evaluate_imagen2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import configparser\n",
    "\n",
    "# Loading configurations\n",
    "configParser = configparser.RawConfigParser()   \n",
    "configFilePath = r'configuration.txt'\n",
    "configParser.read(configFilePath)\n",
    "\n",
    "insert_amd_env_vars =  int(configParser.get('COMMON', 'insert_amd_env_vars'))\n",
    "HSA_OVERRIDE_GFX_VERSION =  configParser.get('COMMON', 'HSA_OVERRIDE_GFX_VERSION')\n",
    "ROCM_PATH =  configParser.get('COMMON', 'ROCM_PATH')\n",
    "\n",
    "if(insert_amd_env_vars != 0):\n",
    "    os.environ[\"HSA_OVERRIDE_GFX_VERSION\"] = HSA_OVERRIDE_GFX_VERSION\n",
    "    os.environ[\"ROCM_PATH\"] = ROCM_PATH\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from textwrap import wrap\n",
    "import os\n",
    "import gc\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#TODO document jupyter\n",
    "import pickle\n",
    "import configparser\n",
    "import sqlite3 as sl\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from pathlib import Path\n",
    "\n",
    "configParser = configparser.RawConfigParser()   \n",
    "configFilePath = r'configuration.txt'\n",
    "configParser.read(configFilePath)\n",
    "datasetPathDatabase =  configParser.get('COMMON', 'datasetPathDatabase') + '/dataset.db'\n",
    "datasetPathDatabaseAdditional =  configParser.get('COMMON', 'datasetPathDatabase') + '/datasetAdditional.db'\n",
    "datasetPathDatabaseVgg =  configParser.get('COMMON', 'datasetPathDatabase') + '/datasetFacesBlurred.db'\n",
    "image_size =  int(configParser.get('COMMON', 'resizeImageTo'))\n",
    "begin_with_image_size = int(configParser.get('COMMON', 'begin_with_image_size'))\n",
    "unet1_dim =  int(configParser.get('COMMON', 'unet1_dim'))\n",
    "unet2_dim =  int(configParser.get('COMMON', 'unet2_dim'))\n",
    "unet1_image_size =  int(configParser.get('COMMON', 'unet1_image_size'))\n",
    "audio_embs =  str(configParser.get('COMMON', 'audio_embs'))\n",
    "audio_length_used =  configParser.get('evaluate_imagen', 'audio_length_used') \n",
    "model_filename =  configParser.get('evaluate_imagen', 'model_filename')\n",
    "cond_scale =  int(configParser.get('evaluate_imagen', 'cond_scale'))\n",
    "datasetPathVideo =  configParser.get('COMMON', 'test_datasetPathVideo')\n",
    "datasetPathGeneratedFaces =  configParser.get('evaluate_imagen', 'test_datasetPathGeneratedFaces')\n",
    "generated_face_table_name =  configParser.get('evaluate_imagen', 'generated_face_table_name')\n",
    "\n",
    "\n",
    "timesteps= int(configParser.get('COMMON', 'timesteps') )\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#db_sampling_chunk = int(configParser.get('train_imagen', 'db_sampling_chunk'))\n",
    "sampling_chunk = int(configParser.get('evaluate_imagen', 'sampling_chunk'))\n",
    "preprocessing_chunk = int(configParser.get('evaluate_imagen', 'preprocessing_chunk'))\n",
    "\n",
    "\n",
    "\n",
    "ignore_speaker_embedding = bool(int(configParser.get('evaluate_imagen', 'ignore_speaker_embedding') ))\n",
    "ignore_speech_brain = bool(int(configParser.get('evaluate_imagen', 'ignore_speech_brain') ))\n",
    "ignore_pyannote_titanet_speakernet = bool(int(configParser.get('evaluate_imagen', 'ignore_pyannote_titanet_speakernet') ))\n",
    "ignore_audio_features = bool(int(configParser.get('evaluate_imagen', 'ignore_audio_features') ))\n",
    "ignore_pyAudioAnalysis = bool(int(configParser.get('evaluate_imagen', 'ignore_pyAudioAnalysis') ))\n",
    "ignore_librosa = bool(int(configParser.get('evaluate_imagen', 'ignore_librosa') ))\n",
    "ignore_image_guide = bool(int(configParser.get('evaluate_imagen', 'ignore_image_guide') ))\n",
    "ignore_additional_attributes = bool(int(configParser.get('evaluate_imagen', 'ignore_additional_attributes') ))\n",
    "ignore_age = bool(int(configParser.get('evaluate_imagen', 'ignore_age') ))\n",
    "ignore_gender = bool(int(configParser.get('evaluate_imagen', 'ignore_gender') ))\n",
    "ignore_ethnicity = bool(int(configParser.get('evaluate_imagen', 'ignore_ethnicity') ))\n",
    "ignore_language_spoken = bool(int(configParser.get('evaluate_imagen', 'ignore_language_spoken') ))\n",
    "\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.options.mode.chained_assignment = None\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasetPathDatabase =  configParser.get('COMMON', 'test_datasetPathDatabase') + '/dataset.db'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import lancedb\n",
    "#uri = datasetPathDatabase\n",
    "#db = lancedb.connect(uri)\n",
    "\n",
    "#table = db.open_table(\"video_stage1\")\n",
    "#videos = table.to_pandas()\n",
    "#videos\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def speaker_emb_preprocess(speaker_emb2):\n",
    "\n",
    "    if(ignore_speaker_embedding or ignore_speech_brain):\n",
