зеркало из
https://github.com/ssciwr/AMMICO.git
synced 2025-10-29 13:06:04 +02:00
fixed test multimodal search on cpu and gpu machines
Этот коммит содержится в:
Petr Andriushchenko
родитель
ea275cdd09
Коммит
b0cfab05e9
@ -1,4 +1,5 @@
|
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import pytest
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import math
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from PIL import Image
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import numpy
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from torch import device, cuda, no_grad
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@ -17,6 +18,8 @@ TEST_IMAGE_9 = "./test/data/IMG_3755.jpg"
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TEST_IMAGE_10 = "./test/data/IMG_3756.jpg"
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TEST_IMAGE_11 = "./test/data/IMG_3757.jpg"
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TEST_IMAGE_12 = "./test/data/pic1.png"
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related_error = 1e-3
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gpu_is_not_available = not cuda.is_available()
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def test_read_img():
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@ -51,8 +54,7 @@ def test_load_feature_extractor_model_blip2():
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extracted_feature_text = model.extract_features(
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{"image": "", "text_input": processed_text}, mode="text"
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)
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assert processed_pic[0, 0, 0, 25:35].tolist() == [
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check_list_processed_pic = [
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-1.0039474964141846,
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-1.0039474964141846,
|
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-0.8433647751808167,
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@ -64,13 +66,18 @@ def test_load_feature_extractor_model_blip2():
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-0.7119789123535156,
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-1.0623412132263184,
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]
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for i, num in zip(range(10), processed_pic[0, 0, 0, 25:35].tolist()):
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assert (
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math.isclose(num, check_list_processed_pic[i], rel_tol=related_error)
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is True
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)
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assert (
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processed_text
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== "the bird sat on a tree located at the intersection of 23rd and 43rd streets"
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)
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assert extracted_feature_img["image_embeds_proj"][0, 0, 10:20].tolist() == [
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check_list_extracted_feature_img = [
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0.04566730558872223,
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-0.042554520070552826,
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-0.06970272958278656,
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@ -82,8 +89,17 @@ def test_load_feature_extractor_model_blip2():
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0.12923966348171234,
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0.006452132016420364,
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]
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for i, num in zip(
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range(10), extracted_feature_img["image_embeds_proj"][0, 0, 10:20].tolist()
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):
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assert (
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math.isclose(
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num, check_list_extracted_feature_img[i], rel_tol=related_error
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)
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is True
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)
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assert extracted_feature_text["text_embeds_proj"][0, 0, 10:20].tolist() == [
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check_list_extracted_feature_text = [
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-0.1384519338607788,
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-0.008663734421133995,
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0.006240826100111008,
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@ -95,14 +111,57 @@ def test_load_feature_extractor_model_blip2():
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-0.05823372304439545,
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0.036941494792699814,
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]
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for i, num in zip(
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range(10), extracted_feature_text["text_embeds_proj"][0, 0, 10:20].tolist()
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):
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assert (
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math.isclose(
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num, check_list_extracted_feature_text[i], rel_tol=related_error
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)
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is True
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)
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image_paths = [TEST_IMAGE_2, TEST_IMAGE_3]
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raw_images, images_tensors = ms.MultimodalSearch.read_and_process_images(
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my_dict, image_paths, vis_processor
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)
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check_list_images_tensors = [
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-1.0039474964141846,
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-1.0039474964141846,
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-0.8433647751808167,
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-0.6097899675369263,
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-0.5951915383338928,
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-0.6243883967399597,
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-0.6827820539474487,
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-0.6097899675369263,
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-0.7119789123535156,
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-1.0623412132263184,
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]
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for i, num in zip(range(10), images_tensors[0, 0, 0, 0, 25:35].tolist()):
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assert (
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math.isclose(num, check_list_images_tensors[i], rel_tol=related_error)
