docs/transformers/tests/pipelines/test_pipelines_image_to_text.py

# Copyright 2022 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 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,
    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_vision
class ImageToTextPipelineTests(unittest.TestCase):
    model_mapping = MODEL_FOR_VISION_2_SEQ_MAPPING
    tf_model_mapping = TF_MODEL_FOR_VISION_2_SEQ_MAPPING

    def get_test_pipeline(
        self,
        model,
        tokenizer=None,
        image_processor=None,
        feature_extractor=None,
        processor=None,
        torch_dtype="float32",
    ):
        pipe = ImageToTextPipeline(
            model=model,
            tokenizer=tokenizer,
            feature_extractor=feature_extractor,
            image_processor=image_processor,
            processor=processor,
            torch_dtype=torch_dtype,
        )
        examples = [
            Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]
        return pipe, examples

    def run_pipeline_test(self, pipe, examples):
        outputs = pipe(examples)
        self.assertEqual(
            outputs,
            [
                [{"generated_text": ANY(str)}],
                [{"generated_text": ANY(str)}],
            ],
        )

    @require_tf
    def test_small_model_tf(self):
        pipe = pipeline("image-to-text", model="hf-internal-testing/tiny-random-vit-gpt2", framework="tf")
        image = "./tests/fixtures/tests_samples/COCO/000000039769.png"

        outputs = pipe(image)
        self.assertEqual(
            outputs,
            [
                {
                    "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                },
            ],
        )

        outputs = pipe([image, image])
        self.assertEqual(
            outputs,
            [
                [
                    {
                        "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                    }
                ],
                [
                    {
                        "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                    }
                ],
            ],
        )

        outputs = pipe(image, max_new_tokens=1)
        self.assertEqual(
            outputs,
            [{"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")
        image = "./tests/fixtures/tests_samples/COCO/000000039769.png"

        outputs = pipe(image)
        self.assertEqual(
            outputs,
            [
                {
                    "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                },
            ],
        )

        outputs = pipe([image, image])
        self.assertEqual(
            outputs,
            [
                [
                    {
                        "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                    }
                ],
                [
                    {
                        "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                    }
                ],
            ],
        )

    @require_torch
    def test_small_model_pt_conditional(self):
        pipe = pipeline("image-to-text", model="hf-internal-testing/tiny-random-BlipForConditionalGeneration")
        image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
        prompt = "a photo of"

        outputs = pipe(image, prompt=prompt)
        self.assertTrue(outputs[0]["generated_text"].startswith(prompt))

    @require_torch
    def test_consistent_batching_behaviour(self):
        pipe = pipeline("image-to-text", model="hf-internal-testing/tiny-random-BlipForConditionalGeneration")
        image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
        prompt = "a photo of"

        outputs = pipe([image, image], prompt=prompt)
        self.assertTrue(outputs[0][0]["generated_text"].startswith(prompt))
        self.assertTrue(outputs[1][0]["generated_text"].startswith(prompt))

        outputs = pipe([image, image], prompt=prompt, batch_size=2)
        self.assertTrue(outputs[0][0]["generated_text"].startswith(prompt))
        self.assertTrue(outputs[1][0]["generated_text"].startswith(prompt))

        from torch.utils.data import Dataset

        class MyDataset(Dataset):
            def __len__(self):
                return 5

            def __getitem__(self, i):
                return "./tests/fixtures/tests_samples/COCO/000000039769.png"

        dataset = MyDataset()
        for batch_size in (1, 2, 4):
            outputs = pipe(dataset, prompt=prompt, batch_size=batch_size if batch_size > 1 else None)
            self.assertTrue(list(outputs)[0][0]["generated_text"].startswith(prompt))
            self.assertTrue(list(outputs)[1][0]["generated_text"].startswith(prompt))

    @slow
    @require_torch
    def test_large_model_pt(self):
        pipe = pipeline("image-to-text", model="ydshieh/vit-gpt2-coco-en")
        image = "./tests/fixtures/tests_samples/COCO/000000039769.png"

        outputs = pipe(image)
        self.assertEqual(outputs, [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}])

        outputs = pipe([image, image])
        self.assertEqual(
            outputs,
            [
                [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
                [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
            ],
        )

    @slow
    @require_torch
    def test_generation_pt_blip(self):
        pipe = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
        url = "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
        image = Image.open(requests.get(url, stream=True).raw)

        outputs = pipe(image)
        self.assertEqual(outputs, [{"generated_text": "a pink pokemon pokemon with a blue shirt and a blue shirt"}])

    @slow
    @require_torch
    def test_generation_pt_git(self):
        pipe = pipeline("image-to-text", model="microsoft/git-base-coco")
        url = "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
        image = Image.open(requests.get(url, stream=True).raw)

        outputs = pipe(image)
        self.assertEqual(outputs, [{"generated_text": "a cartoon of a purple character."}])

    @slow
    @require_torch
    def test_conditional_generation_pt_blip(self):
        pipe = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
        url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
        image = Image.open(requests.get(url, stream=True).raw)

        prompt = "a photography of"

        outputs = pipe(image, prompt=prompt)
        self.assertEqual(outputs, [{"generated_text": "a photography of a volcano"}])

        with self.assertRaises(ValueError):
            outputs = pipe([image, image], prompt=[prompt, prompt])

    @slow
    @require_torch
    def test_conditional_generation_pt_git(self):
        pipe = pipeline("image-to-text", model="microsoft/git-base-coco")
        url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
        image = Image.open(requests.get(url, stream=True).raw)

        prompt = "a photo of a"

        outputs = pipe(image, prompt=prompt)
        self.assertEqual(outputs, [{"generated_text": "a photo of a tent with a tent and a tent in the background."}])

        with self.assertRaises(ValueError):
            outputs = pipe([image, image], prompt=[prompt, prompt])

    @slow
    @require_torch
    def test_conditional_generation_pt_pix2struct(self):
        pipe = pipeline("image-to-text", model="google/pix2struct-ai2d-base")
        url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
        image = Image.open(requests.get(url, stream=True).raw)

        prompt = "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"

        outputs = pipe(image, prompt=prompt)
        self.assertEqual(outputs, [{"generated_text": "ash cloud"}])

        with self.assertRaises(ValueError):
            outputs = pipe([image, image], prompt=[prompt, prompt])

    @slow
    @require_tf
    def test_large_model_tf(self):
        pipe = pipeline("image-to-text", model="ydshieh/vit-gpt2-coco-en", framework="tf")
        image = "./tests/fixtures/tests_samples/COCO/000000039769.png"

        outputs = pipe(image)
        self.assertEqual(outputs, [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}])

        outputs = pipe([image, image])
        self.assertEqual(
            outputs,
            [
                [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
                [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
            ],
        )

    @slow
    @require_torch
    def test_conditional_generation_llava(self):
        pipe = pipeline("image-to-text", model="llava-hf/bakLlava-v1-hf")

        prompt = (
            "<image>\nUSER: What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud?\nASSISTANT:"
        )

        outputs = pipe(
            "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg",
            prompt=prompt,
            generate_kwargs={"max_new_tokens": 200},
        )
        self.assertEqual(
            outputs,
            [
                {
                    "generated_text": "\nUSER: What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud?\nASSISTANT: Lava"
                }
            ],
        )

    @slow
    @require_torch
    def test_nougat(self):
        pipe = pipeline("image-to-text", "facebook/nougat-base")

        outputs = pipe("https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/nougat_paper.png")

        self.assertEqual(
            outputs,
            [{"generated_text": "# Nougat: Neural Optical Understanding for Academic Documents\n\n Lukas Blec"}],
        )