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In interpolate, join short lines (#37170)

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Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37170

ghstack-source-id: 102773588

Test Plan: CI

Reviewed By: kimishpatel

Differential Revision: D21209998

fbshipit-source-id: 9386e54aa85a5576678d21d443017079028f8dca
This commit is contained in:
David Reiss 2020-05-06 12:59:44 -07:00 committed by Facebook GitHub Bot
parent 59f03c69ab
commit d6b51e4adf
1 changed files with 5 additions and 10 deletions
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15
torch/nn/functional.py
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@ -3121,11 +3121,9 @@ def interpolate(input, size=None, scale_factor=None, mode='nearest', align_corne
if input.dim() == 3 and mode == 'nearest': if input.dim() == 3 and mode == 'nearest':
return torch._C._nn.upsample_nearest1d(input, output_size, sfl[0]) return torch._C._nn.upsample_nearest1d(input, output_size, sfl[0])
elif input.dim() == 4 and mode == 'nearest': elif input.dim() == 4 and mode == 'nearest':
return torch._C._nn.upsample_nearest2d(input, output_size, return torch._C._nn.upsample_nearest2d(input, output_size, sfl[0], sfl[1])
sfl[0], sfl[1])
elif input.dim() == 5 and mode == 'nearest': elif input.dim() == 5 and mode == 'nearest':
return torch._C._nn.upsample_nearest3d(input, output_size, return torch._C._nn.upsample_nearest3d(input, output_size, sfl[0], sfl[1], sfl[2])
sfl[0], sfl[1], sfl[2])
elif input.dim() == 3 and mode == 'area': elif input.dim() == 3 and mode == 'area':
return adaptive_avg_pool1d(input, output_size) return adaptive_avg_pool1d(input, output_size)
elif input.dim() == 4 and mode == 'area': elif input.dim() == 4 and mode == 'area':
@ -3143,8 +3141,7 @@ def interpolate(input, size=None, scale_factor=None, mode='nearest', align_corne
raise NotImplementedError("Got 4D input, but linear mode needs 3D input") raise NotImplementedError("Got 4D input, but linear mode needs 3D input")
elif input.dim() == 4 and mode == 'bilinear': elif input.dim() == 4 and mode == 'bilinear':
assert align_corners is not None assert align_corners is not None
return torch._C._nn.upsample_bilinear2d(input, output_size, align_corners, return torch._C._nn.upsample_bilinear2d(input, output_size, align_corners, sfl[0], sfl[1])
sfl[0], sfl[1])
elif input.dim() == 4 and mode == 'trilinear': elif input.dim() == 4 and mode == 'trilinear':
raise NotImplementedError("Got 4D input, but trilinear mode needs 5D input") raise NotImplementedError("Got 4D input, but trilinear mode needs 5D input")
elif input.dim() == 5 and mode == 'linear': elif input.dim() == 5 and mode == 'linear':
@ -3153,12 +3150,10 @@ def interpolate(input, size=None, scale_factor=None, mode='nearest', align_corne
raise NotImplementedError("Got 5D input, but bilinear mode needs 4D input") raise NotImplementedError("Got 5D input, but bilinear mode needs 4D input")
elif input.dim() == 5 and mode == 'trilinear': elif input.dim() == 5 and mode == 'trilinear':
assert align_corners is not None assert align_corners is not None
return torch._C._nn.upsample_trilinear3d(input, output_size, align_corners, return torch._C._nn.upsample_trilinear3d(input, output_size, align_corners, sfl[0], sfl[1], sfl[2])
sfl[0], sfl[1], sfl[2])
elif input.dim() == 4 and mode == 'bicubic': elif input.dim() == 4 and mode == 'bicubic':
assert align_corners is not None assert align_corners is not None
return torch._C._nn.upsample_bicubic2d(input, output_size, align_corners, return torch._C._nn.upsample_bicubic2d(input, output_size, align_corners, sfl[0], sfl[1])
sfl[0], sfl[1])
else: else:
raise NotImplementedError("Input Error: Only 3D, 4D and 5D input Tensors supported" raise NotImplementedError("Input Error: Only 3D, 4D and 5D input Tensors supported"
" (got {}D) for the modes: nearest | linear | bilinear | bicubic | trilinear" " (got {}D) for the modes: nearest | linear | bilinear | bicubic | trilinear"