diff --git a/docs/source/user_guide/notebooks/Video-get_video_union_predictions.ipynb b/docs/source/user_guide/notebooks/Video-get_video_union_predictions.ipynb index 64dad34..05820e9 100644 --- a/docs/source/user_guide/notebooks/Video-get_video_union_predictions.ipynb +++ b/docs/source/user_guide/notebooks/Video-get_video_union_predictions.ipynb @@ -62,7 +62,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:05:26] OCEANAI - персональные качества личности человека:**
    Авторы:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
        Карпов Алексей [karpov@iias.spb.su]
    Сопровождающие:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
    Версия: 1.0.0a16
    Лицензия: BSD License

" + "**[2024-10-08 16:35:32] OCEANAI - персональные качества личности человека:**
    Авторы:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
        Карпов Алексей [karpov@iias.spb.su]
    Сопровождающие:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
    Версия: 1.0.0a40
    Лицензия: BSD License

" ], "text/plain": [ "" @@ -128,130 +128,118 @@ " \n", " \n", " 1\n", - " TensorFlow\n", - " 2.15.0\n", - " \n", - " \n", - " 2\n", - " Keras\n", - " 2.15.0\n", - " \n", - " \n", - " 3\n", " OpenCV\n", - " 4.8.1\n", + " 4.10.0\n", " \n", " \n", - " 4\n", + " 2\n", " MediaPipe\n", - " 0.9.0\n", + " 0.10.14\n", " \n", " \n", - " 5\n", + " 3\n", " NumPy\n", - " 1.26.2\n", + " 1.26.4\n", " \n", " \n", - " 6\n", + " 4\n", " SciPy\n", - " 1.11.4\n", + " 1.14.1\n", " \n", " \n", - " 7\n", + " 5\n", " Pandas\n", - " 2.1.3\n", + " 2.2.3\n", " \n", " \n", - " 8\n", + " 6\n", " Scikit-learn\n", - " 1.3.2\n", + " 1.5.2\n", " \n", " \n", - " 9\n", + " 7\n", " OpenSmile\n", " 2.5.0\n", " \n", " \n", - " 10\n", + " 8\n", " Librosa\n", - " 0.10.1\n", + " 0.10.2.post1\n", " \n", " \n", - " 11\n", + " 9\n", " AudioRead\n", " 3.0.1\n", " \n", " \n", - " 12\n", + " 10\n", " IPython\n", - " 8.18.1\n", - " \n", - " \n", - " 13\n", - " PyMediaInfo\n", - " 6.1.0\n", + " 8.28.0\n", " \n", " \n", - " 14\n", + " 11\n", " Requests\n", - " 2.31.0\n", + " 2.32.3\n", " \n", " \n", - " 15\n", + " 12\n", " JupyterLab\n", - " 4.0.9\n", + " 4.2.5\n", " \n", " \n", - " 16\n", + " 13\n", " LIWC\n", " 0.5.0\n", " \n", " \n", - " 17\n", + " 14\n", " Transformers\n", - " 4.36.0\n", + " 4.45.1\n", " \n", " \n", - " 18\n", + " 15\n", " Sentencepiece\n", - " 0.1.99\n", + " 0.2.0\n", " \n", " \n", - " 19\n", + " 16\n", " Torch\n", - " 2.0.1+cpu\n", + " 2.4.1+cu118\n", " \n", " \n", - " 20\n", + " 17\n", " Torchaudio\n", - " 2.0.2+cpu\n", + " 2.4.1+cu118\n", + " \n", + " \n", + " 18\n", + " Torchvision\n", + " 0.19.1+cu118\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Package Version\n", - "1 TensorFlow 2.15.0\n", - "2 Keras 2.15.0\n", - "3 OpenCV 4.8.1\n", - "4 MediaPipe 0.9.0\n", - "5 NumPy 1.26.2\n", - "6 SciPy 1.11.4\n", - "7 Pandas 2.1.3\n", - "8 Scikit-learn 1.3.2\n", - "9 OpenSmile 2.5.0\n", - "10 Librosa 0.10.1\n", - "11 AudioRead 3.0.1\n", - "12 IPython 8.18.1\n", - "13 PyMediaInfo 6.1.0\n", - "14 Requests 2.31.0\n", - "15 JupyterLab 4.0.9\n", - "16 LIWC 0.5.0\n", - "17 Transformers 4.36.0\n", - "18 Sentencepiece 0.1.99\n", - "19 Torch 2.0.1+cpu\n", - "20 Torchaudio 2.0.2+cpu" + " Package Version\n", + "1 OpenCV 4.10.0\n", + "2 MediaPipe 0.10.14\n", + "3 NumPy 1.26.4\n", + "4 SciPy 1.14.1\n", + "5 Pandas 2.2.3\n", + "6 Scikit-learn 1.5.2\n", + "7 OpenSmile 2.5.0\n", + "8 Librosa 0.10.2.post1\n", + "9 AudioRead 3.0.1\n", + "10 IPython 8.28.0\n", + "11 Requests 2.32.3\n", + "12 JupyterLab 4.2.5\n", + "13 LIWC 0.5.0\n", + "14 Transformers 4.45.1\n", + "15 Sentencepiece 0.2.0\n", + "16 Torch 2.4.1+cu118\n", + "17 Torchaudio 2.4.1+cu118\n", + "18 Torchvision 0.19.1+cu118" ] }, "metadata": {}, @@ -260,7 +248,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 0.005 сек. ---**" + "**--- Время выполнения: 0.002 