Merge pull request #248 from martinpopel/biger-encs

Bigger translate_encs_wmt32k training data, tsv support

Author: lukaszkaiser

tensor2tensor/data_generators/generator_utils.py

@@ -350,19 +350,18 @@ def generate():
     for source in sources:
       url = source[0]
       filename = os.path.basename(url)
-      read_type = "r:gz" if "tgz" in filename else "r"
-
       compressed_file = maybe_download(tmp_dir, filename, url)
 
-      with tarfile.open(compressed_file, read_type) as corpus_tar:
-        corpus_tar.extractall(tmp_dir)
-
       for lang_file in source[1]:
         tf.logging.info("Reading file: %s" % lang_file)
         filepath = os.path.join(tmp_dir, lang_file)
+        if not tf.gfile.Exists(filepath):
+          read_type = "r:gz" if filename.endswith("tgz") else "r"
+          with tarfile.open(compressed_file, read_type) as corpus_tar:
+            corpus_tar.extractall(tmp_dir)
 
         # For some datasets a second extraction is necessary.
-        if ".gz" in lang_file:
+        if lang_file.endswith(".gz"):
           new_filepath = os.path.join(tmp_dir, lang_file[:-3])
           if tf.gfile.Exists(new_filepath):
             tf.logging.info(

tensor2tensor/data_generators/wmt.py

@@ -19,7 +19,9 @@
 from __future__ import division
 from __future__ import print_function
 
+import glob
 import os
+import stat
 import tarfile
 
 # Dependency imports
@@ -264,6 +266,10 @@ def bi_vocabs_token_generator(source_path,
 
 # English-Czech datasets
 _ENCS_TRAIN_DATASETS = [
+    [
+        "https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-1458/data-plaintext-format.tar",
+        ('tsv', 3, 2, 'data.plaintext-format/*train.gz')
+    ],
     [
         "http://data.statmt.org/wmt17/translation-task/training-parallel-nc-v12.tgz",  # pylint: disable=line-too-long
         ("training/news-commentary-v12.cs-en.en",
@@ -369,38 +375,64 @@ def _compile_data(tmp_dir, datasets, filename):
         url = dataset[0]
         compressed_filename = os.path.basename(url)
         compressed_filepath = os.path.join(tmp_dir, compressed_filename)
-
-        lang1_filename, lang2_filename = dataset[1]
-        lang1_filepath = os.path.join(tmp_dir, lang1_filename)
-        lang2_filepath = os.path.join(tmp_dir, lang2_filename)
-        is_sgm = (lang1_filename.endswith("sgm") and
-                  lang2_filename.endswith("sgm"))
-
         generator_utils.maybe_download(tmp_dir, compressed_filename, url)
-        if not (os.path.exists(lang1_filepath) and
-                os.path.exists(lang2_filepath)):
-          # For .tar.gz and .tgz files, we read compressed.
-          mode = "r:gz" if compressed_filepath.endswith("gz") else "r"
-          with tarfile.open(compressed_filepath, mode) as corpus_tar:
-            corpus_tar.extractall(tmp_dir)
-        if lang1_filepath.endswith(".gz"):
-          new_filepath = lang1_filepath.strip(".gz")
-          generator_utils.gunzip_file(lang1_filepath, new_filepath)
-          lang1_filepath = new_filepath
-        if lang2_filepath.endswith(".gz"):
-          new_filepath = lang2_filepath.strip(".gz")
-          generator_utils.gunzip_file(lang2_filepath, new_filepath)
-          lang2_filepath = new_filepath
-        with tf.gfile.GFile(lang1_filepath, mode="r") as lang1_file:
-          with tf.gfile.GFile(lang2_filepath, mode="r") as lang2_file:
-            line1, line2 = lang1_file.readline(), lang2_file.readline()
-            while line1 or line2:
-              line1res = _preprocess_sgm(line1, is_sgm)
-              line2res = _preprocess_sgm(line2, is_sgm)
-              if line1res or line2res:
-                lang1_resfile.write(line1res.strip() + "\n")
-                lang2_resfile.write(line2res.strip() + "\n")
+
+        if dataset[1][0] == 'tsv':
+          _, src_column, trg_column, glob_pattern = dataset[1]
+          filenames = glob.glob(os.path.join(tmp_dir, glob_pattern))
+          if not filenames:
+            mode = "r:gz" if compressed_filepath.endswith("gz") else "r"  # *.tgz *.tar.gz
+            with tarfile.open(compressed_filepath, mode) as corpus_tar:
+              corpus_tar.extractall(tmp_dir)
+            filenames = glob.glob(os.path.join(tmp_dir, glob_pattern))
+          for tsv_filename in filenames:
+            if tsv_filename.endswith(".gz"):
+              new_filename = tsv_filename.strip(".gz")
+              try:
+                generator_utils.gunzip_file(tsv_filename, new_filename)
+              except PermissionError:
+                tsvdir = os.path.dirname(tsv_filename)
+                os.chmod(tsvdir, os.stat(tsvdir).st_mode | stat.S_IWRITE)
+                generator_utils.gunzip_file(tsv_filename, new_filename)
+              tsv_filename = new_filename
+            with tf.gfile.GFile(tsv_filename, mode="r") as tsv_file:
+              for line in tsv_file:
+                if line and "\t" in line:
+                  parts = line.split("\t")
+                  source, target = parts[src_column], parts[trg_column]
+                  lang1_resfile.write(source.strip() + "\n")
+                  lang2_resfile.write(target.strip() + "\n")
+        else:
+          lang1_filename, lang2_filename = dataset[1]
+          lang1_filepath = os.path.join(tmp_dir, lang1_filename)
+          lang2_filepath = os.path.join(tmp_dir, lang2_filename)
+          is_sgm = (lang1_filename.endswith("sgm") and
+                    lang2_filename.endswith("sgm"))
+
+          if not (os.path.exists(lang1_filepath) and
+                  os.path.exists(lang2_filepath)):
+            # For .tar.gz and .tgz files, we read compressed.
+            mode = "r:gz" if compressed_filepath.endswith("gz") else "r"
+            with tarfile.open(compressed_filepath, mode) as corpus_tar:
+              corpus_tar.extractall(tmp_dir)
+          if lang1_filepath.endswith(".gz"):
+            new_filepath = lang1_filepath.strip(".gz")
+            generator_utils.gunzip_file(lang1_filepath, new_filepath)
+            lang1_filepath = new_filepath
+          if lang2_filepath.endswith(".gz"):
+            new_filepath = lang2_filepath.strip(".gz")
+            generator_utils.gunzip_file(lang2_filepath, new_filepath)
+            lang2_filepath = new_filepath
+          with tf.gfile.GFile(lang1_filepath, mode="r") as lang1_file:
+            with tf.gfile.GFile(lang2_filepath, mode="r") as lang2_file:
               line1, line2 = lang1_file.readline(), lang2_file.readline()
+              while line1 or line2:
+                line1res = _preprocess_sgm(line1, is_sgm)
+                line2res = _preprocess_sgm(line2, is_sgm)
+                if line1res or line2res:
+                  lang1_resfile.write(line1res.strip() + "\n")
+                  lang2_resfile.write(line2res.strip() + "\n")
+                line1, line2 = lang1_file.readline(), lang2_file.readline()
 
