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1828 строки
60 KiB
Plaintext
1828 строки
60 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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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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"attachments": {},
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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 how to carry out an image multimodal search with the [LAVIS](https://github.com/salesforce/LAVIS) library. \n",
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"\n",
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"The first cell is only run on google colab and installs the [ammico](https://github.com/ssciwr/AMMICO) package.\n",
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"\n",
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"After that, we can import `ammico` and read in the files given a folder 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": 1,
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"id": "0b0a6bdf",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-09-04T04:50:39.974583Z",
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"iopub.status.busy": "2023-09-04T04:50:39.974111Z",
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"iopub.status.idle": "2023-09-04T04:50:39.984394Z",
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"shell.execute_reply": "2023-09-04T04:50:39.983616Z"
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}
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},
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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 ammico\n",
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" %pip install git+https://github.com/ssciwr/ammico.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": 2,
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"id": "f10ad6c9-b1a0-4043-8c5d-ed660d77be37",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-09-04T04:50:39.988065Z",
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"iopub.status.busy": "2023-09-04T04:50:39.987571Z",
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"iopub.status.idle": "2023-09-04T04:50:53.092242Z",
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"shell.execute_reply": "2023-09-04T04:50:53.091369Z"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"import ammico.utils as mutils\n",
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"import ammico.multimodal_search as ms"
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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": 3,
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"id": "8d3fe589-ff3c-4575-b8f5-650db85596bc",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-09-04T04:50:53.097229Z",
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"iopub.status.busy": "2023-09-04T04:50:53.096405Z",
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"iopub.status.idle": "2023-09-04T04:50:53.103333Z",
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"shell.execute_reply": "2023-09-04T04:50:53.102563Z"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"images = mutils.find_files(\n",
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" path=\"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": 4,
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"id": "a08bd3a9-e954-4a0e-ad64-6817abd3a25a",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-09-04T04:50:53.106982Z",
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"iopub.status.busy": "2023-09-04T04:50:53.106447Z",
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"iopub.status.idle": "2023-09-04T04:50:53.114665Z",
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"shell.execute_reply": "2023-09-04T04:50:53.113863Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'102141_2_eng': {'filename': 'data/102141_2_eng.png'},\n",
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" '106349S_por': {'filename': 'data/106349S_por.png'},\n",
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" '102730_eng': {'filename': 'data/102730_eng.png'}}"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"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": 5,
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"id": "adf3db21-1f8b-4d44-bbef-ef0acf4623a0",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-09-04T04:50:53.119582Z",
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"iopub.status.busy": "2023-09-04T04:50:53.119086Z",
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"iopub.status.idle": "2023-09-04T04:50:53.122878Z",
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"shell.execute_reply": "2023-09-04T04:50:53.122062Z"
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},
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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": "code",
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"execution_count": 6,
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"id": "4c091f95-07cf-42c3-82c8-5f3a3c5929f8",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-09-04T04:50:53.126811Z",
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"iopub.status.busy": "2023-09-04T04:50:53.126211Z",
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"iopub.status.idle": "2023-09-04T04:50:53.131303Z",
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"shell.execute_reply": "2023-09-04T04:50:53.130482Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'102141_2_eng': {'filename': '102141_2_eng'},\n",
