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44682e7131
* Adapt and test huggingface_hub v1.0.0.rc0 * forgot to bump hfh * bump * code quality * code quality * relax dependency table * fix has_file * install hfh 1.0.0.rc0 in circle ci jobs * repostiryo * push to hub now returns a commit url * catch HfHubHTTPError * check commit on branch * add it back * fix ? * remove deprecated test * uncomment another test * trigger * no proxies * many more small changes * fix load PIL Image from httpx * require 1.0.0.rc0 * fix mocked tests * fix others * unchange * unchange * args * Update .circleci/config.yml * Bump to 1.0.0.rc1 * bump kernels version * fix deps
270 lines
9.8 KiB
Python
270 lines
9.8 KiB
Python
# Copyright 2022 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import io
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import unittest
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import httpx
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from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
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from transformers.pipelines import ImageToTextPipeline, pipeline
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from transformers.testing_utils import (
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is_pipeline_test,
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require_torch,
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require_vision,
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slow,
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)
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from .test_pipelines_common import ANY
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if is_vision_available():
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from PIL import Image
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else:
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class Image:
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@staticmethod
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def open(*args, **kwargs):
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pass
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@is_pipeline_test
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@require_vision
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class ImageToTextPipelineTests(unittest.TestCase):
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model_mapping = MODEL_FOR_VISION_2_SEQ_MAPPING
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def get_test_pipeline(
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self,
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model,
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tokenizer=None,
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image_processor=None,
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feature_extractor=None,
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processor=None,
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dtype="float32",
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):
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pipe = ImageToTextPipeline(
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model=model,
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tokenizer=tokenizer,
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feature_extractor=feature_extractor,
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image_processor=image_processor,
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processor=processor,
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dtype=dtype,
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max_new_tokens=20,
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)
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examples = [
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Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
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"./tests/fixtures/tests_samples/COCO/000000039769.png",
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]
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return pipe, examples
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def run_pipeline_test(self, pipe, examples):
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outputs = pipe(examples)
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self.assertEqual(
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outputs,
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[
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[{"generated_text": ANY(str)}],
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[{"generated_text": ANY(str)}],
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],
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)
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@require_torch
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def test_small_model_pt(self):
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pipe = pipeline("image-to-text", model="hf-internal-testing/tiny-random-vit-gpt2", max_new_tokens=19)
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image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
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outputs = pipe(image)
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self.assertEqual(
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outputs,
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[
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{
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"generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
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},
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],
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)
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outputs = pipe([image, image])
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self.assertEqual(
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outputs,
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[
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[
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{
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"generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
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}
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],
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[
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{
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"generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
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}
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],
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],
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)
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@require_torch
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def test_small_model_pt_conditional(self):
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pipe = pipeline("image-to-text", model="hf-internal-testing/tiny-random-BlipForConditionalGeneration")
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image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
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prompt = "a photo of"
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outputs = pipe(image, prompt=prompt)
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self.assertTrue(outputs[0]["generated_text"].startswith(prompt))
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@require_torch
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def test_consistent_batching_behaviour(self):
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pipe = pipeline(
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"image-to-text", model="hf-internal-testing/tiny-random-BlipForConditionalGeneration", max_new_tokens=10
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)
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image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
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prompt = "a photo of"
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outputs = pipe([image, image], prompt=prompt)
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self.assertTrue(outputs[0][0]["generated_text"].startswith(prompt))
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self.assertTrue(outputs[1][0]["generated_text"].startswith(prompt))
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outputs = pipe([image, image], prompt=prompt, batch_size=2)
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self.assertTrue(outputs[0][0]["generated_text"].startswith(prompt))
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self.assertTrue(outputs[1][0]["generated_text"].startswith(prompt))
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from torch.utils.data import Dataset
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class MyDataset(Dataset):
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def __len__(self):
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return 5
