rely on patched datasets home rather than passing it around (#5998)

* rely on patched datasets home rather than passing it around

* add comment

test/builtin_dataset_mocks.py

@@ -62,8 +62,10 @@ class DatasetMock:
 
         return mock_info
 
-    def prepare(self, home, config):
-        root = home / self.name
+    def prepare(self, config):
+        # `datasets.home()` is patched to a temporary directory through the autouse fixture `test_home` in
+        # test/test_prototype_builtin_datasets.py
+        root = pathlib.Path(datasets.home()) / self.name
         root.mkdir(exist_ok=True)
 
         mock_info = self._parse_mock_info(self.mock_data_fn(root, config))

test/test_prototype_builtin_datasets.py

@@ -25,9 +25,10 @@ def extract_datapipes(dp):
     return get_all_graph_pipes(traverse(dp, only_datapipe=True))
 
 
-@pytest.fixture
+@pytest.fixture(autouse=True)
 def test_home(mocker, tmp_path):
     mocker.patch("torchvision.prototype.datasets._api.home", return_value=str(tmp_path))
+    mocker.patch("torchvision.prototype.datasets.home", return_value=str(tmp_path))
     yield tmp_path
 
 
@@ -54,8 +55,8 @@ class TestCommon:
             raise AssertionError("Info should be a dictionary with string keys.")
 
     @parametrize_dataset_mocks(DATASET_MOCKS)
-    def test_smoke(self, test_home, dataset_mock, config):
-        dataset_mock.prepare(test_home, config)
+    def test_smoke(self, dataset_mock, config):
+        dataset_mock.prepare(config)
 
         dataset = datasets.load(dataset_mock.name, **config)
 
@@ -63,8 +64,8 @@ class TestCommon:
             raise AssertionError(f"Loading the dataset should return an Dataset, but got {type(dataset)} instead.")
 
     @parametrize_dataset_mocks(DATASET_MOCKS)
-    def test_sample(self, test_home, dataset_mock, config):
-        dataset_mock.prepare(test_home, config)
+    def test_sample(self, dataset_mock, config):
+        dataset_mock.prepare(config)
 
         dataset = datasets.load(dataset_mock.name, **config)
 
@@ -82,16 +83,16 @@ class TestCommon:
             raise AssertionError("Sample dictionary is empty.")
 
     @parametrize_dataset_mocks(DATASET_MOCKS)
-    def test_num_samples(self, test_home, dataset_mock, config):
-        mock_info = dataset_mock.prepare(test_home, config)
+    def test_num_samples(self, dataset_mock, config):
+        mock_info = dataset_mock.prepare(config)
 
         dataset = datasets.load(dataset_mock.name, **config)
 
         assert len(list(dataset)) == mock_info["num_samples"]
 
     @parametrize_dataset_mocks(DATASET_MOCKS)
-    def test_no_vanilla_tensors(self, test_home, dataset_mock, config):
-        dataset_mock.prepare(test_home, config)
+    def test_no_vanilla_tensors(self, dataset_mock, config):
+        dataset_mock.prepare(config)
 
         dataset = datasets.load(dataset_mock.name, **config)
 
@@ -103,8 +104,8 @@ class TestCommon:
             )
 
     @parametrize_dataset_mocks(DATASET_MOCKS)
-    def test_transformable(self, test_home, dataset_mock, config):
-        dataset_mock.prepare(test_home, config)
+    def test_transformable(self, dataset_mock, config):
+        dataset_mock.prepare(config)
 
         dataset = datasets.load(dataset_mock.name, **config)
 
@@ -112,15 +113,15 @@ class TestCommon:
 
     @pytest.mark.parametrize("only_datapipe", [False, True])
     @parametrize_dataset_mocks(DATASET_MOCKS)
-    def test_traversable(self, test_home, dataset_mock, config, only_datapipe):
-        dataset_mock.prepare(test_home, config)
+    def test_traversable(self, dataset_mock, config, only_datapipe):
+        dataset_mock.prepare(config)
         dataset = datasets.load(dataset_mock.name, **config)
 
         traverse(dataset, only_datapipe=only_datapipe)
 
     @parametrize_dataset_mocks(DATASET_MOCKS)
-    def test_serializable(self, test_home, dataset_mock, config):
-        dataset_mock.prepare(test_home, config)
+    def test_serializable(self, dataset_mock, config):
+        dataset_mock.prepare(config)
         dataset = datasets.load(dataset_mock.name, **config)
 
         pickle.dumps(dataset)
@@ -133,8 +134,8 @@ class TestCommon:
 