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" '106349S_por': {'filename': '106349S_por'},\n",
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" '102730_eng': {'filename': '102730_eng'}}"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"mydict"
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]
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},
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{
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"attachments": {},
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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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"attachments": {},
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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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"First you need to select a model. You can choose one of the following models: \n",
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"- [blip](https://github.com/salesforce/BLIP)\n",
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"- [blip2](https://huggingface.co/docs/transformers/main/model_doc/blip-2) \n",
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"- [albef](https://github.com/salesforce/ALBEF) \n",
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"- [clip_base](https://github.com/openai/CLIP/blob/main/model-card.md)\n",
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"- [clip_vitl14](https://github.com/mlfoundations/open_clip) \n",
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"- [clip_vitl14_336](https://github.com/mlfoundations/open_clip)"
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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": 7,
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"id": "7bbca1f0-d4b0-43cd-8e05-ee39d37c328e",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-09-04T04:50:53.135942Z",
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"iopub.status.busy": "2023-09-04T04:50:53.135446Z",
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"iopub.status.idle": "2023-09-04T04:50:53.138921Z",
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"shell.execute_reply": "2023-09-04T04:50:53.138149Z"
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},
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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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"attachments": {},
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"cell_type": "markdown",
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"id": "357828c9",
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"metadata": {},
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"source": [
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"To process the loaded images using the selected model, use the below code:"
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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": 8,
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"id": "f6f2c9b1-4a91-47cb-86b5-2c9c67e4837b",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-09-04T04:50:53.142524Z",
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"iopub.status.busy": "2023-09-04T04:50:53.142129Z",
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"iopub.status.idle": "2023-09-04T04:50:53.145748Z",
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"shell.execute_reply": "2023-09-04T04:50:53.144946Z"
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}
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},
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"outputs": [],
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"source": [
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"my_obj = ms.MultimodalSearch(mydict)"
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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": 9,
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"id": "16603ded-078e-4362-847b-57ad76829327",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-09-04T04:50:53.149358Z",
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"iopub.status.busy": "2023-09-04T04:50:53.148936Z",
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"iopub.status.idle": "2023-09-04T04:50:53.153966Z",
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"shell.execute_reply": "2023-09-04T04:50:53.153236Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'102141_2_eng': {'filename': '102141_2_eng'},\n",
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" '106349S_por': {'filename': '106349S_por'},\n",
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" '102730_eng': {'filename': '102730_eng'}}"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"my_obj.subdict"
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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": 10,
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"id": "ca095404-57d0-4f5d-aeb0-38c232252b17",
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"metadata": {
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"execution": {
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"iopub.status.idle": "2023-09-04T04:51:24.013974Z",
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"shell.execute_reply": "2023-09-04T04:51:24.012584Z"
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{
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"ename": "FileNotFoundError",
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"evalue": "[Errno 2] No such file or directory: '102141_2_eng'",
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"output_type": "error",
