[proto] Small optimization for gaussian_blur functional op (#6762)

* Use softmax in _get_gaussian_kernel1d

* Revert "Use softmax in _get_gaussian_kernel1d"

This reverts commit eb8fba3.

* Code update

* Relaxed tolerance in consistency tests for GaussianBlur and ElasticTransform

* Code review updates

* Update test_prototype_transforms_consistency.py

Author: vfdev-5

test/test_prototype_transforms_consistency.py

@@ -308,22 +308,28 @@ def __init__(
             ArgsKwargs(brightness=0.1, contrast=0.4, saturation=0.7, hue=0.3),
         ],
     ),
-    ConsistencyConfig(
-        prototype_transforms.ElasticTransform,
-        legacy_transforms.ElasticTransform,
-        [
-            ArgsKwargs(),
-            ArgsKwargs(alpha=20.0),
-            ArgsKwargs(alpha=(15.3, 27.2)),
-            ArgsKwargs(sigma=3.0),
-            ArgsKwargs(sigma=(2.5, 3.9)),
-            ArgsKwargs(interpolation=prototype_transforms.InterpolationMode.NEAREST),
-            ArgsKwargs(interpolation=prototype_transforms.InterpolationMode.BICUBIC),
-            ArgsKwargs(fill=1),
-        ],
-        # 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)]),
-    ),
+    *[
+        ConsistencyConfig(
+            prototype_transforms.ElasticTransform,
+            legacy_transforms.ElasticTransform,
+            [
+                ArgsKwargs(),
+                ArgsKwargs(alpha=20.0),
+                ArgsKwargs(alpha=(15.3, 27.2)),
+                ArgsKwargs(sigma=3.0),
+                ArgsKwargs(sigma=(2.5, 3.9)),
+                ArgsKwargs(interpolation=prototype_transforms.InterpolationMode.NEAREST),
+                ArgsKwargs(interpolation=prototype_transforms.InterpolationMode.BICUBIC),
+                ArgsKwargs(fill=1),
+            ],
+            # 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)], dtypes=[dt]),
+            # 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=ckw,
+        )
+        for dt, ckw in [(torch.uint8, {"rtol": 1e-1, "atol": 1}), (torch.float32, {"rtol": 1e-2, "atol": 1e-3})]
+    ],
     ConsistencyConfig(
         prototype_transforms.GaussianBlur,
         legacy_transforms.GaussianBlur,
@@ -333,6 +339,7 @@ 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,
@@ -506,7 +513,6 @@ def check_call_consistency(
         image_repr = f"[{tuple(image.shape)}, {str(image.dtype).rsplit('.')[-1]}]"
 
         image_tensor = torch.Tensor(image)
-
         try:
             torch.manual_seed(0)
             output_legacy_tensor = legacy_transform(image_tensor)

torchvision/prototype/transforms/functional/_misc.py

@@ -1,7 +1,9 @@
+import math
 from typing import List, Optional, Union
 
 import PIL.Image
 import torch
+from torch.nn.functional import conv2d, pad as torch_pad
 from torchvision.prototype import features
 from torchvision.transforms import functional_tensor as _FT
 from torchvision.transforms.functional import pil_to_tensor, to_pil_image
@@ -32,6 +34,22 @@ def normalize(
     return normalize_image_tensor(inpt, mean=mean, std=std, inplace=inplace)
 
 
+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)
+    return kernel1d
+
+
+def _get_gaussian_kernel2d(
+    kernel_size: List[int], sigma: List[float], dtype: torch.dtype, device: torch.device
+) -> torch.Tensor:
+    kernel1d_x = _get_gaussian_kernel1d(kernel_size[0], sigma[0]).to(device, dtype=dtype)
+    kernel1d_y = _get_gaussian_kernel1d(kernel_size[1], sigma[1]).to(device, dtype=dtype)
+    kernel2d = kernel1d_y.unsqueeze(-1) * kernel1d_x
+    return kernel2d
+
+
 def gaussian_blur_image_tensor(
     image: torch.Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None
 ) -> torch.Tensor:
@@ -70,7 +88,18 @@ def gaussian_blur_image_tensor(
     else:
         needs_unsquash = False
 
-    output = _FT.gaussian_blur(image, kernel_size, sigma)
+    dtype = image.dtype if torch.is_floating_point(image) else torch.float32
+    kernel = _get_gaussian_kernel2d(kernel_size, sigma, dtype=dtype, device=image.device)
+    kernel = kernel.expand(image.shape[-3], 1, kernel.shape[0], kernel.shape[1])
+
+    image, need_cast, need_squeeze, out_dtype = _FT._cast_squeeze_in(image, [kernel.dtype])
+
+    # padding = (left, right, top, bottom)
+    padding = [kernel_size[0] // 2, kernel_size[0] // 2, kernel_size[1] // 2, kernel_size[1] // 2]
+    output = torch_pad(image, padding, mode="reflect")
+    output = conv2d(output, kernel, groups=output.shape[-3])
+
+    output = _FT._cast_squeeze_out(output, need_cast, need_squeeze, out_dtype)
 
     if needs_unsquash:
         output = output.reshape(shape)