1_local_platform_image_captioning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b21a55de-bfd3-481c-9917-a96fb48231ce",
   "metadata": {},
   "source": [
    "# 플랫폼 업로드를 쉽게하기 위한 로컬 개발 코드\n",
    "- T3Q.ai(T3Q.cep + T3Q.dl): 빅데이터/인공지능 통합 플랫폼\n",
    "- 플랫폼 업로드를 쉽게하기 위하여 로컬에서 아래의 코드(파일1)를 개발한다.\n",
    "- 파일 1(파일명): 1_local_platform_image_classification.ipynb\n",
    "\n",
    "### 전처리 객체 또는 학습모델 객체\n",
    "- 전처리 객체나 학습모델 객체는 meta_data 폴더 아래에 저장한다.\n",
    "\n",
    "### 데이터셋(학습 데이터/테스트 데이터)\n",
    "- 학습과 테스트에 사용되는 데이터를 나누어 관리한다.\n",
    "- 학습 데이터: dataset 폴더 아래에 저장하거나 dataset.zip 파일 형태로 저장한다.\n",
    "- 테스트 데이터: test_dataset 폴더 아래에 저장하거나 test_dataset.zip 파일 형태로 저장한다.\n",
    "\n",
    "### 로컬 개발 워크플로우(workflow)  \n",
    "- 로컬 개발 워크플로우를 다음의 4단계로 분리한다.\n",
    "\n",
    "1. 데이터셋 준비(Data Setup)\n",
    "- 로컬 저장소에서 전처리 및 학습에 필요한 학습 데이터셋을 준비한다.\n",
    "\n",
    "2. 데이터 전처리(Data Preprocessing)\n",
    "- 데이터셋의 분석 및 정규화(Normalization)등의 전처리를 수행한다.\n",
    "- 데이터를 모델 학습에 사용할 수 있도록 가공한다.\n",
    "- 추론과정에서 필요한 경우, 데이터 전처리에 사용된 객체를 meta_data 폴더 아래에 저장한다.\n",
    "\n",
    "3. 학습 모델 훈련(Train Model)\n",
    "- 데이터를 훈련에 사용할 수 있도록 가공한 뒤에 학습 모델을 구성한다. \n",
    "- 학습 모델을 준비된 데이터셋으로 훈련시킨다.\n",
    "- 정확도(Accuracy)나 손실(Loss)등 학습 모델의 성능을 검증한다.\n",
    "- 학습 모델의 성능 검증 후, 학습 모델을 배포한다.\n",
    "- 배포할 학습 모델을 meta_data 폴더 아래에 저장한다.\n",
    "\n",
    "4. 추론(Inference)\n",
    "- 저장된 전처리 객체나 학습 모델 객체를 준비한다.\n",
    "- 추론에 필요한 테스트 데이터셋을 준비한다.\n",
    "- 배포된 학습 모델을 통해 테스트 데이터에 대한 추론을 진행한다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "06a49447-78e0-4d73-af2e-1747a610e054",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "#Image(filename='./T3Q.ai.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27499345-4512-444e-8c88-c8ad22a314fc",
   "metadata": {},
   "source": [
    "# 인공지능 통합플랫폼(T3Q.ai) 프로세스를 이해하고 인공지능 쉽게 하기"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f137ba3a-5ef5-408b-b768-50706d7e4ac7",
   "metadata": {},
   "source": [
    "1. 머신러닝(Machine Learning)과 딥러닝(Deep Learning) 프로그래밍 패턴\n",
    "\n",
    "(1) 데이터셋 불러오기(Dataset Loading)\n",
    "(2) 데이터 전처리(Data Preprocessing)\n",
    "   - 데이터 정규화(Normalization)\n",
    "   - 학습과 테스트 데이터 분할(Train/Test Data Split) 등\n",
    "(3) 학습 모델 구성(Train Model Build)\n",
    "(4) 학습(Model Training)\n",
    "(5) 학습 모델 성능 검증(Model Performance Validation)\n",
    "(6) 학습 모델 저장(배포) 하기(Model Save)\n",
    "(7) 추론 데이터 전처리((Data Preprocessing)\n",
    "(8) 추론(Inference) 또는 예측(Prediction) \n",
    "(9) 추론 결과 데이터 후처리(Data Postprocessing) "
   ]
  },
  {
   "cell_type": "raw",
   "id": "093fe2a0-6478-41a3-b0cd-91126412f286",
   "metadata": {},
   "source": [
    "2. 빅데이터/인공지능 통합 플랫폼[ T3Q.ai ]에서 딥러닝 프로그래밍 하고 인공지능 서비스 실시간 운용하기\n",
    " - 6개의 함수로 딥러닝 프로그래밍 하고 인공지능 서비스 실시간 운용하기\n",
    "\n",
    "(1) process_for_train(pm) 함수\n",
    " - 데이터셋 준비(Dataset Setup) \n",
    "   에 필요한 코드 작성\n",
    "\n",
    "(2) init_svc(im, rule) 함수\n",
    " - 전처리 객체 불러오기\n",
    "   에 필요한 코드 작성(생략 가능)\n",
    "\n",
    "(3) transform(df, params, batch_id) 함수\n",
    "- 추론 데이터 전처리((Data Preprocessing)\n",
    "  에 필요한 코드 작성(생략 가능)\n",
    "\n",
    "(4) train(tm) 함수 \n",
    " - 데이터셋 불러오기(Dataset Loading)\n",
    " - 데이터 전처리(Data Preprocessing)\n",
    " - 학습 모델 구성(Train Model Build)\n",
    " - 학습(Model Training)\n",
    " - 학습 모델 성능 검증(Model Performance Validation)\n",
    " - 전처리 객체 저장\n",
    " - 학습 모델 저장(배포) 하기\n",
    "   에 필요한 코드 작성\n",
    "\n",
    "(5) init_svc(im) 함수 \n",
    " - 전처리 객체 불러오기\n",
    " - 학습모델 객체 불러오기\n",
    "   에 필요한 코드 작성\n",
    "\n",
    "(6) inference(df, params, batch_id) 함수\n",
    " - 추론 데이터 전처리((Data Preprocessing)\n",
    " - 추론(Inference) 또는 예측(Prediction) \n",
    " - 추론 결과 데이터 후처리(Data Postprocessing) \n",
    "   에 필요한 코드 작성"
   ]
  },
  {
   "cell_type": "raw",
   "id": "9b5db594-87d6-4a01-a9e9-788dfbf442cc",
   "metadata": {},
   "source": [
    "3. 전처리 모듈 관리, 학습 알고리즘 관리 함수 설명\n",
    "\n",
    "1) 프로젝트 설정/전처리모듈 관리 함수 \n",
    "\n",
    "def process_for_train(pm):\n",
    "    \"\"\"\n",
    "    (1) 입력: pm\n",
    "      # pm.source_path: 학습플랫폼/데이터셋 관리 메뉴에서 저장한 데이터를 불러오는 경로\n",
    "      # pm.target_path: 처리 완료된 데이터를 저장하는 경로\n",
    "    (2) 출력: None\n",
    "    (3) 설명: \n",
    "      # 데이터셋 관리 메뉴에서 저장한 데이터를 불러와서 필요한 처리를 수행\n",
    "      # 처리 완료된 데이터를 저장하는 기능, pm.target_path에 저장\n",
    "      # train(tm) 함수의 tm.train_data_path를 통해 데이터를 불러와서 전처리와 학습을 수행 \n",
    "    \"\"\"\n",
    "\n",
    "def init_svc(im, rule):\n",
    "    \"\"\"\n",
    "    (1) 입력: im, rule\n",
    "    (2) 출력: None\n",
    "    (3) 설명: \n",
    "      # process_for_train(pm) 함수에서 저장한 전처리 객체와 데이터에 적용된 룰(rule)을 불러오는 기능\n",
    "      # 전처리 객체, 룰(rule) 불러오기 기능 없이 처리\n",
    "    \"\"\"\n",
    "\n",
    "    return {}\n",
    "\n",
    "def transform(df, params, batch_id):\n",
    "    \"\"\"\n",
    "    (1) 입력: df, params, batch_id\n",
    "      # df: 추론모델관리와 추론API관리, 실시간 추론을 통해 전달되는 추론 입력 데이터(dataframe 형태)\n",
    "      # params: init_svc(im, rule) 함수의 리턴(return) 값을 params 변수로 전달\n",
    "    (2) 출력: df\n",
    "    (3) 설명: \n",
    "      # df(추론 입력 데이터)에 대한 전처리를 수행한 후 전처리 된 데이터를 inference(df, ...) 함수의 입력 df에 전달하는 기능\n",
    "      # df(추론 입력 데이터)를 전처리 없이 inference(df, params, batch_id) 함수의 입력 df에 리턴(return)\n",
    "    \"\"\"\n",
    "    \n",
    "    return df\n",
    "\n",
    "\n",
    "2) 프로젝트 설정/학습 알고리즘 관리 함수\n",
    "\n",
    "def train(tm):\n",
    "    \"\"\"\n",
    "    (1) 입력: tm\n",
    "      # tm.train_data_path: pm.target_path에 저장한 데이터를 불러오는 경로\n",
    "      # tm.model_path: 전처리 객체와 학습 모델 객체를 저장하는 경로\n",
    "    (2) 출력: None\n",
    "    (3) 설명: \n",
    "      # pm.target_path에 저장한 데이터를 tm.train_data_path를 통해 데이터를 불러오는 기능\n",
    "      # 데이터 전처리와 학습 모델을 구성하고 모델 학습을 수행\n",
    "      # 학습 모델의 성능을 검증하고 배포할 학습 모델을 저장\n",
    "      # 전처리 객체와 학습 모델 객체를 저장, tm.model_path에 저장\n",
    "      # init_svc(im) 함수의 im.model_path를 통해 전처리 객체와 학습 모델 객체를 준비\n",
    "    \"\"\"\n",
    "\n",
    "def init_svc(im):\n",
    "    \"\"\"\n",
    "    (1) 입력: im\n",
    "      # im.model_path: tm.model_path에 저장한 전처리 객체와 학습 모델 객체 등을 불러오는 경로\n",
    "    (2) 출력: 전처리 객체와 학습 모델 객체 등을 딕셔너리(dictionary) 형태로 리턴(return)\n",
    "    (3) 설명: \n",
    "      # tm.model_path에 저장한 전처리 객체와 학습 모델 객체 등을 불러오는 기능\n",
    "      # 전처리 객체, 룰(rule) 불러오기 기능 없이 처리\n",
    "      # 전처리 객체와 학습 모델 객체 등을 딕셔너리(dictionary) 형태로 리턴(return)\n",
    "      # 리턴(return) 값을 inference(df, params, batch_id) 함수의 입력 params 변수로 전달\n",
    "    \"\"\"\n",
    "\n",
    "    return { \"model\": model, \"param\": param }\n",
    "\n",
    "def inference(df, params, batch_id):\n",
    "    \"\"\"\n",
    "    (1) 입력: df, params, batch_id\n",
    "      # df: transform(df, params, batch_id)함수의 리턴(return) 값으로 전달된 df, 추론 입력 데이터(dataframe 형태)\n",
    "      # params  init_svc(im) 함수의 return 값을 params 변수로 전달\n",
    "        ## 학습 모델 객체 사용 예시       model=params['model']\n",
    "        ## 전처리(pca) 객체 사용 예시     pca=params['pca']\n",
    "    (2) 출력: 추론 결과를 딕셔너리(dictionary) 형태로 리턴(return)\n",
    "    (3) 설명: \n",
    "      # 전처리 객체를 사용하여 df(추론 입력 데이터)에 대한 전처리 수행\n",
    "      # 배포된 학습 모델(model)을 사용하여 df(추론 입력 데이터)에 추론(예측)을 수행\n",
    "      # 추론 결과를 딕셔너리(dictionary) 형태로 리턴(return)\n",
    "    \"\"\"\n",
    "    \n",
    "    return {\"inference\": result}"
   ]
  },
  {
   "cell_type": "raw",
   "id": "70dd01f0-97f9-414f-9653-2b7dba5466fd",
   "metadata": {},
   "source": [
    "4. 전처리 모듈 관리, 학습 알고리즘 관리 함수 설명(AI 훈민정음 프로젝트)\n",
    "\n",
    "1) 프로젝트 설정/전처리모듈 관리 함수(AI 훈민정음 프로젝트) \n",
    "\n",
    "import logging\n",
    "\n",
    "def process_for_train(pm):\n",
    "    \"\"\"\n",
    "    (1) 입력: pm\n",
    "      # pm.source_path: 학습플랫폼/데이터셋 관리 메뉴에서 저장한 데이터를 불러오는 경로\n",
    "      # pm.target_path: 처리 완료된 데이터를 저장하는 경로\n",
    "    (2) 출력: None\n",
    "    (3) 설명: \n",
