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[WIP] Add OneformerFastImageProcessor (#38343)

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* [WIP] OneformerFastImageProcessor

* update init

* Fully working oneformer image processor fast

* change Nearest to Neares exact interpolation where needed

* fix doc

---------

Co-authored-by: yonigozlan <yoni.gozlan@huggingface.co>
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
This commit is contained in:
space_samurai
2025-07-23 02:11:39 +05:30
committed by GitHub
parent 4884b6bf41
commit c6d0500d15
11 changed files with 1224 additions and 128 deletions
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10
docs/source/en/model_doc/oneformer.md
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@@ -69,7 +69,14 @@ The resource should ideally demonstrate something new instead of duplicating an
[[autodoc]] OneFormerImageProcessor
- preprocess
- encode_inputs
- post_process_semantic_segmentation
- post_process_instance_segmentation
- post_process_panoptic_segmentation
## OneFormerImageProcessorFast
[[autodoc]] OneFormerImageProcessorFast
- preprocess
- post_process_semantic_segmentation
- post_process_instance_segmentation
- post_process_panoptic_segmentation
@@ -87,4 +94,3 @@ The resource should ideally demonstrate something new instead of duplicating an
[[autodoc]] OneFormerForUniversalSegmentation
- forward

2
src/transformers/models/auto/image_processing_auto.py
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@@ -130,7 +130,7 @@ else:
("mobilevitv2", ("MobileViTImageProcessor", "MobileViTImageProcessorFast")),
("nat", ("ViTImageProcessor", "ViTImageProcessorFast")),
("nougat", ("NougatImageProcessor", "NougatImageProcessorFast")),
("oneformer", ("OneFormerImageProcessor",)),
("oneformer", ("OneFormerImageProcessor", "OneFormerImageProcessorFast")),
("owlv2", ("Owlv2ImageProcessor",)),
("owlvit", ("OwlViTImageProcessor", "OwlViTImageProcessorFast")),
("paligemma", ("SiglipImageProcessor", "SiglipImageProcessorFast")),

4
src/transformers/models/conditional_detr/image_processing_conditional_detr_fast.py
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@@ -454,10 +454,10 @@ class ConditionalDetrImageProcessorFast(BaseImageProcessorFast):
The target size of the image, as returned by the preprocessing `resize` step.
threshold (`float`, *optional*, defaults to 0.5):
The threshold used to binarize the segmentation masks.
resample (`InterpolationMode`, defaults to `InterpolationMode.NEAREST`):
resample (`InterpolationMode`, defaults to `InterpolationMode.NEAREST_EXACT`):
The resampling filter to use when resizing the masks.
"""
interpolation = interpolation if interpolation is not None else F.InterpolationMode.NEAREST
interpolation = interpolation if interpolation is not None else F.InterpolationMode.NEAREST_EXACT
ratio_height, ratio_width = [target / orig for target, orig in zip(target_size, orig_size)]
new_annotation = {}

4
src/transformers/models/deformable_detr/image_processing_deformable_detr_fast.py
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@@ -445,10 +445,10 @@ class DeformableDetrImageProcessorFast(BaseImageProcessorFast):
The target size of the image, as returned by the preprocessing `resize` step.
threshold (`float`, *optional*, defaults to 0.5):
The threshold used to binarize the segmentation masks.
resample (`InterpolationMode`, defaults to `InterpolationMode.NEAREST`):
resample (`InterpolationMode`, defaults to `InterpolationMode.NEAREST_EXACT`):
The resampling filter to use when resizing the masks.
"""
interpolation = interpolation if interpolation is not None else F.InterpolationMode.NEAREST
interpolation = interpolation if interpolation is not None else F.InterpolationMode.NEAREST_EXACT
ratio_height, ratio_width = [target / orig for target, orig in zip(target_size, orig_size)]
new_annotation = {}

