309 lines
11 KiB
Python
309 lines
11 KiB
Python
# 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 transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
|
|
from transformers.pipelines import pipeline
|
|
from transformers.testing_utils import (
|
|
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, processor):
|
|
pipe = pipeline("image-to-text", model=model, tokenizer=tokenizer, image_processor=processor)
|
|
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"}],
|
|
)
|
|
|
|
@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"}],
|
|
)
|