    "      # 데이터셋 관리 메뉴에서 저장한 데이터를 불러와서 필요한 처리를 수행\n",
    "      # 처리 완료된 데이터를 저장하는 기능, pm.target_path에 저장\n",
    "      # train(tm) 함수의 tm.train_data_path를 통해 데이터를 불러와서 전처리와 학습을 수행 \n",
    "    (4) 추가 설명: \n",
    "      # 함수 구조는 원형대로 유지\n",
    "      # 실질적인 기능을 하는 함수를 서브모듈 함수(exec_process)로 정의하여 사용함\n",
    "      # 함수명                            서브함수명\n",
    "      # process_for_train(pm)          exec_process(pm) \n",
    "      # 함수의 정상적인 동작 체크를 위해 마지막 라인(the end line)에 로그 출력 수행\n",
    "    \"\"\"\n",
    "\n",
    "    exec_process(pm)\n",
    "    \n",
    "    logging.info('[hunmin log] the end line of the function [process_for_train]')\n",
    "\n",
    "def init_svc(im, rule):\n",
    "    \"\"\"\n",
    "    (1) 입력: im, rule\n",
    "    (2) 출력: None\n",
    "    (3) 설명: \n",
    "      # process_for_train(pm) 함수에서 저장한 전처리 객체와 데이터에 적용된 룰(rule)을 불러오는 기능\n",
    "      # 전처리 객체, 룰(rule) 불러오기 기능 없이 처리\n",
    "    \"\"\"\n",
    "\n",
    "    return {}\n",
    "\n",
    "def transform(df, params, batch_id):\n",
    "    \"\"\"\n",
    "    (1) 입력: df, params, batch_id\n",
    "      # df: 추론모델관리와 추론API관리, 실시간 추론을 통해 전달되는 추론 입력 데이터(dataframe 형태)\n",
    "      # params: init_svc(im, rule) 함수의 리턴(return) 값을 params 변수로 전달\n",
    "    (2) 출력: df\n",
    "    (3) 설명: \n",
    "      # df(추론 입력 데이터)에 대한 전처리를 수행한 후 전처리 된 데이터를 inference(df, ...) 함수의 입력 df에 전달하는 기능\n",
    "      # df(추론 입력 데이터)를 전처리 없이 inference(df, params, batch_id) 함수의 입력 df에 리턴(return)\n",
    "    (4) 추가 설명: \n",
    "      # 함수 구조는 원형대로 유지\n",
    "      # 함수의 정상적인 동작 체크를 위해 마지막 라인(the end line)에 로그 출력 수행            \n",
    "    \"\"\"\n",
    "    \n",
    "    logging.info('[hunmin log] the end line of the function [transform]')\n",
    "    \n",
    "    return df\n",
    "\n",
    "\n",
    "2) 프로젝트 설정/학습 알고리즘 관리 함수(AI 훈민정음 프로젝트)\n",
    "\n",
    "import logging\n",
    "\n",
    "def train(tm):\n",
    "    \"\"\"\n",
    "    (1) 입력: tm\n",
    "      # tm.train_data_path: pm.target_path에 저장한 데이터를 불러오는 경로\n",
    "      # tm.model_path: 전처리 객체와 학습 모델 객체를 저장하는 경로\n",
    "    (2) 출력: None\n",
    "    (3) 설명: \n",
    "      # pm.target_path에 저장한 데이터를 tm.train_data_path를 통해 데이터를 불러오는 기능\n",
    "      # 데이터 전처리와 학습 모델을 구성하고 모델 학습을 수행\n",
    "      # 학습 모델의 성능을 검증하고 배포할 학습 모델을 저장\n",
    "      # 전처리 객체와 학습 모델 객체를 저장, tm.model_path에 저장\n",
    "      # init_svc(im) 함수의 im.model_path를 통해 전처리 객체와 학습 모델 객체를 준비\n",
    "    (4) 추가 설명: \n",
    "      # 함수 구조는 원형대로 유지\n",
    "      # 실질적인 기능을 하는 함수를 서브모듈 함수(exec_train)로 정의하여 사용함\n",
    "      # 함수명                         서브함수명\n",
    "      # train(tm)                      exec_train(tm)\n",
    "      # 함수의 정상적인 동작 체크를 위해 마지막 라인(the end line)에 로그 출력 수행\n",
    "    \"\"\"\n",
    "\n",
    "    exec_train(tm)\n",
    "    \n",
    "    logging.info('[hunmin log] the end line of the function [train]')\n",
    "\n",
    "def init_svc(im):\n",
    "    \"\"\"\n",
    "    (1) 입력: im\n",
    "      # im.model_path: tm.model_path에 저장한 전처리 객체와 학습 모델 객체 등을 불러오는 경로\n",
    "    (2) 출력: 전처리 객체와 학습 모델 객체 등을 딕셔너리(dictionary) 형태로 리턴(return)\n",
    "    (3) 설명: \n",
    "      # tm.model_path에 저장한 전처리 객체와 학습 모델 객체 등을 불러오는 기능\n",
    "      # 전처리 객체, 룰(rule) 불러오기 기능 없이 처리\n",
    "      # 전처리 객체와 학습 모델 객체 등을 딕셔너리(dictionary) 형태로 리턴(return)\n",
    "      # 리턴(return) 값을 inference(df, params, batch_id) 함수의 입력 params 변수로 전달\n",
    "    (4) 추가 설명: \n",
    "      # 함수 구조는 원형대로 유지\n",
    "      # 실질적인 기능을 하는 함수를 서브모듈 함수(exec_init_svc)로 정의하여 사용함\n",
    "      # 함수명                            서브함수명\n",
    "      # init_svc(im)                      exec_init_svc(im)\n",
    "      # 함수의 정상적인 동작 체크를 위해 마지막 라인(the end line)에 로그 출력 수행      \n",
    "    \"\"\"\n",
    "\n",
    "    params = exec_init_svc(im)\n",
    "    \n",
    "    logging.info('[hunmin log] the end line of the function [init_svc]')\n",
    "    \n",
    "    return { **params }\n",
    "\n",
    "def inference(df, params, batch_id):\n",
    "    \"\"\"\n",
    "    (1) 입력: df, params, batch_id\n",
    "      # df: transform(df, params, batch_id)함수의 리턴(return) 값으로 전달된 df, 추론 입력 데이터(dataframe 형태)\n",
    "      # params  init_svc(im) 함수의 return 값을 params 변수로 전달\n",
    "        ## 학습 모델 객체 사용 예시       model=params['model']\n",
    "        ## 전처리 객체 사용 예시          pca=params['pre_model']\n",
    "    (2) 출력: 추론 결과를 딕셔너리(dictionary) 형태로 리턴(return)\n",
    "    (3) 설명: \n",
    "      # 전처리 객체를 사용하여 df(추론 입력 데이터)에 대한 전처리 수행\n",
    "      # 배포된 학습 모델(model)을 사용하여 df(추론 입력 데이터)에 추론(예측)을 수행\n",
    "      # 추론 결과를 딕셔너리(dictionary) 형태로 리턴(return)\n",
    "    (4) 추가 설명: \n",
    "      # 함수 구조는 원형대로 유지\n",
    "      # 실질적인 기능을 하는 함수를 서브모듈 함수(exec_inference)로 정의하여 사용함\n",
    "      # 함수명                                                     서브함수명\n",
    "      # inference(df, params, batch_id)                      exec_inference(df, params, batch_id)\n",
    "      # 함수의 정상적인 동작 체크를 위해 마지막 라인(the end line)에 로그 출력 수행            \n",
    "    \"\"\"\n",
    "    \n",
    "    result = exec_inference(df, params, batch_id)\n",
    "    \n",
    "    logging.info('[hunmin log] the end line of the function [inference]')\n",
    "\n",
    "    return { **result }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f78f8718-0eda-41a6-a83e-7464a3c494c9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7c151c56-513c-44a5-9e13-fd154883b3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 파일명: image_captioning_preprocess.py\n",
    "\n",
    "from platform_image_captioning_preprocess_sub import exec_process\n",
    "import logging\n",
    "import logging\n",
    "\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "\n",
    "def process_for_train(pm):\n",
    "    \n",
    "    exec_process(pm)\n",
    "    \n",
    "    logging.info('[hunmin log] the end line of the function [process_for_train]')\n",
    "    \n",
    "    \n",
    "def init_svc(im, rule):\n",
    "    return {}\n",
    "\n",
    "\n",
    "def transform(df, params, batch_id):\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81a6e7b5-37a9-40bb-bede-54ac21c5568c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:numexpr.utils:Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
      "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n"
     ]
    }
   ],
   "source": [
    "# 파일명: image_captioning_preprocess_sub.py\n",
    "\n",
    "import os\n",
    "import zipfile\n",
    "import logging\n",
    "\n",
    "def exec_process(pm):\n",
    "\n",
    "    logging.info('[hunmin log]  the start line of the function [exec_process]')\n",
    "\n",
    "    logging.info('[hunmin log] pm.source_path : {}'.format(pm.source_path))\n",
    "\n",
    "    # 저장 파일 확인\n",
    "    list_files_directories(pm.source_path)\n",
    "    \n",
    "    # pm.source_path의 dataset.zip 파일을 \n",
    "    # pm.target_path의 dataset 폴더에 압축을 풀어준다.\n",
    "    # 이미지, 캡션(라벨), preprocessor zip파일\n",
    "    my_zip_path = os.path.join(pm.source_path,'dataset.zip') \n",
    "    extract_zip_file = zipfile.ZipFile(my_zip_path)\n",
    "    #extract_zip_file.extractall(os.path.join(pm.target_path, 'image_dataset'))\n",
    "    extract_zip_file.extractall(pm.target_path)\n",
    "    extract_zip_file.close()\n",
    "    \n",
    "    # 저장 파일 확인\n",
    "    list_files_directories(pm.target_path)\n",
    "\n",
    "    logging.info('[hunmin log]  the finish line of the function [exec_process]')\n",
    "\n",
    "\n",
    "\n",
    "# 저장 파일 확인\n",
    "def list_files_directories(path):\n",
    "    # Get the list of all files and directories in current working directory\n",
    "    dir_list = os.listdir(path)\n",
    "    logging.info('[hunmin log] Files and directories in {} :'.format(path))\n",
