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Image Feature Extraction pipeline (#28216)

Browse Source 
* Draft pipeline

* Fixup

* Fix docstrings

* Update doctest

* Update pipeline_model_mapping

* Update docstring

* Update tests

* Update src/transformers/pipelines/image_feature_extraction.py

Co-authored-by: Omar Sanseviero <osanseviero@gmail.com>

* Fix docstrings - review comments

* Remove pipeline mapping for composite vision models

* Add to pipeline tests

* Remove for flava (multimodal)

* safe pil import

* Add requirements for pipeline run

* Account for super slow efficientnet

* Review comments

* Fix tests

* Swap order of kwargs

* Use build_pipeline_init_args

* Add back FE pipeline for Vilt

* Include image_processor_kwargs in docstring

* Mark test as flaky

* Update TODO

* Update tests/pipelines/test_pipelines_image_feature_extraction.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Add license header

---------

Co-authored-by: Omar Sanseviero <osanseviero@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
This commit is contained in:
amyeroberts
2024-02-05 14:50:07 +00:00
committed by GitHub
parent 7addc9346c
commit ba3264b4e8
60 changed files with 387 additions and 53 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
tests/models/beit/test_modeling_beit.py
View File

@@ -242,7 +242,7 @@ class BeitModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
)
pipeline_model_mapping = (
{
"feature-extraction": BeitModel,
"image-feature-extraction": BeitModel,
"image-classification": BeitForImageClassification,
"image-segmentation": BeitForSemanticSegmentation,
}

2
tests/models/bit/test_modeling_bit.py
View File

@@ -162,7 +162,7 @@ class BitModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": BitModel, "image-classification": BitForImageClassification}
{"image-feature-extraction": BitModel, "image-classification": BitForImageClassification}
if is_torch_available()
else {}
)

5
tests/models/blip/test_modeling_blip.py
View File

@@ -429,7 +429,10 @@ class BlipModelTester:
class BlipModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (BlipModel,) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": BlipModel, "image-to-text": BlipForConditionalGeneration}
{
"feature-extraction": BlipModel,
"image-to-text": BlipForConditionalGeneration,
}
if is_torch_available()
else {}
)

4
tests/models/clip/test_modeling_clip.py
View File

@@ -477,7 +477,9 @@ class CLIPModelTester:
@require_torch
class CLIPModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (CLIPModel,) if is_torch_available() else ()
pipeline_model_mapping = {"feature-extraction": CLIPModel} if is_torch_available() else {}
pipeline_model_mapping = (
{"feature-extraction": CLIPModel, "image-feature-extraction": CLIPVisionModel} if is_torch_available() else {}
)
fx_compatible = True
test_head_masking = False
test_pruning = False

2
tests/models/conditional_detr/test_modeling_conditional_detr.py
View File

@@ -185,7 +185,7 @@ class ConditionalDetrModelTest(ModelTesterMixin, GenerationTesterMixin, Pipeline
else ()
)
pipeline_model_mapping = (
{"feature-extraction": ConditionalDetrModel, "object-detection": ConditionalDetrForObjectDetection}
{"image-feature-extraction": ConditionalDetrModel, "object-detection": ConditionalDetrForObjectDetection}
if is_torch_available()
else {}
)

2
tests/models/convnext/test_modeling_convnext.py
View File

@@ -172,7 +172,7 @@ class ConvNextModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase
else ()
)
pipeline_model_mapping = (
{"feature-extraction": ConvNextModel, "image-classification": ConvNextForImageClassification}
{"image-feature-extraction": ConvNextModel, "image-classification": ConvNextForImageClassification}
if is_torch_available()
else {}
)

2
tests/models/convnextv2/test_modeling_convnextv2.py
View File

@@ -180,7 +180,7 @@ class ConvNextV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCa
else ()
)
pipeline_model_mapping = (
{"feature-extraction": ConvNextV2Model, "image-classification": ConvNextV2ForImageClassification}
{"image-feature-extraction": ConvNextV2Model, "image-classification": ConvNextV2ForImageClassification}
if is_torch_available()
else {}
)

2
tests/models/cvt/test_modeling_cvt.py
View File

@@ -151,7 +151,7 @@ class CvtModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
{"image-feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
if is_torch_available()
else {}
)