4
src/transformers/models/detr/image_processing_detr_fast.py
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@@ -466,10 +466,10 @@ class DetrImageProcessorFast(BaseImageProcessorFast):
The target size of the image, as returned by the preprocessing `resize` step.
threshold (`float`, *optional*, defaults to 0.5):
The threshold used to binarize the segmentation masks.
resample (`InterpolationMode`, defaults to `InterpolationMode.NEAREST`):
resample (`InterpolationMode`, defaults to `InterpolationMode.NEAREST_EXACT`):
The resampling filter to use when resizing the masks.
"""
interpolation = interpolation if interpolation is not None else F.InterpolationMode.NEAREST
interpolation = interpolation if interpolation is not None else F.InterpolationMode.NEAREST_EXACT
ratio_height, ratio_width = [target / orig for target, orig in zip(target_size, orig_size)]
new_annotation = {}

4
src/transformers/models/grounding_dino/image_processing_grounding_dino_fast.py
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@@ -476,10 +476,10 @@ class GroundingDinoImageProcessorFast(BaseImageProcessorFast):
The target size of the image, as returned by the preprocessing `resize` step.
threshold (`float`, *optional*, defaults to 0.5):
The threshold used to binarize the segmentation masks.
resample (`InterpolationMode`, defaults to `InterpolationMode.NEAREST`):
resample (`InterpolationMode`, defaults to `InterpolationMode.NEAREST_EXACT`):
The resampling filter to use when resizing the masks.
"""
interpolation = interpolation if interpolation is not None else F.InterpolationMode.NEAREST
interpolation = interpolation if interpolation is not None else F.InterpolationMode.NEAREST_EXACT
ratio_height, ratio_width = [target / orig for target, orig in zip(target_size, orig_size)]
new_annotation = {}

1
src/transformers/models/oneformer/__init__.py
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@@ -20,6 +20,7 @@ from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_oneformer import *
from .image_processing_oneformer import *
from .image_processing_oneformer_fast import *
from .modeling_oneformer import *
from .processing_oneformer import *
else:

1002
src/transformers/models/oneformer/image_processing_oneformer_fast.py Normal file
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4
src/transformers/models/rt_detr/image_processing_rt_detr_fast.py
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@@ -264,10 +264,10 @@ class RTDetrImageProcessorFast(BaseImageProcessorFast):
The target size of the image, as returned by the preprocessing `resize` step.
threshold (`float`, *optional*, defaults to 0.5):
The threshold used to binarize the segmentation masks.
resample (`InterpolationMode`, defaults to `InterpolationMode.NEAREST`):
resample (`InterpolationMode`, defaults to `InterpolationMode.NEAREST_EXACT`):
The resampling filter to use when resizing the masks.
"""
interpolation = interpolation if interpolation is not None else F.InterpolationMode.NEAREST
interpolation = interpolation if interpolation is not None else F.InterpolationMode.NEAREST_EXACT
ratio_height, ratio_width = [target / orig for target, orig in zip(target_size, orig_size)]
new_annotation = {}

4
src/transformers/models/yolos/image_processing_yolos_fast.py
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@@ -493,10 +493,10 @@ class YolosImageProcessorFast(BaseImageProcessorFast):
The target size of the image, as returned by the preprocessing `resize` step.
threshold (`float`, *optional*, defaults to 0.5):
The threshold used to binarize the segmentation masks.
resample (`InterpolationMode`, defaults to `InterpolationMode.NEAREST`):
resample (`InterpolationMode`, defaults to `InterpolationMode.NEAREST_EXACT`):
The resampling filter to use when resizing the masks.
"""
interpolation = interpolation if interpolation is not None else F.InterpolationMode.NEAREST
interpolation = interpolation if interpolation is not None else F.InterpolationMode.NEAREST_EXACT
ratio_height, ratio_width = [target / orig for target, orig in zip(target_size, orig_size)]
new_annotation = {}

