS]
contexts = tf.gather(all_contexts, rand_indices) # (max_contexts,)
else:
#print('we are using the else statement')
contexts = row_parts[1:(self.config.MAX_CONTEXTS + 1)] # (max_contexts,)
# contexts: (max_contexts, )
split_contexts = tf.strings.split(contexts, sep=',')
sparse_split_contexts = split_contexts.to_sparse()
#print('\n----------')
#print(f"the amount of contexts are: {split_contexts.shape}")
# print(f"here is the tensor: {sparse_split_contexts}")
#print(f"here is the shape: {sparse_split_contexts.shape}")
#################
# TEMPORARY FIX #
#################
# desired_shape = (100, 3)
# sliced_sparse_contexts = tf.sparse.slice(sparse_split_contexts, [0, 0], [desired_shape[0], desired_shape[1]])
# sliced_sparse_contexts = tf.sparse.reshape(sliced_sparse_contexts, desired_shape)
# sparse_split_contexts = sliced_sparse_contexts
# print(f"here is the shape: {sparse_split_contexts.shape}")
#################
# TEMPORARY FIX #
#################
dense_split_contexts = tf.reshape(
tf.sparse.to_dense(sp_input=sparse_split_contexts, default_value=Common.PAD),
shape=[self.config.MAX_CONTEXTS, 3]) # (batch, max_contexts, 3)
split_target_labels = tf.strings.split(tf.expand_dims(word, -1), sep='|')
sparse_target_labels = split_target_labels.to_sparse()
sparse_target_labels = tf.sparse.reset_shape(sparse_target_labels,
[1, tf.maximum(tf.cast(self.config.MAX_TARGET_PARTS, tf.int64),
sparse_target_labels.dense_shape[1] + 1)])
dense_target_label = tf.reshape(tf.sparse.to_dense(sp_input=sparse_target_labels,
default_value=Common.PAD),
shape=[-1])
index_of_blank = tf.where(tf.equal(dense_target_label, Common.PAD))
target_length = tf.reduce_min(index_of_blank)
dense_target_label = dense_target_label[:self.config.MAX_TARGET_PARTS]
clipped_target_lengths = tf.clip_by_value(target_length, clip_value_min=0,
clip_value_max=self.config.MAX_TARGET_PARTS)
target_word_labels = tf.concat([
self.target_table.lookup(dense_target_label), [0]], axis=-1) # (max_target_parts + 1) of int
path_source_strings = tf.slice(dense_split_contexts, [0, 0], [self.config.MAX_CONTEXTS, 1]) # (max_contexts, 1)
flat_source_strings = tf.reshape(path_source_strings, [-1]) # (max_contexts)
split_source = tf.strings.split(flat_source_strings, sep='|') # (max_contexts, max_name_parts)
sparse_split_source = split_source.to_sparse()
sparse_split_source = tf.sparse.reset_shape(sparse_split_source,
[self.config.MAX_CONTEXTS,
tf.maximum(
tf.cast(self.config.MAX_NAME_PARTS, tf.int64),
sparse_split_source.dense_shape[1])])
dense_split_source = tf.sparse.to_dense(sp_input=sparse_split_source,
default_value=Common.PAD) # (max_contexts, max_name_parts)
dense_split_source = tf.slice(dense_split_source, [0, 0], [-1, self.config.MAX_NAME_PARTS])
path_source_indices = self.subtoken_table.lookup(dense_split_source) # (max_contexts, max_name_parts)
path_source_lengths = tf.reduce_sum(tf.cast(tf.not_equal(dense_split_source, Common.PAD), tf.int32),
-1) # (max_contexts)
path_strings = tf.slice(dense_split_contexts, [0, 1], [self.config.MAX_CONTEXTS, 1])
flat_path_strings = tf.reshape(path_strings, [-1])
split_path = tf.strings.split(flat_path_strings, sep='|')
sparse_split_path = split_path.to_sparse()
if self.config.MAX_PATH_LENGTH < sparse_split_path.dense_shape[1]:
sparse_split_path = tf.sparse.slice(sparse_split_path, [0, 0],
