Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Remove copied froms for deprecated models #31153

Merged
merged 2 commits into from
Jun 3, 2024
Merged

Remove copied froms for deprecated models #31153

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
8c03921
Remove copied froms for deprecated models
amyeroberts May 30, 2024
c19f925
Remove automatically in script
amyeroberts May 30, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
21 changes: 0 additions & 21 deletions src/transformers/models/deprecated/deta/image_processing_deta.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -78,7 +78,6 @@
SUPPORTED_ANNOTATION_FORMATS = (AnnotationFormat.COCO_DETECTION, AnnotationFormat.COCO_PANOPTIC)


# Copied from transformers.models.detr.image_processing_detr.get_size_with_aspect_ratio
def get_size_with_aspect_ratio(image_size, size, max_size=None) -> Tuple[int, int]:
"""
Computes the output image size given the input image size and the desired output size.
Expand Down Expand Up @@ -110,7 +109,6 @@ def get_size_with_aspect_ratio(image_size, size, max_size=None) -> Tuple[int, in
return (oh, ow)


# Copied from transformers.models.detr.image_processing_detr.get_resize_output_image_size
def get_resize_output_image_size(
input_image: np.ndarray,
size: Union[int, Tuple[int, int], List[int]],
Expand Down Expand Up @@ -139,7 +137,6 @@ def get_resize_output_image_size(
return get_size_with_aspect_ratio(image_size, size, max_size)


# Copied from transformers.models.detr.image_processing_detr.get_image_size_for_max_height_width
def get_image_size_for_max_height_width(
input_image: np.ndarray,
max_height: int,
Expand Down Expand Up @@ -175,7 +172,6 @@ def get_image_size_for_max_height_width(
return new_height, new_width


# Copied from transformers.models.detr.image_processing_detr.get_numpy_to_framework_fn
def get_numpy_to_framework_fn(arr) -> Callable:
"""
Returns a function that converts a numpy array to the framework of the input array.
Expand All @@ -200,7 +196,6 @@ def get_numpy_to_framework_fn(arr) -> Callable:
raise ValueError(f"Cannot convert arrays of type {type(arr)}")


# Copied from transformers.models.detr.image_processing_detr.safe_squeeze
def safe_squeeze(arr: np.ndarray, axis: Optional[int] = None) -> np.ndarray:
"""
Squeezes an array, but only if the axis specified has dim 1.
Expand All @@ -214,7 +209,6 @@ def safe_squeeze(arr: np.ndarray, axis: Optional[int] = None) -> np.ndarray:
return arr


# Copied from transformers.models.detr.image_processing_detr.normalize_annotation
def normalize_annotation(annotation: Dict, image_size: Tuple[int, int]) -> Dict:
image_height, image_width = image_size
norm_annotation = {}
Expand All @@ -229,15 +223,13 @@ def normalize_annotation(annotation: Dict, image_size: Tuple[int, int]) -> Dict:
return norm_annotation


# Copied from transformers.models.detr.image_processing_detr.max_across_indices
def max_across_indices(values: Iterable[Any]) -> List[Any]:
"""
Return the maximum value across all indices of an iterable of values.
"""
return [max(values_i) for values_i in zip(*values)]


# Copied from transformers.models.detr.image_processing_detr.get_max_height_width
def get_max_height_width(
images: List[np.ndarray], input_data_format: Optional[Union[str, ChannelDimension]] = None
) -> List[int]:
Expand All @@ -256,7 +248,6 @@ def get_max_height_width(
return (max_height, max_width)


# Copied from transformers.models.detr.image_processing_detr.make_pixel_mask
def make_pixel_mask(
image: np.ndarray, output_size: Tuple[int, int], input_data_format: Optional[Union[str, ChannelDimension]] = None
) -> np.ndarray:
Expand All @@ -275,7 +266,6 @@ def make_pixel_mask(
return mask


# Copied from transformers.models.detr.image_processing_detr.convert_coco_poly_to_mask
def convert_coco_poly_to_mask(segmentations, height: int, width: int) -> np.ndarray:
"""
Convert a COCO polygon annotation to a mask.
Expand Down Expand Up @@ -310,7 +300,6 @@ def convert_coco_poly_to_mask(segmentations, height: int, width: int) -> np.ndar
return masks


# Copied from transformers.models.detr.image_processing_detr.prepare_coco_detection_annotation with DETR->DETA
def prepare_coco_detection_annotation(
image,
target,
Expand Down Expand Up @@ -371,7 +360,6 @@ def prepare_coco_detection_annotation(
return new_target


# Copied from transformers.models.detr.image_processing_detr.masks_to_boxes
def masks_to_boxes(masks: np.ndarray) -> np.ndarray:
"""
Compute the bounding boxes around the provided panoptic segmentation masks.
Expand Down Expand Up @@ -406,7 +394,6 @@ def masks_to_boxes(masks: np.ndarray) -> np.ndarray:
return np.stack([x_min, y_min, x_max, y_max], 1)


# Copied from transformers.models.detr.image_processing_detr.prepare_coco_panoptic_annotation with DETR->DETA
def prepare_coco_panoptic_annotation(
image: np.ndarray,
target: Dict,
Expand Down Expand Up @@ -448,7 +435,6 @@ def prepare_coco_panoptic_annotation(
return new_target


# Copied from transformers.models.detr.image_processing_detr.resize_annotation
def resize_annotation(
annotation: Dict[str, Any],
orig_size: Tuple[int, int],
Expand Down Expand Up @@ -594,7 +580,6 @@ def __init__(
self.do_pad = do_pad
self.pad_size = pad_size

# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_annotation with DETR->DETA
def prepare_annotation(
self,
image: np.ndarray,
Expand Down Expand Up @@ -683,7 +668,6 @@ def resize(
)
return image

# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.resize_annotation
def resize_annotation(
self,
annotation,
Expand All @@ -697,7 +681,6 @@ def resize_annotation(
"""
return resize_annotation(annotation, orig_size=orig_size, target_size=size, resample=resample)

# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.rescale
def rescale(
self,
image: np.ndarray,
Expand Down Expand Up @@ -726,15 +709,13 @@ def rescale(
"""
return rescale(image, rescale_factor, data_format=data_format, input_data_format=input_data_format)

# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.normalize_annotation
def normalize_annotation(self, annotation: Dict, image_size: Tuple[int, int]) -> Dict:
"""
Normalize the boxes in the annotation from `[top_left_x, top_left_y, bottom_right_x, bottom_right_y]` to
`[center_x, center_y, width, height]` format and from absolute to relative pixel values.
"""
return normalize_annotation(annotation, image_size=image_size)

# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor._update_annotation_for_padded_image
def _update_annotation_for_padded_image(
self,
annotation: Dict,
Expand Down Expand Up @@ -778,7 +759,6 @@ def _update_annotation_for_padded_image(
new_annotation[key] = value
return new_annotation

# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor._pad_image
def _pad_image(
self,
image: np.ndarray,
Expand Down Expand Up @@ -812,7 +792,6 @@ def _pad_image(
)
return padded_image, annotation

# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.pad
def pad(
self,
images: List[np.ndarray],
Expand Down
Loading
Loading