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def __getitem__(self, i):
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return "./tests/fixtures/tests_samples/COCO/000000039769.png"
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dataset = MyDataset()
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for batch_size in (1, 2, 4):
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outputs = pipe(dataset, prompt=prompt, batch_size=batch_size if batch_size > 1 else None)
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self.assertTrue(list(outputs)[0][0]["generated_text"].startswith(prompt))
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self.assertTrue(list(outputs)[1][0]["generated_text"].startswith(prompt))
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@slow
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@require_torch
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def test_large_model_pt(self):
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pipe = pipeline("image-to-text", model="ydshieh/vit-gpt2-coco-en")
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image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
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outputs = pipe(image)
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self.assertEqual(outputs, [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}])
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outputs = pipe([image, image])
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self.assertEqual(
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outputs,
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[
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[{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
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[{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
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],
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)
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@slow
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@require_torch
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def test_generation_pt_blip(self):
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pipe = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
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url = "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
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image = Image.open(io.BytesIO(httpx.get(url, follow_redirects=True).content))
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outputs = pipe(image)
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self.assertEqual(outputs, [{"generated_text": "a pink pokemon pokemon with a blue shirt and a blue shirt"}])
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@slow
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@require_torch
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def test_generation_pt_git(self):
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pipe = pipeline("image-to-text", model="microsoft/git-base-coco")
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url = "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
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image = Image.open(io.BytesIO(httpx.get(url, follow_redirects=True).content))
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outputs = pipe(image)
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self.assertEqual(outputs, [{"generated_text": "a cartoon of a purple character."}])
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@slow
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@require_torch
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def test_conditional_generation_pt_blip(self):
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pipe = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
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image = Image.open(io.BytesIO(httpx.get(url, follow_redirects=True).content))
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prompt = "a photography of"
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outputs = pipe(image, prompt=prompt)
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self.assertEqual(outputs, [{"generated_text": "a photography of a volcano"}])
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with self.assertRaises(ValueError):
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outputs = pipe([image, image], prompt=[prompt, prompt])
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@slow
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@require_torch
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def test_conditional_generation_pt_git(self):
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pipe = pipeline("image-to-text", model="microsoft/git-base-coco")
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
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image = Image.open(io.BytesIO(httpx.get(url, follow_redirects=True).content))
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prompt = "a photo of a"
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outputs = pipe(image, prompt=prompt)
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self.assertEqual(outputs, [{"generated_text": "a photo of a tent with a tent and a tent in the background."}])
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with self.assertRaises(ValueError):
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outputs = pipe([image, image], prompt=[prompt, prompt])
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@slow
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@require_torch
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def test_conditional_generation_pt_pix2struct(self):
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pipe = pipeline("image-to-text", model="google/pix2struct-ai2d-base")
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
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image = Image.open(io.BytesIO(httpx.get(url, follow_redirects=True).content))
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prompt = "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"
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outputs = pipe(image, prompt=prompt)
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self.assertEqual(outputs, [{"generated_text": "ash cloud"}])
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with self.assertRaises(ValueError):
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outputs = pipe([image, image], prompt=[prompt, prompt])
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@slow
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@require_torch
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@unittest.skip("TODO (joao, raushan): there is something wrong with image processing in the model/pipeline")
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def test_conditional_generation_llava(self):
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pipe = pipeline("image-to-text", model="llava-hf/bakLlava-v1-hf")
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prompt = (
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"<image>\nUSER: What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud?\nASSISTANT:"
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)
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outputs = pipe(
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"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg",
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prompt=prompt,
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generate_kwargs={"max_new_tokens": 200},
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)
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self.assertEqual(
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outputs,
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[
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{
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"generated_text": "\nUSER: What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud?\nASSISTANT: Lava"
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}
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],
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)
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@slow
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@require_torch
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def test_nougat(self):
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pipe = pipeline("image-to-text", "facebook/nougat-base", max_new_tokens=19)
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outputs = pipe("https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/nougat_paper.png")
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self.assertEqual(
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outputs,
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[{"generated_text": "# Nougat: Neural Optical Understanding for Academic Documents\n\n Lukas Blec"}],
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)
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