2
tests/models/data2vec/test_modeling_data2vec_vision.py
View File

@@ -178,7 +178,7 @@ class Data2VecVisionModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.Te
)
pipeline_model_mapping = (
{
"feature-extraction": Data2VecVisionModel,
"image-feature-extraction": Data2VecVisionModel,
"image-classification": Data2VecVisionForImageClassification,
"image-segmentation": Data2VecVisionForSemanticSegmentation,
}

2
tests/models/deformable_detr/test_modeling_deformable_detr.py
View File

@@ -191,7 +191,7 @@ class DeformableDetrModelTester:
class DeformableDetrModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (DeformableDetrModel, DeformableDetrForObjectDetection) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": DeformableDetrModel, "object-detection": DeformableDetrForObjectDetection}
{"image-feature-extraction": DeformableDetrModel, "object-detection": DeformableDetrForObjectDetection}
if is_torch_available()
else {}
)

2
tests/models/deit/test_modeling_deit.py
View File

@@ -206,7 +206,7 @@ class DeiTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
)
pipeline_model_mapping = (
{
"feature-extraction": DeiTModel,
"image-feature-extraction": DeiTModel,
"image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()

2
tests/models/deta/test_modeling_deta.py
View File

@@ -217,7 +217,7 @@ class DetaModelTester:
class DetaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (DetaModel, DetaForObjectDetection) if is_torchvision_available() else ()
pipeline_model_mapping = (
{"feature-extraction": DetaModel, "object-detection": DetaForObjectDetection}
{"image-feature-extraction": DetaModel, "object-detection": DetaForObjectDetection}
if is_torchvision_available()
else {}
)

2
tests/models/detr/test_modeling_detr.py
View File

@@ -182,7 +182,7 @@ class DetrModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
)
pipeline_model_mapping = (
{
"feature-extraction": DetrModel,
"image-feature-extraction": DetrModel,
"image-segmentation": DetrForSegmentation,
"object-detection": DetrForObjectDetection,
}

2
tests/models/dinat/test_modeling_dinat.py
View File

@@ -207,7 +207,7 @@ class DinatModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
else ()
)
pipeline_model_mapping = (
{"feature-extraction": DinatModel, "image-classification": DinatForImageClassification}
{"image-feature-extraction": DinatModel, "image-classification": DinatForImageClassification}
if is_torch_available()
else {}
)

2
tests/models/dinov2/test_modeling_dinov2.py
View File

@@ -217,7 +217,7 @@ class Dinov2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
else ()
)
pipeline_model_mapping = (
{"feature-extraction": Dinov2Model, "image-classification": Dinov2ForImageClassification}
{"image-feature-extraction": Dinov2Model, "image-classification": Dinov2ForImageClassification}
if is_torch_available()
else {}
)

2
tests/models/donut/test_modeling_donut_swin.py
View File

@@ -145,7 +145,7 @@ class DonutSwinModelTester:
@require_torch
class DonutSwinModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (DonutSwinModel,) if is_torch_available() else ()
pipeline_model_mapping = {"feature-extraction": DonutSwinModel} if is_torch_available() else {}
pipeline_model_mapping = {"image-feature-extraction": DonutSwinModel} if is_torch_available() else {}
fx_compatible = True
test_pruning = False

2
tests/models/dpt/test_modeling_dpt.py
View File

@@ -163,7 +163,7 @@ class DPTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
pipeline_model_mapping = (
{
"depth-estimation": DPTForDepthEstimation,
"feature-extraction": DPTModel,
"image-feature-extraction": DPTModel,
"image-segmentation": DPTForSemanticSegmentation,
}
if is_torch_available()

2
tests/models/efficientformer/test_modeling_efficientformer.py
View File

@@ -190,7 +190,7 @@ class EfficientFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.T
)
pipeline_model_mapping = (
{
"feature-extraction": EfficientFormerModel,
"image-feature-extraction": EfficientFormerModel,
"image-classification": (
EfficientFormerForImageClassification,
EfficientFormerForImageClassificationWithTeacher,