сек. ---**" ], "text/plain": [ "" @@ -280,7 +268,7 @@ "source": [ "### Формирование нейросетевой архитектуры модели для получения оценок по экспертным признакам\n", "\n", - "> - `_b5.video_model_hc_` - Нейросетевая модель **tf.keras.Model** для получения оценок по экспертным признакам" + "> - `_b5.video_model_hc_` - Нейросетевая модель **nn.Module** для получения оценок по экспертным признакам" ] }, { @@ -291,7 +279,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:05:26] Формирование нейросетевой архитектуры модели для получения оценок по экспертным признакам (видео модальность) ...** " + "**[2024-10-08 16:35:32] Формирование нейросетевой архитектуры модели для получения оценок по экспертным признакам (видео модальность) ...** " ], "text/plain": [ "" @@ -303,7 +291,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 0.321 сек. ---**" + "**--- Время выполнения: 0.002 сек. ---**" ], "text/plain": [ "" @@ -329,7 +317,7 @@ "source": [ "### Загрузка весов нейросетевой модели для получения оценок по экспертным признакам\n", "\n", - "> - `_b5.video_model_hc_` - Нейросетевая модель **tf.keras.Model** для получения оценок по экспертным признакам" + "> - `_b5.video_model_hc_` - Нейросетевая модель **nn.Module** для получения оценок по экспертным признакам" ] }, { @@ -340,7 +328,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:05:27] Загрузка весов нейросетевой модели для получения оценок по экспертным признакам (видео модальность) ...** " + "**[2024-10-08 16:35:32] Загрузка весов нейросетевой модели для получения оценок по экспертным признакам (видео модальность) ...** " ], "text/plain": [ "" @@ -352,7 +340,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:05:27] Загрузка файла \"weights_2022-08-27_18-53-35.h5\" 100.0% ...** " + "**[2024-10-08 16:35:35] Загрузка файла \"weights_2022-08-27_18-53-35.pth\" 100.0% ...** " ], "text/plain": [ "" @@ -364,7 +352,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 0.249 сек. ---**" + "**--- Время выполнения: 2.727 сек. ---**" ], "text/plain": [ "" @@ -379,7 +367,7 @@ "_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_['video']['fi']['hc']['sberdisk']\n", + "url = _b5.weights_for_big5_['video']['fi']['hc']['googledisk']\n", "\n", "res_load_video_model_weights_hc = _b5.load_video_model_weights_hc(\n", " url = url, # Полный путь к файлу с весами нейросетевой модели\n", @@ -396,7 +384,7 @@ "source": [ "### Формирование нейросетевой архитектуры для получения нейросетевых признаков\n", "\n", - "> - `_b5.video_model_deep_fe_` - Нейросетевая модель **tf.keras.Model** для получения нейросетевых признаков" + "> - `_b5.video_model_deep_fe_` - Нейросетевая модель **nn.Module** для получения нейросетевых признаков" ] }, { @@ -407,7 +395,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:05:27] Формирование нейросетевой архитектуры для получения нейросетевых признаков (видео модальность) ...** " + "**[2024-10-08 16:35:35] Формирование нейросетевой архитектуры для получения нейросетевых признаков (видео модальность) ...** " ], "text/plain": [ "" @@ -419,7 +407,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 0.823 сек. ---**" + "**--- Время выполнения: 0.109 сек. ---**" ], "text/plain": [ "" @@ -444,7 +432,7 @@ "source": [ "### Загрузка весов нейросетевой модели для получения нейросетевых признаков\n", "\n", - "> - `_b5.video_model_deep_fe_` - Нейросетевая модель **tf.keras.Model** для получения нейросетевых признаков" + "> - `_b5.video_model_deep_fe_` - Нейросетевая модель **nn.Module** для получения нейросетевых признаков" ] }, { @@ -455,7 +443,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:05:28] Загрузка весов нейросетевой модели для получения нейросетевых признаков (видео модальность) ...** " + "**[2024-10-08 16:35:35] Загрузка весов нейросетевой модели для получения нейросетевых признаков (видео модальность) ...