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Unverified Commit ce06e478 authored by NielsRogge's avatar NielsRogge Committed by GitHub
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Update warning levels (#22727) 

* Use different level

* Remove futurewarning

* Use warning_once

* Update copies
parent 98581954
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Showing with 103 additions and 101 deletions
src/transformers/models/conditional_detr/image_processing_conditional_detr.py
View file @ ce06e478
......@@ -16,7 +16,6 @@
import io
import pathlib
import warnings
from collections import defaultdict
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union
......@@ -61,6 +60,7 @@ from ...utils import (
is_torch_available,
is_torch_tensor,
is_vision_available,
logging,
)
......@@ -78,6 +78,8 @@ if is_scipy_available():
import scipy.stats
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
AnnotationType = Dict[str, Union[int, str, List[Dict]]]
......@@ -795,10 +797,9 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
do_pad = kwargs.pop("pad_and_return_pixel_mask")
if "max_size" in kwargs:
warnings.warn(
logger.warning_once(
"The `max_size` parameter is deprecated and will be removed in v4.26. "
"Please specify in `size['longest_edge'] instead`.",
FutureWarning,
)
max_size = kwargs.pop("max_size")
else:
......@@ -822,10 +823,9 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
@property
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.max_size
def max_size(self):
warnings.warn(
logger.warning(
"The `max_size` parameter is deprecated and will be removed in v4.27. "
"Please specify in `size['longest_edge'] instead`.",
FutureWarning,
)
return self.size["longest_edge"]
......@@ -872,7 +872,7 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare
def prepare(self, image, target, return_segmentation_masks=False, masks_path=None):
warnings.warn(
logger.warning_once(
"The `prepare` method is deprecated and will be removed in a future version. "
"Please use `prepare_annotation` instead. Note: the `prepare_annotation` method "
"does not return the image anymore.",
......@@ -882,17 +882,23 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.convert_coco_poly_to_mask
def convert_coco_poly_to_mask(self, *args, **kwargs):
warnings.warn("The `convert_coco_poly_to_mask` method is deprecated and will be removed in a future version. ")
logger.warning_once(
"The `convert_coco_poly_to_mask` method is deprecated and will be removed in a future version. "
)
return convert_coco_poly_to_mask(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_detection with DETR->ConditionalDetr
def prepare_coco_detection(self, *args, **kwargs):
warnings.warn("The `prepare_coco_detection` method is deprecated and will be removed in a future version. ")
logger.warning_once(
"The `prepare_coco_detection` method is deprecated and will be removed in a future version. "
)
return prepare_coco_detection_annotation(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_panoptic
def prepare_coco_panoptic(self, *args, **kwargs):
warnings.warn("The `prepare_coco_panoptic` method is deprecated and will be removed in a future version. ")
logger.warning_once(
"The `prepare_coco_panoptic` method is deprecated and will be removed in a future version. "
)
return prepare_coco_panoptic_annotation(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.resize
......@@ -909,10 +915,9 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
int, smaller edge of the image will be matched to this number.
"""
if "max_size" in kwargs:
warnings.warn(
logger.warning_once(
"The `max_size` parameter is deprecated and will be removed in v4.26. "
"Please specify in `size['longest_edge'] instead`.",
FutureWarning,
)
max_size = kwargs.pop("max_size")
else:
......@@ -998,9 +1003,7 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the image. If not provided, it will be the same as the input image.
"""
warnings.warn(
"This method is deprecated and will be removed in v4.27.0. Please use pad instead.", FutureWarning
)
logger.warning_once("This method is deprecated and will be removed in v4.27.0. Please use pad instead.")
# pad expects a list of np.ndarray, but the previous feature extractors expected torch tensors
images = [to_numpy_array(image) for image in pixel_values_list]
return self.pad(
......@@ -1139,19 +1142,17 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
The channel dimension format of the image. If not provided, it will be the same as the input image.