    "    logging.info('[hunmin log] dir_list : {}'.format(dir_list))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5a377ae8-1201-46fa-96eb-547a8ddd6172",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 파일명: image_classification_train.py\n",
    "\n",
    "from platform_image_captioning_train_sub import exec_train, exec_inference, exec_init_svc\n",
    "\n",
    "import logging\n",
    "\n",
    "\n",
    "def train(tm):\n",
    "    \n",
    "    exec_train(tm)\n",
    "    logging.info('[hunmin log] the end line of the function [train]')\n",
    "\n",
    "\n",
    "def init_svc(im):\n",
    "    \n",
    "    params = exec_init_svc(im)\n",
    "    logging.info('[hunmin log] the end line of the function [init_svc]')\n",
    "    \n",
    "    return { **params }\n",
    "\n",
    "\n",
    "def inference(df, params, batch_id):\n",
    "    \n",
    "    result = exec_inference(df, params, batch_id)\n",
    "    logging.info('[hunmin log] the end line of the function [inference]')\n",
    "    \n",
    "    return { **result }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "82ccb934-12d1-4071-9a9c-e26ce1a66548",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:[hunmin log] tensorflow ver : 2.9.0\n",
      "INFO:root:[hunmin log] gpu set complete\n",
      "INFO:root:[hunmin log] num of gpu: 1\n"
     ]
    }
   ],
   "source": [
    "# 파일명: platform_image_classification_train_sub.py\n",
    "\n",
    "import logging\n",
    "from torch.utils.data import Dataset\n",
    "from torch.utils.data import DataLoader\n",
    "import os\n",
    "import torch\n",
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import base64\n",
    "import io\n",
    "from transformers import BlipProcessor, BlipForConditionalGeneration, Swin2SRImageProcessor, Swin2SRForImageSuperResolution\n",
    "import cv2, time, sys\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.compat.v1 import ConfigProto, InteractiveSession\n",
    "\n",
    "from absl import flags\n",
    "from absl.flags import FLAGS\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "physical_devices = tf.config.experimental.list_physical_devices('GPU')\n",
    "if len(physical_devices) > 0:\n",
    "    logging.info(\"len(physical_devices): \", len(physical_devices))\n",
    "    tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
    "    \n",
    "import core.utils as utils\n",
    "from core.yolov4 import filter_boxes\n",
    "from core.config import cfg\n",
    "\n",
    "from deep_sort import preprocessing, nn_matching\n",
    "from deep_sort.detection import Detection\n",
    "from deep_sort.tracker import Tracker\n",
    "from tools import generate_detections as gdet\n",
    "\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "\n",
    "def exec_train(tm):\n",
    "    \n",
    "    class ImageCaptioningDataset(Dataset):\n",
    "        def __init__(self, dataset, processor):\n",
    "            self.dataset = dataset\n",
    "            self.processor = processor\n",
    "\n",
    "        def __len__(self):\n",
    "            return len(self.dataset)\n",
    "\n",
    "        def __getitem__(self, idx):\n",
    "            item = self.dataset[idx]\n",
    "            encoding = self.processor(images=item[\"image\"], text=item[\"text\"], padding=\"max_length\", return_tensors=\"pt\")\n",
    "\n",
    "            encoding = {k:v.squeeze() for k,v in encoding.items()}\n",
    "            return encoding\n",
    "        \n",
    "    def image_list(dir, captions):\n",
    "        imagelist = []\n",
    "        for item in captions:\n",
    "            image_name = item['image']\n",
    "            path = dir+'/'+image_name\n",
    "            imagelist.append(Image.open(path))\n",
    "        return imagelist\n",
    "    \n",
    "    def gen_captions(captions,filename):\n",
    "        gen = []\n",
    "        for i in range(len(captions)):\n",
    "            gen.append({'image_id': i+1, 'caption': captions[i]})\n",
    "\n",
    "        with open(filename,'w') as f:\n",
    "            json.dump(gen,f)\n",
    "\n",
    "\n",
    "    ####################################################\n",
    "    # 본격 시작 \n",
    "    ###################################################\n",
    "    ## 1. 데이터셋 준비(Data Setup)\n",
    "    with open(os.path.join(tm.train_data_path, 'dataset/annotations/shuffled_captions.json'),'r',encoding='utf-8' or 'cp949') as f: # caption 불러오기\n",
    "        captions = json.load(f)\n",
    "\n",
    "    logging.info('[hunmin log] :caption load ok')\n",
    "    imagelist = image_list(os.path.join(tm.train_data_path, 'dataset/image_augmented'),captions) # train_data_path로 불러오기 \n",
    "    \n",
    "    ## 2. 데이터 전처리\n",
    "    pro_sr = Swin2SRImageProcessor.from_pretrained(\"caidas/swin2SR-lightweight-x2-64\")\n",
    "    model_sr = Swin2SRForImageSuperResolution.from_pretrained(\"caidas/swin2SR-lightweight-x2-64\")\n",
    "    device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "    model_sr.to(device)\n",
    "    images = []\n",
    "    for image in imagelist:\n",
    "        image = super_reso(image,pro_sr,model_sr) if image.size[0]<50 or image.size[1]<100 else image\n",
    "        images.append(image)\n",
    "    model_sr.to('cpu')\n",
    "\n",
    "    data = [{'text':captions[i]['label'],'image':images[i]} for i in range(len(images))] # 최종 학습을 위한 데이터셋\n",
    "    processor = BlipProcessor.from_pretrained(os.path.join(tm.train_data_path, 'dataset/preprocessor'))\n",
    "\n",
    "    train_dataset = ImageCaptioningDataset(data[:int(0.8*100)], processor)\n",
    "    val_dataset = ImageCaptioningDataset(data[int(0.8*100):], processor)\n",
    "\n",
    "\n",
    "    # 모델 불러오기\n",
    "    mode = \"Salesforce/blip-image-captioning-base\"\n",
    "    model = BlipForConditionalGeneration.from_pretrained(mode)\n",
    "    batch_size = int(tm.param_info['batch_size'])\n",
    "    epochs = int(tm.param_info['epoch'])\n",
    "    lr = float(tm.param_info['learning_rate'])\n",
    "    val_cpath = tm.model_path\n",
    "\n",
    "    train_dataloader = DataLoader(train_dataset,shuffle=False,batch_size = batch_size)\n",
    "    val_dataloader = DataLoader(val_dataset,shuffle=False,batch_size = batch_size)\n",
    "\n",
    "    #train 진행\n",
    "    train_hist = []\n",
    "    val_hist = []\n",
    "\n",
    "    import torch.optim as optim\n",
    "    from torch.optim.lr_scheduler import CosineAnnealingLR\n",
    "\n",
    "    optimizer = optim.AdamW(model.parameters(), lr=lr, weight_decay=0.05)\n",
    "    scheduler = CosineAnnealingLR(optimizer, T_max=epochs)\n",
    "\n",
    "    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "    model.to(device)\n",
    "\n",
    "    for epoch in range(epochs):\n",
    "        model.train()\n",
    "        Loss = 0\n",
    "\n",
    "        for idx, batch in enumerate(train_dataloader):\n",
    "            input_ids = batch.pop(\"input_ids\").to(device)\n",
    "            pixel_values = batch.pop(\"pixel_values\").to(device)\n",
    "            outputs = model(input_ids=input_ids,pixel_values=pixel_values, labels=input_ids)\n",
    "            loss = outputs.loss\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            Loss+=loss.tolist()\n",
    "        train_hist.append(Loss/len(train_dataloader))\n",
    "\n",
    "\n",
    "        #validation진행\n",
    "        val = 0\n",
    "        val_caption =[]\n",
    "        with torch.no_grad():\n",
    "            model.eval()\n",
    "            for idx, batch in enumerate(val_dataloader):\n",
    "                input_ids = batch.pop(\"input_ids\").to(device)\n",
    "                pixel_values = batch.pop(\"pixel_values\").to(device)\n",
    "                outputs = model(input_ids=input_ids,pixel_values=pixel_values, labels=input_ids)\n",
    "\n",
    "                #성능을 보기위한 작업\n",