    "        #print('ignore speech brain')\n",
    "        return np.zeros((1,768))\n",
    "    else:\n",
    "        speaker_emb2 = pickle.loads(speaker_emb2)\n",
    "        #print(speaker_emb2.print)\n",
    "        speaker_emb2 = speaker_emb2.squeeze()\n",
    "        speaker_emb2 = np.pad(speaker_emb2, (288), 'constant', constant_values=(0))\n",
    "        speaker_emb2 = np.tile(speaker_emb2, (1, 1))\n",
    "        speaker_emb2 = speaker_emb2 / 200.0\n",
    "        #print(speaker_emb2.shape)\n",
    "        #speaker_emb2 = np.array(speaker_emb2).tolist()\n",
    "        #print(type(speaker_emb2))\n",
    "        return speaker_emb2\n",
    "\n",
    "\n",
    "def audio_emb_preprocess2(speaker_emb2):\n",
    "    \n",
    "    if(ignore_pyannote_titanet_speakernet or ignore_speaker_embedding):\n",
    "        #print('ignore speech brain')\n",
    "        return np.zeros((28,768))\n",
    "    else:\n",
    "        #print(speaker_emb2)\n",
    "        speaker_emb2 = pickle.loads(speaker_emb2)\n",
    "        a = speaker_emb2 #np.zeros(shape=(24, 512))\n",
    "        a = a[-28:,:] # Actually only last 39 are the relevant ones\n",
    "        #print('au')\n",
    "        #print(a.shape)\n",
    "        b = np.zeros(shape=(a.shape[0], 768-a.shape[1]))\n",
    "        #c = np.zeros(shape=(28-a.shape[0], 768))\n",
    "        arr = np.concatenate((a, b), axis=1)\n",
    "        #arr = np.concatenate((arr, c), axis=0)\n",
    "        arr = arr / (1 if(audio_embs == 'wav2vec') else 10 if(audio_embs == 'openl3')  else 1 if(audio_embs == 'pyannoteTitaNet') else 1)\n",
    "        #print(str(arr.max()))\n",
    "        #print(arr.shape)\n",
    "        speaker_emb2 = np.array(arr)\n",
    "        return speaker_emb2\n",
    "\n",
    "def audio_features_preprocess(audio_features):\n",
    "    #  79 belong to pyaudioanalysis\n",
    "    # 111 belogn to liborsa\n",
    "    if(ignore_audio_features):\n",
    "        #print('ignore speech brain')\n",
    "        return np.zeros((190,768))\n",
    "    else:\n",
    "        audio_features = pickle.loads(audio_features)\n",
    "        #print(audio_features.shape) # 190 x 128\n",
    "        audio_features = audio_features[0:190]\n",
    "        \n",
    "        #import sys\n",
    "        #np.set_printoptions(threshold=sys.maxsize)\n",
    "        #print(np.argwhere(audio_features == 0))\n",
    "\n",
    "        if(ignore_pyAudioAnalysis and not ignore_librosa):\n",
    "            zpa = np.zeros((190-110,128))\n",
    "            audio_features = audio_features[80:190]\n",
    "            audio_features = np.vstack((audio_features,zpa))\n",
    "            #print('ignore pyAudioAnalysis')\n",
    "            #print(audio_features.shape)\n",
    "        elif(ignore_librosa and not ignore_pyAudioAnalysis):\n",
    "            zpa = np.zeros((190-80,128))\n",
    "            audio_features = audio_features[0:80]\n",
    "            audio_features = np.vstack((audio_features,zpa))\n",
    "            #print('ignore librosa')\n",
    "            #print(audio_features.shape)\n",
    "        else:\n",
    "            audio_features = np.zeros((190,128))\n",
    "            #print('ignore pyaudioanalysis and librosa')\n",
    "\n",
    "\n",
    "        z1 = np.zeros((190,768-128))\n",
    "        audio_features = np.hstack((audio_features,z1))\n",
    "        audio_features = audio_features / 100.0\n",
    "        #print(audio_features.shape)\n",
    "    return audio_features\n",
    "\n",
    "def audio_transformer_features_preprocess(video_id):\n",
    "    #print(video_id)\n",
    "    conAdditional = sl.connect(datasetPathDatabaseAdditional)\n",
    "    cur = conAdditional.cursor()\n",
    "    sql = '''SELECT AUDIO_FEATURES FROM AUDIO_TRANSFORMER WHERE VIDEO_ID = ?'''\n",
    "    cur.execute(sql, [video_id])\n",
    "    audio_features = cur.fetchall()\n",
    "    #print(audio_features[0])\n",
    "    audio_features = pickle.loads(audio_features[0][0])\n",
    "    #print(audio_features.shape) # 514 x 768\n",
    "    audio_features = audio_features.squeeze()\n",
    "    audio_features = audio_features[0::3]#172*768\n",
    "    #z1 = np.zeros((161,768-128))\n",
    "    #audio_features = np.hstack((audio_features,z1))\n",
    "    #audio_features = audio_features / 100.0\n",
    "    return audio_features\n",
    "\n",
    "import random\n",
    "def image_guide_preprocess(face_id):\n",
    "    #print(face_id)\n",
    "\n",
    "    if(random.random() > 2):\n",
    "        image_guide = np.zeros((49,768))\n",
    "        #print(image_guide.shape)\n",
    "    else:\n",
    "        conAdditional = sl.connect(datasetPathDatabaseVgg)\n",
    "        cur = conAdditional.cursor()\n",
    "        sql = '''SELECT BLURRED_FACE_EMB FROM FACES_BLURRED WHERE FACE_ID = ?'''\n",
