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[fbsync] Enabled torch compile on _compute_affine_output_size (#8218)
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Reviewed By: vmoens

Differential Revision: D55062779

fbshipit-source-id: 72d16f6739c07855a7297fbb6ab48eb06a9fd384
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NicolasHug authored and facebook-github-bot committed Mar 19, 2024
1 parent 1aa9a5e commit 0a5354e
Showing 1 changed file with 30 additions and 0 deletions.
30 changes: 30 additions & 0 deletions torchvision/transforms/v2/functional/_geometry.py
Original file line number Diff line number Diff line change
Expand Up @@ -525,6 +525,13 @@ def _get_inverse_affine_matrix(


def _compute_affine_output_size(matrix: List[float], w: int, h: int) -> Tuple[int, int]:
if torch._dynamo.is_compiling() and not torch.jit.is_scripting():
return _compute_affine_output_size_python(matrix, w, h)
else:
return _compute_affine_output_size_tensor(matrix, w, h)


def _compute_affine_output_size_tensor(matrix: List[float], w: int, h: int) -> Tuple[int, int]:
# Inspired of PIL implementation:
# https://github.com/python-pillow/Pillow/blob/11de3318867e4398057373ee9f12dcb33db7335c/src/PIL/Image.py#L2054

Expand Down Expand Up @@ -559,6 +566,29 @@ def _compute_affine_output_size(matrix: List[float], w: int, h: int) -> Tuple[in
return int(size[0]), int(size[1]) # w, h


def _compute_affine_output_size_python(matrix: List[float], w: int, h: int) -> Tuple[int, int]:
# Mostly copied from PIL implementation:
# The only difference is with transformed points as input matrix has zero translation part here and
# PIL has a centered translation part.
# https://github.com/python-pillow/Pillow/blob/11de3318867e4398057373ee9f12dcb33db7335c/src/PIL/Image.py#L2054

a, b, c, d, e, f = matrix
xx = []
yy = []

half_w = 0.5 * w
half_h = 0.5 * h
for x, y in ((-half_w, -half_h), (half_w, -half_h), (half_w, half_h), (-half_w, half_h)):
nx = a * x + b * y + c
ny = d * x + e * y + f
xx.append(nx + half_w)
yy.append(ny + half_h)

nw = math.ceil(max(xx)) - math.floor(min(xx))
nh = math.ceil(max(yy)) - math.floor(min(yy))
return int(nw), int(nh) # w, h


def _apply_grid_transform(img: torch.Tensor, grid: torch.Tensor, mode: str, fill: _FillTypeJIT) -> torch.Tensor:
input_shape = img.shape
output_height, output_width = grid.shape[1], grid.shape[2]
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