[sparse_split_path.dense_shape[0], self.config.MAX_PATH_LENGTH])
sparse_split_path = tf.sparse.reset_shape(sparse_split_path,
[self.config.MAX_CONTEXTS, self.config.MAX_PATH_LENGTH])
dense_split_path = tf.sparse.to_dense(sp_input=sparse_split_path,
default_value=Common.PAD) # (batch, max_contexts, max_path_length)
node_indices = self.node_table.lookup(dense_split_path) # (max_contexts, max_path_length)
path_lengths = tf.reduce_sum(tf.cast(tf.not_equal(dense_split_path, Common.PAD), tf.int32),
-1) # (max_contexts)
path_target_strings = tf.slice(dense_split_contexts, [0, 2], [self.config.MAX_CONTEXTS, 1]) # (max_contexts, 1)
flat_target_strings = tf.reshape(path_target_strings, [-1]) # (max_contexts)
split_target = tf.strings.split(flat_target_strings, sep='|') # (max_contexts, max_name_parts)
sparse_split_target = split_target.to_sparse()
sparse_split_target = tf.sparse.reset_shape(sparse_split_target, [self.config.MAX_CONTEXTS,
tf.maximum(
tf.cast(self.config.MAX_NAME_PARTS,
tf.int64),
sparse_split_target.dense_shape[1])])
dense_split_target = tf.sparse.to_dense(sp_input=sparse_split_target,
default_value=Common.PAD) # (max_contexts, max_name_parts)
dense_split_target = tf.slice(dense_split_target, [0, 0], [-1, self.config.MAX_NAME_PARTS])
path_target_indices = self.subtoken_table.lookup(dense_split_target) # (max_contexts, max_name_parts)
path_target_lengths = tf.reduce_sum(tf.cast(tf.not_equal(dense_split_target, Common.PAD), tf.int32),
-1) # (max_contexts)
valid_contexts_mask = tf.cast(tf.not_equal(
tf.reduce_max(path_source_indices, -1) + tf.reduce_max(node_indices, -1) + tf.reduce_max(
path_target_indices, -1), 0), tf.float32)
return {TARGET_STRING_KEY: word, TARGET_INDEX_KEY: target_word_labels,
TARGET_LENGTH_KEY: clipped_target_lengths,
PATH_SOURCE_INDICES_KEY: path_source_indices, NODE_INDICES_KEY: node_indices,
PATH_TARGET_INDICES_KEY: path_target_indices, VALID_CONTEXT_MASK_KEY: valid_contexts_mask,
PATH_SOURCE_LENGTHS_KEY: path_source_lengths, PATH_LENGTHS_KEY: path_lengths,
PATH_TARGET_LENGTHS_KEY: path_target_lengths, PATH_SOURCE_STRINGS_KEY: path_source_strings,
PATH_STRINGS_KEY: path_strings, PATH_TARGET_STRINGS_KEY: path_target_strings
}
def get_dataset(self):
self.init_dataset()
return self.dataset
def init_dataset(self):
self.dataset = tf.data.experimental.CsvDataset(self.file_path, record_defaults=self.record_defaults,
field_delim=' ',
use_quote_delim=False, buffer_size=self.config.CSV_BUFFER_SIZE)
if not self.is_evaluating:
self.dataset = self.dataset.shuffle(self.config.SHUFFLE_BUFFER_SIZE, reshuffle_each_iteration=True)
self.dataset = self.dataset \
.map(map_func=self.process_dataset, num_parallel_calls=self.config.READER_NUM_PARALLEL_BATCHES) \
.batch(batch_size=self.batch_size, drop_remainder=True) \
.prefetch(tf.data.experimental.AUTOTUNE)
if __name__ == '__main__':
# tf.config.experimental_run_functions_eagerly(True)
print("tf executing eagerly: " + str(tf.executing_eagerly()))
args = read_args()
config = Config.get_default_config(args)
with open('{}.dict.c2s'.format(config.TRAIN_PATH), 'rb') as file:
subtoken_to_count = pickle.load(file)
node_to_count = pickle.load(file)
target_to_count = pickle.load(file)
max_contexts = pickle.load(file)
num_training_examples = pickle.load(file)
print('Dictionaries loaded.')