8
tests/models/efficientnet/test_modeling_efficientnet.py
View File

@@ -130,7 +130,7 @@ class EfficientNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.Test
all_model_classes = (EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": EfficientNetModel, "image-classification": EfficientNetForImageClassification}
{"image-feature-extraction": EfficientNetModel, "image-classification": EfficientNetForImageClassification}
if is_torch_available()
else {}
)
@@ -216,6 +216,12 @@ class EfficientNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.Test
model = EfficientNetModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@is_pipeline_test
@require_vision
@slow
def test_pipeline_image_feature_extraction(self):
super().test_pipeline_image_feature_extraction()
@is_pipeline_test
@require_vision
@slow

2
tests/models/focalnet/test_modeling_focalnet.py
View File

@@ -238,7 +238,7 @@ class FocalNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase
else ()
)
pipeline_model_mapping = (
{"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
{"image-feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
if is_torch_available()
else {}
)

4
tests/models/glpn/test_modeling_glpn.py
View File

@@ -146,7 +146,9 @@ class GLPNModelTester:
class GLPNModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
pipeline_model_mapping = (
{"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
{"depth-estimation": GLPNForDepthEstimation, "image-feature-extraction": GLPNModel}
if is_torch_available()
else {}
)
test_head_masking = False

2
tests/models/imagegpt/test_modeling_imagegpt.py
View File

@@ -271,7 +271,7 @@ class ImageGPTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterM
)
all_generative_model_classes = (ImageGPTForCausalImageModeling,) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": ImageGPTModel, "image-classification": ImageGPTForImageClassification}
{"image-feature-extraction": ImageGPTModel, "image-classification": ImageGPTForImageClassification}
if is_torch_available()
else {}
)

2
tests/models/levit/test_modeling_levit.py
View File

@@ -176,7 +176,7 @@ class LevitModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
)
pipeline_model_mapping = (
{
"feature-extraction": LevitModel,
"image-feature-extraction": LevitModel,
"image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
}
if is_torch_available()

2
tests/models/mask2former/test_modeling_mask2former.py
View File

@@ -197,7 +197,7 @@ class Mask2FormerModelTester:
@require_torch
class Mask2FormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (Mask2FormerModel, Mask2FormerForUniversalSegmentation) if is_torch_available() else ()
pipeline_model_mapping = {"feature-extraction": Mask2FormerModel} if is_torch_available() else {}
pipeline_model_mapping = {"image-feature-extraction": Mask2FormerModel} if is_torch_available() else {}
is_encoder_decoder = False
test_pruning = False

2
tests/models/maskformer/test_modeling_maskformer.py
View File

@@ -197,7 +197,7 @@ class MaskFormerModelTester:
class MaskFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
{"image-feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)

8
tests/models/mgp_str/test_modeling_mgp_str.py
View File

@@ -31,7 +31,7 @@ if is_torch_available():
import torch
from torch import nn
from transformers import MgpstrForSceneTextRecognition
from transformers import MgpstrForSceneTextRecognition, MgpstrModel
if is_vision_available():
@@ -118,7 +118,11 @@ class MgpstrModelTester:
@require_torch
class MgpstrModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (MgpstrForSceneTextRecognition,) if is_torch_available() else ()
pipeline_model_mapping = {"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
pipeline_model_mapping = (
{"feature-extraction": MgpstrForSceneTextRecognition, "image-feature-extraction": MgpstrModel}
if is_torch_available()
else {}
)
fx_compatible = False
test_pruning = False

2
tests/models/mobilenet_v1/test_modeling_mobilenet_v1.py
View File

@@ -147,7 +147,7 @@ class MobileNetV1ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestC
all_model_classes = (MobileNetV1Model, MobileNetV1ForImageClassification) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": MobileNetV1Model, "image-classification": MobileNetV1ForImageClassification}
{"image-feature-extraction": MobileNetV1Model, "image-classification": MobileNetV1ForImageClassification}
if is_torch_available()
else {}
)

2
tests/models/mobilenet_v2/test_modeling_mobilenet_v2.py
View File

@@ -195,7 +195,7 @@ class MobileNetV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestC
)
pipeline_model_mapping = (
{
"feature-extraction": MobileNetV2Model,
"image-feature-extraction": MobileNetV2Model,
"image-classification": MobileNetV2ForImageClassification,
"image-segmentation": MobileNetV2ForSemanticSegmentation,
}