"""
if "pad_and_return_pixel_mask" in kwargs:
warnings.warn(
logger.warning_once(
"The `pad_and_return_pixel_mask` argument is deprecated and will be removed in a future version, "
"use `do_pad` instead.",
FutureWarning,
"use `do_pad` instead."
)
do_pad = kwargs.pop("pad_and_return_pixel_mask")
max_size = None
if "max_size" in kwargs:
warnings.warn(
logger.warning_once(
"The `max_size` argument is deprecated and will be removed in a future version, use"
" `size['longest_edge']` instead.",
FutureWarning,
" `size['longest_edge']` instead."
)
size = kwargs.pop("max_size")
......@@ -1296,10 +1297,9 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
in the batch as predicted by the model.
"""
warnings.warn(
logging.warning_once(
"`post_process` is deprecated and will be removed in v5 of Transformers, please use"
" `post_process_object_detection`",
FutureWarning,
)
out_logits, out_bbox = outputs.logits, outputs.pred_boxes
......@@ -1560,7 +1560,7 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
"""
if label_ids_to_fuse is None:
warnings.warn("`label_ids_to_fuse` unset. No instance will be fused.")
logger.warning_once("`label_ids_to_fuse` unset. No instance will be fused.")
label_ids_to_fuse = set()
class_queries_logits = outputs.logits # [batch_size, num_queries, num_classes+1]
......
src/transformers/models/deformable_detr/image_processing_deformable_detr.py
View file @ ce06e478
......@@ -16,7 +16,6 @@
import io
import pathlib
import warnings
from collections import defaultdict
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union
......@@ -61,6 +60,7 @@ from ...utils import (
is_torch_available,
is_torch_tensor,
is_vision_available,
logging,
)
......@@ -77,6 +77,8 @@ if is_scipy_available():
import scipy.stats
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
AnnotationType = Dict[str, Union[int, str, List[Dict]]]
......@@ -793,10 +795,9 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
do_pad = kwargs.pop("pad_and_return_pixel_mask")
if "max_size" in kwargs:
warnings.warn(
logger.warning_once(
"The `max_size` parameter is deprecated and will be removed in v4.26. "
"Please specify in `size['longest_edge'] instead`.",
FutureWarning,
)
max_size = kwargs.pop("max_size")
else:
......@@ -820,10 +821,9 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
@property
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.max_size
def max_size(self):
warnings.warn(
logger.warning(
"The `max_size` parameter is deprecated and will be removed in v4.27. "
"Please specify in `size['longest_edge'] instead`.",
FutureWarning,
)
return self.size["longest_edge"]
......@@ -870,7 +870,7 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare
def prepare(self, image, target, return_segmentation_masks=None, masks_path=None):
warnings.warn(
logger.warning_once(
"The `prepare` method is deprecated and will be removed in a future version. "
"Please use `prepare_annotation` instead. Note: the `prepare_annotation` method "
"does not return the image anymore.",
......@@ -880,17 +880,23 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.convert_coco_poly_to_mask
def convert_coco_poly_to_mask(self, *args, **kwargs):
warnings.warn("The `convert_coco_poly_to_mask` method is deprecated and will be removed in a future version. ")
logger.warning_once(
"The `convert_coco_poly_to_mask` method is deprecated and will be removed in a future version. "
)
return convert_coco_poly_to_mask(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_detection
def prepare_coco_detection(self, *args, **kwargs):
warnings.warn("The `prepare_coco_detection` method is deprecated and will be removed in a future version. ")
logger.warning_once(
"The `prepare_coco_detection` method is deprecated and will be removed in a future version. "
)
return prepare_coco_detection_annotation(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_panoptic
def prepare_coco_panoptic(self, *args, **kwargs):
warnings.warn("The `prepare_coco_panoptic` method is deprecated and will be removed in a future version. ")
logger.warning_once(
"The `prepare_coco_panoptic` method is deprecated and will be removed in a future version. "
)
return prepare_coco_panoptic_annotation(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.resize
......@@ -907,10 +913,9 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
int, smaller edge of the image will be matched to this number.
