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docs/source/user_guide/notebooks/Prediction-load_avt_models_oceanai.ipynb
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Формирование нейросетевой архитектуры модели и загрузка ее весов для получения результатов оценки персональных качеств (аудио, видео и текст объединение)\n", | ||
| "\n", | ||
| "<hr>\n", | ||
| "\n", | ||
| "> - `_b5.av_models_b5_` - Нейросетевые модели **tf.keras.Model** для получения результатов оценки персональных качеств" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": { | ||
| "nbsphinx": "hidden", | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import os # Взаимодействие с файловой системой\n", | ||
| "import sys # Доступ к некоторым переменным и функциям Python\n", | ||
| "\n", | ||
| "PATH_TO_SOURCE = os.path.abspath(os.path.dirname(globals()['_dh'][0]))\n", | ||
| "PATH_TO_ROOT = os.path.join(PATH_TO_SOURCE, '..', '..', '..')\n", | ||
| "\n", | ||
| "sys.path.insert(0, os.path.abspath(PATH_TO_ROOT))" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Импорт необходимых инструментов" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from oceanai.modules.lab.build import Run" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Сборка" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/markdown": [ | ||
| "<span style=\"color:#333\">**[</span><span style=\"color:#1776D2\">2023-12-11 09:46:45</span><span style=\"color:#333\">]</span> <span style=\"color:#333\">OCEANAI - персональные качества личности человека:</span>**<br /><span style=\"color:#333\"> Авторы:</span><br /><span style=\"color:#333\"> Рюмина Елена [<u>ryumina_ev@mail.ru</u>]</span><br /><span style=\"color:#333\"> Рюмин Дмитрий [<u>dl_03.03.1991@mail.ru</u>]</span><br /><span style=\"color:#333\"> Карпов Алексей [<u>karpov@iias.spb.su</u>]</span><br /><span style=\"color:#333\"> Сопровождающие:</span><br /><span style=\"color:#333\"> Рюмина Елена [<u>ryumina_ev@mail.ru</u>]</span><br /><span style=\"color:#333\"> Рюмин Дмитрий [<u>dl_03.03.1991@mail.ru</u>]</span><br /><span style=\"color:#333\"> Версия: <u>1.0.0a5</u></span><br /><span style=\"color:#333\"> Лицензия: <u>BSD License</u></span></p>" | ||
| ], | ||
| "text/plain": [ | ||
| "<IPython.core.display.Markdown object>" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "_b5 = Run(\n", | ||
| " lang = 'ru', # Язык\n", | ||
| " color_simple = '#333', # Цвет обычного текста (шестнадцатеричный код)\n", | ||
| " color_info = '#1776D2', # Цвет текста содержащего информацию (шестнадцатеричный код)\n", | ||
| " color_err = '#FF0000', # Цвет текста содержащего ошибку (шестнадцатеричный код)\n", | ||
| " color_true = '#008001', # Цвет текста содержащего положительную информацию (шестнадцатеричный код)\n", | ||
| " bold_text = True, # Жирное начертание текста\n", | ||
| " num_to_df_display = 30, # Количество строк для отображения в таблицах\n", | ||
| " text_runtime = 'Время выполнения', # Текст времени выполнения\n", | ||
| " metadata = True # Отображение информации о библиотеке\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Формирование нейросетевой архитектуры модели" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/markdown": [ | ||
| "<span style=\"color:#333\">**[</span><span style=\"color:#1776D2\">2023-12-11 09:46:45</span><span style=\"color:#333\">]</span> <span style=\"color:#333\">Формирование нейросетевой архитектуры модели для получения оценок персональных качеств (мультимодальное объединение) ...</span>** " | ||
| ], | ||
| "text/plain": [ | ||
| "<IPython.core.display.Markdown object>" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| }, | ||
| { | ||
| "data": { | ||
| "text/markdown": [ | ||
| "<span style=\"color:#333\">**--- Время выполнения: 0.814 сек. ---**</span>" | ||
| ], | ||
| "text/plain": [ | ||
| "<IPython.core.display.Markdown object>" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "res_load_av_models_b5 = _b5.load_avt_model_b5(\n", | ||
| " show_summary = False, # Отображение сформированной нейросетевой архитектуры модели\n", | ||
| " out = True, # Отображение\n", | ||
| " runtime = True, # Подсчет времени выполнения\n", | ||
| " run = True # Блокировка выполнения\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Загрузка весов нейросетевой модели" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/markdown": [ | ||
