diff --git a/demo/seqToseq/api_train_v2.py b/demo/seqToseq/api_train_v2.py index f100ef80cb156ada475e4593d8e17d7e4574e0f6..177fd26d681d19b70ea71c12faac471520c6fff1 100644 --- a/demo/seqToseq/api_train_v2.py +++ b/demo/seqToseq/api_train_v2.py @@ -115,7 +115,7 @@ def main(): 'target_language_word': 1, 'target_language_next_word': 2 } - wmt14_reader = paddle.reader.batched( + wmt14_reader = paddle.batch( paddle.reader.shuffle( paddle.dataset.wmt14.train(dict_size=dict_size), buf_size=8192), batch_size=5) diff --git a/demo/seqToseq/preprocess.py b/demo/seqToseq/preprocess.py index 03f371331a0755e5939e457f4bdfb1770b8dad88..afa7bd5e0f4191053a834e29313ad40ca0c110e4 100755 --- a/demo/seqToseq/preprocess.py +++ b/demo/seqToseq/preprocess.py @@ -23,167 +23,9 @@ Options: -m --mergeDict merge source and target dictionary """ import os -import sys - -import string from optparse import OptionParser -from paddle.utils.preprocess_util import save_list, DatasetCreater - - -class SeqToSeqDatasetCreater(DatasetCreater): - """ - A class to process data for sequence to sequence application. - """ - - def __init__(self, data_path, output_path): - """ - data_path: the path to store the train data, test data and gen data - output_path: the path to store the processed dataset - """ - DatasetCreater.__init__(self, data_path) - self.gen_dir_name = 'gen' - self.gen_list_name = 'gen.list' - self.output_path = output_path - - def concat_file(self, file_path, file1, file2, output_path, output): - """ - Concat file1 and file2 to be one output file - The i-th line of output = i-th line of file1 + '\t' + i-th line of file2 - file_path: the path to store file1 and file2 - output_path: the path to store output file - """ - file1 = os.path.join(file_path, file1) - file2 = os.path.join(file_path, file2) - output = os.path.join(output_path, output) - if not os.path.exists(output): - os.system('paste ' + file1 + ' ' + file2 + ' > ' + output) - - def cat_file(self, dir_path, suffix, output_path, output): - """ - Cat all the files in dir_path with suffix to be one output file - dir_path: the base directory to store input file - suffix: suffix of file name - output_path: the path to store output file - """ - cmd = 'cat ' - file_list = os.listdir(dir_path) - file_list.sort() - for file in file_list: - if file.endswith(suffix): - cmd += os.path.join(dir_path, file) + ' ' - output = os.path.join(output_path, output) - if not os.path.exists(output): - os.system(cmd + '> ' + output) - - def build_dict(self, file_path, dict_path, dict_size=-1): - """ - Create the dictionary for the file, Note that - 1. Valid characters include all printable characters - 2. There is distinction between uppercase and lowercase letters - 3. There is 3 special token: - : the start of a sequence - : the end of a sequence - : a word not included in dictionary - file_path: the path to store file - dict_path: the path to store dictionary - dict_size: word count of dictionary - if is -1, dictionary will contains all the words in file - """ - if not os.path.exists(dict_path): - dictory = dict() - with open(file_path, "r") as fdata: - for line in fdata: - line = line.split('\t') - for line_split in line: - words = line_split.strip().split() - for word in words: - if word not in dictory: - dictory[word] = 1 - else: - dictory[word] += 1 - output = open(dict_path, "w+") - output.write('\n\n\n') - count = 3 - for key, value in sorted( - dictory.items(), key=lambda d: d[1], reverse=True): - output.write(key + "\n") - count += 1 - if count == dict_size: - break - self.dict_size = count - - def create_dataset(self, - dict_size=-1, - mergeDict=False, - suffixes=['.src', '.trg']): - """ - Create seqToseq dataset - """ - # dataset_list and dir_list has one-to-one relationship - train_dataset = os.path.join(self.data_path, self.train_dir_name) - test_dataset = os.path.join(self.data_path, self.test_dir_name) - gen_dataset = os.path.join(self.data_path, self.gen_dir_name) - dataset_list = [train_dataset, test_dataset, gen_dataset] - - train_dir = os.path.join(self.output_path, self.train_dir_name) - test_dir = os.path.join(self.output_path, self.test_dir_name) - gen_dir = os.path.join(self.output_path, self.gen_dir_name) - dir_list = [train_dir, test_dir, gen_dir] - - # create directory - for dir in dir_list: - if not os.path.exists(dir): - os.mkdir(dir) - - # checkout dataset should be parallel corpora - suffix_len = len(suffixes[0]) - for dataset in dataset_list: - file_list = os.listdir(dataset) - if len(file_list) % 2 == 1: - raise RuntimeError("dataset should be parallel corpora") - file_list.sort() - for i in range(0, len(file_list), 2): - if file_list[i][:-suffix_len] != file_list[i + 1][:-suffix_len]: - raise RuntimeError( - "source and target file name should be equal") - - # cat all the files with the same suffix in dataset - for suffix in suffixes: - for dataset in dataset_list: - outname = os.path.basename(dataset) + suffix - self.cat_file(dataset, suffix, dataset, outname) - - # concat parallel corpora and create file.list - print 'concat parallel corpora for dataset' - id = 0 - list = ['train.list', 'test.list', 'gen.list'] - for dataset in dataset_list: - outname = os.path.basename(dataset) - self.concat_file(dataset, outname + suffixes[0], - outname + suffixes[1], dir_list[id], outname) - save_list([os.path.join(dir_list[id], outname)], - os.path.join(self.output_path, list[id])) - id += 1 - # build dictionary for train data - dict = ['src.dict', 'trg.dict'] - dict_path = [ - os.path.join(self.output_path, dict[0]), - os.path.join(self.output_path, dict[1]) - ] - if mergeDict: - outname = os.path.join(train_dir, train_dataset.split('/')[-1]) - print 'build src dictionary for train data' - self.build_dict(outname, dict_path[0], dict_size) - print 'build trg dictionary for train data' - os.system('cp ' + dict_path[0] + ' ' + dict_path[1]) - else: - outname = os.path.join(train_dataset, self.train_dir_name) - for id in range(0, 2): - suffix = suffixes[id] - print 'build ' + suffix[1:] + ' dictionary for train data' - self.build_dict(outname + suffix, dict_path[id], dict_size) - print 'dictionary size is', self.dict_size +from paddle.v2.dataset.wmt14_util import SeqToSeqDatasetCreater def main():