    "        cur.execute(sql, [face_id])\n",
    "        image_guide = cur.fetchall()\n",
    "        #print(image_guide)\n",
    "        image_guide = pickle.loads(image_guide[0][0])\n",
    "        image_guide = image_guide.squeeze() #197 x 768\n",
    "        image_guide = image_guide[1::4] #49 x 768\n",
    "    return image_guide\n",
    "\n",
    "\n",
    "#boxBlurMin =  int(configParser.get('extractVggBlurred', 'boxBlurMin'))\n",
    "#boxBlurMax =  int(configParser.get('extractVggBlurred', 'boxBlurMax'))\n",
    "\n",
    "#gaussianBlurMin =  int(configParser.get('extractVggBlurred', 'gaussianBlurMin'))\n",
    "#gaussianBlurMax =  int(configParser.get('extractVggBlurred', 'gaussianBlurMax'))\n",
    "\n",
    "\n",
    "from PIL import Image,ImageFilter\n",
    "import random\n",
    "#import cv2\n",
    "import numpy as np\n",
    "#from matplotlib import pyplot as plt\n",
    "  \n",
    "\n",
    "\n",
    "def image_guide_preprocess_low_res_dummy(path):\n",
    "    #print(face_id)\n",
    "\n",
    "    image_guide = np.zeros((1, 768))\n",
    "    return image_guide\n",
    "\n",
    "def image_guide_preprocess_low_res(path):\n",
    "    #print(face_id)\n",
    "\n",
    "\n",
    "    if(ignore_image_guide):\n",
    "        return np.nan\n",
    "    else:\n",
    "\n",
    "        image = Image.open(path).convert('RGB')\n",
    "\n",
    "        #print(image.size)\n",
    "\n",
    "        w_s = image_size / (1+2 * 0.4)\n",
    "        h_s = image_size / (1+2 * 0.4)\n",
    "\n",
    "        image = image.crop((0.2*w_s, 0.0*h_s, 1.6*w_s, 1.4*h_s))\n",
    "\n",
    "        image = image.resize((image_size,image_size))\n",
    "\n",
    "        image = image.resize((begin_with_image_size,begin_with_image_size))\n",
    "        im = image\n",
    "        \n",
    "\n",
    "        #print('saving')\n",
    "        #image.save('opop.png')\n",
    "\n",
    "        #print(np.array(image,np.float32).shape)\n",
    "\n",
    "        pix = np.array(image, np.float32)\n",
    "        pix = np.moveaxis(pix, -1, 0)\n",
    "\n",
    "        pix = pix / 255\n",
    "        image.close()\n",
    "        im.close()\n",
    "        return pix\n",
    "    return image_guide\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "def process_age(age):\n",
    "    if(ignore_age or ignore_additional_attributes):\n",
    "        x = np.zeros(768)\n",
    "        x[767] = 1\n",
    "        return x\n",
    "    else:\n",
    "        x = np.ones(768) * (age / 100.0)\n",
    "        x[767] = 0\n",
    "        return x\n",
    "\n",
    "def process_gender(gender):\n",
    "    if(ignore_gender or ignore_additional_attributes):\n",
    "        return np.zeros(768)\n",
    "    elif(gender == 'man'):\n",
    "        return np.ones(768)\n",
    "    elif(gender == \"woman\"):\n",
    "        return np.ones(768) * -1\n",
    "    else:\n",
    "        return np.zeros(768)\n",
    "    \n",
    "# TODO\n",
    "def process_ethnicity(eth):\n",
    "    x = np.zeros(768)\n",
    "    if(ignore_ethnicity or ignore_additional_attributes):\n",
    "        x = x\n",
    "    elif(eth == \"indian\"):\n",
    "        x[0] = 1\n",
    "    elif(eth == \"asian\"):\n",
    "        x[16]=1\n",
    "    elif(eth == \"latino hispanic\"):\n",
    "        x[2]=1\n",
    "    elif(eth == \"black\"):\n",
    "        x[3]=1\n",
    "    elif(eth == \"middle eastern\"):\n",
    "        x[4]=1\n",
    "    elif(eth == \"white\"):\n",
    "        x[5]=1 \n",
    "    else:\n",
    "        x = x\n",
    "    return x     \n",
    "\n",
    "def process_language(lan):\n",
    "    x = np.zeros(768)\n",
    "    if(ignore_language_spoken or ignore_additional_attributes):\n",
    "        x = x\n",
    "    elif(lan == \"Arabic\"):\n",
    "        x[0] = 1\n",
    "    elif(lan == \"Portuguese\"):\n",
    "        x[16]=1\n",
    "    elif(lan == \"Romansh_Sursilvan\"):\n",
    "        x[2]=1\n",
    "    elif(lan == \"Japanese\"):\n",
    "        x[3]=1\n",
    "    elif(lan == \"Ukranian\"):\n",
    "        x[4]=1\n",
    "    elif(lan == \"German\"):\n",
    "        x[5]=1   \n",
    "    elif(lan == \"Chinese_China\"):\n",
    "        x[6]=1   \n",
    "    elif(lan == \"Welsh\"):\n",
    "        x[7]=1  \n",
    "    elif(lan == \"English\"):\n",
    "        x[8]=1\n",
    "    elif(lan == \"Kabyle\"):\n",
    "        x[9]=1 \n",
    "    elif(lan == \"Kyrgyz\"):\n",
    "        x[10]=1\n",
    "    elif(lan == \"Georgian\"):\n",
    "        x[11]=1\n",
    "    elif(lan == \"Persian\"):\n",
    "        x[12]=1 \n",
    "    elif(lan == \"French\"):\n",
    "        x[13]=1\n",
    "    elif(lan == \"Interlingua\"):\n",
    "        x[14]=1\n",
    "    elif(lan == \"Swedish\"):\n",
    "        x[15]=1\n",
    "    elif(lan == \"Spanish\"):\n",
    "        x[16]=1 \n",
    "    elif(lan == \"Dhivehi\"):\n",