117
tests/models/oneformer/test_image_processing_oneformer.py
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@@ -19,9 +19,10 @@ import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
@@ -31,6 +32,9 @@ if is_torch_available():
if is_vision_available():
from transformers import OneFormerImageProcessor
if is_torchvision_available():
from transformers import OneFormerImageProcessorFast
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle, prepare_metadata
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
@@ -152,12 +156,24 @@ class OneFormerImageProcessorTester:
)
# Copied from transformers.tests.models.beit.test_image_processing_beit.prepare_semantic_single_inputs
def prepare_semantic_single_inputs():
ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
example = ds[0]
return example["image"], example["map"]
# Copied from transformers.tests.models.beit.test_image_processing_beit.prepare_semantic_batch_inputs
def prepare_semantic_batch_inputs():
ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
return list(ds["image"][:2]), list(ds["map"][:2])
@require_torch
@require_vision
class OneFormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
image_processing_class = image_processing_class
fast_image_processing_class = OneFormerImageProcessorFast if is_torchvision_available() else None
def setUp(self):
super().setUp()
@@ -168,7 +184,8 @@ class OneFormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_proc_properties(self):
image_processor = self.image_processing_class(**self.image_processor_dict)
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processor, "image_mean"))
self.assertTrue(hasattr(image_processor, "image_std"))
self.assertTrue(hasattr(image_processor, "do_normalize"))
@@ -182,9 +199,9 @@ class OneFormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
self.assertTrue(hasattr(image_processor, "do_reduce_labels"))
def comm_get_image_processor_inputs(
self, with_segmentation_maps=False, is_instance_map=False, segmentation_type="np"
self, with_segmentation_maps=False, is_instance_map=False, segmentation_type="np", image_processing_class=None
):
image_processor = self.image_processing_class(**self.image_processor_dict)
image_processor = image_processing_class(**self.image_processor_dict)
# prepare image and target
num_labels = self.image_processor_tester.num_labels
annotations = None
@@ -218,8 +235,12 @@ class OneFormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
def test_call_with_segmentation_maps(self):
def common(is_instance_map=False, segmentation_type=None):
for image_processing_class in self.image_processor_list:
inputs = self.comm_get_image_processor_inputs(
with_segmentation_maps=True, is_instance_map=is_instance_map, segmentation_type=segmentation_type
with_segmentation_maps=True,
is_instance_map=is_instance_map,
segmentation_type=segmentation_type,
image_processing_class=image_processing_class,
)
mask_labels = inputs["mask_labels"]
@@ -251,7 +272,8 @@ class OneFormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
self.assertEqual(rle[1], 45)
def test_post_process_semantic_segmentation(self):
fature_extractor = self.image_processing_class(
for image_processing_class in self.image_processor_list:
fature_extractor = image_processing_class(
num_labels=self.image_processor_tester.num_classes,
max_seq_length=77,
task_seq_length=77,
@@ -278,7 +300,8 @@ class OneFormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
self.assertEqual(segmentation[0].shape, target_sizes[0])
def test_post_process_instance_segmentation(self):
image_processor = self.image_processing_class(
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class(
num_labels=self.image_processor_tester.num_classes,
max_seq_length=77,
task_seq_length=77,
@@ -312,7 +335,8 @@ class OneFormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
self.assertEqual(el["segmentation"].shape, (1, 4))
def test_post_process_panoptic_segmentation(self):
image_processor = self.image_processing_class(
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class(
num_labels=self.image_processor_tester.num_classes,
max_seq_length=77,
task_seq_length=77,
@@ -340,7 +364,7 @@ class OneFormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
"2": {"isthing": 1, "name": "baz"},
}
metadata = prepare_metadata(class_info)
for image_processing_class in self.image_processor_list:
with tempfile.TemporaryDirectory() as tmpdirname:
