v2.8.1

.github/workflows/test_datasets.yml

@@ -0,0 +1,15 @@
+name: datasets
+
+on:
+  pull_request:
+    branches: [ master, dev ]
+    paths:
+      - 'src/**'
+      - 'tests/**'
+      - '.github/workflows/**'
+
+jobs:
+  call-base-test-workflow:
+    uses: ./.github/workflows/base_test_workflow.yml
+    with:
+      module-to-test: datasets

docs/datasets.md

@@ -40,7 +40,7 @@ datasets.base_dataset.BaseDataset(
 ## CUB-200-2011
 
 ```python
-datasets.cub.CUB(*args, **kwargs)
+datasets.CUB(*args, **kwargs)
 ```
 
 **Defined splits**: 
@@ -75,7 +75,7 @@ train_and_test_dataset = CUB(root="data",
 ## Cars196
 
 ```python
-datasets.cars196.Cars196(*args, **kwargs)
+datasets.Cars196(*args, **kwargs)
 ```
 
 **Defined splits**: 
@@ -110,7 +110,7 @@ train_and_test_dataset = Cars196(root="data",
 ## INaturalist2018
 
 ```python
-datasets.inaturalist2018.INaturalist2018(*args, **kwargs)
+datasets.INaturalist2018(*args, **kwargs)
 ```
 
 **Defined splits**: 
@@ -146,7 +146,7 @@ train_and_test_dataset = INaturalist2018(root="data",
 ## StanfordOnlineProducts
 
 ```python
-datasets.sop.StanfordOnlineProducts(*args, **kwargs)
+datasets.StanfordOnlineProducts(*args, **kwargs)
 ```
 
 **Defined splits**: 

src/pytorch_metric_learning/__init__.py

@@ -1 +1 @@
-__version__ = "2.8.0"
+__version__ = "2.8.1"

src/pytorch_metric_learning/datasets/__init__.py

@@ -0,0 +1,5 @@
+from .base_dataset import BaseDataset
+from .cars196 import Cars196
+from .cub import CUB
+from .inaturalist2018 import INaturalist2018
+from .sop import StanfordOnlineProducts

tests/__init__.py

@@ -12,13 +12,15 @@
 )
 device_from_environ = os.environ.get("TEST_DEVICE", "cuda")
 with_collect_stats = os.environ.get("WITH_COLLECT_STATS", "false")
+test_datasets = os.environ.get("TEST_DATASETS", "false")
 
 TEST_DTYPES = [getattr(torch, x) for x in dtypes_from_environ]
 TEST_DEVICE = torch.device(device_from_environ)
 
 assert c_f.COLLECT_STATS is False
 
 WITH_COLLECT_STATS = True if with_collect_stats == "true" else False
+TEST_DATASETS = True if test_datasets == "true" else False
 c_f.COLLECT_STATS = WITH_COLLECT_STATS
 
 print(

tests/datasets/test_cars196.py

@@ -5,7 +5,8 @@
 import torchvision.transforms as transforms
 from torch.utils.data import DataLoader
 
-from pytorch_metric_learning.datasets.cars196 import Cars196
+from pytorch_metric_learning.datasets import Cars196
+from .. import TEST_DATASETS
 
 
 class TestCars196(unittest.TestCase):
@@ -19,6 +20,7 @@ def setUpClass(cls):
         if os.path.exists(cls.CARS_196_ROOT):
             cls.ALREADY_EXISTS = True
 
+    @unittest.skipUnless(TEST_DATASETS, "TEST_DATASETS is false")
     def test_Cars196(self):
         train_test_data = Cars196(
             root=TestCars196.CARS_196_ROOT, split="train+test", download=True
@@ -34,6 +36,7 @@ def test_Cars196(self):
         self.assertTrue(len(train_data) == 8054)
         self.assertTrue(len(test_data) == 8131)
 
+    @unittest.skipUnless(TEST_DATASETS, "TEST_DATASETS is false")
     def test_CARS_196_dataloader(self):
         test_data = Cars196(
             root=TestCars196.CARS_196_ROOT,
@@ -50,5 +53,5 @@ def test_CARS_196_dataloader(self):
 
     @classmethod
     def tearDownClass(cls):
-        if not cls.ALREADY_EXISTS:
+        if not cls.ALREADY_EXISTS and os.path.isdir(cls.CARS_196_ROOT):
             shutil.rmtree(cls.CARS_196_ROOT)

tests/datasets/test_cub.py

@@ -5,7 +5,8 @@
 import torchvision.transforms as transforms
 from torch.utils.data import DataLoader
 