2
tests/models/mobilevit/test_modeling_mobilevit.py
View File

@@ -188,7 +188,7 @@ class MobileViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCas
)
pipeline_model_mapping = (
{
"feature-extraction": MobileViTModel,
"image-feature-extraction": MobileViTModel,
"image-classification": MobileViTForImageClassification,
"image-segmentation": MobileViTForSemanticSegmentation,
}

2
tests/models/mobilevitv2/test_modeling_mobilevitv2.py
View File

@@ -190,7 +190,7 @@ class MobileViTV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestC
pipeline_model_mapping = (
{
"feature-extraction": MobileViTV2Model,
"image-feature-extraction": MobileViTV2Model,
"image-classification": MobileViTV2ForImageClassification,
"image-segmentation": MobileViTV2ForSemanticSegmentation,
}

2
tests/models/nat/test_modeling_nat.py
View File

@@ -204,7 +204,7 @@ class NatModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
else ()
)
pipeline_model_mapping = (
{"feature-extraction": NatModel, "image-classification": NatForImageClassification}
{"image-feature-extraction": NatModel, "image-classification": NatForImageClassification}
if is_torch_available()
else {}
)

5
tests/models/owlv2/test_modeling_owlv2.py
View File

@@ -433,7 +433,10 @@ class Owlv2ModelTester:
class Owlv2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (Owlv2Model,) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": Owlv2Model, "zero-shot-object-detection": Owlv2ForObjectDetection}
{
"feature-extraction": Owlv2Model,
"zero-shot-object-detection": Owlv2ForObjectDetection,
}
if is_torch_available()
else {}
)

5
tests/models/owlvit/test_modeling_owlvit.py
View File

@@ -428,7 +428,10 @@ class OwlViTModelTester:
class OwlViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (OwlViTModel,) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": OwlViTModel, "zero-shot-object-detection": OwlViTForObjectDetection}
{
"feature-extraction": OwlViTModel,
"zero-shot-object-detection": OwlViTForObjectDetection,
}
if is_torch_available()
else {}
)

2
tests/models/poolformer/test_modeling_poolformer.py
View File

@@ -124,7 +124,7 @@ class PoolFormerModelTester:
class PoolFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
{"image-feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
if is_torch_available()
else {}
)

2
tests/models/pvt/test_modeling_pvt.py
View File

@@ -158,7 +158,7 @@ def prepare_img():
class PvtModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (PvtModel, PvtForImageClassification) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": PvtModel, "image-classification": PvtForImageClassification}
{"image-feature-extraction": PvtModel, "image-classification": PvtForImageClassification}
if is_torch_available()
else {}
)

2
tests/models/regnet/test_modeling_regnet.py
View File

@@ -126,7 +126,7 @@ class RegNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
{"image-feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
if is_torch_available()
else {}
)

2
tests/models/resnet/test_modeling_resnet.py
View File

@@ -170,7 +170,7 @@ class ResNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
else ()
)
pipeline_model_mapping = (
{"feature-extraction": ResNetModel, "image-classification": ResNetForImageClassification}
{"image-feature-extraction": ResNetModel, "image-classification": ResNetForImageClassification}
if is_torch_available()
else {}
)

2
tests/models/segformer/test_modeling_segformer.py
View File

@@ -171,7 +171,7 @@ class SegformerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCas
)
pipeline_model_mapping = (
{
"feature-extraction": SegformerModel,
"image-feature-extraction": SegformerModel,
"image-classification": SegformerForImageClassification,
"image-segmentation": SegformerForSemanticSegmentation,
}

2
tests/models/swiftformer/test_modeling_swiftformer.py
View File

@@ -139,7 +139,7 @@ class SwiftFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestC
all_model_classes = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
{"image-feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
if is_torch_available()
else {}
)

2
tests/models/swin/test_modeling_swin.py
View File

@@ -232,7 +232,7 @@ class SwinModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
else ()
)
pipeline_model_mapping = (
{"feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
{"image-feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
if is_torch_available()
else {}
)

2
tests/models/swin2sr/test_modeling_swin2sr.py
View File

@@ -162,7 +162,7 @@ class Swin2SRModelTester:
class Swin2SRModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (Swin2SRModel, Swin2SRForImageSuperResolution) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": Swin2SRModel, "image-to-image": Swin2SRForImageSuperResolution}
{"image-feature-extraction": Swin2SRModel, "image-to-image": Swin2SRForImageSuperResolution}
if is_torch_available()
else {}
)

