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test_image_diffusion_mapper.py
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import os
import shutil
import unittest
from data_juicer import _cuda_device_count
from data_juicer.core.data import NestedDataset as Dataset
from data_juicer.ops.mapper.image_diffusion_mapper import ImageDiffusionMapper
from data_juicer.utils.mm_utils import SpecialTokens
from data_juicer.utils.unittest_utils import DataJuicerTestCaseBase
class ImageDiffusionMapperTest(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_diffusion = 'CompVis/stable-diffusion-v1-4'
hf_img2seq = 'Salesforce/blip2-opt-2.7b'
# dir to save the images produced in the tests
output_dir = '../diffusion_output/'
@classmethod
def tearDownClass(cls) -> None:
super().tearDownClass(cls.hf_diffusion)
super().tearDownClass(cls.hf_img2seq)
def _run_mapper(self,
dataset: Dataset,
op,
move_to_dir,
num_proc=1,
total_num=1):
dataset = dataset.map(op.process, num_proc=num_proc, with_rank=True)
dataset_list = dataset.select_columns(
column_names=['images']).to_list()
self.assertEqual(len(dataset_list), total_num)
if not os.path.exists(move_to_dir):
os.makedirs(move_to_dir)
for data in dataset_list:
for image_path in data['images']:
if str(image_path) != str(self.cat_path) \
and str(image_path) != str(self.img3_path):
cp_to_path = os.path.join(move_to_dir,
os.path.basename(image_path))
shutil.copyfile(image_path, cp_to_path)
def test_for_strength(self):
ds_list = [{
'text': f'{SpecialTokens.image}a photo of a cat',
'caption': 'a women with an umbrella',
'images': [self.cat_path]
}]
aug_num = 3
dataset = Dataset.from_list(ds_list)
op = ImageDiffusionMapper(hf_diffusion=self.hf_diffusion,
strength=1.0,
aug_num=aug_num,
keep_original_sample=True,
caption_key='caption')
self._run_mapper(dataset,
op,
os.path.join(self.output_dir, 'test_for_strength'),
total_num=(aug_num + 1) * len(ds_list))
def test_for_given_caption_list(self):
ds_list = [{
'text': f'{SpecialTokens.image}, {SpecialTokens.image}',
'captions': ['A photo of a cat', 'a women with an umbrella'],
'images': [self.cat_path, self.img3_path]
}]
aug_num = 2
dataset = Dataset.from_list(ds_list)
op = ImageDiffusionMapper(hf_diffusion=self.hf_diffusion,
aug_num=aug_num,
keep_original_sample=False,
caption_key='captions')
self._run_mapper(dataset,
op,
os.path.join(self.output_dir,
'test_for_given_caption_list'),
total_num=aug_num * len(ds_list))
def test_for_given_caption_string(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]
}]
aug_num = 1
dataset = Dataset.from_list(ds_list)
op = ImageDiffusionMapper(hf_diffusion=self.hf_diffusion,
aug_num=aug_num,
keep_original_sample=False,
caption_key='text')
self._run_mapper(dataset,
op,
os.path.join(self.output_dir,
'test_for_given_caption_string'),
total_num=aug_num * len(ds_list))
def test_for_no_given_caption(self):
ds_list = [{
'text': f'{SpecialTokens.image}',
'images': [self.cat_path]
}, {
'text': f'{SpecialTokens.image}',
'images': [self.img3_path]
}]
aug_num = 2
dataset = Dataset.from_list(ds_list)
op = ImageDiffusionMapper(hf_diffusion=self.hf_diffusion,
aug_num=aug_num,
keep_original_sample=False,
hf_img2seq=self.hf_img2seq)
self._run_mapper(dataset,
op,
os.path.join(self.output_dir,
'test_for_no_given_caption'),
total_num=aug_num * len(ds_list))
def test_for_fp16_given_caption_string(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]
}]
aug_num = 1
dataset = Dataset.from_list(ds_list)
op = ImageDiffusionMapper(hf_diffusion=self.hf_diffusion,
torch_dtype='fp16',
revision='fp16',
aug_num=aug_num,
keep_original_sample=False,
caption_key='text')
self._run_mapper(dataset,
op,
os.path.join(self.output_dir,
'test_for_fp16_given_caption_string'),
total_num=aug_num * len(ds_list))
def test_for_multi_process_given_caption_string(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]
}]
aug_num = 1
dataset = Dataset.from_list(ds_list)
op = ImageDiffusionMapper(hf_diffusion=self.hf_diffusion,
aug_num=aug_num,
keep_original_sample=False,
caption_key='text')
# set num_proc <= the number of CUDA if it is available
num_proc = 2
if _cuda_device_count() == 1:
num_proc = 1
self._run_mapper(dataset,
op,
os.path.join(self.output_dir,
'test_for_given_caption_string'),
num_proc=num_proc,
total_num=aug_num * len(ds_list))
if __name__ == '__main__':
unittest.main()