    "                val_caption+=processor.batch_decode(model.generate(pixel_values=pixel_values,max_length = 300),skip_special_tokens=True)\n",
    "                val+=outputs.loss.tolist()\n",
    "\n",
    "        val_hist.append(val/len(val_dataloader))\n",
    "\n",
    "        #checkpoint\n",
    "        if val_hist[-1]==min(val_hist):\n",
    "            torch.save(model,tm.model_path+'/model.pt')\n",
    "\n",
    "        #Epoch 출력\n",
    "        logging.info(\"Epoch {}회차 - val_Loss:{}, \".format(epoch+1,val/313))\n",
    "\n",
    "        gen_captions(val_caption,val_cpath+'/'+str(epoch+1)+'.json')\n",
    "        #train_eval.append(coco_caption_eval(train_rpath,train_cpath+'/'+str(epoch+1)+'.json').eval.items())\n",
    "        scheduler.step()\n",
    "\n",
    "    history = [train_hist,val_hist]    \n",
    "    # 학습 결과 표출 함수화 일단 하지 말자. \n",
    "    #plot_metrics(tm, history, model,val_dataloader, processor) # epoch별로 보여줄 예정\n",
    "\n",
    "def super_reso(image,pro_sr,model_sr):\n",
    "    device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "    inputs = pro_sr(image, return_tensors=\"pt\").to(device)\n",
    "\n",
    "    # forward pass\n",
    "    with torch.no_grad():\n",
    "        outputs = model_sr(**inputs)\n",
    "\n",
    "    output = outputs.reconstruction.data.squeeze().cpu().float().clamp_(0, 1).numpy()\n",
    "    output = np.moveaxis(output, source=0, destination=-1)\n",
    "    output = (output * 255.0).round().astype(np.uint8)\n",
    "    return Image.fromarray(output)   \n",
    "\n",
    "# 저장 파일 확인\n",
    "def list_files_directories(path):\n",
    "    # Get the list of all files and directories in current working directory\n",
    "    dir_list = os.listdir(path)\n",
    "    logging.info('[hunmin log] Files and directories in {} :'.format(path))\n",
    "    logging.info('[hunmin log] dir_list : {}'.format(dir_list))\n",
    "\n",
    "\n",
    "\n",
    "###########################################################################\n",
    "## exec_train(tm) 호출 함수 \n",
    "###########################################################################\n",
    "    \n",
    "# 시각화\n",
    "def plot_metrics(tm, history, model,val_dataloader, processor):\n",
    "    from pycocotools.coco import COCO\n",
    "    from pycocoevalcap.eval import COCOEvalCap\n",
    "\n",
    "    def coco_caption_eval(annotation_file, results_file):\n",
    "\n",
    "        coco = COCO(annotation_file)\n",
    "        coco_result = coco.loadRes(results_file)\n",
    "\n",
    "        coco_eval = COCOEvalCap(coco, coco_result)\n",
    "        coco_eval.evaluate()\n",
    "\n",
    "        return coco_eval\n",
    "    \n",
    "    acc = []\n",
    "    y_test = tm.model_path+'/'+'gt.json'\n",
    "    for epoch in range(len(tm.param_info['epoch'])):\n",
    "        y_predict = tm.model_path+'/'+str(epoch+1)+'.json'\n",
    "        acc.append(coco_caption_eval(y_test,y_predict).eval['METEOR'])\n",
    "     \n",
    "    for epoch, (acc,loss) in enumerate(zip(acc,history[0])):\n",
    "        metric={}\n",
    "        metric['accuracy'] = acc[epoch]\n",
    "        metric['loss'] = loss\n",
    "        metric['step'] = epoch\n",
    "        tm.save_stat_metrics(metric)   \n",
    "\n",
    "    logging.info('[user log] loss, acc graph made!!!')    \n",
    "    # 최종모델에 대한 성능보기 \n",
    "    val = 0\n",
    "    val_caption =[]\n",
    "    device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "    with torch.no_grad():\n",
    "        model.eval()\n",
    "        for idx, batch in enumerate(val_dataloader):\n",
    "            input_ids = batch.pop(\"input_ids\").to(device)\n",
    "            pixel_values = batch.pop(\"pixel_values\").to(device)\n",
    "            outputs = model(input_ids=input_ids,pixel_values=pixel_values, labels=input_ids)\n",
    "\n",
    "            #성능을 보기위한 작업\n",
    "            val_caption+=processor.batch_decode(model.generate(pixel_values=pixel_values,max_length = 300),skip_special_tokens=True)\n",
    "            val+=outputs.loss.tolist()\n",
    "    \n",
    "    # 이건 할지 안할지 고민 중 => confusion matrix를 사용할 수 없음 \n",
    "    eval_results={}\n",
    "    eval_results['accuracy'] = coco_caption_eval(y_test,y_predict).eval['METEOR']\n",
    "    eval_results['loss'] = val/len(val_dataloader)\n",
    "    tm.save_result_metrics(eval_results)\n",
    "    logging.info('[user log] accuracy and loss curve plot for platform')\n",
    "\n",
    "\n",
    "###########################################################################\n",
    "    ## 학습 모델 준비\n",
    "###########################################################################\n",
    "\n",
    "def exec_init_svc(im):\n",
    "    logging.info('[hunmin log] im.model_path : {}'.format(im.model_path))\n",
    "    \n",
    "    # 저장 파일 확인\n",
    "    list_files_directories(im.model_path) \n",
    "    \n",
    "    # load the model\n",
    "    mode = \"Salesforce/blip-image-captioning-base\"\n",
    "    processor_blip = BlipProcessor.from_pretrained(mode)\n",
    "    model_blip = torch.load(os.path.join(im.model_path, 'model.pt'), map_location=torch.device('cpu'))\n",
    "    \n",
    "    return {'model' : model_blip, 'processor': processor_blip}\n",
    "\n",
    "\n",
    "###########################################################################\n",
    "## exec_inference(df, params, batch_id)함수, 하위 호출 함수 \n",
    "###########################################################################\n",
    "\n",
    "\n",
    "\n",
    "def exec_inference(df, params, batch_id):\n",
    "    logging.info('exec_inference start')\n",
    "\n",
    "    def decode_base64_video(encoded_video, output_file):\n",
    "        # Base64 데이터를 디코드하여 바이너리 데이터로 변환\n",
    "        decoded_video = base64.b64decode(encoded_video)\n",
    "\n",
    "        # 변환된 바이너리 데이터를 파일로 저장\n",
    "        with open(output_file, 'wb') as file:\n",
    "            file.write(decoded_video)\n",
    "\n",
    "\n",
    "    def draw_box(table, input_keyword):\n",
    "        table_len = len(table)\n",
    "\n",
    "        logging.info('-'*70)\n",
    "        logging.info('input_keyword: ', input_keyword)\n",
    "        try:\n",
    "            selected_row = []\n",
    "\n",
    "            if (len(input_keyword) == 0):\n",
    "                for i in range(table_len):\n",
    "                    row = table.iloc[i]\n",
    "                    selected_row.append(row)\n",
    "            else:\n",
    "                for i in range(table_len):\n",
    "                    row = table.iloc[i]\n",
    "                    object_cap = row['caption']\n",
    "                    matched_keywords = [True for keyword in input_keyword if keyword in object_cap]\n",
    "                    if sum(matched_keywords) == len(input_keyword):\n",
    "                        selected_row.append(row)\n",
    "                        logging.info(\"-\"*70)\n",
    "                        logging.info(\"keyword를 포함하는 객체 id\")\n",
    "                        logging.info(row['object_id'])\n",
    "                        logging.info(\"해당 캡션 문장\")\n",
    "                        logging.info(row['caption'])\n",
    "        except:\n",
    "            logging.info('draw_box Error')\n",
    "\n",
    "        return selected_row\n",
    "    \n",
    "    def video_tracking(input_type, table, input_keyword):\n",
    "        for name in list(flags.FLAGS):\n",
    "            delattr(flags.FLAGS, name)\n",
    "\n",
    "        # yolo4_deepsort1  unzip\n",
    "        flags.DEFINE_string('framework', 'tf', 'tf, tflite, trt')\n",
    "        flags.DEFINE_string('weights', '/data/aip/logs/t3qai/mods/algo_561/1/checkpoints/yolov4-tiny-416','path to weights file')\n",