"""
if "max_size" in kwargs:
warnings.warn(
logger.warning_once(
"The `max_size` parameter is deprecated and will be removed in v4.26. "
"Please specify in `size['longest_edge'] instead`.",
FutureWarning,
)
max_size = kwargs.pop("max_size")
else:
......@@ -996,9 +1001,7 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the image. If not provided, it will be the same as the input image.
"""
warnings.warn(
"This method is deprecated and will be removed in v4.27.0. Please use pad instead.", FutureWarning
)
logger.warning_once("This method is deprecated and will be removed in v4.27.0. Please use pad instead.")
# pad expects a list of np.ndarray, but the previous feature extractors expected torch tensors
images = [to_numpy_array(image) for image in pixel_values_list]
return self.pad(
......@@ -1137,19 +1140,17 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
The channel dimension format of the image. If not provided, it will be the same as the input image.
"""
if "pad_and_return_pixel_mask" in kwargs:
warnings.warn(
logger.warning_once(
"The `pad_and_return_pixel_mask` argument is deprecated and will be removed in a future version, "
"use `do_pad` instead.",
FutureWarning,
"use `do_pad` instead."
)
do_pad = kwargs.pop("pad_and_return_pixel_mask")
max_size = None
if "max_size" in kwargs:
warnings.warn(
logger.warning_once(
"The `max_size` argument is deprecated and will be removed in a future version, use"
" `size['longest_edge']` instead.",
FutureWarning,
" `size['longest_edge']` instead."
)
size = kwargs.pop("max_size")
......@@ -1294,10 +1295,9 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
in the batch as predicted by the model.
"""
warnings.warn(
logger.warning_once(
"`post_process` is deprecated and will be removed in v5 of Transformers, please use"
" `post_process_object_detection`.",
FutureWarning,
)
out_logits, out_bbox = outputs.logits, outputs.pred_boxes
......
src/transformers/models/deta/image_processing_deta.py
View file @ ce06e478
......@@ -15,7 +15,6 @@
"""Image processor class for Deformable DETR."""
import pathlib
import warnings
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
......@@ -56,6 +55,7 @@ from ...utils import (
is_torch_tensor,
is_torchvision_available,
is_vision_available,
logging,
)
from ...utils.generic import ExplicitEnum, TensorType
......@@ -71,6 +71,9 @@ if is_vision_available():
import PIL
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class AnnotionFormat(ExplicitEnum):
COCO_DETECTION = "coco_detection"
COCO_PANOPTIC = "coco_panoptic"
......@@ -540,7 +543,7 @@ class DetaImageProcessor(BaseImageProcessor):
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare
def prepare(self, image, target, return_segmentation_masks=None, masks_path=None):
warnings.warn(
logger.warning_once(
"The `prepare` method is deprecated and will be removed in a future version. "
"Please use `prepare_annotation` instead. Note: the `prepare_annotation` method "
"does not return the image anymore.",
......@@ -550,17 +553,23 @@ class DetaImageProcessor(BaseImageProcessor):
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.convert_coco_poly_to_mask
def convert_coco_poly_to_mask(self, *args, **kwargs):
warnings.warn("The `convert_coco_poly_to_mask` method is deprecated and will be removed in a future version. ")
logger.warning_once(
"The `convert_coco_poly_to_mask` method is deprecated and will be removed in a future version. "
)
return convert_coco_poly_to_mask(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_detection
def prepare_coco_detection(self, *args, **kwargs):
warnings.warn("The `prepare_coco_detection` method is deprecated and will be removed in a future version. ")
logger.warning_once(
"The `prepare_coco_detection` method is deprecated and will be removed in a future version. "
)
return prepare_coco_detection_annotation(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_panoptic
def prepare_coco_panoptic(self, *args, **kwargs):
warnings.warn("The `prepare_coco_panoptic` method is deprecated and will be removed in a future version. ")
logger.warning_once(
"The `prepare_coco_panoptic` method is deprecated and will be removed in a future version. "
)
return prepare_coco_panoptic_annotation(*args, **kwargs)
def resize(
......@@ -656,9 +665,7 @@ class DetaImageProcessor(BaseImageProcessor):
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the image. If not provided, it will be the same as the input image.
