fix broken tests

Author: pmeier

test/datasets_utils.py

@@ -549,7 +549,7 @@ def test_feature_types(self, config):
     @test_all_configs
     def test_num_examples(self, config):
         with self.create_dataset(config) as (dataset, info):
-            assert len(dataset) == info["num_examples"]
+            assert len(list(dataset)) == len(dataset) == info["num_examples"]
 
     @test_all_configs
     def test_transforms(self, config):

test/test_datasets.py

@@ -194,7 +194,7 @@ class Caltech256TestCase(datasets_utils.ImageDatasetTestCase):
     def inject_fake_data(self, tmpdir, config):
         tmpdir = pathlib.Path(tmpdir) / "caltech256" / "256_ObjectCategories"
 
-        categories = ((1, "ak47"), (127, "laptop-101"), (257, "clutter"))
+        categories = ((1, "ak47"), (2, "american-flag"), (3, "backpack"))
         num_images_per_category = 2
 
         for idx, category in categories:

torchvision/datasets/widerface.py

@@ -137,13 +137,13 @@ def parse_train_val_annotations_file(self) -> None:
                             {
                                 "img_path": img_path,
                                 "annotations": {
-                                    "bbox": labels_tensor[:, 0:4],  # x, y, width, height
-                                    "blur": labels_tensor[:, 4],
-                                    "expression": labels_tensor[:, 5],
-                                    "illumination": labels_tensor[:, 6],
-                                    "occlusion": labels_tensor[:, 7],
-                                    "pose": labels_tensor[:, 8],
-                                    "invalid": labels_tensor[:, 9],
+                                    "bbox": labels_tensor[:, 0:4].clone(),  # x, y, width, height
+                                    "blur": labels_tensor[:, 4].clone(),
+                                    "expression": labels_tensor[:, 5].clone(),
+                                    "illumination": labels_tensor[:, 6].clone(),
+                                    "occlusion": labels_tensor[:, 7].clone(),
+                                    "pose": labels_tensor[:, 8].clone(),
+                                    "invalid": labels_tensor[:, 9].clone(),
                                 },
                             }
                         )