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"traceback": [
|
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[10], line 8\u001b[0m\n\u001b[1;32m 1\u001b[0m (\n\u001b[1;32m 2\u001b[0m model,\n\u001b[1;32m 3\u001b[0m vis_processors,\n\u001b[1;32m 4\u001b[0m txt_processors,\n\u001b[1;32m 5\u001b[0m image_keys,\n\u001b[1;32m 6\u001b[0m image_names,\n\u001b[1;32m 7\u001b[0m features_image_stacked,\n\u001b[0;32m----> 8\u001b[0m ) \u001b[38;5;241m=\u001b[39m \u001b[43mmy_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparsing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath_to_save_tensors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
|
|
"File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/multimodal_search.py:371\u001b[0m, in \u001b[0;36mMultimodalSearch.parsing_images\u001b[0;34m(self, model_type, path_to_save_tensors, path_to_load_tensors)\u001b[0m\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 367\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mSyntaxError\u001b[39;00m(\n\u001b[1;32m 368\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease, use one of the following models: blip2, blip, albef, clip_base, clip_vitl14, clip_vitl14_336\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 369\u001b[0m )\n\u001b[0;32m--> 371\u001b[0m _, images_tensors \u001b[38;5;241m=\u001b[39m \u001b[43mMultimodalSearch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_and_process_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 372\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimage_names\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvis_processors\u001b[49m\n\u001b[1;32m 373\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 374\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m path_to_load_tensors \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 375\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n",
|
|
"File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/multimodal_search.py:168\u001b[0m, in \u001b[0;36mMultimodalSearch.read_and_process_images\u001b[0;34m(self, image_paths, vis_processor)\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread_and_process_images\u001b[39m(\u001b[38;5;28mself\u001b[39m, image_paths: \u001b[38;5;28mlist\u001b[39m, vis_processor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mtuple\u001b[39m:\n\u001b[1;32m 157\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 158\u001b[0m \u001b[38;5;124;03m Read and process images with vis_processor.\u001b[39;00m\n\u001b[1;32m 159\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[38;5;124;03m images_tensors (torch.Tensor): tensors of images stacked in device.\u001b[39;00m\n\u001b[1;32m 167\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 168\u001b[0m raw_images \u001b[38;5;241m=\u001b[39m [MultimodalSearch\u001b[38;5;241m.\u001b[39mread_img(\u001b[38;5;28mself\u001b[39m, path) \u001b[38;5;28;01mfor\u001b[39;00m path \u001b[38;5;129;01min\u001b[39;00m image_paths]\n\u001b[1;32m 169\u001b[0m images \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 170\u001b[0m vis_processor[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meval\u001b[39m\u001b[38;5;124m\"\u001b[39m](r_img)\n\u001b[1;32m 171\u001b[0m \u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 172\u001b[0m \u001b[38;5;241m.\u001b[39mto(MultimodalSearch\u001b[38;5;241m.\u001b[39mmultimodal_device)\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m r_img \u001b[38;5;129;01min\u001b[39;00m raw_images\n\u001b[1;32m 174\u001b[0m ]\n\u001b[1;32m 175\u001b[0m images_tensors \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mstack(images)\n",
|
|
"File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/multimodal_search.py:168\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread_and_process_images\u001b[39m(\u001b[38;5;28mself\u001b[39m, image_paths: \u001b[38;5;28mlist\u001b[39m, vis_processor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mtuple\u001b[39m:\n\u001b[1;32m 157\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 158\u001b[0m \u001b[38;5;124;03m Read and process images with vis_processor.\u001b[39;00m\n\u001b[1;32m 159\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[38;5;124;03m images_tensors (torch.Tensor): tensors of images stacked in device.\u001b[39;00m\n\u001b[1;32m 167\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 168\u001b[0m raw_images \u001b[38;5;241m=\u001b[39m [\u001b[43mMultimodalSearch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_img\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m path \u001b[38;5;129;01min\u001b[39;00m image_paths]\n\u001b[1;32m 169\u001b[0m images \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 170\u001b[0m vis_processor[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meval\u001b[39m\u001b[38;5;124m\"\u001b[39m](r_img)\n\u001b[1;32m 171\u001b[0m \u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 172\u001b[0m \u001b[38;5;241m.\u001b[39mto(MultimodalSearch\u001b[38;5;241m.\u001b[39mmultimodal_device)\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m r_img \u001b[38;5;129;01min\u001b[39;00m raw_images\n\u001b[1;32m 174\u001b[0m ]\n\u001b[1;32m 175\u001b[0m images_tensors \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mstack(images)\n",
|
|
