1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
|
import os
import tarfile
from collections import defaultdict
from unittest.mock import patch
from torchtext.datasets import Multi30k
from ..common.case_utils import TempDirMixin, zip_equal, get_random_unicode
from ..common.parameterized_utils import nested_params
from ..common.torchtext_test_case import TorchtextTestCase
def _get_mock_dataset(root_dir):
"""
root_dir: directory to the mocked dataset
"""
base_dir = os.path.join(root_dir, "Multi30k")
temp_dataset_dir = os.path.join(base_dir, "temp_dataset_dir")
os.makedirs(temp_dataset_dir, exist_ok=True)
seed = 1
mocked_data = defaultdict(list)
for file_name in ("train.de", "train.en", "val.de", "val.en", "test.de", "test.en"):
txt_file = os.path.join(temp_dataset_dir, file_name)
with open(txt_file, "w", encoding="utf-8") as f:
for i in range(5):
rand_string = get_random_unicode(seed)
f.write(rand_string + "\n")
mocked_data[file_name].append(rand_string)
seed += 1
archive = {}
archive["train"] = os.path.join(base_dir, "training.tar.gz")
archive["val"] = os.path.join(base_dir, "validation.tar.gz")
archive["test"] = os.path.join(base_dir, "mmt16_task1_test.tar.gz")
for split in ("train", "val", "test"):
with tarfile.open(archive[split], "w:gz") as tar:
tar.add(os.path.join(temp_dataset_dir, f"{split}.de"))
tar.add(os.path.join(temp_dataset_dir, f"{split}.en"))
return mocked_data
class TestMulti30k(TempDirMixin, TorchtextTestCase):
@classmethod
def setUpClass(cls):
super().setUpClass()
cls.root_dir = cls.get_base_temp_dir()
cls.samples = _get_mock_dataset(os.path.join(cls.root_dir, "datasets"))
cls.patcher = patch("torchdata.datapipes.iter.util.cacheholder._hash_check", return_value=True)
cls.patcher.start()
@classmethod
def tearDownClass(cls):
cls.patcher.stop()
super().tearDownClass()
@nested_params(["train", "valid", "test"], [("de", "en"), ("en", "de")])
def test_multi30k(self, split, language_pair):
dataset = Multi30k(root=self.root_dir, split=split, language_pair=language_pair)
if split == "valid":
split = "val"
samples = list(dataset)
expected_samples = [
(d1, d2)
for d1, d2 in zip(
self.samples[f"{split}.{language_pair[0]}"],
self.samples[f"{split}.{language_pair[1]}"],
)
]
for sample, expected_sample in zip_equal(samples, expected_samples):
self.assertEqual(sample, expected_sample)
@nested_params(["train", "valid", "test"], [("de", "en"), ("en", "de")])
def test_multi30k_split_argument(self, split, language_pair):
dataset1 = Multi30k(root=self.root_dir, split=split, language_pair=language_pair)
(dataset2,) = Multi30k(root=self.root_dir, split=(split,), language_pair=language_pair)
for d1, d2 in zip_equal(dataset1, dataset2):
self.assertEqual(d1, d2)
|
