riaz.somc/transformers
1
0
Fork 0
mirror of https://github.com/huggingface/transformers.git synced 2025-07-31 02:02:21 +06:00
Code Issues Actions 23 Packages Projects Releases Wiki Activity

Fix DETR tests after #21144 (#21365)

Browse Source 
* Fix annotation check

* Fix annotation check

* Update type annotations
This commit is contained in:
amyeroberts 2023-01-30 15:55:00 +00:00 committed by GitHub
parent 7a2e13204f
commit 59611a0f3a
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 24 additions and 12 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
src/transformers/models/conditional_detr/image_processing_conditional_detr.py
View File

@ -77,6 +77,9 @@ if is_scipy_available():
import scipy.stats
AnnotationType = Dict[str, Union[int, str, List[Dict]]]
class AnnotionFormat(ExplicitEnum):
COCO_DETECTION = "coco_detection"
COCO_PANOPTIC = "coco_panoptic"
@ -1071,7 +1074,7 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
def preprocess(
self,
images: ImageInput,
annotations: Optional[Union[List[Dict], List[List[Dict]]]] = None,
annotations: Optional[Union[AnnotationType, List[AnnotationType]]] = None,
return_segmentation_masks: bool = None,
masks_path: Optional[Union[str, pathlib.Path]] = None,
do_resize: Optional[bool] = None,
@ -1094,7 +1097,7 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
Args:
images (`ImageInput`):
Image or batch of images to preprocess.
annotations (`List[Dict]` or `List[List[Dict]]`, *optional*):
annotations (`AnnotationType` or `List[AnnotationType]`, *optional*):
List of annotations associated with the image or batch of images. If annotionation is for object
detection, the annotations should be a dictionary with the following keys:
- "image_id" (`int`): The image id.
@ -1173,7 +1176,7 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
raise ValueError("Image mean and std must be specified if do_normalize is True.")
images = make_list_of_images(images)
if annotations is not None and isinstance(annotations[0], dict):
if annotations is not None and isinstance(annotations, dict):
annotations = [annotations]
if annotations is not None and len(images) != len(annotations):

9
src/transformers/models/deformable_detr/image_processing_deformable_detr.py
View File

@ -76,6 +76,9 @@ if is_scipy_available():
import scipy.stats
AnnotationType = Dict[str, Union[int, str, List[Dict]]]
class AnnotionFormat(ExplicitEnum):
COCO_DETECTION = "coco_detection"
COCO_PANOPTIC = "coco_panoptic"
@ -1069,7 +1072,7 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
def preprocess(
self,
images: ImageInput,
annotations: Optional[Union[List[Dict], List[List[Dict]]]] = None,
annotations: Optional[Union[AnnotationType, List[AnnotationType]]] = None,
return_segmentation_masks: bool = None,
masks_path: Optional[Union[str, pathlib.Path]] = None,
do_resize: Optional[bool] = None,
@ -1092,7 +1095,7 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
Args:
images (`ImageInput`):
Image or batch of images to preprocess.
annotations (`List[Dict]` or `List[List[Dict]]`, *optional*):
annotations (`AnnotationType` or `List[AnnotationType]`, *optional*):
List of annotations associated with the image or batch of images. If annotionation is for object
detection, the annotations should be a dictionary with the following keys:
- "image_id" (`int`): The image id.
@ -1171,7 +1174,7 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
raise ValueError("Image mean and std must be specified if do_normalize is True.")
images = make_list_of_images(images)
if annotations is not None and isinstance(annotations[0], dict):
if annotations is not None and isinstance(annotations, dict):
annotations = [annotations]
if annotations is not None and len(images) != len(annotations):

9
src/transformers/models/detr/image_processing_detr.py
View File

@ -76,6 +76,9 @@ if is_scipy_available():
import scipy.stats
AnnotationType = Dict[str, Union[int, str, List[Dict]]]
class AnnotionFormat(ExplicitEnum):
COCO_DETECTION = "coco_detection"
COCO_PANOPTIC = "coco_panoptic"
@ -1037,7 +1040,7 @@ class DetrImageProcessor(BaseImageProcessor):
def preprocess(
self,
images: ImageInput,
annotations: Optional[Union[List[Dict], List[List[Dict]]]] = None,
annotations: Optional[Union[AnnotationType, List[AnnotationType]]] = None,
return_segmentation_masks: bool = None,
masks_path: Optional[Union[str, pathlib.Path]] = None,
do_resize: Optional[bool] = None,
@ -1060,7 +1063,7 @@ class DetrImageProcessor(BaseImageProcessor):
Args:
images (`ImageInput`):
Image or batch of images to preprocess.
annotations (`List[Dict]` or `List[List[Dict]]`, *optional*):
annotations (`AnnotationType` or `List[AnnotationType]`, *optional*):
List of annotations associated with the image or batch of images. If annotionation is for object
detection, the annotations should be a dictionary with the following keys:
- "image_id" (`int`): The image id.
@ -1139,7 +1142,7 @@ class DetrImageProcessor(BaseImageProcessor):
raise ValueError("Image mean and std must be specified if do_normalize is True.")
images = make_list_of_images(images)
if annotations is not None and isinstance(annotations[0], dict):
if annotations is not None and isinstance(annotations, dict):
annotations = [annotations]
if annotations is not None and len(images) != len(annotations):

9
src/transformers/models/yolos/image_processing_yolos.py
View File

@ -75,6 +75,9 @@ if is_scipy_available():
import scipy.stats
AnnotationType = Dict[str, Union[int, str, List[Dict]]]
class AnnotionFormat(ExplicitEnum):
COCO_DETECTION = "coco_detection"
COCO_PANOPTIC = "coco_panoptic"
@ -937,7 +940,7 @@ class YolosImageProcessor(BaseImageProcessor):
def preprocess(
self,
images: ImageInput,
annotations: Optional[Union[List[Dict], List[List[Dict]]]] = None,
annotations: Optional[Union[AnnotationType, List[AnnotationType]]] = None,
return_segmentation_masks: bool = None,
masks_path: Optional[Union[str, pathlib.Path]] = None,
do_resize: Optional[bool] = None,
@ -960,7 +963,7 @@ class YolosImageProcessor(BaseImageProcessor):
Args:
images (`ImageInput`):
Image or batch of images to preprocess.
annotations (`List[Dict]` or `List[List[Dict]]`, *optional*):
annotations (`AnnotationType` or `List[AnnotationType]`, *optional*):
List of annotations associated with the image or batch of images. If annotionation is for object
detection, the annotations should be a dictionary with the following keys:
- "image_id" (`int`): The image id.
@ -1039,7 +1042,7 @@ class YolosImageProcessor(BaseImageProcessor):
raise ValueError("Image mean and std must be specified if do_normalize is True.")
images = make_list_of_images(images)
if annotations is not None and isinstance(annotations[0], dict):
if annotations is not None and isinstance(annotations, dict):
annotations = [annotations]
if annotations is not None and len(images) != len(annotations):