** " ], "text/plain": [ "" @@ -467,7 +455,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:05:31] Загрузка файла \"weights_2022-11-01_12-27-07.h5\" 100.0% ...** " + "**[2024-10-08 16:35:39] Загрузка файла \"weights_2022-11-01_12-27-07.pth\" 100.0% ...** " ], "text/plain": [ "" @@ -479,7 +467,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 3.342 сек. ---**" + "**--- Время выполнения: 3.878 сек. ---**" ], "text/plain": [ "" @@ -494,7 +482,7 @@ "_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_['video']['fi']['fe']['sberdisk']\n", + "url = _b5.weights_for_big5_['video']['fi']['fe']['googledisk']\n", "\n", "res_load_video_model_weights_deep_fe = _b5.load_video_model_weights_deep_fe(\n", " url = url, # Полный путь к файлу с весами нейросетевой модели\n", @@ -511,7 +499,7 @@ "source": [ "### Формирование нейросетевой архитектуры модели для получения оценок по нейросетевым признакам\n", "\n", - "> - `_b5.video_model_nn_` - Нейросетевая модель **tf.keras.Model** для получения оценок по нейросетевым признакам" + "> - `_b5.video_model_nn_` - Нейросетевая модель **nn.Module** для получения оценок по нейросетевым признакам" ] }, { @@ -522,7 +510,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:05:31] Формирование нейросетевой архитектуры для получения оценок по нейросетевым признакам (видео модальность) ...** " + "**[2024-10-08 16:35:39] Формирование нейросетевой архитектуры для получения оценок по нейросетевым признакам (видео модальность) ...** " ], "text/plain": [ "" @@ -534,7 +522,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 0.708 сек. ---**" + "**--- Время выполнения: 0.023 сек. ---**" ], "text/plain": [ "" @@ -559,7 +547,7 @@ "source": [ "### Загрузка весов нейросетевой модели для получения оценок по нейросетевым признакам\n", "\n", - "> - `_b5.video_model_nn_` - Нейросетевая модель **tf.keras.Model** для получения оценок по нейросетевым признакам" + "> - `_b5.video_model_nn_` - Нейросетевая модель **nn.Module** для получения оценок по нейросетевым признакам" ] }, { @@ -570,7 +558,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:05:32] Загрузка весов нейросетевой модели для получения оценок по нейросетевым признакам (видео модальность) ...** " + "**[2024-10-08 16:35:39] Загрузка весов нейросетевой модели для получения оценок по нейросетевым признакам (видео модальность) ...** " ], "text/plain": [ "" @@ -582,7 +570,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:05:32] Загрузка файла \"weights_2022-03-22_16-31-48.h5\"** " + "**[2024-10-08 16:35:42] Загрузка файла \"weights_2022-03-22_16-31-48.pth\"** " ], "text/plain": [ "" @@ -594,7 +582,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 0.209 сек. ---**" + "**--- Время выполнения: 2.816 сек. ---**" ], "text/plain": [ "" @@ -609,7 +597,7 @@ "_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_['video']['fi']['nn']['sberdisk']\n", + "url = _b5.weights_for_big5_['video']['fi']['nn']['googledisk']\n", "\n", "res_load_video_model_weights_nn = _b5.load_video_model_weights_nn(\n", " url = url, # Полный путь к файлу с весами нейросетевой модели\n", @@ -626,7 +614,7 @@ "source": [ "### Формирование нейросетевых архитектур моделей для получения результатов оценки персональных качеств\n", "\n", - "> - `_b5.video_models_b5_` - Нейросетевые модели **tf.keras.Model** для получения результатов оценки персональных качеств" + "> - `_b5.video_models_b5_` - Нейросетевые модели **nn.Module** для получения результатов оценки персональных качеств" ] }, { @@ -637,7 +625,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:05:32] Формирование нейросетевых архитектур моделей для получения результатов оценки персональных качеств (видео модальность) ...** " + "**[2024-10-08 16:35:43] Формирование нейросетевых архитектур моделей для получения результатов оценки персональных качеств (видео модальность) ...