"""
warnings.warn(
"This method is deprecated and will be removed in v4.27.0. Please use pad instead.", FutureWarning
)
logger.warning_once("This method is deprecated and will be removed in v4.27.0. Please use pad instead.")
# pad expects a list of np.ndarray, but the previous feature extractors expected torch tensors
images = [to_numpy_array(image) for image in pixel_values_list]
return self.pad(
......@@ -796,10 +803,9 @@ class DetaImageProcessor(BaseImageProcessor):
The channel dimension format of the image. If not provided, it will be the same as the input image.
"""
if "pad_and_return_pixel_mask" in kwargs:
warnings.warn(
logger.warning_once(
"The `pad_and_return_pixel_mask` argument is deprecated and will be removed in a future version, "
"use `do_pad` instead.",
FutureWarning,
)
do_pad = kwargs.pop("pad_and_return_pixel_mask")
......
src/transformers/models/detr/image_processing_detr.py
View file @ ce06e478
......@@ -16,7 +16,6 @@
import io
import pathlib
import warnings
from collections import defaultdict
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union
......@@ -60,6 +59,7 @@ from ...utils import (
is_torch_available,
is_torch_tensor,
is_vision_available,
logging,
)
......@@ -77,6 +77,8 @@ if is_scipy_available():
import scipy.stats
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
AnnotationType = Dict[str, Union[int, str, List[Dict]]]
......@@ -777,10 +779,9 @@ class DetrImageProcessor(BaseImageProcessor):
do_pad = kwargs.pop("pad_and_return_pixel_mask")
if "max_size" in kwargs:
warnings.warn(
logger.warning_once(
"The `max_size` parameter is deprecated and will be removed in v4.26. "
"Please specify in `size['longest_edge'] instead`.",
FutureWarning,
)
max_size = kwargs.pop("max_size")
else:
......@@ -803,10 +804,9 @@ class DetrImageProcessor(BaseImageProcessor):
@property
def max_size(self):
warnings.warn(
logger.warning(
"The `max_size` parameter is deprecated and will be removed in v4.27. "
"Please specify in `size['longest_edge'] instead`.",
FutureWarning,
)
return self.size["longest_edge"]
......@@ -850,7 +850,7 @@ class DetrImageProcessor(BaseImageProcessor):
return target
def prepare(self, image, target, return_segmentation_masks=None, masks_path=None):
warnings.warn(
logger.warning_once(
"The `prepare` method is deprecated and will be removed in a future version. "
"Please use `prepare_annotation` instead. Note: the `prepare_annotation` method "
"does not return the image anymore.",
......@@ -859,15 +859,21 @@ class DetrImageProcessor(BaseImageProcessor):
return image, target
def convert_coco_poly_to_mask(self, *args, **kwargs):
warnings.warn("The `convert_coco_poly_to_mask` method is deprecated and will be removed in a future version. ")
logger.warning_once(
"The `convert_coco_poly_to_mask` method is deprecated and will be removed in a future version. "
)
return convert_coco_poly_to_mask(*args, **kwargs)
def prepare_coco_detection(self, *args, **kwargs):
warnings.warn("The `prepare_coco_detection` method is deprecated and will be removed in a future version. ")
logger.warning_once(
"The `prepare_coco_detection` method is deprecated and will be removed in a future version. "
)
return prepare_coco_detection_annotation(*args, **kwargs)
def prepare_coco_panoptic(self, *args, **kwargs):
warnings.warn("The `prepare_coco_panoptic` method is deprecated and will be removed in a future version. ")
logger.warning_once(
"The `prepare_coco_panoptic` method is deprecated and will be removed in a future version. "
)
return prepare_coco_panoptic_annotation(*args, **kwargs)
def resize(
......@@ -883,10 +889,9 @@ class DetrImageProcessor(BaseImageProcessor):
int, smaller edge of the image will be matched to this number.
