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Fix torchvision interpolation CI #34539

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Merged
yonigozlan merged 1 commit into from
Nov 5, 2024
Merged

Fix torchvision interpolation CI #34539

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10 changes: 6 additions & 4 deletions src/transformers/models/detr/image_processing_detr_fast.py
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Original file line number Diff line number Diff line change
Expand Up @@ -347,7 +347,7 @@ def __init__(
format: Union[str, AnnotationFormat] = AnnotationFormat.COCO_DETECTION,
do_resize: bool = True,
size: Dict[str, int] = None,
resample: [Union[PILImageResampling, F.InterpolationMode]] = PILImageResampling.BILINEAR,
resample: Union[PILImageResampling, "F.InterpolationMode"] = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
Expand Down Expand Up @@ -462,7 +462,7 @@ def resize(
self,
image: torch.Tensor,
size: SizeDict,
interpolation: F.InterpolationMode = F.InterpolationMode.BILINEAR,
interpolation: "F.InterpolationMode" = None,
**kwargs,
) -> torch.Tensor:
"""
Expand All @@ -485,6 +485,7 @@ def resize(
interpolation (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`):
Resampling filter to use if resizing the image.
"""
interpolation = interpolation if interpolation is not None else F.InterpolationMode.BILINEAR
if size.shortest_edge and size.longest_edge:
# Resize the image so that the shortest edge or the longest edge is of the given size
# while maintaining the aspect ratio of the original image.
Expand Down Expand Up @@ -517,7 +518,7 @@ def resize_annotation(
orig_size: Tuple[int, int],
target_size: Tuple[int, int],
threshold: float = 0.5,
interpolation: F.InterpolationMode = F.InterpolationMode.NEAREST,
interpolation: "F.InterpolationMode" = None,
):
"""
Resizes an annotation to a target size.
Expand All @@ -534,6 +535,7 @@ def resize_annotation(
resample (`InterpolationMode`, defaults to `InterpolationMode.NEAREST`):
The resampling filter to use when resizing the masks.
"""
interpolation = interpolation if interpolation is not None else F.InterpolationMode.NEAREST
ratio_height, ratio_width = [target / orig for target, orig in zip(target_size, orig_size)]

new_annotation = {}
Expand Down Expand Up @@ -680,7 +682,7 @@ def preprocess(
masks_path: Optional[Union[str, pathlib.Path]] = None,
do_resize: Optional[bool] = None,
size: Optional[Dict[str, int]] = None,
resample: Optional[Union[PILImageResampling, F.InterpolationMode]] = None,
resample: Optional[Union[PILImageResampling, "F.InterpolationMode"]] = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[Union[int, float]] = None,
do_normalize: Optional[bool] = None,
Expand Down
11 changes: 6 additions & 5 deletions src/transformers/models/rt_detr/image_processing_rt_detr_fast.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,7 +43,6 @@
get_image_type,
infer_channel_dimension_format,
make_list_of_images,
pil_torch_interpolation_mapping,
validate_annotations,
)
from ...utils import (
Expand Down Expand Up @@ -197,7 +196,7 @@ def __init__(
format: Union[str, AnnotationFormat] = AnnotationFormat.COCO_DETECTION,
do_resize: bool = True,
size: Dict[str, int] = None,
resample: Union[PILImageResampling, F.InterpolationMode] = PILImageResampling.BILINEAR,
resample: Union[PILImageResampling, "F.InterpolationMode"] = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = False,
Expand Down Expand Up @@ -256,7 +255,7 @@ def resize(
self,
image: torch.Tensor,
size: SizeDict,
interpolation: F.InterpolationMode = F.InterpolationMode.BILINEAR,
interpolation: "F.InterpolationMode" = None,
**kwargs,
) -> torch.Tensor:
"""
Expand All @@ -279,6 +278,7 @@ def resize(
interpolation (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`):
Resampling filter to use if resizing the image.
"""
interpolation = interpolation if interpolation is not None else F.InterpolationMode.BILINEAR
if size.shortest_edge and size.longest_edge:
# Resize the image so that the shortest edge or the longest edge is of the given size
# while maintaining the aspect ratio of the original image.
Expand Down Expand Up @@ -312,7 +312,7 @@ def resize_annotation(
orig_size: Tuple[int, int],
target_size: Tuple[int, int],
threshold: float = 0.5,
interpolation: F.InterpolationMode = F.InterpolationMode.NEAREST,
interpolation: "F.InterpolationMode" = None,
):
"""
Resizes an annotation to a target size.
Expand All @@ -329,6 +329,7 @@ def resize_annotation(
resample (`InterpolationMode`, defaults to `InterpolationMode.NEAREST`):
The resampling filter to use when resizing the masks.
"""
interpolation = interpolation if interpolation is not None else F.InterpolationMode.NEAREST
ratio_height, ratio_width = [target / orig for target, orig in zip(target_size, orig_size)]

new_annotation = {}
Expand Down Expand Up @@ -480,7 +481,7 @@ def preprocess(
masks_path: Optional[Union[str, pathlib.Path]] = None,
do_resize: Optional[bool] = None,
size: Optional[Dict[str, int]] = None,
resample: Optional[Union[PILImageResampling, F.InterpolationMode]] = None,
resample: Optional[Union[PILImageResampling, "F.InterpolationMode"]] = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[Union[int, float]] = None,
do_normalize: Optional[bool] = None,
Expand Down