    "        flags.DEFINE_integer('size', 416, 'resize images to')\n",
    "        flags.DEFINE_boolean('tiny', True, 'yolo or yolo-tiny')\n",
    "        flags.DEFINE_string('model', 'yolov4', 'yolov3 or yolov4')\n",
    "        flags.DEFINE_string('output_format', 'H264', 'codec used in VideoWriter when saving video to file')\n",
    "        flags.DEFINE_float('iou', 0.45, 'iou threshold')\n",
    "        flags.DEFINE_float('score', 0.50, 'score threshold')\n",
    "        flags.DEFINE_boolean('dont_show', False, 'dont show video output')\n",
    "        flags.DEFINE_boolean('info', False, 'show detailed info of tracked objects')\n",
    "        flags.DEFINE_boolean('count', False, 'count objects being tracked on screen')\n",
    "        flags.DEFINE_string('video', '/data/aip/logs/t3qai/mods/algo_561/1/data/video/input_video.mp4', 'path to input video or set to 0 for webcam')\n",
    "        flags.DEFINE_string('output', '/data/aip/logs/t3qai/mods/algo_561/1/data/output/output_video.mp4', 'path to output video')\n",
    "\n",
    "        FLAGS = flags.FLAGS\n",
    "        FLAGS(sys.argv)\n",
    "\n",
    "        # Definition of the parameters\n",
    "        max_cosine_distance = 0.4\n",
    "        nn_budget = None\n",
    "        nms_max_overlap = 1.0\n",
    "        \n",
    "        # initialize deep sort\n",
    "        # model_filename = 'C:\\\\Users\\\\Sihyun\\\\Desktop\\\\INISW\\\\project\\\\sihyun_track_test\\\\yolov4-deepsort-master\\\\model_data\\\\mars-small128.pb'\n",
    "        model_filename = '/data/aip/logs/t3qai/mods/algo_561/1/model_data/mars-small128.pb'\n",
    "        encoder = gdet.create_box_encoder(model_filename, batch_size=1)\n",
    "        # calculate cosine distance metric\n",
    "        metric = nn_matching.NearestNeighborDistanceMetric(\"cosine\", max_cosine_distance, nn_budget)\n",
    "        # initialize tracker\n",
    "        tracker = Tracker(metric)\n",
    "\n",
    "        # load configuration for object detector\n",
    "        config = ConfigProto()\n",
    "        config.gpu_options.allow_growth = True\n",
    "        session = InteractiveSession(config=config)\n",
    "        STRIDES, ANCHORS, NUM_CLASS, XYSCALE = utils.load_config(FLAGS)\n",
    "        input_size = FLAGS.size\n",
    "        video_path = FLAGS.video\n",
    "\n",
    "        # load tflite model if flag is set\n",
    "        if FLAGS.framework == 'tflite':\n",
    "            interpreter = tf.lite.Interpreter(model_path=FLAGS.weights)\n",
    "            interpreter.allocate_tensors()\n",
    "            input_details = interpreter.get_input_details()\n",
    "            output_details = interpreter.get_output_details()\n",
    "            logging.info(input_details)\n",
    "            logging.info(output_details)\n",
    "        # otherwise load standard tensorflow saved model\n",
    "        else:\n",
    "            infer = keras.models.load_model(FLAGS.weights)\n",
    "\n",
    "        # begin video capture\n",
    "        try:\n",
    "            vid = cv2.VideoCapture(int(video_path))\n",
    "        except:\n",
    "            vid = cv2.VideoCapture(video_path)\n",
    "\n",
    "        total_frame = int(vid.get(cv2.CAP_PROP_FRAME_COUNT))\n",
    "        out = None\n",
    "\n",
    "        # get video ready to save locally if flag is set\n",
    "        if FLAGS.output:\n",
    "            # by default VideoCapture returns float instead of int\n",
    "            width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
    "            height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
    "            fps = int(vid.get(cv2.CAP_PROP_FPS))\n",
    "            codec = cv2.VideoWriter_fourcc(*FLAGS.output_format)\n",
    "            out = cv2.VideoWriter(FLAGS.output, codec, fps, (width, height))\n",
    "\n",
    "        frame_num = 0\n",
    "\n",
    "        result_list = []\n",
    "\n",
    "        if input_type == 'B':\n",
    "            draw_box_res = draw_box(table, input_keyword)\n",
    "            if len(draw_box_res) == 0:\n",
    "                return json.dumps({'status': 'fail', 'message': 'No object to track'})\n",
    "            else:\n",
    "                logging.info(\"## 실제로 Tracking이 되야 하는 객체 ID 리스트 ##\")\n",
    "                \n",
    "                draw_box_res = pd.DataFrame(draw_box_res)\n",
    "                data_rs = draw_box_res.drop_duplicates(subset='object_id',keep='first') \n",
    "\n",
    "                result_list = data_rs[['object_id', 'caption']]\n",
    "\n",
    "                result_obj_id = data_rs['object_id'].tolist()\n",
    "                logging.info(\"result_obj_id: \", result_obj_id)\n",
    "                logging.info(\"-\" * 70)\n",
    "        \n",
    "        # while video is running\n",
    "        # result_list = []\n",
    "        cnt = 0\n",
    "        while True:\n",
    "            return_value, frame = vid.read()\n",
    "            if return_value:\n",
    "                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "                image = Image.fromarray(frame)\n",
    "            else:\n",
    "                logging.info('Video has ended or failed, try a different video format!')\n",
    "                session.close()\n",
    "                break\n",
    "            frame_num +=1\n",
    "            frame_size = frame.shape[:2]\n",
    "            image_data = cv2.resize(frame, (input_size, input_size))\n",
    "            image_data = image_data / 255.\n",
    "            image_data = image_data[np.newaxis, ...].astype(np.float32)\n",
    "            start_time = time.time()\n",
    "\n",
    "            # run detections on tflite if flag is set\n",
    "            if FLAGS.framework == 'tflite':\n",
    "                interpreter.set_tensor(input_details[0]['index'], image_data)\n",
    "                interpreter.invoke()\n",
    "                pred = [interpreter.get_tensor(output_details[i]['index']) for i in range(len(output_details))]\n",
    "                # run detections using yolov3 if flag is set\n",
    "                if FLAGS.model == 'yolov3' and FLAGS.tiny == True:\n",
    "                    boxes, pred_conf = filter_boxes(pred[1], pred[0], score_threshold=0.25,\n",
    "                                                    input_shape=tf.constant([input_size, input_size]))\n",
    "                else:\n",
    "                    boxes, pred_conf = filter_boxes(pred[0], pred[1], score_threshold=0.25,\n",
    "                                                    input_shape=tf.constant([input_size, input_size]))\n",
    "            else:\n",
    "                batch_data = tf.constant(image_data)\n",
    "                pred_bbox = infer.predict(batch_data)\n",
    "\n",
    "                for value in pred_bbox:\n",
    "                    temp_value = np.expand_dims(value, axis=0)\n",
    "                    boxes = temp_value[:, :, 0:4]\n",
    "                    pred_conf = temp_value[:, :, 4:]\n",
    "\n",
    "                    \n",
    "            boxes, scores, classes, valid_detections = tf.image.combined_non_max_suppression(\n",
    "                boxes=tf.reshape(boxes, (tf.shape(boxes)[0], -1, 1, 4)),\n",
    "                scores=tf.reshape(\n",
    "                    pred_conf, (tf.shape(pred_conf)[0], -1, tf.shape(pred_conf)[-1])),\n",
    "                max_output_size_per_class=50,\n",
    "                max_total_size=50,\n",
    "                iou_threshold=FLAGS.iou,\n",
    "                score_threshold=FLAGS.score\n",
    "            )\n",
    "\n",
    "            # convert data to numpy arrays and slice out unused elements\n",
    "            num_objects = valid_detections.numpy()[0]\n",
