Add IMDB Mocked Unit Test

test/datasets/test_imdb.py

@@ -0,0 +1,89 @@
+import os
+import random
+import string
+import tarfile
+from collections import defaultdict
+from unittest.mock import patch
+
+from parameterized import parameterized
+from torchtext.datasets.imdb import IMDB
+
+from ..common.case_utils import TempDirMixin, zip_equal
+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, "IMDB")
+    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 split in ("train", "test"):
+        neg_dir = os.path.join(temp_dataset_dir, split, "neg")
+        pos_dir = os.path.join(temp_dataset_dir, split, "pos")
+        os.makedirs(neg_dir, exist_ok=True)
+        os.makedirs(pos_dir, exist_ok=True)
+
+        for i in range(5):
+            # all negative labels are read first before positive labels in the
+            # IMDB dataset implementation
+            label = "neg" if i < 2 else "pos"
+            cur_dir = pos_dir if label == "pos" else neg_dir
+            txt_file = os.path.join(cur_dir, f"{i}{i}_{i}.txt")
+            with open(txt_file, "w") as f:
+                rand_string = " ".join(
+                    random.choice(string.ascii_letters) for i in range(seed)
+                )
+                dataset_line = (label, rand_string)
+                # append line to correct dataset split
+                mocked_data[split].append(dataset_line)
+                f.write(rand_string)
+                seed += 1
+
+    compressed_dataset_path = os.path.join(base_dir, "aclImdb_v1.tar.gz")
+    # create tar file from dataset folder
+    with tarfile.open(compressed_dataset_path, "w:gz") as tar:
+        tar.add(temp_dataset_dir, arcname="aclImdb_v1")
+
+    return mocked_data
+
+
+class TestIMDB(TempDirMixin, TorchtextTestCase):
+    root_dir = None
+    samples = []
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+        cls.root_dir = cls.get_base_temp_dir()
+        cls.samples = _get_mock_dataset(cls.root_dir)
+        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()
+
+    @parameterized.expand(["train", "test"])
+    def test_imdb(self, split):
+        dataset = IMDB(root=self.root_dir, split=split)
+
+        samples = list(dataset)
+        expected_samples = self.samples[split]
+        for sample, expected_sample in zip_equal(samples, expected_samples):
+            self.assertEqual(sample, expected_sample)
+
+    @parameterized.expand(["train", "test"])
+    def test_imdb_split_argument(self, split):
+        dataset1 = IMDB(root=self.root_dir, split=split)
+        (dataset2,) = IMDB(root=self.root_dir, split=(split,))
+
+        for d1, d2 in zip_equal(dataset1, dataset2):
+            self.assertEqual(d1, d2)