"File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/multimodal_search.py:153\u001b[0m, in \u001b[0;36mMultimodalSearch.read_img\u001b[0;34m(self, filepath)\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread_img\u001b[39m(\u001b[38;5;28mself\u001b[39m, filepath: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Image:\n\u001b[1;32m 144\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;124;03m Load Image from filepath.\u001b[39;00m\n\u001b[1;32m 146\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 151\u001b[0m \u001b[38;5;124;03m raw_image (PIL.Image): image.\u001b[39;00m\n\u001b[1;32m 152\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 153\u001b[0m raw_image \u001b[38;5;241m=\u001b[39m \u001b[43mImage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mconvert(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRGB\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 154\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m raw_image\n",
|
|
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/PIL/Image.py:3236\u001b[0m, in \u001b[0;36mopen\u001b[0;34m(fp, mode, formats)\u001b[0m\n\u001b[1;32m 3233\u001b[0m filename \u001b[38;5;241m=\u001b[39m fp\n\u001b[1;32m 3235\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m filename:\n\u001b[0;32m-> 3236\u001b[0m fp \u001b[38;5;241m=\u001b[39m \u001b[43mbuiltins\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3237\u001b[0m exclusive_fp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 3239\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
|
|
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '102141_2_eng'"
|
|
]
|
|
}
|
|
],
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|
"source": [
|
|
"(\n",
|
|
" model,\n",
|
|
" vis_processors,\n",
|
|
" txt_processors,\n",
|
|
" image_keys,\n",
|
|
" image_names,\n",
|
|
" features_image_stacked,\n",
|
|
") = my_obj.parsing_images(\n",
|
|
" model_type, \n",
|
|
" path_to_save_tensors=\"data/\",\n",
|
|
" )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "f236c3b1-c3a6-471a-9fc5-ef831b675286",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-09-04T04:51:24.019545Z",
|
|
"iopub.status.busy": "2023-09-04T04:51:24.018761Z",
|
|
"iopub.status.idle": "2023-09-04T04:51:24.067851Z",
|
|
"shell.execute_reply": "2023-09-04T04:51:24.066943Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"ename": "NameError",
|
|
"evalue": "name 'features_image_stacked' is not defined",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
|
"Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfeatures_image_stacked\u001b[49m\n",
|
|
"\u001b[0;31mNameError\u001b[0m: name 'features_image_stacked' is not defined"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"features_image_stacked"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"id": "9ff8a894-566b-4c4f-acca-21c50b5b1f52",
|
|
"metadata": {},
|
|
"source": [
|
|
"The images are then processed and stored in a numerical representation, a tensor. These tensors do not change for the same image and same model - so if you run this analysis once, and save the tensors giving a path with the keyword `path_to_save_tensors`, a file with filename `.<Number_of_images>_<model_name>_saved_features_image.pt` will be placed there.\n",
|
|
"\n",
|
|
"This will save you a lot of time if you want to analyse same images with the same model but different questions. To run using the saved tensors, execute the below code giving the path and name of the tensor file."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "56c6d488-f093-4661-835a-5c73a329c874",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-09-04T04:51:24.071978Z",
|
|
"iopub.status.busy": "2023-09-04T04:51:24.071413Z",
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"iopub.status.idle": "2023-09-04T04:51:24.076673Z",
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"shell.execute_reply": "2023-09-04T04:51:24.075906Z"
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},
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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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"# ) = my_obj.parsing_images(\n",
|
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"# model_type,\n",
|
|
"# path_to_load_tensors=\"/content/drive/MyDrive/misinformation-data/5_clip_base_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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"attachments": {},
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"cell_type": "markdown",
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"id": "309923c1-d6f8-4424-8fca-bde5f3a98b38",
|
|
"metadata": {},
|
|
"source": [
|
|
"Here we already processed our image folder with 5 images and the `clip_base` model. So you need just to write the name `5_clip_base_saved_features_image.pt` of the saved file that consists of tensors of all images as keyword argument for `path_to_load_tensors`. "
|
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]
|
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},
|
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{
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"attachments": {},
|
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"cell_type": "markdown",
|
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"id": "162a52e8-6652-4897-b92e-645cab07aaef",
|
|
"metadata": {},
|
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"source": [
|
|
"## Formulate your search queries\n",
|
|
"\n",
|
|
"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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"cell_type": "code",
|
|
"execution_count": 13,
|
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"id": "c4196a52-d01e-42e4-8674-5712f7d6f792",
|
|
"metadata": {
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|
"execution": {
|
|
"iopub.execute_input": "2023-09-04T04:51:24.080378Z",
|
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"iopub.status.busy": "2023-09-04T04:51:24.079877Z",
|