"""
if "max_size" in kwargs:
warnings.warn(
logger.warning_once(
"The `max_size` parameter is deprecated and will be removed in v4.26. "
"Please specify in `size['longest_edge'] instead`.",
FutureWarning,
)
max_size = kwargs.pop("max_size")
else:
......@@ -967,9 +972,7 @@ class DetrImageProcessor(BaseImageProcessor):
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the image. If not provided, it will be the same as the input image.
"""
warnings.warn(
"This method is deprecated and will be removed in v4.27.0. Please use pad instead.", FutureWarning
)
logger.warning_once("This method is deprecated and will be removed in v4.27.0. Please use pad instead.")
# pad expects a list of np.ndarray, but the previous feature extractors expected torch tensors
images = [to_numpy_array(image) for image in pixel_values_list]
return self.pad(
......@@ -1105,19 +1108,17 @@ class DetrImageProcessor(BaseImageProcessor):
The channel dimension format of the image. If not provided, it will be the same as the input image.
"""
if "pad_and_return_pixel_mask" in kwargs:
warnings.warn(
logger.warning_once(
"The `pad_and_return_pixel_mask` argument is deprecated and will be removed in a future version, "
"use `do_pad` instead.",
FutureWarning,
"use `do_pad` instead."
)
do_pad = kwargs.pop("pad_and_return_pixel_mask")
max_size = None
if "max_size" in kwargs:
warnings.warn(
logger.warning_once(
"The `max_size` argument is deprecated and will be removed in a future version, use"
" `size['longest_edge']` instead.",
FutureWarning,
" `size['longest_edge']` instead."
)
size = kwargs.pop("max_size")
......@@ -1263,10 +1264,9 @@ class DetrImageProcessor(BaseImageProcessor):
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
in the batch as predicted by the model.
"""
warnings.warn(
logger.warning_once(
"`post_process` is deprecated and will be removed in v5 of Transformers, please use"
" `post_process_object_detection`",
FutureWarning,
)
out_logits, out_bbox = outputs.logits, outputs.pred_boxes
......@@ -1306,10 +1306,9 @@ class DetrImageProcessor(BaseImageProcessor):
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels, and masks for an image
in the batch as predicted by the model.
"""
warnings.warn(
logger.warning_once(
"`post_process_segmentation` is deprecated and will be removed in v5 of Transformers, please use"
" `post_process_semantic_segmentation`.",
FutureWarning,
)
out_logits, raw_masks = outputs.logits, outputs.pred_masks
empty_label = out_logits.shape[-1] - 1
......@@ -1358,10 +1357,9 @@ class DetrImageProcessor(BaseImageProcessor):
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels, boxes and masks for an
image in the batch as predicted by the model.
"""
warnings.warn(
logger.warning_once(
"`post_process_instance` is deprecated and will be removed in v5 of Transformers, please use"
" `post_process_instance_segmentation`.",
FutureWarning,
)
if len(orig_target_sizes) != len(max_target_sizes):
......@@ -1405,10 +1403,9 @@ class DetrImageProcessor(BaseImageProcessor):
`List[Dict]`: A list of dictionaries, each dictionary containing a PNG string and segments_info values for
an image in the batch as predicted by the model.
"""
warnings.warn(
logger.warning_once(
"`post_process_panoptic is deprecated and will be removed in v5 of Transformers, please use"
" `post_process_panoptic_segmentation`.",
FutureWarning,
)
if target_sizes is None:
target_sizes = processed_sizes
......@@ -1751,7 +1748,7 @@ class DetrImageProcessor(BaseImageProcessor):
"""
if label_ids_to_fuse is None:
warnings.warn("`label_ids_to_fuse` unset. No instance will be fused.")
logger.warning_once("`label_ids_to_fuse` unset. No instance will be fused.")
label_ids_to_fuse = set()
class_queries_logits = outputs.logits # [batch_size, num_queries, num_classes+1]
......
src/transformers/models/yolos/image_processing_yolos.py
View file @ ce06e478
......@@ -15,7 +15,6 @@
"""Image processor class for YOLOS."""
