Redesigned SST2Dataset to subclass IterableDataset (#1421)

* Updated SST2Dataset to subclass IterableDataset. Updated SST2 functional call to return SST2Dataset object

* Updated get_datapipe to be private, passed class parameters directly into get_datapipe function

Co-authored-by: nayef211 <n63ahmed@edu.uwaterloo.ca>

test/experimental/test_datasets.py

@@ -11,24 +11,28 @@ class TestDataset(TorchtextTestCase):
     @skipIfNoModule("torchdata")
     def test_sst2_dataset(self):
         split = ("train", "dev", "test")
-        train_dp, dev_dp, test_dp = sst2.SST2(split=split)
+        train_dataset, dev_dataset, test_dataset = sst2.SST2(split=split)
+
+        # verify datasets objects are instances of SST2Dataset
+        for dataset in (train_dataset, dev_dataset, test_dataset):
+            self.assertTrue(isinstance(dataset, sst2.SST2Dataset))
 
         # verify hashes of first line in dataset
         self.assertEqual(
             hashlib.md5(
-                json.dumps(next(iter(train_dp)), sort_keys=True).encode("utf-8")
+                json.dumps(next(iter(train_dataset)), sort_keys=True).encode("utf-8")
             ).hexdigest(),
             sst2._FIRST_LINE_MD5["train"],
         )
         self.assertEqual(
             hashlib.md5(
-                json.dumps(next(iter(dev_dp)), sort_keys=True).encode("utf-8")
+                json.dumps(next(iter(dev_dataset)), sort_keys=True).encode("utf-8")
             ).hexdigest(),
             sst2._FIRST_LINE_MD5["dev"],
         )
         self.assertEqual(
             hashlib.md5(
-                json.dumps(next(iter(test_dp)), sort_keys=True).encode("utf-8")
+                json.dumps(next(iter(test_dataset)), sort_keys=True).encode("utf-8")
             ).hexdigest(),
             sst2._FIRST_LINE_MD5["test"],
         )

torchtext/experimental/datasets/sst2.py

@@ -1,6 +1,7 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 import os
 
+from torch.utils.data.dataset import IterableDataset
 from torchtext._internal.module_utils import is_module_available
 from torchtext.data.datasets_utils import (
     _add_docstring_header,
@@ -50,10 +51,10 @@
 @_create_dataset_directory(dataset_name=DATASET_NAME)
 @_wrap_split_argument(("train", "dev", "test"))
 def SST2(root, split):
-    return SST2Dataset(root, split).get_datapipe()
+    return SST2Dataset(root, split)
 
 
-class SST2Dataset:
+class SST2Dataset(IterableDataset):
     """The SST2 dataset uses torchdata datapipes end-2-end.
     To avoid download at every epoch, we cache the data on-disk
     We do sanity check on dowloaded and extracted data
@@ -67,26 +68,27 @@ def __init__(self, root, split):
                 "how to install the package."
             )
 
-        self.root = root
-        self.split = split
+        self._dp = self._get_datapipe(root, split)
 
-    def get_datapipe(self):
+    def __iter__(self):
+        for data in self._dp:
+            yield data
+
+    def _get_datapipe(self, root, split):
         # cache data on-disk
         cache_dp = IterableWrapper([URL]).on_disk_cache(
             HttpReader,
             op_map=lambda x: (x[0], x[1].read()),
-            filepath_fn=lambda x: os.path.join(self.root, os.path.basename(x)),
+            filepath_fn=lambda x: os.path.join(root, os.path.basename(x)),
         )
 
         # do sanity check
         check_cache_dp = cache_dp.check_hash(
-            {os.path.join(self.root, "SST-2.zip"): MD5}, "md5"
+            {os.path.join(root, "SST-2.zip"): MD5}, "md5"
         )
 
         # extract data from zip
-        extracted_files = check_cache_dp.read_from_zip().filter(
-            lambda x: self.split in x[0]
-        )
+        extracted_files = check_cache_dp.read_from_zip().filter(lambda x: split in x[0])
 
         # Parse CSV file and yield data samples
         return extracted_files.parse_csv(skip_lines=1, delimiter="\t").map(