Relaxed tolerance in consistency tests for GaussianBlur and ElasticTransform

Author: vfdev-5

test/test_prototype_transforms_consistency.py

@@ -323,6 +323,9 @@ def __init__(
         ],
         # ElasticTransform needs larger images to avoid the needed internal padding being larger than the actual image
         make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, sizes=[(163, 163), (72, 333), (313, 95)]),
+        # We updated gaussian blur kernel generation with a faster and numerically more stable version
+        # This brings float32 accumulation visible in elastic transform -> we need to relax consistency tolerance
+        closeness_kwargs={"rtol": 1e-1, "atol": 1},
     ),
     ConsistencyConfig(
         prototype_transforms.GaussianBlur,
@@ -333,6 +336,7 @@ def __init__(
             ArgsKwargs(kernel_size=3, sigma=0.7),
             ArgsKwargs(kernel_size=5, sigma=(0.3, 1.4)),
         ],
+        closeness_kwargs={"rtol": 1e-5, "atol": 1e-5},
     ),
     ConsistencyConfig(
         prototype_transforms.RandomAffine,

torchvision/transforms/functional_tensor.py

@@ -1,4 +1,3 @@
-import math
 import warnings
 from typing import List, Optional, Tuple, Union
 
@@ -727,10 +726,13 @@ def perspective(
     return _apply_grid_transform(img, grid, interpolation, fill=fill)
 
 
-def _get_gaussian_kernel1d(kernel_size: int, sigma: float) -> torch.Tensor:
-    lim = (kernel_size - 1) / (2 * math.sqrt(2) * sigma)
-    x = torch.linspace(-lim, lim, steps=kernel_size)
-    kernel1d = torch.softmax(-x.pow_(2), dim=0)
+def _get_gaussian_kernel1d(kernel_size: int, sigma: float) -> Tensor:
+    ksize_half = (kernel_size - 1) * 0.5
+
+    x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)
+    pdf = torch.exp(-0.5 * (x / sigma).pow(2))
+    kernel1d = pdf / pdf.sum()
+
     return kernel1d