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is True
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)
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del model, vis_processor, txt_processor
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cuda.empty_cache()
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def test_load_feature_extractor_model_blip():
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@pytest.mark.parametrize(
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("multimodal_device"),
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[
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device("cpu"),
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pytest.param(
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device("cuda"),
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marks=pytest.mark.skipif(
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gpu_is_not_available, reason="gpu_is_not_availible"
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),
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),
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],
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)
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def test_load_feature_extractor_model_blip(multimodal_device):
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my_dict = {}
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multimodal_device = device("cuda" if cuda.is_available() else "cpu")
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(
|
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model,
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vis_processor,
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@ -125,7 +184,7 @@ def test_load_feature_extractor_model_blip():
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{"image": "", "text_input": processed_text}, mode="text"
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)
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assert processed_pic[0, 0, 0, 25:35].tolist() == [
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check_list_processed_pic = [
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-1.0039474964141846,
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-1.0039474964141846,
|
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-0.8433647751808167,
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@ -137,13 +196,18 @@ def test_load_feature_extractor_model_blip():
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-0.7119789123535156,
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-1.0623412132263184,
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]
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for i, num in zip(range(10), processed_pic[0, 0, 0, 25:35].tolist()):
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assert (
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math.isclose(num, check_list_processed_pic[i], rel_tol=related_error)
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is True
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)
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assert (
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processed_text
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== "the bird sat on a tree located at the intersection of 23rd and 43rd streets"
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)
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assert extracted_feature_img["image_embeds_proj"][0, 0, 10:20].tolist() == [
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check_list_extracted_feature_img = [
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-0.02480311505496502,
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0.05037587881088257,
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0.039517853409051895,
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@ -155,8 +219,17 @@ def test_load_feature_extractor_model_blip():
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-0.07324369996786118,
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0.06994668394327164,
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]
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for i, num in zip(
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range(10), extracted_feature_img["image_embeds_proj"][0, 0, 10:20].tolist()
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):
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assert (
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math.isclose(
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num, check_list_extracted_feature_img[i], rel_tol=related_error
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)
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is True
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)
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assert extracted_feature_text["text_embeds_proj"][0, 0, 10:20].tolist() == [
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check_list_extracted_feature_text = [
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0.0118643119931221,
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-0.01291718054562807,
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-0.0009687161073088646,
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@ -168,14 +241,34 @@ def test_load_feature_extractor_model_blip():
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0.0062415082938969135,
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0.0034833091776818037,
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]
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for i, num in zip(
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range(10), extracted_feature_text["text_embeds_proj"][0, 0, 10:20].tolist()
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):
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assert (
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math.isclose(
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num, check_list_extracted_feature_text[i], rel_tol=related_error
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)
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is True
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)
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del model, vis_processor, txt_processor
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cuda.empty_cache()
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def test_load_feature_extractor_model_albef():
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@pytest.mark.parametrize(
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("multimodal_device"),
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[
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device("cpu"),
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pytest.param(
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device("cuda"),
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marks=pytest.mark.skipif(
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gpu_is_not_available, reason="gpu_is_not_availible"
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),
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),
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],
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)