-from pytorch_metric_learning.datasets.cub import CUB
+from pytorch_metric_learning.datasets import CUB
+from .. import TEST_DATASETS
 
 
 class TestCUB(unittest.TestCase):
@@ -19,6 +20,7 @@ def setUpClass(cls):
         if os.path.exists(cls.CUB_ROOT):
             cls.ALREADY_EXISTS = True
 
+    @unittest.skipUnless(TEST_DATASETS, "TEST_DATASETS is false")
     def test_CUB(self):
         train_test_data = CUB(root=TestCUB.CUB_ROOT, split="train+test", download=True)
         train_data = CUB(root=TestCUB.CUB_ROOT, split="train", download=True)
@@ -28,6 +30,7 @@ def test_CUB(self):
         self.assertTrue(len(train_data) == 5864)
         self.assertTrue(len(test_data) == 5924)
 
+    @unittest.skipUnless(TEST_DATASETS, "TEST_DATASETS is false")
     def test_CUB_dataloader(self):
         test_data = CUB(
             root=TestCUB.CUB_ROOT,
@@ -44,5 +47,5 @@ def test_CUB_dataloader(self):
 
     @classmethod
     def tearDownClass(cls):
-        if not cls.ALREADY_EXISTS:
+        if not cls.ALREADY_EXISTS and os.path.isdir(cls.CUB_ROOT):
             shutil.rmtree(cls.CUB_ROOT)

tests/datasets/test_inaturalist2018.py

@@ -5,7 +5,8 @@
 import torchvision.transforms as transforms
 from torch.utils.data import DataLoader
 
-from pytorch_metric_learning.datasets.inaturalist2018 import INaturalist2018
+from pytorch_metric_learning.datasets import INaturalist2018
+from .. import TEST_DATASETS
 
 
 class TestINaturalist2018(unittest.TestCase):
@@ -19,6 +20,7 @@ def setUpClass(cls):
         if os.path.exists(cls.INATURALIST2018_ROOT):
             cls.ALREADY_EXISTS = True
 
+    @unittest.skipUnless(TEST_DATASETS, "TEST_DATASETS is false")
     def test_INaturalist2018(self):
         train_test_data = INaturalist2018(
             root=TestINaturalist2018.INATURALIST2018_ROOT,
@@ -36,6 +38,7 @@ def test_INaturalist2018(self):
         self.assertTrue(len(train_data) == 325846)
         self.assertTrue(len(test_data) == 136093)
 
+    @unittest.skipUnless(TEST_DATASETS, "TEST_DATASETS is false")
     def test_INaturalist2018_dataloader(self):
         test_data = INaturalist2018(
             root=TestINaturalist2018.INATURALIST2018_ROOT,
@@ -52,5 +55,5 @@ def test_INaturalist2018_dataloader(self):
 
     @classmethod
     def tearDownClass(cls):
-        if not cls.ALREADY_EXISTS:
+        if not cls.ALREADY_EXISTS and os.path.isdir(cls.INATURALIST2018_ROOT):
             shutil.rmtree(cls.INATURALIST2018_ROOT)

tests/datasets/test_sop.py

@@ -5,7 +5,8 @@
 import torchvision.transforms as transforms
 from torch.utils.data import DataLoader
 
-from pytorch_metric_learning.datasets.sop import StanfordOnlineProducts
+from pytorch_metric_learning.datasets import StanfordOnlineProducts
+from .. import TEST_DATASETS
 
 
 class TestStanfordOnlineProducts(unittest.TestCase):
@@ -19,6 +20,7 @@ def setUpClass(cls):
         if os.path.exists(cls.SOP_ROOT):
             cls.ALREADY_EXISTS = True
 
+    @unittest.skipUnless(TEST_DATASETS, "TEST_DATASETS is false")
     def test_SOP(self):
         train_test_data = StanfordOnlineProducts(
             root=TestStanfordOnlineProducts.SOP_ROOT, split="train+test", download=True
@@ -34,6 +36,7 @@ def test_SOP(self):
         self.assertTrue(len(train_data) == 59551)
         self.assertTrue(len(test_data) == 60502)
 
+    @unittest.skipUnless(TEST_DATASETS, "TEST_DATASETS is false")
     def test_SOP_dataloader(self):
         test_data = StanfordOnlineProducts(
             root=TestStanfordOnlineProducts.SOP_ROOT,
@@ -48,7 +51,8 @@ def test_SOP_dataloader(self):
         self.assertTupleEqual(tuple(inputs.shape), (8, 3, 224, 224))
         self.assertTupleEqual(tuple(labels.shape), (8,))
 
+    @unittest.skipUnless(TEST_DATASETS, "TEST_DATASETS is false")
     @classmethod
     def tearDownClass(cls):
-        if not cls.ALREADY_EXISTS:
+        if not cls.ALREADY_EXISTS and os.path.isdir(cls.SOP_ROOT):
             shutil.rmtree(cls.SOP_ROOT)