    "        x[17]=1\n",
    "    elif(lan == \"Kinyarwanda\"):\n",
    "        x[18]=1 \n",
    "    elif(lan == \"Tatar\"):\n",
    "        x[19]=1\n",
    "    elif(lan == \"Hakha_Chin\"):\n",
    "        x[20]=1 \n",
    "    elif(lan == \"Tamil\"):\n",
    "        x[21]=1 \n",
    "    elif(lan == \"Greek\"):\n",
    "        x[22]=1\n",
    "    elif(lan == \"Latvian\"):\n",
    "        x[23]=1 \n",
    "    elif(lan == \"Russian\"):\n",
    "        x[24]=1\n",
    "    elif(lan == \"Breton\"):\n",
    "        x[25]=1\n",
    "    elif(lan == \"Catalan\"):\n",
    "        x[26]=1    \n",
    "    elif(lan == \"Maltese\"):\n",
    "        x[27]=1 \n",
    "    elif(lan == \"Slovenian\"):\n",
    "        x[28]=1    \n",
    "    elif(lan == \"Indonesian\"):\n",
    "        x[29]=1    \n",
    "    elif(lan == \"Dutch\"):\n",
    "        x[30]=1\n",
    "    elif(lan == \"Chinese_Taiwan\"):\n",
    "        x[31]=1 \n",
    "    elif(lan == \"Sakha\"):\n",
    "        x[32]=1 \n",
    "    elif(lan == \"Polish\"):\n",
    "        x[33]=1 \n",
    "    elif(lan == \"Czech\"):\n",
    "        x[34]=1 \n",
    "    elif(lan == \"Romanian\"):\n",
    "        x[35]=1 \n",
    "    elif(lan == \"Mangolian\"):\n",
    "        x[36]=1 \n",
    "    elif(lan == \"Italian\"):\n",
    "        x[37]=1 \n",
    "    elif(lan == \"Chinese_Hongkong\"):\n",
    "        x[38]=1 \n",
    "    elif(lan == \"Estonian\"):\n",
    "        x[39]=1 \n",
    "    elif(lan == \"Basque\"):\n",
    "        x[40]=1 \n",
    "    elif(lan == \"Esperanto\"):\n",
    "        x[41]=1 \n",
    "    elif(lan == \"Frisian\"):\n",
    "        x[42]=1 \n",
    "    elif(lan == \"Turkish\"):\n",
    "        x[43]=1 \n",
    "    elif(lan == \"Chuvash\"):\n",
    "        x[44]=1 \n",
    "    else:\n",
    "        x = x\n",
    "    return x       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def process_input(row):\n",
    "    #print(row)\n",
    "    age = row[\"caption_a\"]\n",
    "    ethnicity=row[\"caption_e\"]\n",
    "    gender=row[\"caption_g\"]\n",
    "    language=row[\"caption_l\"]\n",
    "    speaker_emb=row[\"SPEAKER_EMB\"]\n",
    "    audio_emb=row[\"AUDIO_EMB\"]\n",
    "    audio_features=row[\"AUDIO_FEATURES\"]\n",
    "    image_guide=row[\"image_guide\"]\n",
    "    #print(image_guide)\n",
    "    #print(speaker_emb)\n",
    "    speaker_emb = np.asarray(speaker_emb, dtype=np.float32)\n",
    "    speaker_emb = speaker_emb.squeeze()\n",
    "    audio_emb = np.asarray(audio_emb, dtype=np.float32)\n",
    "    audio_emb = audio_emb.squeeze()\n",
    "    #print(speaker_emb.shape)\n",
    "    h = np.vstack((age, ethnicity))\n",
    "    h = np.vstack((h, gender))\n",
    "    h = np.vstack((h, language))\n",
    "    h = np.vstack((h, speaker_emb))\n",
    "    h = np.vstack((h, audio_emb))\n",
    "    h = np.vstack((h,audio_features))\n",
    "    h = np.vstack((h,image_guide))\n",
    "    #print('aaaaaaaaaaa')\n",
    "    #print(h.shape)\n",
    "    j = np.zeros(768)\n",
    "    j = np.tile(j,(256-h.shape[0],1))\n",
    "    h = np.vstack((h, j))\n",
    "    #print(h.shape)\n",
    "    \n",
    "    return h\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def process_image_path(path):\n",
    "    \n",
    "    image = Image.open(path).convert('RGB')\n",
    "\n",
    "    w_s = image_size / (1+2 * 0.4)\n",
    "    h_s = image_size / (1+2 * 0.4)\n",
    "\n",
    "    #print(image.size)\n",
    "    image = image.crop((0.2*w_s,0.0*h_s,1.6*w_s,1.4*h_s))\n",
    "    image = image.resize((image_size,image_size))\n",
    "\n",
    "    #print('saving')\n",
    "    #image.save(str(random.random()) + '.png')\n",
    "\n",
    "    \n",
    "\n",
    "    #print(np.array(image,np.float32).shape)\n",
    "    pix = np.array(image,np.float32)\n",
    "    pix = np.moveaxis(pix, -1, 0)\n",
    "    \n",
    "    pix = pix / 255\n",
    "    image.close()\n",
    "    return pix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pathlib\n",
    "import lancedb\n",
    "uri = datasetPathDatabase\n",
    "db = lancedb.connect(uri)\n",
    "\n",
    "try:\n",
    "    db.drop_table(generated_face_table_name)\n",
    "except:\n",
    "    pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "def save_generated_image(row):\n",
    "    id = row['videos_id']\n",
    "    row_videos = videos_df.loc[videos_df['id'] == id]\n",
    "    generated_image = row['generated']\n",
    "    \n",
    "    absPathVideo = row_videos['video_path'].iloc[0]\n",
    "\n",
    "\n",
    "    absPathGeneratedFace = absPathVideo.replace(datasetPathVideo,datasetPathGeneratedFaces)\n",
    "    absPathGeneratedFace = os.path.splitext(absPathGeneratedFace)[0]\n",
    "    absPathGeneratedFace = absPathGeneratedFace + \"_generated_face_\"+ \".png\"\n",
