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test_image_captioning_mapper.py
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test_image_captioning_mapper.py
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import os
import unittest
from data_juicer.core.data import NestedDataset as Dataset
from data_juicer.ops.mapper.image_captioning_mapper import \
ImageCaptioningMapper
from data_juicer.utils.mm_utils import SpecialTokens
from data_juicer.utils.unittest_utils import (SKIPPED_TESTS,
DataJuicerTestCaseBase)
# Skip tests for this OP in the GitHub actions due to OOM on the current runner
# These tests have been tested locally.
@SKIPPED_TESTS.register_module()
class ImageCaptioningMapperTest(DataJuicerTestCaseBase):
data_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..',
'data')
cat_path = os.path.join(data_path, 'cat.jpg')
img3_path = os.path.join(data_path, 'img3.jpg')
hf_img2seq = 'Salesforce/blip2-opt-2.7b'
@classmethod
def tearDownClass(cls) -> None:
super().tearDownClass(cls.hf_img2seq)
def _run_mapper(self,
dataset: Dataset,
op,
num_proc=1,
caption_num=0):
dataset = dataset.map(op.process, num_proc=num_proc, with_rank=True)
dataset_list = dataset.select_columns(column_names=['text']).to_list()
# assert the caption is generated successfully in terms of not_none
# as the generated content is not deterministic
self.assertEqual(len(dataset_list), caption_num)
def test_no_eoc_special_token(self):
ds_list = [{
'text': f'{SpecialTokens.image}a photo of a cat',
'images': [self.cat_path]
}, {
'text': f'{SpecialTokens.image}a photo, a women with an umbrella',
'images': [self.img3_path]
}]
caption_num = 1
dataset = Dataset.from_list(ds_list)
op = ImageCaptioningMapper(hf_img2seq=self.hf_img2seq,
caption_num=caption_num,
keep_candidate_mode='random_any')
self._run_mapper(dataset, op, caption_num=len(dataset) * 2)
def test_eoc_special_token(self):
ds_list = [
{
'text':
f'{SpecialTokens.image}a photo of a cat{SpecialTokens.eoc}',
'images': [self.cat_path]
},
{
'text':
f'{SpecialTokens.image}a photo, a women with an umbrella{SpecialTokens.eoc}', # noqa: E501
'images': [self.img3_path]
}
]
caption_num = 1
dataset = Dataset.from_list(ds_list)
op = ImageCaptioningMapper(hf_img2seq=self.hf_img2seq,
caption_num=caption_num,
keep_candidate_mode='random_any')
self._run_mapper(dataset, op, caption_num=len(dataset) * 2)
def test_multi_candidate_keep_random_any(self):
ds_list = [{
'text': f'{SpecialTokens.image}a photo of a cat',
'images': [self.cat_path]
}, {
'text': f'{SpecialTokens.image}a photo, a women with an umbrella',
'images': [self.img3_path]
}]
caption_num = 4
dataset = Dataset.from_list(ds_list)
op = ImageCaptioningMapper(hf_img2seq=self.hf_img2seq,
caption_num=caption_num,
keep_candidate_mode='random_any')
self._run_mapper(dataset, op, caption_num=len(dataset) * 2)
def test_multi_candidate_keep_all(self):
ds_list = [{
'text': f'{SpecialTokens.image}a photo of a cat',
'images': [self.cat_path]
}, {
'text': f'{SpecialTokens.image}a photo, a women with an umbrella',
'images': [self.img3_path]
}]
caption_num = 4
dataset = Dataset.from_list(ds_list)
op = ImageCaptioningMapper(hf_img2seq=self.hf_img2seq,
caption_num=caption_num,
keep_candidate_mode='all')
self._run_mapper(dataset,
op,
caption_num=(1 + caption_num) * len(dataset))
def test_multi_candidate_keep_similar_one(self):
ds_list = [{
'text': f'{SpecialTokens.image}a photo of a cat',
'images': [self.cat_path]
}, {
'text': f'{SpecialTokens.image}a photo, a women with an umbrella',
'images': [self.img3_path]
}]
caption_num = 4
dataset = Dataset.from_list(ds_list)
