# 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 io import unittest import httpx from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available from transformers.pipelines import ImageToTextPipeline, pipeline from transformers.testing_utils import ( is_pipeline_test, 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 def get_test_pipeline( self, model, tokenizer=None, image_processor=None, feature_extractor=None, processor=None, dtype="float32", ): pipe = ImageToTextPipeline( model=model, tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor, processor=processor, dtype=dtype, max_new_tokens=20, ) 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_torch def test_small_model_pt(self): pipe = pipeline("image-to-text", model="hf-internal-testing/tiny-random-vit-gpt2", max_new_tokens=19) 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", max_new_tokens=10 ) 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(io.BytesIO(httpx.get(url, follow_redirects=True).content)) 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(io.BytesIO(httpx.get(url, follow_redirects=True).content)) 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(io.BytesIO(httpx.get(url, follow_redirects=True).content)) 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(io.BytesIO(httpx.get(url, follow_redirects=True).content)) 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(io.BytesIO(httpx.get(url, follow_redirects=True).content)) 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_torch @unittest.skip("TODO (joao, raushan): there is something wrong with image processing in the model/pipeline") def test_conditional_generation_llava(self): pipe = pipeline("image-to-text", model="llava-hf/bakLlava-v1-hf") prompt = ( "\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", max_new_tokens=19) 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"}], )