    "    pathlib.Path(os.path.dirname(absPathGeneratedFace)).mkdir(parents=True, exist_ok=True) \n",
    "\n",
    "    #print(row)\n",
    "    #try:\n",
    "    #    print(generated_image)\n",
    "    #except:\n",
    "    #    pass\n",
    "\n",
    "    #try:\n",
    "    #    print(generated_image.shape)\n",
    "    #except:\n",
    "    #    pass\n",
    "\n",
    "    #print(generated_image)\n",
    "    generated_image.save(absPathGeneratedFace)\n",
    "\n",
    "    p_small = pathlib.Path(os.path.dirname(absPathVideo))\n",
    "    p_big = p_small.parent.absolute()\n",
    "    df_table2 = pd.DataFrame()\n",
    "\n",
    "    df_table2 = pd.concat([df_table2, pd.DataFrame([{'id':int(id),'id_true': int(id),\n",
    "                    'generated_face_path': absPathGeneratedFace,'user':p_big.name}])], ignore_index=True)\n",
    "\n",
    "   \n",
    "    \n",
    "    \n",
    "    try:\n",
    "        table_stage2 = db.open_table(generated_face_table_name)\n",
    "        table_stage2.add(df_table2)\n",
    "    except:\n",
    "        db.create_table(generated_face_table_name, df_table2)\n",
    "\n",
    "    return\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask.dataframe as dd\n",
    "\n",
    "db = lancedb.connect(uri)\n",
    "\n",
    "table = db.open_table(\"video_stage1\")\n",
    "videos = table.to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><strong>Dask DataFrame Structure:</strong></div>\n",
       "<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>id</th>\n",
       "      <th>video_path</th>\n",
       "      <th>face_path</th>\n",
       "      <th>user</th>\n",
       "      <th>blurred_face_path</th>\n",
       "      <th>features_path</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>vector</th>\n",
       "      <th>stage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npartitions=1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<div>Dask Name: from_pandas, 1 graph layer</div>"
      ],
      "text/plain": [
       "Dask DataFrame Structure:\n",
       "                  id video_path face_path    user blurred_face_path features_path    age  gender ethnicity  vector  stage\n",
       "npartitions=1                                                                                                            \n",
       "0              int64     object    object  object            object        object  int64  object    object  object  int64\n",
       "117              ...        ...       ...     ...               ...           ...    ...     ...       ...     ...    ...\n",
       "Dask Name: from_pandas, 1 graph layer"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "videos_df = videos\n",
    "videos = dd.from_pandas(videos,chunksize=preprocessing_chunk)\n",
    "videos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of preprocessing chunks: 1\n",
      "118\n",
      "Getting Data\n",
      "      ID  FACE_ID                                         VIDEO_PATH  \\\n",
      "0      0        0  /media/gamal/Passport/Datasets/Voxceleb2TestVi...   \n",
      "1      1        1  /media/gamal/Passport/Datasets/Voxceleb2TestVi...   \n",
      "2      2        2  /media/gamal/Passport/Datasets/Voxceleb2TestVi...   \n",
      "3      3        3  /media/gamal/Passport/Datasets/Voxceleb2TestVi...   \n",
      "4      4        4  /media/gamal/Passport/Datasets/Voxceleb2TestVi...   \n",
      "..   ...      ...                                                ...   \n",
      "113  113      113  /media/gamal/Passport/Datasets/Voxceleb2TestVi...   \n",
      "114  114      114  /media/gamal/Passport/Datasets/Voxceleb2TestVi...   \n",
      "115  115      115  /media/gamal/Passport/Datasets/Voxceleb2TestVi...   \n",
      "116  116      116  /media/gamal/Passport/Datasets/Voxceleb2TestVi...   \n",
      "117  117      117  /media/gamal/Passport/Datasets/Voxceleb2TestVi...   \n",
      "\n",
      "     caption_a        caption_e caption_g  \\\n",
      "0           26            white       Man   \n",
      "1           31            white       Man   \n",
      "2           28  latino hispanic       Man   \n",
      "3           29   middle eastern       Man   \n",
      "4           38            white     Woman   \n",
      "..         ...              ...       ...   \n",
      "113         26            black       Man   \n",
      "114         27            black       Man   \n",
      "115         44            white       Man   \n",
      "116         26            white       Man   \n",
      "117         24            white       Man   \n",
      "\n",
      "                                           SPEAKER_EMB  \\\n",
      "0    b'\\x80\\x04\\x95\\x8f\\x03\\x00\\x00\\x00\\x00\\x00\\x00...   \n",
      "1    b'\\x80\\x04\\x95\\x8f\\x03\\x00\\x00\\x00\\x00\\x00\\x00...   \n",
      "2    b'\\x80\\x04\\x95\\x8f\\x03\\x00\\x00\\x00\\x00\\x00\\x00...   \n",
