2021 the huggingface team 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 orglicenseslicense2 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 accepts url pil image lists rgba la l assert that the pipeline can be initialized with a feature extractor that is not in any mapping perceiver is not tested by runpipelinetest properly that is because the type of featureextractor and model preprocessor need to be kept in sync which is not the case in the current design 2021 the huggingface team 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 accepts url pil image lists rgba la l assert that the pipeline can be initialized with a feature extractor that is not in any mapping perceiver is not tested by run_pipeline_test properly that is because the type of feature_extractor and model preprocessor need to be kept in sync which is not the case in the current design
import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizerBase,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
    from PIL import Image
else:

    class Image:
        @staticmethod
        def open(*args, **kwargs):
            pass


@is_pipeline_test
@require_torch_or_tf
@require_vision
class ImageClassificationPipelineTests(unittest.TestCase):
    model_mapping = MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
    tf_model_mapping = TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

    def get_test_pipeline(self, model, tokenizer, processor):
        image_classifier = ImageClassificationPipeline(model=model, image_processor=processor, top_k=2)
        examples = [
            Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
            "http://images.cocodataset.org/val2017/000000039769.jpg",
        ]
        return image_classifier, examples

    def run_pipeline_test(self, image_classifier, examples):
        outputs = image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png")

        self.assertEqual(
            outputs,
            [
                {"score": ANY(float), "label": ANY(str)},
                {"score": ANY(float), "label": ANY(str)},
            ],
        )

        import datasets

        dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test")

        
        outputs = image_classifier(
            [
                Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                
                dataset[0]["file"],
                
                dataset[1]["file"],
                
                dataset[2]["file"],
            ]
        )
        self.assertEqual(
            outputs,
            [
                [
                    {"score": ANY(float), "label": ANY(str)},
                    {"score": ANY(float), "label": ANY(str)},
                ],
                [
                    {"score": ANY(float), "label": ANY(str)},
                    {"score": ANY(float), "label": ANY(str)},
                ],
                [
                    {"score": ANY(float), "label": ANY(str)},
                    {"score": ANY(float), "label": ANY(str)},
                ],
                [
                    {"score": ANY(float), "label": ANY(str)},
                    {"score": ANY(float), "label": ANY(str)},
                ],
                [
                    {"score": ANY(float), "label": ANY(str)},
                    {"score": ANY(float), "label": ANY(str)},
                ],
            ],
        )

    @require_torch
    def test_small_model_pt(self):
        small_model = "hf-internal-testing/tiny-random-vit"
        image_classifier = pipeline("image-classification", model=small_model)

        outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
        )

        outputs = image_classifier(
            [
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                "http://images.cocodataset.org/val2017/000000039769.jpg",
            ],
            top_k=2,
        )
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
                [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
            ],
        )

    @require_tf
    def test_small_model_tf(self):
        small_model = "hf-internal-testing/tiny-random-vit"
        image_classifier = pipeline("image-classification", model=small_model, framework="tf")

        outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
        )

        outputs = image_classifier(
            [
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                "http://images.cocodataset.org/val2017/000000039769.jpg",
            ],
            top_k=2,
        )
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
                [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
            ],
        )

    def test_custom_tokenizer(self):
        tokenizer = PreTrainedTokenizerBase()

        
        image_classifier = pipeline(
            "image-classification", model="hf-internal-testing/tiny-random-vit", tokenizer=tokenizer
        )

        self.assertIs(image_classifier.tokenizer, tokenizer)

    @slow
    @require_torch
    def test_perceiver(self):
        
        
        
        image_classifier = pipeline("image-classification", model="deepmind/vision-perceiver-conv")
        outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.4385, "label": "tabby, tabby cat"},
                {"score": 0.321, "label": "tiger cat"},
                {"score": 0.0502, "label": "Egyptian cat"},
                {"score": 0.0137, "label": "crib, cot"},
                {"score": 0.007, "label": "radiator"},
            ],
        )

        image_classifier = pipeline("image-classification", model="deepmind/vision-perceiver-fourier")
        outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.5658, "label": "tabby, tabby cat"},
                {"score": 0.1309, "label": "tiger cat"},
                {"score": 0.0722, "label": "Egyptian cat"},
                {"score": 0.0707, "label": "remote control, remote"},
                {"score": 0.0082, "label": "computer keyboard, keypad"},
            ],
        )

        image_classifier = pipeline("image-classification", model="deepmind/vision-perceiver-learned")
        outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.3022, "label": "tabby, tabby cat"},
                {"score": 0.2362, "label": "Egyptian cat"},
                {"score": 0.1856, "label": "tiger cat"},
                {"score": 0.0324, "label": "remote control, remote"},
                {"score": 0.0096, "label": "quilt, comforter, comfort, puff"},
            ],
        )