Image pipelines spec compliance (#33899)

* Update many similar visual pipelines

* Add input tests

* Add ImageToText as well

* Add output tests

* Add output tests

* Add output tests

* OutputElement -> Output

* Correctly test elements

* make fixup

* fix typo in the task list

* Fix VQA testing

* Add copyright to image_classification.py

* Revert changes to VQA pipeline because outputs have differences - will move to another PR

* make fixup

* Remove deprecation warnings

tests/pipelines/test_pipelines_depth_estimation.py

@@ -14,11 +14,13 @@
 
 import unittest
 
+from huggingface_hub import DepthEstimationOutput
 from huggingface_hub.utils import insecure_hashlib
 
 from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
 from transformers.pipelines import DepthEstimationPipeline, pipeline
 from transformers.testing_utils import (
+    compare_pipeline_output_to_hub_spec,
     is_pipeline_test,
     nested_simplify,
     require_tf,
@@ -94,6 +96,9 @@ class DepthEstimationPipelineTests(unittest.TestCase):
             outputs,
         )
 
+        for single_output in outputs:
+            compare_pipeline_output_to_hub_spec(single_output, DepthEstimationOutput)
+
     @require_tf
     @unittest.skip(reason="Depth estimation is not implemented in TF")
     def test_small_model_tf(self):

tests/pipelines/test_pipelines_image_classification.py

@@ -14,6 +14,8 @@
 
 import unittest
 
+from huggingface_hub import ImageClassificationOutputElement
+
 from transformers import (
     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
     TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
@@ -23,6 +25,7 @@ from transformers import (
 )
 from transformers.pipelines import ImageClassificationPipeline, pipeline
 from transformers.testing_utils import (
+    compare_pipeline_output_to_hub_spec,
     is_pipeline_test,
     nested_simplify,
     require_tf,
@@ -121,6 +124,10 @@ class ImageClassificationPipelineTests(unittest.TestCase):
             ],
         )
 
+        for single_output in outputs:
+            for output_element in single_output:
+                compare_pipeline_output_to_hub_spec(output_element, ImageClassificationOutputElement)
+
     @require_torch
     def test_small_model_pt(self):
         small_model = "hf-internal-testing/tiny-random-vit"

tests/pipelines/test_pipelines_image_segmentation.py

@@ -20,6 +20,7 @@ import datasets
 import numpy as np
 import requests
 from datasets import load_dataset
+from huggingface_hub import ImageSegmentationOutputElement
 from huggingface_hub.utils import insecure_hashlib
 
 from transformers import (
@@ -36,6 +37,7 @@ from transformers import (
     pipeline,
 )
 from transformers.testing_utils import (
+    compare_pipeline_output_to_hub_spec,
     is_pipeline_test,
     nested_simplify,
     require_tf,
@@ -168,6 +170,10 @@ class ImageSegmentationPipelineTests(unittest.TestCase):
             f"Expected [{n}, {n}, {n}, {n}, {n}], got {[len(item) for item in outputs]}",
         )
 
+        for single_output in outputs:
+            for output_element in single_output:
+                compare_pipeline_output_to_hub_spec(output_element, ImageSegmentationOutputElement)
+
     @require_tf
     @unittest.skip(reason="Image segmentation not implemented in TF")
     def test_small_model_tf(self):

tests/pipelines/test_pipelines_image_to_text.py

@@ -15,10 +15,12 @@
 import unittest
 
 import requests
+from huggingface_hub import ImageToTextOutput
 
 from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
 from transformers.pipelines import ImageToTextPipeline, pipeline
 from transformers.testing_utils import (
+    compare_pipeline_output_to_hub_spec,
     is_pipeline_test,
     require_tf,
     require_torch,
@@ -103,6 +105,9 @@ class ImageToTextPipelineTests(unittest.TestCase):
             [{"generated_text": "growth"}],
         )
 
+        for single_output in outputs:
+            compare_pipeline_output_to_hub_spec(single_output, ImageToTextOutput)
+
     @require_torch
     def test_small_model_pt(self):
         pipe = pipeline("image-to-text", model="hf-internal-testing/tiny-random-vit-gpt2")

tests/pipelines/test_pipelines_object_detection.py

@@ -14,6 +14,8 @@
 
 import unittest
 
+from huggingface_hub import ObjectDetectionOutputElement
+
 from transformers import (
     MODEL_FOR_OBJECT_DETECTION_MAPPING,
     AutoFeatureExtractor,
@@ -22,7 +24,8 @@ from transformers import (
     is_vision_available,
     pipeline,
 )
-from transformers.testing_utils import (
+from transformers.testing_utils import (  #
+    compare_pipeline_output_to_hub_spec,
     is_pipeline_test,
     nested_simplify,
     require_pytesseract,
@@ -101,6 +104,7 @@ class ObjectDetectionPipelineTests(unittest.TestCase):
                         "box": {"xmin": ANY(int), "ymin": ANY(int), "xmax": ANY(int), "ymax": ANY(int)},
                     },
                 )
+                compare_pipeline_output_to_hub_spec(detected_object, ObjectDetectionOutputElement)
 
     @require_tf
     @unittest.skip(reason="Object detection not implemented in TF")

tests/pipelines/test_pipelines_zero_shot_image_classification.py

@@ -14,9 +14,12 @@
 
 import unittest
 
+from huggingface_hub import ZeroShotImageClassificationOutputElement
+
 from transformers import is_vision_available
 from transformers.pipelines import pipeline
 from transformers.testing_utils import (
+    compare_pipeline_output_to_hub_spec,
     is_pipeline_test,
     nested_simplify,
     require_tf,
@@ -127,6 +130,9 @@ class ZeroShotImageClassificationPipelineTests(unittest.TestCase):
             ],
         )
 
+        for single_output in output:
+            compare_pipeline_output_to_hub_spec(single_output, ZeroShotImageClassificationOutputElement)
+
     @require_torch
     def test_small_model_pt_fp16(self):
         self.test_small_model_pt(torch_dtype="float16")