      "3    b'\\x80\\x04\\x95\\x8f\\x03\\x00\\x00\\x00\\x00\\x00\\x00...   \n",
      "4    b'\\x80\\x04\\x95\\x8f\\x03\\x00\\x00\\x00\\x00\\x00\\x00...   \n",
      "..                                                 ...   \n",
      "113  b'\\x80\\x04\\x95\\x8f\\x03\\x00\\x00\\x00\\x00\\x00\\x00...   \n",
      "114  b'\\x80\\x04\\x95\\x8f\\x03\\x00\\x00\\x00\\x00\\x00\\x00...   \n",
      "115  b'\\x80\\x04\\x95\\x8f\\x03\\x00\\x00\\x00\\x00\\x00\\x00...   \n",
      "116  b\"\\x80\\x04\\x95\\x8f\\x03\\x00\\x00\\x00\\x00\\x00\\x00...   \n",
      "117  b'\\x80\\x04\\x95\\x8f\\x03\\x00\\x00\\x00\\x00\\x00\\x00...   \n",
      "\n",
      "                                             AUDIO_EMB  \\\n",
      "0    b'\\x80\\x04\\x95\\x84\\x00\\x00\\x00\\x00\\x00\\x00\\x00...   \n",
      "1    b'\\x80\\x04\\x95\\x84\\x00\\x00\\x00\\x00\\x00\\x00\\x00...   \n",
      "2    b'\\x80\\x04\\x95\\x84\\x00\\x00\\x00\\x00\\x00\\x00\\x00...   \n",
      "3    b'\\x80\\x04\\x95\\x84\\x00\\x00\\x00\\x00\\x00\\x00\\x00...   \n",
      "4    b'\\x80\\x04\\x95\\x84\\x00\\x00\\x00\\x00\\x00\\x00\\x00...   \n",
      "..                                                 ...   \n",
      "113  b'\\x80\\x04\\x95\\x84\\x00\\x00\\x00\\x00\\x00\\x00\\x00...   \n",
      "114  b'\\x80\\x04\\x95\\x84\\x00\\x00\\x00\\x00\\x00\\x00\\x00...   \n",
      "115  b'\\x80\\x04\\x95\\x84\\x00\\x00\\x00\\x00\\x00\\x00\\x00...   \n",
      "116  b'\\x80\\x04\\x95\\x84\\x00\\x00\\x00\\x00\\x00\\x00\\x00...   \n",
      "117  b'\\x80\\x04\\x95\\x84\\x00\\x00\\x00\\x00\\x00\\x00\\x00...   \n",
      "\n",
      "                                        AUDIO_FEATURES caption_l  \\\n",
      "0    b'\\x80\\x04\\x95\\x83\\x00\\x00\\x00\\x00\\x00\\x00\\x00...   English   \n",
      "1    b'\\x80\\x04\\x95\\x83\\x00\\x00\\x00\\x00\\x00\\x00\\x00...   English   \n",
      "2    b'\\x80\\x04\\x95\\x83\\x00\\x00\\x00\\x00\\x00\\x00\\x00...   English   \n",
      "3    b'\\x80\\x04\\x95\\x83\\x00\\x00\\x00\\x00\\x00\\x00\\x00...   English   \n",
      "4    b'\\x80\\x04\\x95\\x83\\x00\\x00\\x00\\x00\\x00\\x00\\x00...   English   \n",
      "..                                                 ...       ...   \n",
      "113  b'\\x80\\x04\\x95\\x83\\x00\\x00\\x00\\x00\\x00\\x00\\x00...   English   \n",
      "114  b'\\x80\\x04\\x95\\x83\\x00\\x00\\x00\\x00\\x00\\x00\\x00...   English   \n",
      "115  b'\\x80\\x04\\x95\\x83\\x00\\x00\\x00\\x00\\x00\\x00\\x00...   English   \n",
      "116  b'\\x80\\x04\\x95\\x83\\x00\\x00\\x00\\x00\\x00\\x00\\x00...   English   \n",
      "117  b'\\x80\\x04\\x95\\x83\\x00\\x00\\x00\\x00\\x00\\x00\\x00...   English   \n",
      "\n",
      "                                            image_path  \\\n",
      "0    /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "1    /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "2    /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "3    /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "4    /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "..                                                 ...   \n",
      "113  /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "114  /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "115  /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "116  /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "117  /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "\n",
      "                                                   VGG  \\\n",
      "0    [0.02598828449845314, 0.002286622067913413, 0....   \n",
      "1    [0.014909587800502777, 0.010321180336177349, 0...   \n",
      "2    [0.017716582864522934, 0.031231218948960304, 0...   \n",
      "3    [0.015032665804028511, 0.0020783008076250553, ...   \n",
      "4    [0.014704544097185135, -0.004270102363079786, ...   \n",
      "..                                                 ...   \n",
      "113  [0.011644644662737846, 0.006384784821420908, 0...   \n",
      "114  [0.012097470462322235, 0.011594198644161224, 0...   \n",
      "115  [0.020973164588212967, -0.011326933279633522, ...   \n",
      "116  [0.027417417615652084, -0.00149383838288486, 0...   \n",
      "117  [0.01719789206981659, 0.002730456180870533, -0...   \n",
      "\n",
      "                                           image_guide  \\\n",
      "0    /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "1    /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "2    /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "3    /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "4    /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "..                                                 ...   \n",
      "113  /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "114  /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "115  /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "116  /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "117  /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "\n",