metadata_path = os.path.join(tmpdirname, "metadata.json")
with open(metadata_path, "w") as f:
@@ -349,7 +373,7 @@ class OneFormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
config_dict = self.image_processor_dict
config_dict["class_info_file"] = metadata_path
config_dict["repo_path"] = tmpdirname
image_processor = self.image_processing_class(**config_dict)
image_processor = image_processing_class(**config_dict)
self.assertEqual(image_processor.metadata, metadata)
@@ -357,11 +381,74 @@ class OneFormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processor_dict = dict(self.image_processor_dict)
image_processor_dict.pop("do_reduce_labels", None)
image_processor_dict["reduce_labels"] = True
# Only test for OneFormerImageProcessor
image_processing_class = self.image_processing_class
# test we are able to create the image processor with the deprecated kwargs
image_processor = self.image_processing_class(**image_processor_dict)
image_processor = image_processing_class(**image_processor_dict)
self.assertEqual(image_processor.do_reduce_labels, True)
# test we still support reduce_labels with config
image_processor = self.image_processing_class.from_dict(image_processor_dict)
image_processor = image_processing_class.from_dict(image_processor_dict)
self.assertEqual(image_processor.do_reduce_labels, True)
def test_slow_fast_equivalence(self):
if not self.test_slow_image_processor or not self.test_fast_image_processor:
self.skipTest(reason="Skipping slow/fast equivalence test")
if self.image_processing_class is None or self.fast_image_processing_class is None:
self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
dummy_image, dummy_map = prepare_semantic_single_inputs()
image_processor_slow = self.image_processing_class(**self.image_processor_dict)
image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
image_encoding_slow = image_processor_slow(dummy_image, segmentation_maps=dummy_map, return_tensors="pt")
image_encoding_fast = image_processor_fast(dummy_image, segmentation_maps=dummy_map, return_tensors="pt")
self._assert_slow_fast_tensors_equivalence(image_encoding_slow.pixel_values, image_encoding_fast.pixel_values)
for mask_label_slow, mask_label_fast in zip(image_encoding_slow.mask_labels, image_encoding_fast.mask_labels):
self._assert_slow_fast_tensors_equivalence(mask_label_slow, mask_label_fast)
for class_label_slow, class_label_fast in zip(
image_encoding_slow.class_labels, image_encoding_fast.class_labels
):
self._assert_slow_fast_tensors_equivalence(class_label_slow.float(), class_label_fast.float())
self.assertEqual(image_encoding_slow.text_inputs, image_encoding_fast.text_inputs)
self.assertEqual(image_encoding_slow.task_inputs, image_encoding_fast.task_inputs)
def test_slow_fast_equivalence_batched(self):
if not self.test_slow_image_processor or not self.test_fast_image_processor:
self.skipTest(reason="Skipping slow/fast equivalence test")
if self.image_processing_class is None or self.fast_image_processing_class is None:
self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
if hasattr(self.image_processor_tester, "do_center_crop") and self.image_processor_tester.do_center_crop:
self.skipTest(
reason="Skipping as do_center_crop is True and center_crop functions are not equivalent for fast and slow processors"
)
dummy_images, dummy_maps = prepare_semantic_batch_inputs()
image_processor_slow = self.image_processing_class(**self.image_processor_dict)
image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
encoding_slow = image_processor_slow(
dummy_images,
segmentation_maps=dummy_maps,
task_inputs=["instance"] + ["semantic"] * (len(dummy_images) - 1),
return_tensors="pt",
)
encoding_fast = image_processor_fast(
dummy_images,
segmentation_maps=dummy_maps,
task_inputs=["instance"] + ["semantic"] * (len(dummy_images) - 1),
return_tensors="pt",
)
self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values)
for mask_label_slow, mask_label_fast in zip(encoding_slow.mask_labels, encoding_fast.mask_labels):
self._assert_slow_fast_tensors_equivalence(mask_label_slow, mask_label_fast)
for class_label_slow, class_label_fast in zip(encoding_slow.class_labels, encoding_fast.class_labels):
self._assert_slow_fast_tensors_equivalence(class_label_slow.float(), class_label_fast.float())
self.assertEqual(encoding_slow.text_inputs, encoding_fast.text_inputs)
self.assertEqual(encoding_slow.task_inputs, encoding_fast.task_inputs)