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"iopub.status.idle": "2023-09-04T04:51:24.089664Z",
|
|
"shell.execute_reply": "2023-09-04T04:51:24.088912Z"
|
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},
|
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"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"search_query3 = [\n",
|
|
" {\"text_input\": \"politician press conference\"},\n",
|
|
" {\"text_input\": \"a world map\"},\n",
|
|
" {\"text_input\": \"a dog\"},\n",
|
|
"]"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"id": "8bcf3127-3dfd-4ff4-b9e7-a043099b1418",
|
|
"metadata": {},
|
|
"source": [
|
|
"You can filter your results in 3 different ways:\n",
|
|
"- `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",
|
|
"- `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",
|
|
"- `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."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "7f7dc52f-7ee9-4590-96b7-e0d9d3b82378",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-09-04T04:51:24.093645Z",
|
|
"iopub.status.busy": "2023-09-04T04:51:24.092995Z",
|
|
"iopub.status.idle": "2023-09-04T04:51:24.141808Z",
|
|
"shell.execute_reply": "2023-09-04T04:51:24.140906Z"
|
|
},
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"ename": "NameError",
|
|
"evalue": "name 'model' is not defined",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
|
"Cell \u001b[0;32mIn[14], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m similarity, sorted_lists \u001b[38;5;241m=\u001b[39m my_obj\u001b[38;5;241m.\u001b[39mmultimodal_search(\n\u001b[0;32m----> 2\u001b[0m \u001b[43mmodel\u001b[49m,\n\u001b[1;32m 3\u001b[0m vis_processors,\n\u001b[1;32m 4\u001b[0m txt_processors,\n\u001b[1;32m 5\u001b[0m model_type,\n\u001b[1;32m 6\u001b[0m image_keys,\n\u001b[1;32m 7\u001b[0m features_image_stacked,\n\u001b[1;32m 8\u001b[0m search_query3,\n\u001b[1;32m 9\u001b[0m filter_number_of_images\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m,\n\u001b[1;32m 10\u001b[0m )\n",
|
|
"\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"similarity, sorted_lists = my_obj.multimodal_search(\n",
|
|
" model,\n",
|
|
" vis_processors,\n",
|
|
" txt_processors,\n",
|
|
" model_type,\n",
|
|
" image_keys,\n",
|
|
" features_image_stacked,\n",
|
|
" search_query3,\n",
|
|
" filter_number_of_images=20,\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"id": "65210ca2-b674-44bd-807a-4165e14bad74",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-09-04T04:51:24.146031Z",
|
|
"iopub.status.busy": "2023-09-04T04:51:24.145313Z",
|
|
"iopub.status.idle": "2023-09-04T04:51:24.189634Z",
|
|
"shell.execute_reply": "2023-09-04T04:51:24.188769Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"ename": "NameError",
|
|
"evalue": "name 'similarity' is not defined",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
|
"Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msimilarity\u001b[49m\n",
|
|
"\u001b[0;31mNameError\u001b[0m: name 'similarity' is not defined"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"similarity"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "557473df-e2b9-4ef0-9439-3daadf6741ac",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-09-04T04:51:24.193858Z",
|
|
"iopub.status.busy": "2023-09-04T04:51:24.193287Z",
|
|
"iopub.status.idle": "2023-09-04T04:51:24.239406Z",
|
|
"shell.execute_reply": "2023-09-04T04:51:24.238605Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"ename": "NameError",
|
|
"evalue": "name 'sorted_lists' is not defined",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
|
"Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msorted_lists\u001b[49m\n",
|
|
"\u001b[0;31mNameError\u001b[0m: name 'sorted_lists' is not defined"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"sorted_lists"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"id": "c93d7e88-594d-4095-b5f2-7bf01210dc61",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-09-04T04:51:24.244231Z",
|
|
"iopub.status.busy": "2023-09-04T04:51:24.243661Z",
|
|
"iopub.status.idle": "2023-09-04T04:51:24.252026Z",
|
|
"shell.execute_reply": "2023-09-04T04:51:24.250954Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'102141_2_eng': {'filename': '102141_2_eng'},\n",
|
|
" '106349S_por': {'filename': '106349S_por'},\n",
|
|
" '102730_eng': {'filename': '102730_eng'}}"
|
|
]
|
|
},
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"mydict"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"id": "e1cf7e46-0c2c-4fb2-b89a-ef585ccb9339",
|
|
"metadata": {},
|
|
"source": [
|
|
"After launching `multimodal_search` function, the results of each query will be added to the source dictionary. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "9ad74b21-6187-4a58-9ed8-fd3e80f5a4ed",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-09-04T04:51:24.258373Z",
|
|
"iopub.status.busy": "2023-09-04T04:51:24.258098Z",
|
|
"iopub.status.idle": "2023-09-04T04:51:24.268973Z",
|
|
"shell.execute_reply": "2023-09-04T04:51:24.268013Z"
|
|
},
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'filename': '106349S_por'}"
|
|
]
|
|
},
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"mydict[\"106349S_por\"]"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"id": "cd3ee120-8561-482b-a76a-e8f996783325",
|
|
"metadata": {},
|
|
"source": [
|
|
"A special function was written to present the search results conveniently. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "4324e4fd-e9aa-4933-bb12-074d54e0c510",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-09-04T04:51:24.272389Z",
|
|
"iopub.status.busy": "2023-09-04T04:51:24.271964Z",
