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a5c43b6488
* deleted lavis from utils * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fixed test_objects * added 'not gcv' to CI * fixed multimodal search and summary tests * disable doc build on PR for now * restrict ipywidgets version to avoid dummycomm error * limit deepface version * original repositories for retinaface lavis * update gcv test results * update display test outputs * update test env * run all tests * wo xdist to avoid segfault * remove widgets ref * skip long-running tests * skip long * verbose codecov upload * refactor summary test 2 * finish summary test refactor * reduce memory overhead of SummaryDetector * remove VQA models from self * remove VQA models from self * update notebook for changes * update notebook for changes * fixed multimodal search tests * fixed tests in multimodal search after precommit * run all tests * update doc notebook for summary changes * skip long-running multimodal * exclude blip2 from testing --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Inga Ulusoy <inga.ulusoy@uni-heidelberg.de>
99 строки
3.2 KiB
Python
99 строки
3.2 KiB
Python
import os
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import pytest
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from torch import device, cuda
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from lavis.models import load_model_and_preprocess
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import misinformation.summary as sm
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IMAGES = ["d755771b-225e-432f-802e-fb8dc850fff7.png", "IMG_2746.png"]
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SUMMARY_DEVICE = device("cuda" if cuda.is_available() else "cpu")
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TEST_KWARGS = {
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"run1": {
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"name": "blip_caption",
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"model_type": "base_coco",
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"is_eval": True,
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"device": SUMMARY_DEVICE,
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},
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"run2": {
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"name": "blip_caption",
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"model_type": "base_coco",
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"is_eval": True,
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"device": SUMMARY_DEVICE,
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},
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"run3": {
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"name": "blip_caption",
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"model_type": "large_coco",
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"is_eval": True,
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"device": SUMMARY_DEVICE,
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},
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}
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@pytest.fixture
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def get_dict(get_path):
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mydict = {}
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for img in IMAGES:
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id_ = os.path.splitext(os.path.basename(img))[0]
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mydict[id_] = {"filename": get_path + img}
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return mydict
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@pytest.mark.long
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def test_analyse_image(get_dict):
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reference_results = {
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"run1": {
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"d755771b-225e-432f-802e-fb8dc850fff7": "a river running through a city next to tall buildings",
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"IMG_2746": "a crowd of people standing on top of a tennis court",
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},
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"run2": {
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"d755771b-225e-432f-802e-fb8dc850fff7": "a river running through a city next to tall buildings",
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"IMG_2746": "a crowd of people standing on top of a tennis court",
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},
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"run3": {
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"d755771b-225e-432f-802e-fb8dc850fff7": "a river running through a town next to tall buildings",
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"IMG_2746": "a crowd of people standing on top of a track",
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},
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}
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# test three different models
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for test_run in TEST_KWARGS.keys():
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summary_model, summary_vis_processors, _ = load_model_and_preprocess(
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**TEST_KWARGS[test_run]
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)
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# run two different images
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for key in get_dict.keys():
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get_dict[key] = sm.SummaryDetector(get_dict[key]).analyse_image(
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summary_model, summary_vis_processors
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)
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assert len(get_dict) == 2
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for key in get_dict.keys():
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assert len(get_dict[key]["3_non-deterministic summary"]) == 3
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assert (
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get_dict[key]["const_image_summary"] == reference_results[test_run][key]
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)
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cuda.empty_cache()
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summary_model = None
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summary_vis_processors = None
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def test_analyse_questions(get_dict):
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list_of_questions = [
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"How many persons on the picture?",
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"What happends on the picture?",
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]
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for key in get_dict:
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get_dict[key] = sm.SummaryDetector(get_dict[key]).analyse_questions(
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list_of_questions
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)
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assert len(get_dict) == 2
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list_of_questions_ans = ["2", "100"]
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list_of_questions_ans2 = ["flood", "festival"]
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test_answers = []
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test_answers2 = []
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for key in get_dict.keys():
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test_answers.append(get_dict[key][list_of_questions[0]])
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test_answers2.append(get_dict[key][list_of_questions[1]])
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assert sorted(test_answers) == sorted(list_of_questions_ans)
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assert sorted(test_answers2) == sorted(list_of_questions_ans2)
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