From d6b51e4adfb34a177c89ed966e245abd49b14833 Mon Sep 17 00:00:00 2001 From: David Reiss Date: Wed, 6 May 2020 12:59:44 -0700 Subject: [PATCH] In interpolate, join short lines (#37170) 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 --- torch/nn/functional.py | 15 +++++---------- 1 file changed, 5 insertions(+), 10 deletions(-) diff --git a/torch/nn/functional.py b/torch/nn/functional.py index 7dd54599f3b..7540a55e744 100644 --- a/torch/nn/functional.py +++ b/torch/nn/functional.py @@ -3121,11 +3121,9 @@ def interpolate(input, size=None, scale_factor=None, mode='nearest', align_corne if input.dim() == 3 and mode == 'nearest': return torch._C._nn.upsample_nearest1d(input, output_size, sfl[0]) elif input.dim() == 4 and mode == 'nearest': - return torch._C._nn.upsample_nearest2d(input, output_size, - sfl[0], sfl[1]) + return torch._C._nn.upsample_nearest2d(input, output_size, sfl[0], sfl[1]) elif input.dim() == 5 and mode == 'nearest': - return torch._C._nn.upsample_nearest3d(input, output_size, - sfl[0], sfl[1], sfl[2]) + return torch._C._nn.upsample_nearest3d(input, output_size, sfl[0], sfl[1], sfl[2]) elif input.dim() == 3 and mode == 'area': return adaptive_avg_pool1d(input, output_size) 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") elif input.dim() == 4 and mode == 'bilinear': assert align_corners is not None - return torch._C._nn.upsample_bilinear2d(input, output_size, align_corners, - sfl[0], sfl[1]) + return torch._C._nn.upsample_bilinear2d(input, output_size, align_corners, sfl[0], sfl[1]) elif input.dim() == 4 and mode == 'trilinear': raise NotImplementedError("Got 4D input, but trilinear mode needs 5D input") 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") elif input.dim() == 5 and mode == 'trilinear': assert align_corners is not None - return torch._C._nn.upsample_trilinear3d(input, output_size, align_corners, - sfl[0], sfl[1], sfl[2]) + return torch._C._nn.upsample_trilinear3d(input, output_size, align_corners, sfl[0], sfl[1], sfl[2]) elif input.dim() == 4 and mode == 'bicubic': assert align_corners is not None - return torch._C._nn.upsample_bicubic2d(input, output_size, align_corners, - sfl[0], sfl[1]) + return torch._C._nn.upsample_bicubic2d(input, output_size, align_corners, sfl[0], sfl[1]) else: raise NotImplementedError("Input Error: Only 3D, 4D and 5D input Tensors supported" " (got {}D) for the modes: nearest | linear | bilinear | bicubic | trilinear"