|
|
"iopub.status.idle": "2023-09-04T04:51:24.381493Z",
|
|
"shell.execute_reply": "2023-09-04T04:51:24.380564Z"
|
|
},
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Your search query: politician press conference'"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'--------------------------------------------------'"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Results:'"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"ename": "KeyError",
|
|
"evalue": "'politician press conference'",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
|
|
"Cell \u001b[0;32mIn[19], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmy_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow_results\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43msearch_query3\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m)\u001b[49m\n",
|
|
"File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/multimodal_search.py:970\u001b[0m, in \u001b[0;36mMultimodalSearch.show_results\u001b[0;34m(self, query, itm, image_gradcam_with_itm)\u001b[0m\n\u001b[1;32m 967\u001b[0m current_querry_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(query\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 968\u001b[0m current_querry_rank \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrank \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mlist\u001b[39m(query\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m--> 970\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43msorted\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 971\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubdict\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcurrent_querry_val\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreverse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m 972\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 973\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m s[\u001b[38;5;241m1\u001b[39m][current_querry_rank] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 974\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n",
|
|
"File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/multimodal_search.py:971\u001b[0m, in \u001b[0;36mMultimodalSearch.show_results.<locals>.<lambda>\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m 967\u001b[0m current_querry_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(query\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 968\u001b[0m current_querry_rank \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrank \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mlist\u001b[39m(query\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 970\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28msorted\u001b[39m(\n\u001b[0;32m--> 971\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubdict\u001b[38;5;241m.\u001b[39mitems(), key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m t: \u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcurrent_querry_val\u001b[49m\u001b[43m]\u001b[49m, reverse\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 972\u001b[0m ):\n\u001b[1;32m 973\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m s[\u001b[38;5;241m1\u001b[39m][current_querry_rank] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 974\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n",
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"\u001b[0;31mKeyError\u001b[0m: 'politician press conference'"
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]
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}
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],
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"source": [
|
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"my_obj.show_results(\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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"attachments": {},
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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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"## Improve the search results\n",
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"\n",
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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 an `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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{
|
|
"cell_type": "code",
|
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"execution_count": 20,
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"id": "b3af7b39-6d0d-4da3-9b8f-7dfd3f5779be",
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|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-09-04T04:51:24.385581Z",
|
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"iopub.status.busy": "2023-09-04T04:51:24.385076Z",
|
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"iopub.status.idle": "2023-09-04T04:51:24.389324Z",
|
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"shell.execute_reply": "2023-09-04T04:51:24.388428Z"
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},
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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",
|
|
"# 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": 21,
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"id": "caf1f4ae-4b37-4954-800e-7120f0419de5",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-09-04T04:51:24.393177Z",
|
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"iopub.status.busy": "2023-09-04T04:51:24.392662Z",
|
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"iopub.status.idle": "2023-09-04T04:51:24.436892Z",
