From 19975d352d6dfeece1ea5d7050de348b838cf48d Mon Sep 17 00:00:00 2001 From: JiauZhang <1743960454@qq.com> Date: Wed, 15 Apr 2026 21:53:23 +0800 Subject: [PATCH 1/2] remove redundant condition checks in get_image_size method --- .../image_processing_pp_ocrv5_server_det.py | 8 -------- 1 file changed, 8 deletions(-) diff --git a/src/transformers/models/pp_ocrv5_server_det/image_processing_pp_ocrv5_server_det.py b/src/transformers/models/pp_ocrv5_server_det/image_processing_pp_ocrv5_server_det.py index 0ed8e303158b..84cafe5d9c80 100644 --- a/src/transformers/models/pp_ocrv5_server_det/image_processing_pp_ocrv5_server_det.py +++ b/src/transformers/models/pp_ocrv5_server_det/image_processing_pp_ocrv5_server_det.py @@ -358,14 +358,6 @@ def get_image_size( resize_height = max(int(round(resize_height / 32) * 32), 32) resize_width = max(int(round(resize_width / 32) * 32), 32) - if resize_height == height and resize_width == width: - return SizeDict(height=resize_height, width=resize_width), torch.tensor( - [height, width], dtype=torch.float32, device=image.device - ) - - if resize_width <= 0 or resize_height <= 0: - return None, (None, None) - return SizeDict(height=resize_height, width=resize_width), torch.tensor( [height, width], dtype=torch.float32, device=image.device ) From 482c1176584599d2b815876af831d89022c0966a Mon Sep 17 00:00:00 2001 From: JiauZhang <1743960454@qq.com> Date: Fri, 17 Apr 2026 20:05:50 +0800 Subject: [PATCH 2/2] update modular file --- .../pp_ocrv5_server_det/modular_pp_ocrv5_server_det.py | 8 -------- 1 file changed, 8 deletions(-) diff --git a/src/transformers/models/pp_ocrv5_server_det/modular_pp_ocrv5_server_det.py b/src/transformers/models/pp_ocrv5_server_det/modular_pp_ocrv5_server_det.py index 2259f5ff093a..1943891143f2 100644 --- a/src/transformers/models/pp_ocrv5_server_det/modular_pp_ocrv5_server_det.py +++ b/src/transformers/models/pp_ocrv5_server_det/modular_pp_ocrv5_server_det.py @@ -429,14 +429,6 @@ def get_image_size( resize_height = max(int(round(resize_height / 32) * 32), 32) resize_width = max(int(round(resize_width / 32) * 32), 32) - if resize_height == height and resize_width == width: - return SizeDict(height=resize_height, width=resize_width), torch.tensor( - [height, width], dtype=torch.float32, device=image.device - ) - - if resize_width <= 0 or resize_height <= 0: - return None, (None, None) - return SizeDict(height=resize_height, width=resize_width), torch.tensor( [height, width], dtype=torch.float32, device=image.device )