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Unverified Commit bdc55567 authored by Philip Meier's avatar Philip Meier Committed by GitHub
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introduce nearest-exact interpolation (#6754) 

* introduce nearest-exact interpolation

* update prototype tests

* update stable tests
parent 3eafe77a
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Showing with 32 additions and 15 deletions
test/prototype_transforms_kernel_infos.py
View file @ bdc55567
......@@ -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
View file @ bdc55567
......@@ -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
View file @ bdc55567
......@@ -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
View file @ bdc55567
......@@ -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
View file @ bdc55567
......@@ -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
......
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