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453 строки
12 KiB
Plaintext
Generated
453 строки
12 KiB
Plaintext
Generated
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "22df2297-0629-45aa-b88c-6c61f1544db6",
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"metadata": {},
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"source": [
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"# Image Multimodal Search"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9eeeb302-296e-48dc-86c7-254aa02f2b3a",
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"metadata": {},
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"source": [
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"This notebooks shows some preliminary work on Image Multimodal Search with lavis library. It is mainly meant to explore its capabilities and to decide on future research directions. We package our code into a `misinformation` package that is imported here:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0b0a6bdf",
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"metadata": {},
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"outputs": [],
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"source": [
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"# if running on google colab\n",
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"# flake8-noqa-cell\n",
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"import os\n",
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"\n",
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"if \"google.colab\" in str(get_ipython()):\n",
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" # update python version\n",
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" # install setuptools\n",
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" !pip install setuptools==61 -qqq\n",
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" # install misinformation\n",
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" !pip install git+https://github.com/ssciwr/misinformation.git -qqq\n",
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" # mount google drive for data and API key\n",
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" from google.colab import drive\n",
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"\n",
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" drive.mount(\"/content/drive\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f10ad6c9-b1a0-4043-8c5d-ed660d77be37",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import misinformation.utils as mutils\n",
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"import misinformation.multimodal_search as ms"
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]
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},
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{
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"cell_type": "markdown",
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"id": "acf08b44-3ea6-44cd-926d-15c0fd9f39e0",
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"metadata": {},
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"source": [
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"Set an image path as input file path."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8d3fe589-ff3c-4575-b8f5-650db85596bc",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Here you need to provide the path to your google drive folder\n",
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"# or local folder containing the images\n",
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"images = mutils.find_files(\n",
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" path=\"/content/drive/MyDrive/misinformation-data/\",\n",
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" limit=10,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "adf3db21-1f8b-4d44-bbef-ef0acf4623a0",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"mydict = mutils.initialize_dict(images)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "987540a8-d800-4c70-a76b-7bfabaf123fa",
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"metadata": {},
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"source": [
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"## Indexing and extracting features from images in selected folder"
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]
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},
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{
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"cell_type": "markdown",
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"id": "66d6ede4-00bc-4aeb-9a36-e52d7de33fe5",
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"metadata": {},
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"source": [
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"You can choose one of the following models: blip, blip2, albef, clip_base, clip_vitl14, clip_vitl14_336"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7bbca1f0-d4b0-43cd-8e05-ee39d37c328e",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"model_type = \"blip\"\n",
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"# model_type = \"blip2\"\n",
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"# model_type = \"albef\"\n",
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"# model_type = \"clip_base\"\n",
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"# model_type = \"clip_vitl14\"\n",
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"# model_type = \"clip_vitl14_336\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ca095404-57d0-4f5d-aeb0-38c232252b17",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"(\n",
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" model,\n",
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" vis_processors,\n",
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" txt_processors,\n",
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" image_keys,\n",
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" image_names,\n",
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" features_image_stacked,\n",
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") = ms.MultimodalSearch.parsing_images(mydict, model_type, path_to_saved_tensors=\".\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9ff8a894-566b-4c4f-acca-21c50b5b1f52",
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"metadata": {},
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"source": [
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"The tensors of all images `features_image_stacked` was saved in `<Number_of_images>_<model_name>_saved_features_image.pt`. If you run it once for current model and current set of images you do not need to repeat it again. Instead you can load this features with the command:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "56c6d488-f093-4661-835a-5c73a329c874",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# (\n",
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"# model,\n",
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"# vis_processors,\n",
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"# txt_processors,\n",
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"# image_keys,\n",
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"# image_names,\n",
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"# features_image_stacked,\n",
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"# ) = ms.MultimodalSearch.parsing_images(\n",
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"# mydict,\n",
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"# model_type,\n",
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"# path_to_load_tensors=\".5_blip_saved_features_image.pt\",\n",
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"# )"
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]
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},
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{
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"cell_type": "markdown",
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"id": "309923c1-d6f8-4424-8fca-bde5f3a98b38",
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"metadata": {},
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"source": [
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"Here we already processed our image folder with 18 images with `clip_base` model. So you need just write the name `18_clip_base_saved_features_image.pt` of the saved file that consists of tensors of all images as a 3rd argument to the previous function. "
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]
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},
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{
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"cell_type": "markdown",
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"id": "162a52e8-6652-4897-b92e-645cab07aaef",
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"metadata": {},
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"source": [
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"Next, you need to form search queries. You can search either by image or by text. You can search for a single query, or you can search for several queries at once, the computational time should not be much different. The format of the queries is as follows:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c4196a52-d01e-42e4-8674-5712f7d6f792",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"search_query3 = [\n",
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" {\"text_input\": \"politician press conference\"},\n",
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" {\"text_input\": \"a world map\"},\n",
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" {\"text_input\": \"a dog\"},\n",
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"]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8bcf3127-3dfd-4ff4-b9e7-a043099b1418",
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"metadata": {},