|
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"shell.execute_reply": "2023-09-04T04:51:24.435955Z"
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},
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"tags": []
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},
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"outputs": [
|
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{
|
|
"ename": "NameError",
|
|
"evalue": "name 'image_keys' is not defined",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
|
"Cell \u001b[0;32mIn[21], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m itm_scores, image_gradcam_with_itm \u001b[38;5;241m=\u001b[39m my_obj\u001b[38;5;241m.\u001b[39mimage_text_match_reordering(\n\u001b[1;32m 2\u001b[0m search_query3,\n\u001b[1;32m 3\u001b[0m itm_model,\n\u001b[0;32m----> 4\u001b[0m \u001b[43mimage_keys\u001b[49m,\n\u001b[1;32m 5\u001b[0m sorted_lists,\n\u001b[1;32m 6\u001b[0m batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 7\u001b[0m need_grad_cam\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 8\u001b[0m )\n",
|
|
"\u001b[0;31mNameError\u001b[0m: name 'image_keys' is not defined"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"itm_scores, image_gradcam_with_itm = my_obj.image_text_match_reordering(\n",
|
|
" search_query3,\n",
|
|
" itm_model,\n",
|
|
" image_keys,\n",
|
|
" sorted_lists,\n",
|
|
" batch_size=1,\n",
|
|
" need_grad_cam=True,\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"id": "9e98c150-5fab-4251-bce7-0d8fc7b385b9",
|
|
"metadata": {},
|
|
"source": [
|
|
"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. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"id": "6a829b99-5230-463a-8b11-30ffbb67fc3a",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-09-04T04:51:24.441402Z",
|
|
"iopub.status.busy": "2023-09-04T04:51:24.440853Z",
|
|
"iopub.status.idle": "2023-09-04T04:51:24.485206Z",
|
|
"shell.execute_reply": "2023-09-04T04:51:24.484182Z"
|
|
},
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"ename": "NameError",
|
|
"evalue": "name 'image_gradcam_with_itm' is not defined",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
|
"Cell \u001b[0;32mIn[22], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m my_obj\u001b[38;5;241m.\u001b[39mshow_results(\n\u001b[0;32m----> 2\u001b[0m search_query3[\u001b[38;5;241m0\u001b[39m], itm\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, image_gradcam_with_itm\u001b[38;5;241m=\u001b[39m\u001b[43mimage_gradcam_with_itm\u001b[49m\n\u001b[1;32m 3\u001b[0m )\n",
|
|
"\u001b[0;31mNameError\u001b[0m: name 'image_gradcam_with_itm' is not defined"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"my_obj.show_results(\n",
|
|
" search_query3[0], itm=True, image_gradcam_with_itm=image_gradcam_with_itm\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"id": "d86ab96b-1907-4b7f-a78e-3983b516d781",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"source": [
|
|
"## Save search results to csv"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"id": "4bdbc4d4-695d-4751-ab7c-d2d98e2917d7",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"source": [
|
|
"Convert the dictionary of dictionarys into a dictionary with lists:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"id": "6c6ddd83-bc87-48f2-a8d6-1bd3f4201ff7",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-09-04T04:51:24.489469Z",
|
|
"iopub.status.busy": "2023-09-04T04:51:24.488734Z",
|
|
"iopub.status.idle": "2023-09-04T04:51:24.495000Z",
|
|
"shell.execute_reply": "2023-09-04T04:51:24.494279Z"
|
|
},
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"outdict = mutils.append_data_to_dict(mydict)\n",
|
|
"df = mutils.dump_df(outdict)"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"id": "ea2675d5-604c-45e7-86d2-080b1f4559a0",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"source": [
|
|
"Check the dataframe:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"id": "e78646d6-80be-4d3e-8123-3360957bcaa8",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-09-04T04:51:24.498638Z",
|
|
"iopub.status.busy": "2023-09-04T04:51:24.497955Z",
|
|
"iopub.status.idle": "2023-09-04T04:51:24.513508Z",
|
|
"shell.execute_reply": "2023-09-04T04:51:24.512731Z"
|
|
},
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>filename</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>102141_2_eng</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>106349S_por</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>102730_eng</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" filename\n",
|
|
"0 102141_2_eng\n",
|
|
"1 106349S_por\n",
|
|
"2 102730_eng"
|
|
]
|
|
},
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df.head(10)"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"id": "05546d99-afab-4565-8f30-f14e1426abcf",
|
|
"metadata": {},
|
|
"source": [
|
|
"Write the csv file:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"id": "185f7dde-20dc-44d8-9ab0-de41f9b5734d",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-09-04T04:51:24.517172Z",
|
|
"iopub.status.busy": "2023-09-04T04:51:24.516616Z",
|
|
"iopub.status.idle": "2023-09-04T04:51:24.523471Z",
|
|
"shell.execute_reply": "2023-09-04T04:51:24.522769Z"
|
|
},
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"df.to_csv(\"data/data_out.csv\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "b6a79201-7c17-496c-a6a1-b8ecfd3dd1e8",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
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|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.17"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|