** " ], "text/plain": [ "" @@ -649,7 +637,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 0.046 сек. ---**" + "**--- Время выполнения: 0.002 сек. ---**" ], "text/plain": [ "" @@ -674,7 +662,7 @@ "source": [ "### Загрузка весов нейросетевых моделей для получения результатов оценки персональных качеств\n", "\n", - "> - `_b5.video_models_b5_` - Нейросетевые модели **tf.keras.Model** для получения результатов оценки персональных качеств" + "> - `_b5.video_models_b5_` - Нейросетевые модели **nn.Module** для получения результатов оценки персональных качеств" ] }, { @@ -685,7 +673,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:05:32] Загрузка весов нейросетевых моделей для получения результатов оценки персональных качеств (видео модальность) ...** " + "**[2024-10-08 16:35:45] Загрузка весов нейросетевых моделей для получения результатов оценки персональных качеств (видео модальность) ...** " ], "text/plain": [ "" @@ -697,7 +685,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:05:32] Загрузка файла \"weights_2022-06-15_16-46-30.h5\"** **Открытость опыту**" + "**[2024-10-08 16:35:48] Загрузка файла \"weights_2022-06-15_16-46-30.pth\"** **Открытость опыту**" ], "text/plain": [ "" @@ -709,7 +697,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:05:32] Загрузка файла \"weights_2022-06-15_16-48-50.h5\"** **Добросовестность**" + "**[2024-10-08 16:35:50] Загрузка файла \"weights_2022-06-15_16-48-50.pth\"** **Добросовестность**" ], "text/plain": [ "" @@ -721,7 +709,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:05:33] Загрузка файла \"weights_2022-06-15_16-54-06.h5\"** **Экстраверсия**" + "**[2024-10-08 16:35:52] Загрузка файла \"weights_2022-06-15_16-54-06.pth\"** **Экстраверсия**" ], "text/plain": [ "" @@ -733,7 +721,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:05:33] Загрузка файла \"weights_2022-06-15_17-02-03.h5\"** **Доброжелательность**" + "**[2024-10-08 16:35:54] Загрузка файла \"weights_2022-06-15_17-02-03.pth\"** **Доброжелательность**" ], "text/plain": [ "" @@ -745,7 +733,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:05:33] Загрузка файла \"weights_2022-06-15_17-06-15.h5\"** **Эмоциональная стабильность**" + "**[2024-10-08 16:35:57] Загрузка файла \"weights_2022-06-15_17-06-15.pth\"** **Эмоциональная стабильность**" ], "text/plain": [ "" @@ -757,7 +745,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 1.013 сек. ---**" + "**--- Время выполнения: 11.525 сек. ---**" ], "text/plain": [ "" @@ -772,11 +760,11 @@ "_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_openness = _b5.weights_for_big5_['video']['fi']['b5']['openness']['sberdisk']\n", - "url_conscientiousness = _b5.weights_for_big5_['video']['fi']['b5']['conscientiousness']['sberdisk']\n", - "url_extraversion = _b5.weights_for_big5_['video']['fi']['b5']['extraversion']['sberdisk']\n", - "url_agreeableness = _b5.weights_for_big5_['video']['fi']['b5']['agreeableness']['sberdisk']\n", - "url_non_neuroticism = _b5.weights_for_big5_['video']['fi']['b5']['non_neuroticism']['sberdisk']\n", + "url_openness = _b5.weights_for_big5_['video']['fi']['b5']['openness']['googledisk']\n", + "url_conscientiousness = _b5.weights_for_big5_['video']['fi']['b5']['conscientiousness']['googledisk']\n", + "url_extraversion = _b5.weights_for_big5_['video']['fi']['b5']['extraversion']['googledisk']\n", + "url_agreeableness = _b5.weights_for_big5_['video']['fi']['b5']['agreeableness']['googledisk']\n", + "url_non_neuroticism = _b5.weights_for_big5_['video']['fi']['b5']['non_neuroticism']['googledisk']\n", "\n", "res_load_video_models_weights_b5 = _b5.load_video_models_weights_b5(\n", " url_openness = url_openness, # Открытость опыту\n", @@ -809,7 +797,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 22:24:55] Получение прогнозов и вычисление точности (видео модальность) ...**

    2000 из 2000 (100.0%) ... test80_25\\_Q4wOgixh7E.004.mp4 ...