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"source": [
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"You can filter your results in 3 different ways:\n",
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"- `filter_number_of_images` limits the number of images found. That is, if the parameter `filter_number_of_images = 10`, then the first 10 images that best match the query will be shown. The other images ranks will be set to `None` and the similarity value to `0`.\n",
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"- `filter_val_limit` limits the output of images with a similarity value not bigger than `filter_val_limit`. That is, if the parameter `filter_val_limit = 0.2`, all images with similarity less than 0.2 will be discarded.\n",
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"- `filter_rel_error` (percentage) limits the output of images with a similarity value not bigger than `100 * abs(current_simularity_value - best_simularity_value_in_current_search)/best_simularity_value_in_current_search < filter_rel_error`. That is, if we set filter_rel_error = 30, it means that if the top1 image have 0.5 similarity value, we discard all image with similarity less than 0.35."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7f7dc52f-7ee9-4590-96b7-e0d9d3b82378",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"similarity, sorted_lists = ms.MultimodalSearch.multimodal_search(\n",
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" mydict,\n",
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" model,\n",
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" vis_processors,\n",
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" txt_processors,\n",
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" model_type,\n",
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" image_keys,\n",
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" features_image_stacked,\n",
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" search_query3,\n",
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" filter_number_of_images=20,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e1cf7e46-0c2c-4fb2-b89a-ef585ccb9339",
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"metadata": {},
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"source": [
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"After launching `multimodal_search` function, the results of each query will be added to the source dictionary. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9ad74b21-6187-4a58-9ed8-fd3e80f5a4ed",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"mydict[\"109237S_spa\"]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cd3ee120-8561-482b-a76a-e8f996783325",
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"metadata": {},
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"source": [
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"A special function was written to present the search results conveniently. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4324e4fd-e9aa-4933-bb12-074d54e0c510",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"ms.MultimodalSearch.show_results(\n",
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" mydict,\n",
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" search_query3[0],\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0b750e9f-fe64-4028-9caf-52d7187462f1",
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"metadata": {},
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"source": [
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"For even better results, a slightly different approach has been prepared that can improve search results. It is quite resource-intensive, so it is applied after the main algorithm has found the most relevant images. This approach works only with text queries. Among the parameters you can choose 3 models: `\"blip_base\"`, `\"blip_large\"`, `\"blip2_coco\"`. If you get the Out of Memory error, try reducing the batch_size value (minimum = 1), which is the number of images being processed simultaneously. With the parameter `need_grad_cam = True/False` you can enable the calculation of the heat map of each image to be processed. Thus the `image_text_match_reordering` function calculates new similarity values and new ranks for each image. The resulting values are added to the general dictionary."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b3af7b39-6d0d-4da3-9b8f-7dfd3f5779be",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"itm_model = \"blip_base\"\n",
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"# itm_model = \"blip_large\"\n",
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"# itm_model = \"blip2_coco\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "caf1f4ae-4b37-4954-800e-7120f0419de5",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"itm_scores, image_gradcam_with_itm = ms.MultimodalSearch.image_text_match_reordering(\n",
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" mydict,\n",
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" search_query3,\n",
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" itm_model,\n",
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" image_keys,\n",
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" sorted_lists,\n",
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" batch_size=1,\n",
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" need_grad_cam=True,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9e98c150-5fab-4251-bce7-0d8fc7b385b9",
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"metadata": {},
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"source": [
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"Then using the same output function you can add the `ITM=True` arguments to output the new image order. You can also add the `image_gradcam_with_itm` argument to output the heat maps of the calculated images. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6a829b99-5230-463a-8b11-30ffbb67fc3a",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"ms.MultimodalSearch.show_results(\n",
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" mydict, search_query3[0], itm=True, image_gradcam_with_itm=image_gradcam_with_itm\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d86ab96b-1907-4b7f-a78e-3983b516d781",
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"metadata": {
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"tags": []
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},
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"source": [
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"## Save searhing results to csv"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4bdbc4d4-695d-4751-ab7c-d2d98e2917d7",
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"metadata": {
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"tags": []
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},
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"source": [
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"Convert the dictionary of dictionarys into a dictionary with lists:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6c6ddd83-bc87-48f2-a8d6-1bd3f4201ff7",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"outdict = misinformation.utils.append_data_to_dict(mydict)\n",
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"df = misinformation.utils.dump_df(outdict)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ea2675d5-604c-45e7-86d2-080b1f4559a0",
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"metadata": {
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"tags": []
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},
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"source": [
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"Check the dataframe:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e78646d6-80be-4d3e-8123-3360957bcaa8",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"df.head(10)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "05546d99-afab-4565-8f30-f14e1426abcf",
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"metadata": {},
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"source": [
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"Write the csv file:"
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]
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},
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{
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"cell_type": "code",
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|
"execution_count": null,
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"id": "185f7dde-20dc-44d8-9ab0-de41f9b5734d",
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"metadata": {
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|
"tags": []
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},
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"outputs": [],
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"source": [
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"df.to_csv(\"./data_out.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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|
"id": "b6a79201-7c17-496c-a6a1-b8ecfd3dd1e8",
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|
"metadata": {},
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|
"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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|
"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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|
"file_extension": ".py",
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|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.0"
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}
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},
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|
"nbformat": 4,
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|
"nbformat_minor": 5
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|
}
|