      "                                         low_res_image  \\\n",
      "0    /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "1    /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "2    /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "3    /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "4    /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "..                                                 ...   \n",
      "113  /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "114  /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "115  /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "116  /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "117  /media/gamal/Passport/Datasets/VoxCeleb2Test/V...   \n",
      "\n",
      "                                    path_var_len_audio  videos_id  \n",
      "0    /home/gamal/vsc/DiffusionSpeech2Face/imagen_te...          0  \n",
      "1    /home/gamal/vsc/DiffusionSpeech2Face/imagen_te...          1  \n",
      "2    /home/gamal/vsc/DiffusionSpeech2Face/imagen_te...          2  \n",
      "3    /home/gamal/vsc/DiffusionSpeech2Face/imagen_te...          3  \n",
      "4    /home/gamal/vsc/DiffusionSpeech2Face/imagen_te...          4  \n",
      "..                                                 ...        ...  \n",
      "113  /home/gamal/vsc/DiffusionSpeech2Face/imagen_te...        113  \n",
      "114  /home/gamal/vsc/DiffusionSpeech2Face/imagen_te...        114  \n",
      "115  /home/gamal/vsc/DiffusionSpeech2Face/imagen_te...        115  \n",
      "116  /home/gamal/vsc/DiffusionSpeech2Face/imagen_te...        116  \n",
      "117  /home/gamal/vsc/DiffusionSpeech2Face/imagen_te...        117  \n",
      "\n",
      "[118 rows x 16 columns]\n",
      "Data Preprocessed...\n",
      "Using model file /data_store/checkpoint/checkpoint.49085.pt\n",
      "checkpoint loaded from /data_store/checkpoint/checkpoint.49085.pt\n"
     ]
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      "Resting...\n"
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      "Resting...\n"
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      "Resting...\n",
      "images\n",
      "[<PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CD30>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CC10>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CC40>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CAF0>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CB20>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CB50>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CB80>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CD00>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CBE0>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CD60>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CD90>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CDC0>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CDF0>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CE20>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CE50>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CE80>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CEB0>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CEE0>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CF10>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CF40>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CF70>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CFA0>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD0CFD0>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD12040>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD12070>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD120A0>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD120D0>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD12100>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD12130>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD12160>, <PIL.Image.Image image mode=RGB 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<PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD1C040>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD1C070>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD1C0A0>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD1C0D0>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD1C100>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD1C130>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD1C160>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD1C190>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD1C1C0>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD1C1F0>, <PIL.Image.Image image mode=RGB size=128x128 at 0x7F937AD1C220>]\n",
      "no of chunks:1\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "import multiprocessing\n",