    "            bboxes = boxes.numpy()[0]\n",
    "            bboxes = bboxes[0:int(num_objects)]\n",
    "            scores = scores.numpy()[0]\n",
    "            scores = scores[0:int(num_objects)]\n",
    "            classes = classes.numpy()[0]\n",
    "            classes = classes[0:int(num_objects)]\n",
    "\n",
    "            # format bounding boxes from normalized ymin, xmin, ymax, xmax ---> xmin, ymin, width, height\n",
    "            original_h, original_w, _ = frame.shape\n",
    "            bboxes = utils.format_boxes(bboxes, original_h, original_w)\n",
    "\n",
    "            # store all predictions in one parameter for simplicity when calling functions\n",
    "            pred_bbox = [bboxes, scores, classes, num_objects]\n",
    "\n",
    "            # read in all class names from config\n",
    "            class_names = utils.read_class_names(cfg.YOLO.CLASSES)\n",
    "\n",
    "            # by default allow all classes in .names file\n",
    "            # allowed_classes = list(class_names.values())\n",
    "            \n",
    "            # custom allowed classes (uncomment line below to customize tracker for only people)\n",
    "            allowed_classes = ['person']\n",
    "\n",
    "            # loop through objects and use class index to get class name, allow only classes in allowed_classes list\n",
    "            names = []\n",
    "            deleted_indx = []\n",
    "            for i in range(num_objects):\n",
    "                class_indx = int(classes[i])\n",
    "                class_name = class_names[class_indx]\n",
    "                if class_name not in allowed_classes:\n",
    "                    deleted_indx.append(i)\n",
    "                else:\n",
    "                    names.append(class_name)\n",
    "            names = np.array(names)\n",
    "            count = len(names)\n",
    "            if FLAGS.count:\n",
    "                cv2.putText(frame, \"Objects being tracked: {}\".format(count), (5, 35), cv2.FONT_HERSHEY_COMPLEX_SMALL, 2, (0, 255, 0), 2)\n",
    "                logging.info(\"Objects being tracked: {}\".format(count))\n",
    "            # delete detections that are not in allowed_classes\n",
    "            bboxes = np.delete(bboxes, deleted_indx, axis=0)\n",
    "            scores = np.delete(scores, deleted_indx, axis=0)\n",
    "\n",
    "            # encode yolo detections and feed to tracker\n",
    "            features = encoder(frame, bboxes)\n",
    "            detections = [Detection(bbox, score, class_name, feature) for bbox, score, class_name, feature in zip(bboxes, scores, names, features)]\n",
    "\n",
    "            #initialize color map\n",
    "            cmap = plt.get_cmap('tab20b')\n",
    "            colors = [cmap(i)[:3] for i in np.linspace(0, 1, 20)]\n",
    "\n",
    "            # run non-maxima supression\n",
    "            boxs = np.array([d.tlwh for d in detections])\n",
    "            scores = np.array([d.confidence for d in detections])\n",
    "            classes = np.array([d.class_name for d in detections])\n",
    "            indices = preprocessing.non_max_suppression(boxs, classes, nms_max_overlap, scores)\n",
    "            detections = [detections[i] for i in indices]       \n",
    "\n",
    "            # Call the tracker\n",
    "            tracker.predict()\n",
    "            tracker.update(detections)\n",
    "\n",
    "            # update tracks\n",
    "            for track in tracker.tracks:\n",
    "                if not track.is_confirmed() or track.time_since_update > 1:\n",
    "                    continue \n",
    "                bbox = track.to_tlbr()\n",
    "                class_name = track.get_class()\n",
    "                \n",
    "            # draw bbox on screen\n",
    "                if input_type == 'A':\n",
    "                    fps = vid.get(cv2.CAP_PROP_FPS)\n",
    "\n",
    "                    time_in_seconds = frame_num / fps\n",
    "\n",
    "                    minutes = int(time_in_seconds // 60)\n",
    "                    seconds = int(time_in_seconds % 60)\n",
    "\n",
    "                    bbox_0 = int(bbox[0]) if int(bbox[0]) > 0 else 0\n",
    "                    bbox_1 = int(bbox[1]) if int(bbox[1]) > 0 else 0\n",
    "                    bbox_2 = int(bbox[2]) if int(bbox[2]) > 0 else 0\n",
    "                    bbox_3 = int(bbox[3]) if int(bbox[3]) > 0 else 0\n",
    "                    \n",
    "                    logging.info(\"*\"*70)\n",
    "                    logging.info(\"객체 좌표값\")\n",
    "                    logging.info(bbox_0, bbox_1, bbox_2, bbox_3)\n",
    "                    # insert_result = db_handler.insert_video_info(frame_num-1, track.track_id, bbox_0, bbox_1, bbox_2, bbox_3, video_id, minutes, seconds)\n",
    "                    result_list.append({\"frame_id\": frame_num-1, \"object_id\": track.track_id, \"x1\": bbox_0, \"y1\": bbox_1, \"x2\": bbox_2, \"y2\": bbox_3, \"minutes\": minutes, \"seconds\": seconds})\n",
    "                    cnt += 1\n",
    "\n",
    "                    logging.info(\"DB Input Result: \", result_list)\n",
    "                    # logging.info(\"DB Input Result: \", insert_result)\n",
    "                    logging.info(\"*\"*70)\n",
    "\n",
    "                elif input_type =='B':\n",
    "                    if (int(track.track_id) in result_obj_id):\n",
    "                        logging.info(int(track.track_id) in result_obj_id)\n",
    "\n",
    "                        color = colors[int(track.track_id) % len(colors)]\n",
    "                        color = [i * 255 for i in color]\n",
    "                        cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), color, 2)\n",
    "                        cv2.rectangle(frame, (int(bbox[0]), int(bbox[1]-30)), (int(bbox[0])+(len(class_name)+len(str(track.track_id)))*17, int(bbox[1])), color, -1)\n",
    "                        cv2.putText(frame, class_name + \"-\" + str(track.track_id),(int(bbox[0]), int(bbox[1]-10)),0, 0.75, (255,255,255),2)\n",
    "                    else:\n",
    "                        continue\n",
    "\n",
    "            result = np.asarray(frame)\n",
    "            result = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)\n",
    "            \n",
    "            if FLAGS.output and input_type == 'B':\n",
    "                logging.info(\"*\"*70)\n",
    "                logging.info(\"Tracking이 되어야 하는 Object만 영상에 표시\")\n",
    "                logging.info(\"*\"*70)\n",
    "                out.write(result)\n",
    "\n",
    "        return result_list\n",
    "\n",
    "    # 캡션 생성 및 table에 객체 id별 캡션 넣어주기\n",
    "    # 캡션 생성 함수\n",
    "\n",
    "    def single_image_caption(image, model, vis_processors, decode, device):\n",
    "        generated_ids = model.generate(pixel_values= vis_processors(images=image, return_tensors=\"pt\").to(device).pixel_values,max_length=300)\n",
    "        caption= decode(generated_ids, skip_special_tokens=True)[0]\n",
    "        return caption\n",
    "\n",
    "    def multi_image_caption(params, images): \n",
    "        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "        model = params[\"model\"].to(device)\n",
    "        model.eval()\n",
    "        vis_processors = params[\"processor\"].image_processor\n",
    "        decode = params[\"processor\"].batch_decode\n",
    "\n",
    "        captions = []\n",
    "        for image in images:\n",
    "            captions.append(single_image_caption(image,model,vis_processors,decode,device))\n",
    "        return captions\n",
    "    \n",
    "    def cropping(cap, frame_id, box):\n",
    "        cap.set(cv2.CAP_PROP_POS_FRAMES, frame_id) # 이미지 불러오기\n",
    "        T, image = cap.read()\n",
