introduce nearest-exact interpolation (#6754)

* introduce nearest-exact interpolation

* update prototype tests

* update stable tests

test/prototype_transforms_kernel_infos.py

@@ -232,6 +232,7 @@ def reference_inputs_resize_image_tensor():
         make_image_loaders(extra_dims=[()]),
         [
             F.InterpolationMode.NEAREST,
+            F.InterpolationMode.NEAREST_EXACT,
             F.InterpolationMode.BILINEAR,
             F.InterpolationMode.BICUBIC,
         ],
@@ -881,6 +882,7 @@ def reference_inputs_resized_crop_image_tensor():
         make_image_loaders(extra_dims=[()]),
         [
             F.InterpolationMode.NEAREST,
+            F.InterpolationMode.NEAREST_EXACT,
             F.InterpolationMode.BILINEAR,
             F.InterpolationMode.BICUBIC,
         ],

test/test_functional_tensor.py

@@ -25,7 +25,12 @@ from common_utils import (
 )
 from torchvision.transforms import InterpolationMode
 
-NEAREST, BILINEAR, BICUBIC = InterpolationMode.NEAREST, InterpolationMode.BILINEAR, InterpolationMode.BICUBIC
+NEAREST, NEAREST_EXACT, BILINEAR, BICUBIC = (
+    InterpolationMode.NEAREST,
+    InterpolationMode.NEAREST_EXACT,
+    InterpolationMode.BILINEAR,
+    InterpolationMode.BICUBIC,
+)
 
 
 @pytest.mark.parametrize("device", cpu_and_gpu())
@@ -506,7 +511,7 @@ def test_perspective_interpolation_warning():
     ],
 )
 @pytest.mark.parametrize("max_size", [None, 34, 40, 1000])
-@pytest.mark.parametrize("interpolation", [BILINEAR, BICUBIC, NEAREST])
+@pytest.mark.parametrize("interpolation", [BILINEAR, BICUBIC, NEAREST, NEAREST_EXACT])
 def test_resize(device, dt, size, max_size, interpolation):
 
     if dt == torch.float16 and device == "cpu":
@@ -966,7 +971,7 @@ def test_pad(device, dt, pad, config):
 
 
 @pytest.mark.parametrize("device", cpu_and_gpu())
-@pytest.mark.parametrize("mode", [NEAREST, BILINEAR, BICUBIC])
+@pytest.mark.parametrize("mode", [NEAREST, NEAREST_EXACT, BILINEAR, BICUBIC])
 def test_resized_crop(device, mode):
     # test values of F.resized_crop in several cases:
     # 1) resize to the same size, crop to the same size => should be identity

test/test_transforms_tensor.py

@@ -20,7 +20,12 @@ from torchvision import transforms as T
 from torchvision.transforms import functional as F, InterpolationMode
 from torchvision.transforms.autoaugment import _apply_op
 
-NEAREST, BILINEAR, BICUBIC = InterpolationMode.NEAREST, InterpolationMode.BILINEAR, InterpolationMode.BICUBIC
+NEAREST, NEAREST_EXACT, BILINEAR, BICUBIC = (
+    InterpolationMode.NEAREST,
+    InterpolationMode.NEAREST_EXACT,
+    InterpolationMode.BILINEAR,
+    InterpolationMode.BICUBIC,
+)
 
