port sample input smoke test

test/common_utils.py

@@ -406,6 +406,7 @@ def make_bounding_boxes(
     canvas_size=DEFAULT_SIZE,
     *,
     format=tv_tensors.BoundingBoxFormat.XYXY,
+    num_objects=1,
     dtype=None,
     device="cpu",
 ):
@@ -419,8 +420,7 @@ def sample_position(values, max_value):
 
     dtype = dtype or torch.float32
 
-    num_objects = 1
-    h, w = [torch.randint(1, c, (num_objects,)) for c in canvas_size]
+    h, w = [torch.randint(1, s, (num_objects,)) for s in canvas_size]
     y = sample_position(h, canvas_size[0])
     x = sample_position(w, canvas_size[1])
 

test/test_transforms_v2.py

@@ -17,7 +17,7 @@
 from torchvision.ops.boxes import box_iou
 from torchvision.transforms.functional import to_pil_image
 from torchvision.transforms.v2 import functional as F
-from torchvision.transforms.v2._utils import check_type, is_pure_tensor, query_chw
+from torchvision.transforms.v2._utils import is_pure_tensor, query_chw
 from transforms_v2_legacy_utils import (
     make_bounding_boxes,
     make_detection_mask,
@@ -62,22 +62,6 @@ def parametrize(transforms_with_inputs):
     )
 
 
-def auto_augment_adapter(transform, input, device):
-    adapted_input = {}
-    image_or_video_found = False
-    for key, value in input.items():
-        if isinstance(value, (tv_tensors.BoundingBoxes, tv_tensors.Mask)):
-            # AA transforms don't support bounding boxes or masks
-            continue
-        elif check_type(value, (tv_tensors.Image, tv_tensors.Video, is_pure_tensor, PIL.Image.Image)):
-            if image_or_video_found:
-                # AA transforms only support a single image or video
-                continue
-            image_or_video_found = True
-        adapted_input[key] = value
-    return adapted_input
-
-
 def linear_transformation_adapter(transform, input, device):
     flat_inputs = list(input.values())
     c, h, w = query_chw(
@@ -93,58 +77,19 @@ def linear_transformation_adapter(transform, input, device):
     return {key: value for key, value in input.items() if not isinstance(value, PIL.Image.Image)}
 
 
-def normalize_adapter(transform, input, device):
-    adapted_input = {}
-    for key, value in input.items():
-        if isinstance(value, PIL.Image.Image):
-            # normalize doesn't support PIL images
-            continue
-        elif check_type(value, (tv_tensors.Image, tv_tensors.Video, is_pure_tensor)):
-            # normalize doesn't support integer images
-            value = F.to_dtype(value, torch.float32, scale=True)
-        adapted_input[key] = value
-    return adapted_input
-
-
 class TestSmoke:
     @pytest.mark.parametrize(
         ("transform", "adapter"),
         [
-            (transforms.RandomErasing(p=1.0), None),
-            (transforms.AugMix(), auto_augment_adapter),
-            (transforms.AutoAugment(), auto_augment_adapter),
-            (transforms.RandAugment(), auto_augment_adapter),
-            (transforms.TrivialAugmentWide(), auto_augment_adapter),
             (transforms.ColorJitter(brightness=0.1, contrast=0.2, saturation=0.3, hue=0.15), None),
             (transforms.Grayscale(), None),
-            (transforms.RandomAdjustSharpness(sharpness_factor=0.5, p=1.0), None),
-            (transforms.RandomAutocontrast(p=1.0), None),
-            (transforms.RandomEqualize(p=1.0), None),
             (transforms.RandomGrayscale(p=1.0), None),
-            (transforms.RandomInvert(p=1.0), None),
             (transforms.RandomChannelPermutation(), None),
             (transforms.RandomPhotometricDistort(p=1.0), None),
-            (transforms.RandomPosterize(bits=4, p=1.0), None),
-            (transforms.RandomSolarize(threshold=0.5, p=1.0), None),
-            (transforms.CenterCrop([16, 16]), None),
-            (transforms.ElasticTransform(sigma=1.0), None),
-            (transforms.Pad(4), None),
-            (transforms.RandomAffine(degrees=30.0), None),
-            (transforms.RandomCrop([16, 16], pad_if_needed=True), None),
-            (transforms.RandomHorizontalFlip(p=1.0), None),
-            (transforms.RandomPerspective(p=1.0), None),
-            (transforms.RandomResize(min_size=10, max_size=20, antialias=True), None),
-            (transforms.RandomResizedCrop([16, 16], antialias=True), None),
-            (transforms.RandomRotation(degrees=30), None),
             (transforms.RandomShortestSize(min_size=10, antialias=True), None),
-            (transforms.RandomVerticalFlip(p=1.0), None),
             (transforms.RandomZoomOut(p=1.0), None),
-            (transforms.Resize([16, 16], antialias=True), None),
             (transforms.ScaleJitter((16, 16), scale_range=(0.8, 1.2), antialias=True), None),
-            (transforms.ClampBoundingBoxes(), None),
-            (transforms.ConvertBoundingBoxFormat(tv_tensors.BoundingBoxFormat.CXCYWH), None),
             (transforms.ConvertImageDtype(), None),
-            (transforms.GaussianBlur(kernel_size=3), None),
             (
                 transforms.LinearTransformation(
                     # These are just dummy values that will be filled by the adapter. We can't define them upfront,
@@ -154,9 +99,6 @@ class TestSmoke:
                 ),
                 linear_transformation_adapter,
             ),
-            (transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), normalize_adapter),
-            (transforms.ToDtype(torch.float64), None),
-            (transforms.UniformTemporalSubsample(num_samples=2), None),
         ],
         ids=lambda transform: type(transform).__name__,
     )