     @pytest.mark.parametrize("num_workers", [0, 1])
     @parametrize_dataset_mocks(DATASET_MOCKS)
-    def test_data_loader(self, test_home, dataset_mock, config, num_workers):
-        dataset_mock.prepare(test_home, config)
+    def test_data_loader(self, dataset_mock, config, num_workers):
+        dataset_mock.prepare(config)
         dataset = datasets.load(dataset_mock.name, **config)
 
         dl = DataLoader(
@@ -151,17 +152,17 @@ class TestCommon:
     #  contain a custom test for that, but we opted to wait for a potential solution / test from torchdata for now.
     @parametrize_dataset_mocks(DATASET_MOCKS)
     @pytest.mark.parametrize("annotation_dp_type", (Shuffler, ShardingFilter))
-    def test_has_annotations(self, test_home, dataset_mock, config, annotation_dp_type):
+    def test_has_annotations(self, dataset_mock, config, annotation_dp_type):
 
-        dataset_mock.prepare(test_home, config)
+        dataset_mock.prepare(config)
         dataset = datasets.load(dataset_mock.name, **config)
 
         if not any(isinstance(dp, annotation_dp_type) for dp in extract_datapipes(dataset)):
             raise AssertionError(f"The dataset doesn't contain a {annotation_dp_type.__name__}() datapipe.")
 
     @parametrize_dataset_mocks(DATASET_MOCKS)
-    def test_save_load(self, test_home, dataset_mock, config):
-        dataset_mock.prepare(test_home, config)
+    def test_save_load(self, dataset_mock, config):
+        dataset_mock.prepare(config)
         dataset = datasets.load(dataset_mock.name, **config)
         sample = next(iter(dataset))
 
@@ -171,8 +172,8 @@ class TestCommon:
             assert_samples_equal(torch.load(buffer), sample)
 
     @parametrize_dataset_mocks(DATASET_MOCKS)
-    def test_infinite_buffer_size(self, test_home, dataset_mock, config):
-        dataset_mock.prepare(test_home, config)
+    def test_infinite_buffer_size(self, dataset_mock, config):
+        dataset_mock.prepare(config)
         dataset = datasets.load(dataset_mock.name, **config)
 
         for dp in extract_datapipes(dataset):
@@ -182,8 +183,8 @@ class TestCommon:
                 assert dp.buffer_size == INFINITE_BUFFER_SIZE
 
     @parametrize_dataset_mocks(DATASET_MOCKS)
-    def test_has_length(self, test_home, dataset_mock, config):
-        dataset_mock.prepare(test_home, config)
+    def test_has_length(self, dataset_mock, config):
+        dataset_mock.prepare(config)
         dataset = datasets.load(dataset_mock.name, **config)
 
         assert len(dataset) > 0
@@ -191,8 +192,8 @@ class TestCommon:
 
 @parametrize_dataset_mocks(DATASET_MOCKS["qmnist"])
 class TestQMNIST:
-    def test_extra_label(self, test_home, dataset_mock, config):
-        dataset_mock.prepare(test_home, config)
+    def test_extra_label(self, dataset_mock, config):
+        dataset_mock.prepare(config)
 
         dataset = datasets.load(dataset_mock.name, **config)
 
@@ -211,13 +212,13 @@ class TestQMNIST:
 
 @parametrize_dataset_mocks(DATASET_MOCKS["gtsrb"])
 class TestGTSRB:
-    def test_label_matches_path(self, test_home, dataset_mock, config):
+    def test_label_matches_path(self, dataset_mock, config):
         # We read the labels from the csv files instead. But for the trainset, the labels are also part of the path.
         # This test makes sure that they're both the same
         if config["split"] != "train":
             return
 
-        dataset_mock.prepare(test_home, config)
+        dataset_mock.prepare(config)
 
         dataset = datasets.load(dataset_mock.name, **config)
 
@@ -228,8 +229,8 @@ class TestGTSRB:
 
 @parametrize_dataset_mocks(DATASET_MOCKS["usps"])
 class TestUSPS:
-    def test_sample_content(self, test_home, dataset_mock, config):
-        dataset_mock.prepare(test_home, config)
+    def test_sample_content(self, dataset_mock, config):
+        dataset_mock.prepare(config)
 
         dataset = datasets.load(dataset_mock.name, **config)