" + "**[2024-10-08 17:23:11] Получение прогнозов и вычисление точности (видео модальность) ...**

    2000 из 2000 (100.0%) ... test80_25\\_Q4wOgixh7E.004.mp4 ...

" ], "text/plain": [ "" @@ -847,7 +835,7 @@ " Non-Neuroticism\n", " \n", " \n", - " ID\n", + " Person ID\n", " \n", " \n", " \n", @@ -859,344 +847,344 @@ " \n", " \n", " 1\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.526971\n", - " 0.460063\n", - " 0.422793\n", - " 0.502726\n", - " 0.450519\n", + " 13kjwEtSyXc.003.mp4\n", + " 0.528491\n", + " 0.461952\n", + " 0.423844\n", + " 0.504062\n", + " 0.451862\n", " \n", " \n", " 2\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.559385\n", - " 0.432843\n", - " 0.504231\n", - " 0.578673\n", - " 0.513424\n", + " 1Lv72Si4GnY.000.mp4\n", + " 0.556622\n", + " 0.432502\n", + " 0.502193\n", + " 0.578062\n", + " 0.512365\n", " \n", " \n", " 3\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.466969\n", - " 0.51701\n", - " 0.331863\n", - " 0.451395\n", - " 0.406188\n", + " 1uC-2TZqplE.003.mp4\n", + " 0.465167\n", + " 0.518381\n", + " 0.331894\n", + " 0.453452\n", + " 0.407971\n", " \n", " \n", " 4\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.626113\n", - " 0.597363\n", - " 0.564068\n", - " 0.574056\n", - " 0.589245\n", + " 2Z8Xi_DTlpI.000.mp4\n", + " 0.62796\n", + " 0.599573\n", + " 0.564308\n", + " 0.574992\n", + " 0.591495\n", " \n", " \n", " 5\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.5925\n", - " 0.507246\n", - " 0.505394\n", - " 0.585405\n", - " 0.493066\n", + " 3df_Uk9EmwU.002.mp4\n", + " 0.582687\n", + " 0.505265\n", + " 0.497748\n", + " 0.579352\n", + " 0.484003\n", " \n", " \n", " 6\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.671855\n", - " 0.642559\n", - " 0.614689\n", - " 0.613508\n", - " 0.619511\n", + " 3gmc2kLV4Bo.003.mp4\n", + " 0.673677\n", + " 0.64423\n", + " 0.622223\n", + " 0.617483\n", + " 0.624848\n", " \n", " \n", " 7\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.411555\n", - " 0.394029\n", - " 0.329323\n", - " 0.488684\n", - " 0.39105\n", + " 3hKgh9AB3tk.003.mp4\n", + " 0.41655\n", + " 0.394264\n", + " 0.328741\n", + " 0.487896\n", + " 0.392852\n", " \n", " \n", " 8\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.583696\n", - " 0.568682\n", - " 0.505574\n", - " 0.625314\n", - " 0.587337\n", + " 3S72dDIm1fM.005.mp4\n", + " 0.582322\n", + " 0.568466\n", + " 0.501585\n", + " 0.624629\n", + " 0.587418\n", " \n", " \n", " 9\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.551353\n", - " 0.450333\n", - " 0.449763\n", - " 0.495501\n", - " 0.438009\n", + " 3tPq9fNOXZQ.000.mp4\n", + " 0.549069\n", + " 0.451225\n", + " 0.44713\n", + " 0.493269\n", + " 0.43745\n", " \n", " \n", " 10\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.575084\n", - " 0.517972\n", - " 0.46315\n", - " 0.582468\n", - " 0.537961\n", + " 43tayteIFRk.001.mp4\n", + " 0.571851\n", + " 0.507518\n", + " 0.456708\n", + " 0.57585\n", + " 0.52462\n", " \n", " \n", " 11\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.559182\n", - " 0.398618\n", - " 0.433806\n", - " 0.480592\n", - " 0.492383\n", + " 4RKQGZzPClk.000.mp4\n", + " 0.563701\n", + " 0.402969\n", + " 0.438301\n", + " 0.482963\n", + " 0.497794\n", " \n", " \n", " 12\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.50948\n", - " 0.432549\n", - " 0.3319\n", - " 0.495221\n", - " 0.486891\n", + " 6zm71IHOCZA.005.mp4\n", + " 0.508313\n", + " 0.436114\n", + " 