зеркало из
https://github.com/ssciwr/AMMICO.git
synced 2025-10-30 05:26:05 +02:00
57 строки
1.5 KiB
Python
57 строки
1.5 KiB
Python
import os
|
|
import numpy
|
|
from torch import device, cuda
|
|
from lavis.models import load_model_and_preprocess
|
|
import misinformation.multimodal_search as ms
|
|
|
|
TEST_IMAGE_1 = "./test/data/d755771b-225e-432f-802e-fb8dc850fff7.png"
|
|
TEST_IMAGE_2 = "./test/data/IMG_2746.png"
|
|
TEST_IMAGE_3 = "./test/data/IMG_2750.png"
|
|
TEST_IMAGE_4 = "./test/data/IMG_2805.png"
|
|
TEST_IMAGE_5 = "./test/data/IMG_2806.png"
|
|
TEST_IMAGE_6 = "./test/data/IMG_2807.png"
|
|
TEST_IMAGE_7 = "./test/data/IMG_2808.png"
|
|
TEST_IMAGE_8 = "./test/data/IMG_2809.png"
|
|
TEST_IMAGE_9 = "./test/data/IMG_3755.jpg"
|
|
TEST_IMAGE_10 = "./test/data/IMG_3756.jpg"
|
|
TEST_IMAGE_11 = "./test/data/IMG_3757.jpg"
|
|
TEST_IMAGE_12 = "./test/data/pic1.png"
|
|
|
|
|
|
def test_read_img():
|
|
test_img = ms.read_img(TEST_IMAGE_2)
|
|
assert numpy.array(test_img)[257][34] == [70, 66, 63]
|
|
|
|
|
|
|
|
#def test_load_feature_extractor_model_blip2():
|
|
# multimodal_device = device("cuda" if cuda.is_available() else "cpu")
|
|
# (model, vis_processors, txt_processors,) = ms.load_feature_extractor_model_blip2(multimodal_device)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mydict = {}
|
|
for img_path in images:
|
|
id_ = os.path.splitext(os.path.basename(img_path))[0]
|
|
mydict[id_] = {"filename": img_path}
|
|
|
|
for key in mydict:
|
|
mydict[key] = sm.SummaryDetector(mydict[key]).analyse_image()
|
|
keys = list(mydict.keys())
|
|
assert len(mydict) == 12
|
|
for key in keys:
|
|
assert len(mydict[key]["3_non-deterministic summary"]) == 3 |