   return filename
 
@@ -630,13 +662,18 @@ def vocab_name(self):
 
   def generator(self, data_dir, tmp_dir, train):
     datasets = _ENCS_TRAIN_DATASETS if train else _ENCS_TEST_DATASETS
-    source_datasets = [[item[0], [item[1][0]]] for item in datasets]
-    target_datasets = [[item[0], [item[1][1]]] for item in datasets]
-    symbolizer_vocab = generator_utils.get_or_generate_vocab(
-        data_dir, tmp_dir, self.vocab_file, self.targeted_vocab_size,
-        source_datasets + target_datasets)
     tag = "train" if train else "dev"
     data_path = _compile_data(tmp_dir, datasets, "wmt_encs_tok_%s" % tag)
+    vocab_datasets = []
+    # CzEng contains 100 gz files with tab-separated columns, so let's expect
+    # it is the first dataset in datasets and use the newly created *.lang{1,2} files instead.
+    if datasets[0][0].endswith("data-plaintext-format.tar"):
+      vocab_datasets.append([datasets[0][0],
+                            ["wmt_encs_tok_%s.lang1" % tag, "wmt_encs_tok_%s.lang2" % tag]])
+      datasets = datasets[1:]
+    vocab_datasets += [[item[0], [item[1][0], item[1][1]]] for item in datasets]
+    symbolizer_vocab = generator_utils.get_or_generate_vocab(
+        data_dir, tmp_dir, self.vocab_file, self.targeted_vocab_size, vocab_datasets)
     return token_generator(data_path + ".lang1", data_path + ".lang2",
                            symbolizer_vocab, EOS)