2
tests/models/swinv2/test_modeling_swinv2.py
View File

@@ -217,7 +217,7 @@ class Swinv2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
else ()
)
pipeline_model_mapping = (
{"feature-extraction": Swinv2Model, "image-classification": Swinv2ForImageClassification}
{"image-feature-extraction": Swinv2Model, "image-classification": Swinv2ForImageClassification}
if is_torch_available()
else {}
)

2
tests/models/table_transformer/test_modeling_table_transformer.py
View File

@@ -200,7 +200,7 @@ class TableTransformerModelTest(ModelTesterMixin, GenerationTesterMixin, Pipelin
else ()
)
pipeline_model_mapping = (
{"feature-extraction": TableTransformerModel, "object-detection": TableTransformerForObjectDetection}
{"image-feature-extraction": TableTransformerModel, "object-detection": TableTransformerForObjectDetection}
if is_torch_available()
else {}
)

2
tests/models/vilt/test_modeling_vilt.py
View File

@@ -228,7 +228,7 @@ class ViltModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
else ()
)
pipeline_model_mapping = (
{"feature-extraction": ViltModel, "visual-question-answering": ViltForQuestionAnswering}
{"image-feature-extraction": ViltModel, "visual-question-answering": ViltForQuestionAnswering}
if is_torch_available()
else {}
)

2
tests/models/vit/test_modeling_vit.py
View File

@@ -193,7 +193,7 @@ class ViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
else ()
)
pipeline_model_mapping = (
{"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
{"image-feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
if is_torch_available()
else {}
)

2
tests/models/vit_hybrid/test_modeling_vit_hybrid.py
View File

@@ -156,7 +156,7 @@ class ViTHybridModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCas
all_model_classes = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
{"image-feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
if is_torch_available()
else {}
)

2
tests/models/vit_mae/test_modeling_vit_mae.py
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@@ -164,7 +164,7 @@ class ViTMAEModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
all_model_classes = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
pipeline_model_mapping = {"feature-extraction": ViTMAEModel} if is_torch_available() else {}
pipeline_model_mapping = {"image-feature-extraction": ViTMAEModel} if is_torch_available() else {}
test_pruning = False
test_torchscript = False

2
tests/models/vit_msn/test_modeling_vit_msn.py
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@@ -152,7 +152,7 @@ class ViTMSNModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
{"image-feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
if is_torch_available()
else {}
)

4
tests/models/yolos/test_modeling_yolos.py
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@@ -168,7 +168,9 @@ class YolosModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
{"image-feature-extraction": YolosModel, "object-detection": YolosForObjectDetection}
if is_torch_available()
else {}
)
test_pruning = False