import pathlib
import warnings
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union
import numpy as np
......@@ -707,10 +706,9 @@ class YolosImageProcessor(BaseImageProcessor):
do_pad = kwargs.pop("pad_and_return_pixel_mask")
if "max_size" in kwargs:
warnings.warn(
logger.warning_once(
"The `max_size` parameter is deprecated and will be removed in v4.26. "
"Please specify in `size['longest_edge'] instead`.",
FutureWarning,
)
max_size = kwargs.pop("max_size")
else:
......@@ -734,10 +732,9 @@ class YolosImageProcessor(BaseImageProcessor):
@property
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.max_size
def max_size(self):
warnings.warn(
logger.warning(
"The `max_size` parameter is deprecated and will be removed in v4.27. "
"Please specify in `size['longest_edge'] instead`.",
FutureWarning,
)
return self.size["longest_edge"]
......@@ -784,7 +781,7 @@ class YolosImageProcessor(BaseImageProcessor):
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare
def prepare(self, image, target, return_segmentation_masks=False, masks_path=None):
warnings.warn(
logger.warning_once(
"The `prepare` method is deprecated and will be removed in a future version. "
"Please use `prepare_annotation` instead. Note: the `prepare_annotation` method "
"does not return the image anymore.",
......@@ -794,17 +791,23 @@ class YolosImageProcessor(BaseImageProcessor):
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.convert_coco_poly_to_mask
def convert_coco_poly_to_mask(self, *args, **kwargs):
warnings.warn("The `convert_coco_poly_to_mask` method is deprecated and will be removed in a future version. ")
logger.warning_once(
"The `convert_coco_poly_to_mask` method is deprecated and will be removed in a future version. "
)
return convert_coco_poly_to_mask(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_detection with DETR->Yolos
def prepare_coco_detection(self, *args, **kwargs):
warnings.warn("The `prepare_coco_detection` method is deprecated and will be removed in a future version. ")
logger.warning_once(
"The `prepare_coco_detection` method is deprecated and will be removed in a future version. "
)
return prepare_coco_detection_annotation(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_panoptic
def prepare_coco_panoptic(self, *args, **kwargs):
warnings.warn("The `prepare_coco_panoptic` method is deprecated and will be removed in a future version. ")
logger.warning_once(
"The `prepare_coco_panoptic` method is deprecated and will be removed in a future version. "
)
return prepare_coco_panoptic_annotation(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.resize
......@@ -821,10 +824,9 @@ class YolosImageProcessor(BaseImageProcessor):
int, smaller edge of the image will be matched to this number.
"""
if "max_size" in kwargs:
warnings.warn(
logger.warning_once(
"The `max_size` parameter is deprecated and will be removed in v4.26. "
"Please specify in `size['longest_edge'] instead`.",
FutureWarning,
)
max_size = kwargs.pop("max_size")
else:
......@@ -1007,19 +1009,17 @@ class YolosImageProcessor(BaseImageProcessor):
The channel dimension format of the image. If not provided, it will be the same as the input image.
"""
if "pad_and_return_pixel_mask" in kwargs:
warnings.warn(
logger.warning_once(
"The `pad_and_return_pixel_mask` argument is deprecated and will be removed in a future version, "
"use `do_pad` instead.",
FutureWarning,
)
do_pad = kwargs.pop("pad_and_return_pixel_mask")
max_size = None
if "max_size" in kwargs:
warnings.warn(
logger.warning_once(
"The `max_size` argument is deprecated and will be removed in a future version, use"
" `size['longest_edge']` instead.",
FutureWarning,
)
size = kwargs.pop("max_size")
......@@ -1164,10 +1164,9 @@ class YolosImageProcessor(BaseImageProcessor):
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
in the batch as predicted by the model.
"""
warnings.warn(
logger.warning_once(
"`post_process` is deprecated and will be removed in v5 of Transformers, please use"
" `post_process_object_detection`",
FutureWarning,
)
out_logits, out_bbox = outputs.logits, outputs.pred_boxes
......
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