if config.DATA_NUM_CONTEXTS <= 0:
config.DATA_NUM_CONTEXTS = max_contexts
subtoken_to_index, index_to_subtoken, subtoken_vocab_size = \
Common.load_vocab_from_dict(subtoken_to_count, add_values=[Common.PAD, Common.UNK],
max_size=config.SUBTOKENS_VOCAB_MAX_SIZE)
print('Loaded subtoken vocab. size: %d' % subtoken_vocab_size)
target_to_index, index_to_target, target_vocab_size = \
Common.load_vocab_from_dict(target_to_count, add_values=[Common.PAD, Common.UNK, Common.SOS],
max_size=config.TARGET_VOCAB_MAX_SIZE)
print('Loaded target word vocab. size: %d' % target_vocab_size)
node_to_index, index_to_node, nodes_vocab_size = \
Common.load_vocab_from_dict(node_to_count, add_values=[Common.PAD, Common.UNK], max_size=None)
print('Loaded nodes vocab. size: %d' % nodes_vocab_size)
reader = Reader(subtoken_to_index, target_to_index, node_to_index, config, False)
test_manually = True
#################
# TEMPORARY FIX #
#################
if test_manually:
print('we are evaluating the data manually')
with open('{}.cleaned_train_output_file.txt'.format(config.TRAIN_PATH), 'w') as txt_file:
print('we are cleaning the train set')
with open('{}.train.c2s'.format(config.TRAIN_PATH), 'r') as data_file:
for test_sample in data_file.readlines():
datum = test_sample
test_sample = test_sample.strip()
contexts_num = sum(ch.isspace() for ch in test_sample)
space_padding = ' ' * (config.DATA_NUM_CONTEXTS - contexts_num)
test_sample += space_padding
try:
reader.process_from_placeholder(test_sample)
except:
continue
txt_file.write(datum.strip() + '\n')
with open('{}.cleaned_test_output_file.txt'.format(config.TRAIN_PATH), 'w') as txt_file:
print('we are cleaning the test set')
with open('{}.test.c2s'.format(config.TRAIN_PATH), 'r') as data_file:
for test_sample in data_file.readlines():
datum = test_sample
test_sample = test_sample.strip()
contexts_num = sum(ch.isspace() for ch in test_sample)
space_padding = ' ' * (config.DATA_NUM_CONTEXTS - contexts_num)
test_sample += space_padding
try:
reader.process_from_placeholder(test_sample)
except:
continue
txt_file.write(datum.strip() + '\n')
with open('{}.cleaned_valid_output_file.txt'.format(config.TRAIN_PATH), 'w') as txt_file:
print('we are cleaning the validation set')
with open('{}.val.c2s'.format(config.TRAIN_PATH), 'r') as data_file:
for test_sample in data_file.readlines():
datum = test_sample
test_sample = test_sample.strip()
contexts_num = sum(ch.isspace() for ch in test_sample)
space_padding = ' ' * (config.DATA_NUM_CONTEXTS - contexts_num)
test_sample += space_padding
try:
reader.process_from_placeholder(test_sample)
except:
continue
txt_file.write(datum.strip() + '\n')
else:
dataset = reader.get_dataset()
try:
for output in dataset:
target_indices = output[TARGET_INDEX_KEY].numpy()
target_strings = output[TARGET_STRING_KEY].numpy()
target_lengths = output[TARGET_LENGTH_KEY].numpy()
path_source_indices = output[PATH_SOURCE_INDICES_KEY].numpy()
node_indices = output[NODE_INDICES_KEY].numpy()
path_target_indices = output[PATH_TARGET_INDICES_KEY].numpy()
valid_context_mask = output[VALID_CONTEXT_MASK_KEY].numpy()
path_source_lengths = output[PATH_SOURCE_LENGTHS_KEY].numpy()
path_lengths = output[PATH_LENGTHS_KEY].numpy()
path_target_lengths = output[PATH_TARGET_LENGTHS_KEY].numpy()
path_source_strings = output[PATH_SOURCE_STRINGS_KEY].numpy()
path_strings = output[PATH_STRINGS_KEY].numpy()
path_target_strings = output[PATH_TARGET_STRINGS_KEY].numpy()
print('Target strings: ', Common.binary_to_string_list(target_strings))
print('Context strings: ', Common.binary_to_string_3d(
np.concatenate([path_source_strings, path_strings, path_target_strings], -1)))
print('Target indices: ', target_indices)
print('Target lengths: ', target_lengths)
print('Path source strings: ', Common.binary_to_string_3d(path_source_strings))
print('Path source indices: ', path_source_indices)
print('Path source lengths: ', path_source_lengths)
print('Path strings: ', Common.binary_to_string_3d(path_strings))
print('Node indices: ', node_indices)
print('Path lengths: ', path_lengths)
print('Path target strings: ', Common.binary_to_string_3d(path_target_strings))
print('Path target indices: ', path_target_indices)
print('Path target lengths: ', path_target_lengths)
print('Valid context mask: ', valid_context_mask)
except tf.errors.OutOfRangeError:
print('Done training, epoch reached')
'''
if test_manually:
print('we are testing the data manually')
with open('{}.train.c2s'.format(config.TRAIN_PATH), 'r') as data_file:
for test_sample in data_file.readlines():
test_sample = test_sample.strip()
contexts_num = sum(ch.isspace() for ch in test_sample)
space_padding = ' ' * (config.DATA_NUM_CONTEXTS - contexts_num)
test_sample += space_padding
reader.process_from_placeholder(test_sample)
'''
#################
# TEMPORARY FIX #
#################