    "        image = image[box[1]:box[3],box[0]:box[2]] if T else logging.info(\"cropping error\")\n",
    "\n",
    "        return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) if T else None\n",
    "    \n",
    "    ###########################################################################\n",
    "    ## 4. 추론(Inference) 시작\n",
    "    ###########################################################################\n",
    "\n",
    "    # 디코딩된 동영상을 저장할 파일 경로 및 이름\n",
    "    output_file = \"/data/aip/logs/t3qai/mods/algo_561/1/data/video/input_video.mp4\"\n",
    "    # 디코딩 함수 호출\n",
    "    decode_base64_video(df.iloc[0,0], output_file)\n",
    "    video_file = os.path.join(\"/data/aip/logs/t3qai/mods/algo_561/1/data/video\", output_file)\n",
    "\n",
    "    result_list = video_tracking(input_type='A', table=None, input_keyword='')\n",
    "    result = pd.DataFrame(result_list)\n",
    "\n",
    "    table = result.drop_duplicates(subset='object_id', keep='first')\n",
    "    table = table.reset_index(drop=True)\n",
    "\n",
    "    logging.info(\"*\" * 70)\n",
    "    logging.info(table)\n",
    "    logging.info(\"*\" * 70)\n",
    "    \n",
    "    pro_sr = Swin2SRImageProcessor.from_pretrained(\"caidas/swin2SR-lightweight-x2-64\")\n",
    "    model_sr = Swin2SRForImageSuperResolution.from_pretrained(\"caidas/swin2SR-lightweight-x2-64\")\n",
    "\n",
    "    cap =cv2.VideoCapture(video_file)\n",
    "    images = []\n",
    "    for _, info in table.iterrows(): # 이미지 전체 다 가져오기\n",
    "        image = Image.fromarray(cropping(cap, info['frame_id'], [info['x1'],info['y1'],info['x2'],info['y2']]))\n",
    "        # 작은 이미지의 경우 SR 적용 \n",
    "        image = super_reso(image,pro_sr,model_sr) if image.size[0]<50 or image.size[1]<100 else image \n",
    "        images.append(image)\n",
    "\n",
    "    captions = multi_image_caption(params, images)\n",
    "    table['caption'] = captions\n",
    "\n",
    "    logging.info(\"*\" * 70)\n",
    "    logging.info(table)\n",
    "    logging.info(\"*\" * 70)\n",
    "\n",
    "    input_keyword = [\"\"]  # 키워드 0개\n",
    "    # input_keyword = [\"woman\"]  # 키워드 1개\n",
    "    # input_keyword=[\"man\", \"black\"]  # 키워드 2개\n",
    "    result_list = video_tracking(input_type='B', table=table, input_keyword=input_keyword)\n",
    "    logging.info(\"keyword: \", input_keyword)\n",
    "    logging.info(result_list)\n",
    "    logging.info(\"*\" * 70)\n",
    "    \n",
    "    result_list = result_list.to_dict('records')\n",
    "    \n",
    "    result = {\"tracking_result\": result_list}\n",
    "    logging.info(\"------final_checking------\")\n",
    "    logging.info(result)\n",
    "    logging.info(\"------final_checking------\")\n",
    "\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b2227e4f-4e87-4c28-a467-969f5ca63761",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pm.source_path: ./\n",
      "pm.target_path:  ./meta_data\n",
      "tm.train_data_path:  ./meta_data\n",
      "tm.model_path:  ./meta_data\n",
      "tm.param_info['batch_size']:  10\n",
      "tm.param_info['epoch']:  20\n",
      "im.model_path:  ./meta_data\n",
      "df:                                                     0\n",
      "0  iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAIAAAD9b0jDAA...\n",
      "df.dtypes: 0    object\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=1, step=1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PM 클래스: pm 객체\n",
    "class PM:\n",
    "    def __init__(self):\n",
    "        self.source_path = './'\n",
    "        self.target_path = './meta_data'\n",
    "\n",
    "# TM 클래스: tm 객체\n",
    "class TM:\n",
    "    param_info = {}\n",
    "    def __init__(self):\n",
    "        self.train_data_path = './meta_data'\n",
    "        self.model_path = './meta_data'\n",
    "        self.param_info['batch_size'] = 10\n",
    "        self.param_info['epoch'] = 20\n",
    "\n",
    "# IM 클래스: im 객체\n",
    "class IM:\n",
    "    def __init__(self):\n",
    "        self.model_path = './meta_data'\n",
    "\n",
    "\n",
    "# pm 객체\n",
    "pm = PM()\n",
    "print('pm.source_path:', pm.source_path)\n",
    "print('pm.target_path: ', pm.target_path)\n",
    "\n",
    "# tm 객체\n",
    "tm = TM()\n",
    "print('tm.train_data_path: ', tm.train_data_path)\n",
    "print('tm.model_path: ', tm.model_path)\n",
    "print('tm.param_info[\\'batch_size\\']: ', tm.param_info['batch_size'])\n",
    "print('tm.param_info[\\'epoch\\']: ', tm.param_info['epoch'])\n",
    "\n",
    "# im 객체\n",
    "im = IM()\n",
    "print('im.model_path: ', im.model_path)\n",
    "\n",
    "# inferecne(df, params, batch_id) 함수 입력\n",
    "params = {}\n",
    "batch_id = 0\n",
    "\n",
    "import io\n",
    "import pandas as pd\n",
    "\n",
    "# base64 encoded video\n",
    "data = 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']]\n",
    "df = pd.DataFrame(data)\n",
    "print('df: ', df)\n",
    "print('df.dtypes:', df.dtypes)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8a094ccf-6a82-4593-9dae-1134fbdb5a48",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:[hunmin log]  the start line of the function [exec_process]\n",
      "INFO:root:[hunmin log] pm.source_path : ./\n",
      "INFO:root:[hunmin log] Files and directories in ./ :\n",
      "INFO:root:[hunmin log] dir_list : ['0_local_image_classification.ipynb', '0_local_image_classification_requirement.txt', '1_local_platform_image_classification.ipynb', '2_1_1_platform_image_classification_preprocess.py', '2_1_2_platform_image_classification_preprocess_sub.py', '2_2_1_platform_image_classification_train.py', '2_2_2_platform_image_classification_train_sub.py', 'dataset.zip', 'LICENSE.txt', 'meta_data', 'README.txt', 'T3Q.ai_platform_image_classification', 'test_dataset.zip']\n",
      "INFO:root:[hunmin log] Files and directories in ./meta_data :\n",
      "INFO:root:[hunmin log] dir_list : ['cnn_model.h5', 'dataset', 'test_dataset']\n",
      "INFO:root:[hunmin log]  the finish line of the function [exec_process]\n",
      "INFO:root:[hunmin log] the end line of the function [process_for_train]\n",
      "INFO:root:[hunmin log] the start line of the function [exec_train]\n",
      "INFO:root:[hunmin log] tm.train_data_path : ./meta_data\n",
      "INFO:root:[hunmin log] Files and directories in ./meta_data :\n",
      "INFO:root:[hunmin log] dir_list : ['cnn_model.h5', 'dataset', 'test_dataset']\n",
      "INFO:root:[hunmin log] : (image_number, image_size)\n",
      "INFO:root:[hunmin log] : (124612, 784)\n",
      "INFO:root:[hunmin log] : (144722, 784)\n",
      "INFO:root:[hunmin log] : (166208, 784)\n",
      "INFO:root:[hunmin log] : (117999, 784)\n",
      "INFO:root:[hunmin log] : (130721, 784)\n",
      "INFO:root:[hunmin log] : (134863, 784)\n",
      "INFO:root:[hunmin log] : (134150, 784)\n",
      "INFO:root:[hunmin log] : (127182, 784)\n",
      "INFO:root:[hunmin log] : (120879, 784)\n",
      "INFO:root:[hunmin log] : (186770, 784)\n",
      "INFO:root:[hunmin log] envelope : \n",
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      "INFO:root:[hunmin log] X_train : (8000, 784)\n",
      "INFO:root:[hunmin log] X_train_cnn : (8000, 28, 28, 1)\n",
      "INFO:root:[hunmin log] Y_train : (8000,)\n",
      "INFO:root:[hunmin log] Y_train_cnn : (8000, 10)\n",
      "INFO:root:[hunmin log] class number : 10\n",
      "INFO:root:[hunmin log] model.summary() :\n",
      "INFO:root:Model: \"sequential\"\n",
      "INFO:root:_________________________________________________________________\n",
      "INFO:root: Layer (type)                Output Shape              Param #   \n",
      "INFO:root:=================================================================\n",