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def test_load_feature_extractor_model_albef(multimodal_device):
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my_dict = {}
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multimodal_device = device("cuda" if cuda.is_available() else "cpu")
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(
|
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model,
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vis_processor,
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@ -198,7 +291,7 @@ def test_load_feature_extractor_model_albef():
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{"image": "", "text_input": processed_text}, mode="text"
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)
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assert processed_pic[0, 0, 0, 25:35].tolist() == [
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check_list_processed_pic = [
|
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-1.0039474964141846,
|
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-1.0039474964141846,
|
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-0.8433647751808167,
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@ -210,13 +303,18 @@ def test_load_feature_extractor_model_albef():
|
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-0.7119789123535156,
|
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-1.0623412132263184,
|
||||
]
|
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for i, num in zip(range(10), processed_pic[0, 0, 0, 25:35].tolist()):
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assert (
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math.isclose(num, check_list_processed_pic[i], rel_tol=related_error)
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is True
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)
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assert (
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processed_text
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== "the bird sat on a tree located at the intersection of 23rd and 43rd streets"
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)
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assert extracted_feature_img["image_embeds_proj"][0, 0, 10:20].tolist() == [
|
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check_list_extracted_feature_img = [
|
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0.08971136063337326,
|
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-0.10915573686361313,
|
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-0.020636577159166336,
|
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@ -228,8 +326,17 @@ def test_load_feature_extractor_model_albef():
|
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-0.03284582123160362,
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-0.1037328764796257,
|
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]
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for i, num in zip(
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range(10), extracted_feature_img["image_embeds_proj"][0, 0, 10:20].tolist()
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):
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assert (
|
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math.isclose(
|
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num, check_list_extracted_feature_img[i], rel_tol=related_error
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)
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is True
|
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)
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assert extracted_feature_text["text_embeds_proj"][0, 0, 10:20].tolist() == [
|
||||
check_list_extracted_feature_text = [
|
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-0.06229640915989876,
|
||||
0.11278597265481949,
|
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0.06628583371639252,
|
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@ -241,14 +348,34 @@ def test_load_feature_extractor_model_albef():
|
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0.050752390176057816,
|
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-0.0421440489590168,
|
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]
|
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for i, num in zip(
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range(10), extracted_feature_text["text_embeds_proj"][0, 0, 10:20].tolist()
|
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):
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assert (
|
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math.isclose(
|
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num, check_list_extracted_feature_text[i], rel_tol=related_error
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)
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is True
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)
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del model, vis_processor, txt_processor
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cuda.empty_cache()
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def test_load_feature_extractor_model_clip_base():
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@pytest.mark.parametrize(
|
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("multimodal_device"),
|
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[
|
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device("cpu"),
|
||||
pytest.param(
|
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device("cuda"),
|
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marks=pytest.mark.skipif(
|
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gpu_is_not_available, reason="gpu_is_not_availible"
|
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),
|
||||
),
|
||||
],
|
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)
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def test_load_feature_extractor_model_clip_base(multimodal_device):
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my_dict = {}
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multimodal_device = device("cuda" if cuda.is_available() else "cpu")
|
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(
|
||||
model,
|
||||
vis_processor,
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||||
@ -267,7 +394,7 @@ def test_load_feature_extractor_model_clip_base():
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extracted_feature_img = model.extract_features({"image": processed_pic})
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extracted_feature_text = model.extract_features({"text_input": processed_text})
|
||||
|
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assert processed_pic[0, 0, 0, 25:35].tolist() == [
|
||||
check_list_processed_pic = [
|
||||
-0.7995694875717163,
|
||||
-0.7849710583686829,
|
||||
-0.7849710583686829,
|
||||
@ -279,13 +406,18 @@ def test_load_feature_extractor_model_clip_base():
|
||||
-0.7703726291656494,
|
||||
-0.7703726291656494,
|
||||
]
|
||||
for i, num in zip(range(10), processed_pic[0, 0, 0, 25:35].tolist()):
|
||||
assert (
|
||||
math.isclose(num, check_list_processed_pic[i], rel_tol=related_error)
|
||||
is True
|
||||
)
|
||||
|
||||
assert (
|
||||
processed_text
|
||||
== "The bird sat on a tree located at the intersection of 23rd and 43rd streets."
