apply changes

tests/conftest.py

@@ -1,4 +1,5 @@
 import json
+import shutil
 from io import BytesIO
 
 import hdf5storage
@@ -154,28 +155,22 @@ def mock_svhn_dataset(tmpdir_factory, mock_image_stream):
     svhn_root = root.mkdir('svhn')
     file = BytesIO(mock_image_stream)
     # ascii image names
-    first = np.array([[49], [46], [112], [110], [103]], dtype=np.int16)  # 0.png
-    second = np.array([[50], [46], [112], [110], [103]], dtype=np.int16)  # 1.png
-    third = np.array([[51], [46], [112], [110], [103]], dtype=np.int16)  # 2.png
+    first = np.array([[49], [46], [112], [110], [103]], dtype=np.int16)  # 1.png
+    second = np.array([[50], [46], [112], [110], [103]], dtype=np.int16)  # 2.png
+    third = np.array([[51], [46], [112], [110], [103]], dtype=np.int16)  # 3.png
     # labels: label is also ascii
     label = {'height': [35, 35, 35, 35], 'label': [1, 1, 3, 7],
              'left': [116, 128, 137, 151], 'top': [27, 29, 29, 26],
              'width': [15, 10, 17, 17]}
 
     matcontent = {'digitStruct': {'name': [first, second, third], 'bbox': [label, label, label]}}
-    # mock train data
-    root = svhn_root.mkdir('train')
-    hdf5storage.write(matcontent, filename=root.join('digitStruct.mat'))
-    for i in range(4):
-        fn = root.join(f'{i}.png')
-        with open(fn, 'wb') as f:
-            f.write(file.getbuffer())
-    # mock test data
-    root = svhn_root.mkdir('test')
-    hdf5storage.write(matcontent, filename=root.join('digitStruct.mat'))
-    for i in range(4):
-        fn = root.join(f'{i}.png')
+    # Mock train data
+    train_root = svhn_root.mkdir('train')
+    hdf5storage.write(matcontent, filename=train_root.join('digitStruct.mat'))
+    for i in range(3):
+        fn = train_root.join(f'{i+1}.png')
         with open(fn, 'wb') as f:
             f.write(file.getbuffer())
-
-    return str(svhn_root)
+    # Packing data into an archive to simulate the real data set and bypass archive extraction
+    shutil.make_archive(svhn_root.join('svhn_train'), 'tar', str(svhn_root))
+    return str(root)

tests/pytorch/test_datasets_pt.py

@@ -235,23 +235,23 @@ def test_ic13_dataset(mock_ic13, size, rotate):
 
 
 @pytest.mark.parametrize(
-    "train, input_size, size, rotate",
+    "input_size, size, rotate",
     [
-        [True, [32, 128], 3, True],  # Actual set has 33402 samples
-        [False, [32, 128], 3, False],  # Actual set has 13068 samples
+        [[32, 128], 3, True],  # Actual set has 33402 training samples and 13068 test samples
+        [[32, 128], 3, False],
     ],
 )
-def test_svhn(train, input_size, size, rotate, mock_svhn_dataset):
+def test_svhn(input_size, size, rotate, mock_svhn_dataset):
     # monkeypatch the path to temporary dataset
     datasets.SVHN.TRAIN = (mock_svhn_dataset, None, "svhn_train.tar")
-    datasets.SVHN.TEST = (mock_svhn_dataset, None, "svhn_test.tar")
 
     ds = datasets.SVHN(
-        train=train, download=True, sample_transforms=Resize(input_size), rotated_bbox=rotate,
+        train=True, download=True, sample_transforms=Resize(input_size), rotated_bbox=rotate,
+        cache_dir=mock_svhn_dataset, cache_subdir="svhn",
     )
 
     assert len(ds) == size
-    assert repr(ds) == f"SVHN(train={train})"
+    assert repr(ds) == f"SVHN(train={True})"
     img, target = ds[0]
     assert isinstance(img, torch.Tensor)
     assert img.shape == (3, *input_size)

tests/tensorflow/test_datasets_tf.py

@@ -221,23 +221,23 @@ def test_ic13_dataset(mock_ic13, size, rotate):
 
 
 @pytest.mark.parametrize(
-    "train, input_size, size, rotate",
+    "input_size, size, rotate",
     [
-        [True, [32, 128], 3, True],  # Actual set has 33402 samples
-        [False, [32, 128], 3, False],  # Actual set has 13068 samples
+        [[32, 128], 3, True],  # Actual set has 33402 training samples and 13068 test samples
+        [[32, 128], 3, False],
     ],
 )
-def test_svhn(train, input_size, size, rotate, mock_svhn_dataset):
+def test_svhn(input_size, size, rotate, mock_svhn_dataset):
     # monkeypatch the path to temporary dataset
     datasets.SVHN.TRAIN = (mock_svhn_dataset, None, "svhn_train.tar")
-    datasets.SVHN.TEST = (mock_svhn_dataset, None, "svhn_test.tar")
 
     ds = datasets.SVHN(
-        train=train, download=False, sample_transforms=Resize(input_size), rotated_bbox=rotate,
+        train=True, download=True, sample_transforms=Resize(input_size), rotated_bbox=rotate,
+        cache_dir=mock_svhn_dataset, cache_subdir="svhn",
     )
 
     assert len(ds) == size
-    assert repr(ds) == f"SVHN(train={train})"
+    assert repr(ds) == f"SVHN(train={True})"
     img, target = ds[0]
     assert isinstance(img, tf.Tensor)
     assert img.shape == (*input_size, 3)