      "INFO:root: conv2d (Conv2D)             (None, 28, 28, 32)        320       \n",
      "INFO:root:                                                                 \n",
      "INFO:root: conv2d_1 (Conv2D)           (None, 28, 28, 64)        18496     \n",
      "INFO:root:                                                                 \n",
      "INFO:root: dropout (Dropout)           (None, 28, 28, 64)        0         \n",
      "INFO:root:                                                                 \n",
      "INFO:root: conv2d_2 (Conv2D)           (None, 28, 28, 64)        36928     \n",
      "INFO:root:                                                                 \n",
      "INFO:root: max_pooling2d (MaxPooling2D  (None, 14, 14, 64)       0         \n",
      "INFO:root: )                                                               \n",
      "INFO:root:                                                                 \n",
      "INFO:root: flatten (Flatten)           (None, 12544)             0         \n",
      "INFO:root:                                                                 \n",
      "INFO:root: dense (Dense)               (None, 32)                401440    \n",
      "INFO:root:                                                                 \n",
      "INFO:root: dropout_1 (Dropout)         (None, 32)                0         \n",
      "INFO:root:                                                                 \n",
      "INFO:root: dense_1 (Dense)             (None, 10)                330       \n",
      "INFO:root:                                                                 \n",
      "INFO:root:=================================================================\n",
      "INFO:root:Total params: 457,514\n",
      "INFO:root:Trainable params: 457,514\n",
      "INFO:root:Non-trainable params: 0\n",
      "INFO:root:_________________________________________________________________\n",
      "INFO:root:[hunmin log] For epoch 1, loss is 1.00.\n",
      "INFO:root:[hunmin log] For epoch 2, loss is 0.55.\n",
      "INFO:root:[hunmin log] For epoch 3, loss is 0.44.\n",
      "INFO:root:[hunmin log] For epoch 4, loss is 0.36.\n",
      "INFO:root:[hunmin log] For epoch 5, loss is 0.31.\n",
      "INFO:root:[hunmin log] For epoch 6, loss is 0.28.\n",
      "INFO:root:[hunmin log] For epoch 7, loss is 0.23.\n",
      "INFO:root:[hunmin log] For epoch 8, loss is 0.21.\n",
      "INFO:root:[hunmin log] For epoch 9, loss is 0.19.\n",
      "INFO:root:[hunmin log] For epoch 10, loss is 0.18.\n",
      "INFO:root:[hunmin log] For epoch 11, loss is 0.16.\n",
      "INFO:root:[hunmin log] For epoch 12, loss is 0.15.\n",
      "INFO:root:[hunmin log] For epoch 13, loss is 0.15.\n",
      "INFO:root:[hunmin log] For epoch 14, loss is 0.13.\n",
      "INFO:root:[hunmin log] For epoch 15, loss is 0.13.\n",
      "INFO:root:[hunmin log] For epoch 16, loss is 0.12.\n",
      "INFO:root:[hunmin log] For epoch 17, loss is 0.12.\n",
      "INFO:root:[hunmin log] For epoch 18, loss is 0.12.\n",
      "INFO:root:[hunmin log] For epoch 19, loss is 0.10.\n",
      "INFO:root:[hunmin log] For epoch 20, loss is 0.11.\n",
      "INFO:root:[hunmin log] loss : 0.626572847366333\n",
      "INFO:root:[hunmin log] acc : 0.8870000243186951\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:[hunmin log] tm.model_path : ./meta_data\n",
      "INFO:root:[hunmin log] Files and directories in ./meta_data :\n",
      "INFO:root:[hunmin log] dir_list : ['cnn_model.h5', 'dataset', 'test_dataset']\n",
      "INFO:root:[hunmin log]  the finish line of the function [exec_train]\n",
      "INFO:root:[hunmin log] the end line of the function [train]\n",
      "INFO:root:[hunmin log] df :                                                    0\n",
      "0  iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAIAAAD9b0jDAA...\n",
      "INFO:root:[hunmin log] df.shape : (1, 1)\n",
      "INFO:root:[hunmin log] type(df) : <class 'pandas.core.frame.DataFrame'>\n",
      "INFO:root:[hunmin log] the end line of the function [transform]\n",
      "INFO:root:[hunmin log] im.model_path : ./meta_data\n",
      "INFO:root:[hunmin log] Files and directories in ./meta_data :\n",
      "INFO:root:[hunmin log] dir_list : ['cnn_model.h5', 'dataset', 'test_dataset']\n",
      "INFO:root:[hunmin log] the end line of the function [init_svc]\n",
      "INFO:root:[hunmin log] the start line of the function [exec_inference]\n",
      "INFO:root:[hunmin log] model.summary() :\n",
      "INFO:root:Model: \"sequential\"\n",
      "INFO:root:_________________________________________________________________\n",
      "INFO:root: Layer (type)                Output Shape              Param #   \n",
      "INFO:root:=================================================================\n",
      "INFO:root: conv2d (Conv2D)             (None, 28, 28, 32)        320       \n",
      "INFO:root:                                                                 \n",
      "INFO:root: conv2d_1 (Conv2D)           (None, 28, 28, 64)        18496     \n",
      "INFO:root:                                                                 \n",
      "INFO:root: dropout (Dropout)           (None, 28, 28, 64)        0         \n",
      "INFO:root:                                                                 \n",
      "INFO:root: conv2d_2 (Conv2D)           (None, 28, 28, 64)        36928     \n",
      "INFO:root:                                                                 \n",
      "INFO:root: max_pooling2d (MaxPooling2D  (None, 14, 14, 64)       0         \n",
      "INFO:root: )                                                               \n",
      "INFO:root:                                                                 \n",
      "INFO:root: flatten (Flatten)           (None, 12544)             0         \n",
      "INFO:root:                                                                 \n",
      "INFO:root: dense (Dense)               (None, 32)                401440    \n",
      "INFO:root:                                                                 \n",
      "INFO:root: dropout_1 (Dropout)         (None, 32)                0         \n",
      "INFO:root:                                                                 \n",
      "INFO:root: dense_1 (Dense)             (None, 10)                330       \n",
      "INFO:root:                                                                 \n",
      "INFO:root:=================================================================\n",
      "INFO:root:Total params: 457,514\n",
      "INFO:root:Trainable params: 457,514\n",
      "INFO:root:Non-trainable params: 0\n",
      "INFO:root:_________________________________________________________________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 116ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:[hunmin log] result : {'inference': 'apple'}\n",
      "INFO:root:[hunmin log] the end line of the function [inference]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 12s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'inference': 'apple'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "process_for_train(pm)\n",
    "\n",
    "train(tm)\n",
    "\n",
    "#transform(df, params, batch_id)\n",
    "\n",
    "params = init_svc(im)\n",
    "\n",
    "inference(df, params, batch_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b90f18ab",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "image_classification.ipynb",
   "provenance": []
  },
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   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
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 },
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 "nbformat_minor": 5
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