op = ImageCaptioningMapper(hf_img2seq=self.hf_img2seq,
caption_num=caption_num,
keep_candidate_mode='similar_one_simhash')
self._run_mapper(dataset, op, caption_num=len(dataset) * 2)
def test_multi_process(self):
ds_list = [{
'text': f'{SpecialTokens.image}a photo of a cat',
'images': [self.cat_path]
}] * 10
caption_num = 1
dataset = Dataset.from_list(ds_list)
op = ImageCaptioningMapper(hf_img2seq=self.hf_img2seq,
caption_num=caption_num,
keep_candidate_mode='random_any')
self._run_mapper(dataset, op, num_proc=4, caption_num=len(dataset) * 2)
def test_no_eoc_special_token_remove_original_sample(self):
ds_list = [{
'text': f'{SpecialTokens.image}a photo of a cat',
'images': [self.cat_path]
}, {
'text': f'{SpecialTokens.image}a photo, a women with an umbrella',
'images': [self.img3_path]
}]
caption_num = 1
dataset = Dataset.from_list(ds_list)
op = ImageCaptioningMapper(hf_img2seq=self.hf_img2seq,
caption_num=caption_num,
keep_candidate_mode='random_any',
keep_original_sample=False)
self._run_mapper(dataset, op, caption_num=len(dataset))
def test_eoc_special_token_remove_original_sample(self):
ds_list = [
{
'text':
f'{SpecialTokens.image}a photo of a cat{SpecialTokens.eoc}',
'images': [self.cat_path]
},
{
'text':
f'{SpecialTokens.image}a photo, a women with an umbrella{SpecialTokens.eoc}', # noqa: E501
'images': [self.img3_path]
}
]
caption_num = 1
dataset = Dataset.from_list(ds_list)
op = ImageCaptioningMapper(hf_img2seq=self.hf_img2seq,
caption_num=caption_num,
keep_candidate_mode='random_any',
keep_original_sample=False)
self._run_mapper(dataset, op, caption_num=len(dataset))
def test_multi_candidate_keep_random_any_remove_original_sample(self):
ds_list = [{
'text': f'{SpecialTokens.image}a photo of a cat',
'images': [self.cat_path]
}, {
'text': f'{SpecialTokens.image}a photo, a women with an umbrella',
'images': [self.img3_path]
}]
caption_num = 4
dataset = Dataset.from_list(ds_list)
op = ImageCaptioningMapper(hf_img2seq=self.hf_img2seq,
caption_num=caption_num,
keep_candidate_mode='random_any',
keep_original_sample=False)
self._run_mapper(dataset, op, caption_num=len(dataset))
def test_multi_candidate_keep_all_remove_original_sample(self):
ds_list = [{
'text': f'{SpecialTokens.image}a photo of a cat',
'images': [self.cat_path]
}, {
'text': f'{SpecialTokens.image}a photo, a women with an umbrella',
'images': [self.img3_path]
}]
caption_num = 4
dataset = Dataset.from_list(ds_list)
op = ImageCaptioningMapper(hf_img2seq=self.hf_img2seq,
caption_num=caption_num,
keep_candidate_mode='all',
keep_original_sample=False)
self._run_mapper(dataset, op, caption_num=caption_num * len(dataset))
def test_multi_candidate_keep_similar_one_remove_original_sample(self):
ds_list = [{
'text': f'{SpecialTokens.image}a photo of a cat',
'images': [self.cat_path]
}, {
'text': f'{SpecialTokens.image}a photo, a women with an umbrella',
'images': [self.img3_path]
}]
caption_num = 4
dataset = Dataset.from_list(ds_list)
op = ImageCaptioningMapper(hf_img2seq=self.hf_img2seq,
caption_num=caption_num,
keep_candidate_mode='similar_one_simhash',
keep_original_sample=False)
self._run_mapper(dataset, op, caption_num=len(dataset))
def test_multi_process_remove_original_sample(self):
ds_list = [{
'text': f'{SpecialTokens.image}a photo of a cat',
'images': [self.cat_path]
}] * 10
caption_num = 1
dataset = Dataset.from_list(ds_list)
op = ImageCaptioningMapper(hf_img2seq=self.hf_img2seq,
caption_num=caption_num,
keep_candidate_mode='random_any',
keep_original_sample=False)
self._run_mapper(dataset, op, num_proc=4, caption_num=len(dataset))
if __name__ == '__main__':
unittest.main()