157
tests/pipelines/test_pipelines_image_feature_extraction.py Normal file
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@@ -0,0 +1,157 @@
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import pytest
from transformers import (
MODEL_MAPPING,
TF_MODEL_MAPPING,
TOKENIZER_MAPPING,
ImageFeatureExtractionPipeline,
is_tf_available,
is_torch_available,
is_vision_available,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
if is_vision_available():
from PIL import Image
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@is_pipeline_test
class ImageFeatureExtractionPipelineTests(unittest.TestCase):
model_mapping = MODEL_MAPPING
tf_model_mapping = TF_MODEL_MAPPING
@require_torch
def test_small_model_pt(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="pt"
)
img = prepare_img()
outputs = feature_extractor(img)
self.assertEqual(
nested_simplify(outputs[0][0]),
[-1.417, -0.392, -1.264, -1.196, 1.648, 0.885, 0.56, -0.606, -1.175, 0.823, 1.912, 0.081, -0.053, 1.119, -0.062, -1.757, -0.571, 0.075, 0.959, 0.118, 1.201, -0.672, -0.498, 0.364, 0.937, -1.623, 0.228, 0.19, 1.697, -1.115, 0.583, -0.981]) # fmt: skip
@require_tf
def test_small_model_tf(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="tf"
)
img = prepare_img()
outputs = feature_extractor(img)
self.assertEqual(
nested_simplify(outputs[0][0]),
[-1.417, -0.392, -1.264, -1.196, 1.648, 0.885, 0.56, -0.606, -1.175, 0.823, 1.912, 0.081, -0.053, 1.119, -0.062, -1.757, -0.571, 0.075, 0.959, 0.118, 1.201, -0.672, -0.498, 0.364, 0.937, -1.623, 0.228, 0.19, 1.697, -1.115, 0.583, -0.981]) # fmt: skip
@require_torch
def test_image_processing_small_model_pt(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="pt"
)
# test with image processor parameters
image_processor_kwargs = {"size": {"height": 300, "width": 300}}
img = prepare_img()
with pytest.raises(ValueError):
# Image doesn't match model input size
feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
image_processor_kwargs = {"image_mean": [0, 0, 0], "image_std": [1, 1, 1]}
img = prepare_img()
outputs = feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
self.assertEqual(np.squeeze(outputs).shape, (226, 32))
@require_tf
def test_image_processing_small_model_tf(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="tf"
)
# test with image processor parameters
image_processor_kwargs = {"size": {"height": 300, "width": 300}}
img = prepare_img()
with pytest.raises(ValueError):
# Image doesn't match model input size
feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
image_processor_kwargs = {"image_mean": [0, 0, 0], "image_std": [1, 1, 1]}
img = prepare_img()
outputs = feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
self.assertEqual(np.squeeze(outputs).shape, (226, 32))
@require_torch
def test_return_tensors_pt(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="pt"
)
img = prepare_img()
outputs = feature_extractor(img, return_tensors=True)
self.assertTrue(torch.is_tensor(outputs))
@require_tf
def test_return_tensors_tf(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="tf"
)
img = prepare_img()
outputs = feature_extractor(img, return_tensors=True)
self.assertTrue(tf.is_tensor(outputs))
def get_test_pipeline(self, model, tokenizer, processor):
if processor is None:
self.skipTest("No image processor")
elif type(model.config) in TOKENIZER_MAPPING:
self.skipTest("This is a bimodal model, we need to find a more consistent way to switch on those models.")
elif model.config.is_encoder_decoder:
self.skipTest(
"""encoder_decoder models are trickier for this pipeline.
Do we want encoder + decoder inputs to get some featues?
Do we want encoder only features ?
For now ignore those.
"""
)
feature_extractor = ImageFeatureExtractionPipeline(model=model, image_processor=processor)
img = prepare_img()
return feature_extractor, [img, img]
def run_pipeline_test(self, feature_extractor, examples):
imgs = examples
outputs = feature_extractor(imgs[0])
self.assertEqual(len(outputs), 1)
outputs = feature_extractor(imgs)
self.assertEqual(len(outputs), 2)

9
tests/test_pipeline_mixin.py
View File

@@ -39,6 +39,7 @@ from .pipelines.test_pipelines_document_question_answering import DocumentQuesti
from .pipelines.test_pipelines_feature_extraction import FeatureExtractionPipelineTests
from .pipelines.test_pipelines_fill_mask import FillMaskPipelineTests
from .pipelines.test_pipelines_image_classification import ImageClassificationPipelineTests
from .pipelines.test_pipelines_image_feature_extraction import ImageFeatureExtractionPipelineTests
from .pipelines.test_pipelines_image_segmentation import ImageSegmentationPipelineTests
from .pipelines.test_pipelines_image_to_image import ImageToImagePipelineTests
from .pipelines.test_pipelines_image_to_text import ImageToTextPipelineTests
@@ -70,6 +71,7 @@ pipeline_test_mapping = {
"feature-extraction": {"test": FeatureExtractionPipelineTests},
"fill-mask": {"test": FillMaskPipelineTests},
"image-classification": {"test": ImageClassificationPipelineTests},
"image-feature-extraction": {"test": ImageFeatureExtractionPipelineTests},
"image-segmentation": {"test": ImageSegmentationPipelineTests},
"image-to-image": {"test": ImageToImagePipelineTests},
"image-to-text": {"test": ImageToTextPipelineTests},
@@ -374,6 +376,13 @@ class PipelineTesterMixin:
def test_pipeline_image_to_text(self):
self.run_task_tests(task="image-to-text")
@is_pipeline_test
@require_timm
@require_vision
@require_torch
def test_pipeline_image_feature_extraction(self):
self.run_task_tests(task="image-feature-extraction")
@unittest.skip(reason="`run_pipeline_test` is currently not implemented.")
@is_pipeline_test
@require_vision