|
||||
)
|
||||
|
||||
assert extracted_feature_img[0, 10:20].tolist() == [
|
||||
check_list_extracted_feature_img = [
|
||||
0.15101124346256256,
|
||||
-0.03759124130010605,
|
||||
-0.40093156695365906,
|
||||
@ -297,8 +429,15 @@ def test_load_feature_extractor_model_clip_base():
|
||||
-0.030034437775611877,
|
||||
0.19082790613174438,
|
||||
]
|
||||
for i, num in zip(range(10), extracted_feature_img[0, 10:20].tolist()):
|
||||
assert (
|
||||
math.isclose(
|
||||
num, check_list_extracted_feature_img[i], rel_tol=related_error
|
||||
)
|
||||
is True
|
||||
)
|
||||
|
||||
assert extracted_feature_text[0, 10:20].tolist() == [
|
||||
check_list_extracted_feature_text = [
|
||||
0.15391531586647034,
|
||||
0.3078577518463135,
|
||||
0.21737979352474213,
|
||||
@ -310,14 +449,32 @@ def test_load_feature_extractor_model_clip_base():
|
||||
-0.1858849972486496,
|
||||
0.20347601175308228,
|
||||
]
|
||||
for i, num in zip(range(10), extracted_feature_text[0, 10:20].tolist()):
|
||||
assert (
|
||||
math.isclose(
|
||||
num, check_list_extracted_feature_text[i], rel_tol=related_error
|
||||
)
|
||||
is True
|
||||
)
|
||||
|
||||
del model, vis_processor, txt_processor
|
||||
cuda.empty_cache()
|
||||
|
||||
|
||||
def test_load_feature_extractor_model_clip_vitl14():
|
||||
@pytest.mark.parametrize(
|
||||
("multimodal_device"),
|
||||
[
|
||||
device("cpu"),
|
||||
pytest.param(
|
||||
device("cuda"),
|
||||
marks=pytest.mark.skipif(
|
||||
gpu_is_not_available, reason="gpu_is_not_availible"
|
||||
),
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_load_feature_extractor_model_clip_vitl14(multimodal_device):
|
||||
my_dict = {}
|
||||
multimodal_device = device("cuda" if cuda.is_available() else "cpu")
|
||||
(
|
||||
model,
|
||||
vis_processor,
|
||||
@ -336,7 +493,7 @@ def test_load_feature_extractor_model_clip_vitl14():
|
||||
extracted_feature_img = model.extract_features({"image": processed_pic})
|
||||
extracted_feature_text = model.extract_features({"text_input": processed_text})
|
||||
|
||||
assert processed_pic[0, 0, 0, 25:35].tolist() == [
|
||||
check_list_processed_pic = [
|
||||
-0.7995694875717163,
|
||||
-0.7849710583686829,
|
||||
-0.7849710583686829,
|
||||
@ -348,13 +505,18 @@ def test_load_feature_extractor_model_clip_vitl14():
|
||||
-0.7703726291656494,
|
||||
-0.7703726291656494,
|
||||
]
|
||||
for i, num in zip(range(10), processed_pic[0, 0, 0, 25:35].tolist()):
|
||||
assert (
|
||||
math.isclose(num, check_list_processed_pic[i], rel_tol=related_error)
|
||||
is True
|
||||
)
|
||||
|
||||
assert (
|
||||
processed_text
|
||||
== "The bird sat on a tree located at the intersection of 23rd and 43rd streets."