| "<span style=\"color:#333\">**[</span><span style=\"color:#1776D2\">2023-12-11 09:46:46</span><span style=\"color:#333\">]</span> <span style=\"color:#333\">Загрузка весов нейросетевой модели для получения оценок персональных качеств (мультимодальное объединение) ...</span>** " | ||
| ], | ||
| "text/plain": [ | ||
| "<IPython.core.display.Markdown object>" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| }, | ||
| { | ||
| "data": { | ||
| "text/markdown": [ | ||
| "<span style=\"color:#333\">**[</span><span style=\"color:#1776D2\">2023-12-11 09:46:46</span><span style=\"color:#333\">]</span> <span style=\"color:#333\">Загрузка файла \"<span style=\"color:#1776D2\">avt_fi_2023-12-03_11-36-51.h5</span>\"</span>** " | ||
| ], | ||
| "text/plain": [ | ||
| "<IPython.core.display.Markdown object>" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| }, | ||
| { | ||
| "data": { | ||
| "text/markdown": [ | ||
| "<span style=\"color:#333\">**--- Время выполнения: 0.218 сек. ---**</span>" | ||
| ], | ||
| "text/plain": [ | ||
| "<IPython.core.display.Markdown object>" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# Настройки ядра\n", | ||
| "_b5.path_to_save_ = './models' # Директория для сохранения файла\n", | ||
| "_b5.chunk_size_ = 2000000 # Размер загрузки файла из сети за 1 шаг\n", | ||
| "\n", | ||
| "url = _b5.weights_for_big5_['avt']['fi']['b5']['sberdisk']\n", | ||
| "\n", | ||
| "res_load_avt_model_weights_b5 = _b5.load_avt_model_weights_b5(\n", | ||
| " url = url,\n", | ||
| " force_reload = False, # Принудительная загрузка файла с весами нейросетевой модели из сети\n", | ||
| " out = True, # Отображение\n", | ||
| " runtime = True, # Подсчет времени выполнения\n", | ||
| " run = True # Блокировка выполнения\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Отображение сформированной нейросетевой архитектуры модели" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 6, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Model: \"model\"\n", | ||
| "__________________________________________________________________________________________________\n", | ||
| " Layer (type) Output Shape Param # Connected to \n", | ||
| "==================================================================================================\n", | ||
| " hc_t (InputLayer) [(None, 128)] 0 [] \n", | ||
| " \n", | ||
| " hc_a (InputLayer) [(None, 256)] 0 [] \n", | ||
| " \n", | ||
| " nn_t (InputLayer) [(None, 128)] 0 [] \n", | ||
| " \n", | ||
| " nn_a (InputLayer) [(None, 512)] 0 [] \n", | ||
| " \n", | ||
| " hc_v (InputLayer) [(None, 256)] 0 [] \n", | ||
| " \n", | ||
| " nn_v (InputLayer) [(None, 2048)] 0 [] \n", | ||
| " \n", | ||
| " ln_hc_t (LayerNormalization) (None, 128) 256 ['hc_t[0][0]'] \n", | ||
| " \n", | ||
| " ln_hc_a (LayerNormalization) (None, 256) 512 ['hc_a[0][0]'] \n", | ||
| " \n", | ||
| " ln_nn_t (LayerNormalization) (None, 128) 256 ['nn_t[0][0]'] \n", | ||
| " \n", | ||
| " ln_nn_a (LayerNormalization) (None, 512) 1024 ['nn_a[0][0]'] \n", | ||
| " \n", | ||
| " ln_hc_v (LayerNormalization) (None, 256) 512 ['hc_v[0][0]'] \n", | ||
| " \n", | ||
| " ln_nn_v (LayerNormalization) (None, 2048) 4096 ['nn_v[0][0]'] \n", | ||
| " \n", | ||
| " gata (GFL) (None, 64) 131072 ['ln_hc_t[0][0]', \n", | ||
| " 'ln_hc_a[0][0]', \n", | ||
| " 'ln_nn_t[0][0]', \n", | ||
| " 'ln_nn_a[0][0]'] \n", | ||
| " \n", | ||
| " gatv (GFL) (None, 64) 327680 ['ln_hc_t[0][0]', \n", | ||
| " 'ln_hc_v[0][0]', \n", | ||
| " 'ln_nn_t[0][0]', \n", | ||
| " 'ln_nn_v[0][0]'] \n", | ||
| " \n", | ||
| " gaav (GFL) (None, 64) 393216 ['ln_hc_a[0][0]', \n", | ||
| " 'ln_hc_v[0][0]', \n", | ||
| " 'ln_nn_a[0][0]', \n", | ||
| " 'ln_nn_v[0][0]'] \n", | ||
| " \n", | ||
| " tf.concat (TFOpLambda) (None, 192) 0 ['gata[0][0]', \n", | ||
| " 'gatv[0][0]', \n", | ||
| " 'gaav[0][0]'] \n", | ||
| " \n", | ||
| " dense (Dense) (None, 50) 9650 ['tf.concat[0][0]'] \n", | ||
| " \n", | ||
| " dence_cl (Dense) (None, 5) 255 ['dense[0][0]'] \n", | ||
| " \n", | ||
| "==================================================================================================\n", | ||
| "Total params: 868,529\n", | ||
| "Trainable params: 868,529\n", | ||
| "Non-trainable params: 0\n", | ||
| "__________________________________________________________________________________________________\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "_b5.avt_model_b5_.summary()" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3 (ipykernel)", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.9.13" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 4 | ||
| } |