    "import train_imagen_batch\n",
    "import dask.dataframe as dd\n",
    "import math\n",
    "\n",
    "j = 0\n",
    "\n",
    "no_of_video_chunks = math.ceil(len(videos)/preprocessing_chunk)\n",
    "print(\"No. of preprocessing chunks: \" + str(no_of_video_chunks))\n",
    "\n",
    "while(True):\n",
    "\n",
    "    videos_chunk = videos.partitions[j:j+1]\n",
    "    print(len(videos_chunk))\n",
    "\n",
    "    features = pd.DataFrame()\n",
    "    print(\"Getting Data\")\n",
    "    for index, row in videos_chunk.iterrows():\n",
    "\n",
    "        with open(row['features_path'], 'rb') as handle:\n",
    "            feature = pickle.load(handle)\n",
    "            feature['videos_id'] = row['id']\n",
    "            #print(feature)\n",
    "            #features = features.append(feature)\n",
    "            features = pd.concat([features, pd.DataFrame([feature])], ignore_index=True)\n",
    "\n",
    "    #print(features)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    print(features)\n",
    "    data_frame = features[[\"ID\",\"FACE_ID\",\"image_path\",\"caption_a\",\"caption_e\",\"caption_g\",\"caption_l\",\"videos_id\"]]\n",
    "    data_frame['SPEAKER_EMB'] = features['SPEAKER_EMB']\n",
    "    data_frame['AUDIO_EMB'] = features['AUDIO_EMB']\n",
    "    data_frame['image_guide'] = features['image_guide']\n",
    "    data_frame['AUDIO_FEATURES'] = features['AUDIO_FEATURES']\n",
    "\n",
    "\n",
    "    data_frame['SPEAKER_EMB'] = data_frame['SPEAKER_EMB'].apply(lambda x: speaker_emb_preprocess(x))\n",
    "    data_frame['AUDIO_EMB'] = data_frame['AUDIO_EMB'].apply(lambda x: audio_emb_preprocess2(x))\n",
    "    data_frame['AUDIO_FEATURES'] = data_frame['AUDIO_FEATURES'].apply(lambda x: audio_features_preprocess(x))\n",
    "    data_frame = data_frame.drop(['ID'], axis=1)\n",
    "    data_frame['image_guide'] = data_frame['image_path'].apply(lambda x: image_guide_preprocess_low_res_dummy(x))\n",
    "    data_frame = data_frame.drop(['FACE_ID'], axis=1)\n",
    "    data_frame['caption_a'] = data_frame['caption_a'].apply(lambda x: process_age(x))\n",
    "    data_frame['caption_g'] = data_frame['caption_g'].apply(lambda x: process_gender(x))\n",
    "    data_frame['caption_l'] = data_frame['caption_l'].apply(lambda x: process_language(x))\n",
    "    data_frame['caption_e'] = data_frame['caption_e'].apply(lambda x: process_ethnicity(x))\n",
    "    data_frame['low_res_image'] = data_frame['image_path'].apply(lambda x: image_guide_preprocess_low_res(x))\n",
    "\n",
    "    data_frame['INPUT'] = data_frame['SPEAKER_EMB']\n",
    "\n",
    "    data_frame['INPUT'] = data_frame.apply(process_input,args=(),axis=1)\n",
    "\n",
    "\n",
    "    data_frame = data_frame.drop(['caption_e', 'caption_g','caption_l','SPEAKER_EMB','AUDIO_EMB'], axis=1)\n",
    "\n",
    "\n",
    "    data_frame['image_path'] = data_frame['image_path'].apply(lambda x: process_image_path(x))\n",
    "\n",
    "    print(\"Data Preprocessed...\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    part = data_frame[['INPUT','low_res_image']]\n",
    "\n",
    "\n",
    "\n",
    "    input0 = np.array(part['INPUT'])\n",
    "    input0 = np.array([np.array(xi) for xi in input0])\n",
    "    input0[np.isnan(input0)] = 0\n",
    "    input0[input0 > 10] = 10\n",
    "    input0[input0 < -10] = -10\n",
    "    #print(sys.getsizeof(input))\n",
    "\n",
    "\n",
    "    input2 = np.array(part['low_res_image'])\n",
    "    input2 = np.array([np.array(xi) for xi in input2])\n",
    "    input2.squeeze()\n",
    "    #print(sys.getsizeof(input2))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    proc = multiprocessing.Process(target=train_imagen_batch.sample_batch_unet, args=(input0,input2,model_filename,image_size,unet1_dim,unet2_dim,timesteps,begin_with_image_size,unet1_image_size,cond_scale,sampling_chunk,ignore_image_guide))\n",
    "    proc.start()\n",
    "    proc.join()\n",
    "\n",
    "    with open('evaluate_imagen2.tmp', 'rb') as handle:\n",
    "        images = pickle.load(handle)\n",
    "\n",
    "    print(\"images\")\n",
    "    print(images)\n",
    "\n",
    "\n",
    "    data_frame.reset_index(drop=True, inplace=True)\n",
    "    data_frame['generated'] = pd.DataFrame(data=images)\n",
    "    data_frame.apply(save_generated_image,args=(),axis=1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    del images\n",
    "    del features\n",
    "    del data_frame\n",
    "    del input0\n",
    "    del input2\n",
    "    gc.collect()\n",
    "\n",
    "    j = j+1\n",
    "    print(\"no of chunks:\" + str(no_of_video_chunks))\n",
    "    print(j)\n",
    "    if(j == no_of_video_chunks):\n",
    "        break\n",
    "        \n",
    "\n",
    "\n"
   ]
  }
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