 
 def _test_transform_vs_scripted(transform, s_transform, tensor, msg=None):
@@ -378,7 +383,7 @@ class TestResize:
     @pytest.mark.parametrize("dt", [None, torch.float32, torch.float64])
     @pytest.mark.parametrize("size", [[32], [32, 32], (32, 32), [34, 35]])
     @pytest.mark.parametrize("max_size", [None, 35, 1000])
-    @pytest.mark.parametrize("interpolation", [BILINEAR, BICUBIC, NEAREST])
+    @pytest.mark.parametrize("interpolation", [BILINEAR, BICUBIC, NEAREST, NEAREST_EXACT])
     def test_resize_scripted(self, dt, size, max_size, interpolation, device):
         tensor, _ = _create_data(height=34, width=36, device=device)
         batch_tensors = torch.randint(0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=device)
@@ -402,12 +407,12 @@ class TestResize:
     @pytest.mark.parametrize("scale", [(0.7, 1.2), [0.7, 1.2]])
     @pytest.mark.parametrize("ratio", [(0.75, 1.333), [0.75, 1.333]])
     @pytest.mark.parametrize("size", [(32,), [44], [32], [32, 32], (32, 32), [44, 55]])
-    @pytest.mark.parametrize("interpolation", [NEAREST, BILINEAR, BICUBIC])
+    @pytest.mark.parametrize("interpolation", [NEAREST, BILINEAR, BICUBIC, NEAREST_EXACT])
     @pytest.mark.parametrize("antialias", [None, True, False])
     def test_resized_crop(self, scale, ratio, size, interpolation, antialias, device):
 
-        if antialias and interpolation == NEAREST:
-            pytest.skip("Can not resize if interpolation mode is NEAREST and antialias=True")
+        if antialias and interpolation in {NEAREST, NEAREST_EXACT}:
+            pytest.skip(f"Can not resize if interpolation mode is {interpolation} and antialias=True")
 
         tensor = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8, device=device)
         batch_tensors = torch.randint(0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=device)

torchvision/transforms/functional.py

@@ -20,10 +20,12 @@ from . import functional_pil as F_pil, functional_tensor as F_t
 
 class InterpolationMode(Enum):
     """Interpolation modes
-    Available interpolation methods are ``nearest``, ``bilinear``, ``bicubic``, ``box``, ``hamming``, and ``lanczos``.
+    Available interpolation methods are ``nearest``, ``nearest-exact``, ``bilinear``, ``bicubic``, ``box``, ``hamming``,
+    and ``lanczos``.
     """
 
     NEAREST = "nearest"
+    NEAREST_EXACT = "nearest-exact"
     BILINEAR = "bilinear"
     BICUBIC = "bicubic"
     # For PIL compatibility
@@ -50,6 +52,7 @@ pil_modes_mapping = {
     InterpolationMode.NEAREST: 0,
     InterpolationMode.BILINEAR: 2,
     InterpolationMode.BICUBIC: 3,
+    InterpolationMode.NEAREST_EXACT: 0,
     InterpolationMode.BOX: 4,
     InterpolationMode.HAMMING: 5,
     InterpolationMode.LANCZOS: 1,
@@ -416,7 +419,8 @@ def resize(
         interpolation (InterpolationMode): Desired interpolation enum defined by
             :class:`torchvision.transforms.InterpolationMode`.
             Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``,
-            ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
+            ``InterpolationMode.NEAREST_EXACT``, ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are
+            supported.
             For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
             but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
         max_size (int, optional): The maximum allowed for the longer edge of
@@ -617,7 +621,8 @@ def resized_crop(
         interpolation (InterpolationMode): Desired interpolation enum defined by
             :class:`torchvision.transforms.InterpolationMode`.
             Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``,
-            ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
+            ``InterpolationMode.NEAREST_EXACT``, ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are
+            supported.
             For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
             but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
         antialias (bool, optional): antialias flag. If ``img`` is PIL Image, the flag is ignored and anti-alias

torchvision/transforms/transforms.py

@@ -296,8 +296,8 @@ class Resize(torch.nn.Module):
                 In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``.
         interpolation (InterpolationMode): Desired interpolation enum defined by
             :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
-            If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` and
-            ``InterpolationMode.BICUBIC`` are supported.
+            If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``,
+            ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
             For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
             but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
         max_size (int, optional): The maximum allowed for the longer edge of
@@ -865,8 +865,8 @@ class RandomResizedCrop(torch.nn.Module):
             resizing.
         interpolation (InterpolationMode): Desired interpolation enum defined by
             :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
-            If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` and
-            ``InterpolationMode.BICUBIC`` are supported.
+            If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``,
+            ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
             For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
             but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
         antialias (bool, optional): antialias flag. If ``img`` is PIL Image, the flag is ignored and anti-alias