|
||||
)
|
||||
|
||||
assert extracted_feature_img[0, 10:20].tolist() == [
|
||||
check_list_extracted_feature_img = [
|
||||
-0.3911527395248413,
|
||||
-0.35456305742263794,
|
||||
0.5724918842315674,
|
||||
@ -366,8 +528,15 @@ def test_load_feature_extractor_model_clip_vitl14():
|
||||
0.19491413235664368,
|
||||
0.01419895887374878,
|
||||
]
|
||||
for i, num in zip(range(10), extracted_feature_img[0, 10:20].tolist()):
|
||||
assert (
|
||||
math.isclose(
|
||||
num, check_list_extracted_feature_img[i], rel_tol=related_error
|
||||
)
|
||||
is True
|
||||
)
|
||||
|
||||
assert extracted_feature_text[0, 10:20].tolist() == [
|
||||
check_list_extracted_feature_text = [
|
||||
-0.07539052516222,
|
||||
0.0939129889011383,
|
||||
-0.2643853425979614,
|
||||
@ -379,14 +548,32 @@ def test_load_feature_extractor_model_clip_vitl14():
|
||||
0.18563221395015717,
|
||||
-0.3183072805404663,
|
||||
]
|
||||
for i, num in zip(range(10), extracted_feature_text[0, 10:20].tolist()):
|
||||
assert (
|
||||
math.isclose(
|
||||
num, check_list_extracted_feature_text[i], rel_tol=related_error
|
||||
)
|
||||
is True
|
||||
)
|
||||
|
||||
del model, vis_processor, txt_processor
|
||||
cuda.empty_cache()
|
||||
|
||||
|
||||
def test_load_feature_extractor_model_clip_vitl14_336():
|
||||
@pytest.mark.parametrize(
|
||||
("multimodal_device"),
|
||||
[
|
||||
device("cpu"),
|
||||
pytest.param(
|
||||
device("cuda"),
|
||||
marks=pytest.mark.skipif(
|
||||
gpu_is_not_available, reason="gpu_is_not_availible"
|
||||
),
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_load_feature_extractor_model_clip_vitl14_336(multimodal_device):
|
||||
my_dict = {}
|
||||
multimodal_device = device("cuda" if cuda.is_available() else "cpu")
|
||||
(
|
||||
model,
|
||||
vis_processor,
|
||||
@ -405,7 +592,7 @@ def test_load_feature_extractor_model_clip_vitl14_336():
|
||||
extracted_feature_img = model.extract_features({"image": processed_pic})
|
||||
extracted_feature_text = model.extract_features({"text_input": processed_text})
|
||||
|
||||
assert processed_pic[0, 0, 0, 25:35].tolist() == [
|
||||
check_list_processed_pic = [
|
||||
-0.7995694875717163,
|
||||
-0.7849710583686829,
|
||||
-0.7849710583686829,
|
||||
@ -417,13 +604,18 @@ def test_load_feature_extractor_model_clip_vitl14_336():
|
||||
-1.149931788444519,
|
||||
-1.0039474964141846,
|
||||
]
|
||||
for i, num in zip(range(10), processed_pic[0, 0, 0, 25:35].tolist()):
|
||||
assert (
|
||||
math.isclose(num, check_list_processed_pic[i], rel_tol=related_error)
|
||||
is True
|
||||
)
|
||||
|
||||
assert (
|
||||
processed_text
|
||||
== "The bird sat on a tree located at the intersection of 23rd and 43rd streets."
|
||||
)
|
||||
|
||||
assert extracted_feature_img[0, 10:20].tolist() == [
|
||||
check_list_extracted_feature_img = [
|
||||
-0.15060146152973175,
|
||||
-0.1998099535703659,
|
||||
0.5503129363059998,
|
||||
@ -435,8 +627,15 @@ def test_load_feature_extractor_model_clip_vitl14_336():
|
||||
0.02220013737678528,
|
||||
0.01086437702178955,
|
||||
]
|
||||
for i, num in zip(range(10), extracted_feature_img[0, 10:20].tolist()):
|
||||
assert (
|
||||
math.isclose(
|
||||
num, check_list_extracted_feature_img[i], rel_tol=related_error
|
||||
)
|
||||
is True
|
||||
)
|
||||
|
||||
assert extracted_feature_text[0, 10:20].tolist() == [
|
||||
check_list_extracted_feature_text = [
|
||||
-0.1172553077340126,
|
||||
0.07105237245559692,
|
||||
-0.283934086561203,
|
||||
@ -448,6 +647,13 @@ def test_load_feature_extractor_model_clip_vitl14_336():
|
||||
0.22669515013694763,
|
||||
-0.32044747471809387,
|
||||
]
|
||||
for i, num in zip(range(10), extracted_feature_text[0, 10:20].tolist()):
|
||||
assert (
|
||||
math.isclose(
|
||||
num, check_list_extracted_feature_text[i], rel_tol=related_error
|
||||
)
|
||||
is True
|
||||
)
|
||||
|
||||
del model, vis_processor, txt_processor
|
||||
cuda.empty_cache()
|
||||
|
||||
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