0.333626\n", + " 0.497906\n", + " 0.489626\n", " \n", " \n", " 13\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.330026\n", - " 0.322635\n", - " 0.235595\n", - " 0.369766\n", - " 0.25056\n", + " 7qGYGbIg45c.001.mp4\n", + " 0.333581\n", + " 0.329381\n", + " 0.238896\n", + " 0.375451\n", + " 0.254064\n", " \n", " \n", " 14\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.649351\n", - " 0.550074\n", - " 0.502858\n", - " 0.526621\n", - " 0.566755\n", + " 8YQKwMdiaAE.003.mp4\n", + " 0.65042\n", + " 0.551177\n", + " 0.505542\n", + " 0.531723\n", + " 0.572215\n", " \n", " \n", " 15\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.651914\n", - " 0.83048\n", - " 0.535514\n", - " 0.695223\n", - " 0.734383\n", + " 9Crw2RtrBcY.005.mp4\n", + " 0.655932\n", + " 0.834232\n", + " 0.537374\n", + " 0.695053\n", + " 0.73809\n", " \n", " \n", " 16\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.523986\n", - " 0.435594\n", - " 0.382946\n", - " 0.41001\n", - " 0.466265\n", + " 9eNHxfOV2Kg.005.mp4\n", + " 0.523257\n", + " 0.446606\n", + " 0.387457\n", + " 0.417878\n", + " 0.464942\n", " \n", " \n", " 17\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.575113\n", - " 0.678301\n", - " 0.468646\n", - " 0.602139\n", - " 0.626021\n", + " 9J-KIPMQmqk.002.mp4\n", + " 0.57973\n", + " 0.680401\n", + " 0.474162\n", + " 0.606024\n", + " 0.633491\n", " \n", " \n", " 18\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.566349\n", - " 0.558975\n", - " 0.462116\n", - " 0.606252\n", - " 0.569516\n", + " 9RfE2-aTvaM.002.mp4\n", + " 0.572572\n", + " 0.564212\n", + " 0.468839\n", + " 0.611467\n", + " 0.576528\n", " \n", " \n", " 19\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.672282\n", - " 0.6552\n", - " 0.656699\n", - " 0.627328\n", - " 0.663199\n", + " 9_6auSk_wkY.002.mp4\n", + " 0.669528\n", + " 0.661192\n", + " 0.654868\n", + " 0.627607\n", + " 0.662564\n", " \n", " \n", " 20\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.684442\n", - " 0.602593\n", - " 0.680469\n", - " 0.635343\n", - " 0.652304\n", + " aaylz9A9K80.000.mp4\n", + " 0.685854\n", + " 0.600825\n", + " 0.681677\n", + " 0.63356\n", + " 0.65243\n", " \n", " \n", " 21\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.550788\n", - " 0.492015\n", - " 0.404885\n", - " 0.562745\n", - " 0.478233\n", + " Af_F0IzHK6o.002.mp4\n", + " 0.552855\n", + " 0.491443\n", + " 0.404893\n", + " 0.563796\n", + " 0.482746\n", " \n", " \n", " 22\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.525446\n", - " 0.469039\n", - " 0.428517\n", - " 0.491442\n", - " 0.45359\n", + " Ah5PEPT4xbo.000.mp4\n", + " 0.530697\n", + " 0.474127\n", + " 0.433119\n", + " 0.493364\n", + " 0.461316\n", " \n", " \n", " 23\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.473489\n", - " 0.442729\n", - " 0.353017\n", - " 0.447929\n", - " 0.358706\n", + " AotbiNsU85A.003.mp4\n", + " 0.469729\n", + " 0.443068\n", + " 0.352539\n", + " 0.448632\n", + " 0.355868\n", " \n", " \n", " 24\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.667829\n", - " 0.655159\n", - " 0.603695\n", - " 0.630121\n", - " 0.614812\n", + " BLc_GvsbI1U.001.mp4\n", + " 0.669096\n", + " 0.655879\n", + " 0.604209\n", + " 0.630514\n", + " 0.615415\n", " \n", " \n", " 25\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.469207\n", - " 0.594029\n", - " 0.364701\n", - " 0.522734\n", - " 0.481228\n", + " bLOSPQ8MAC8.005.mp4\n", + " 0.470269\n", + " 0.6047\n", + " 0.364384\n", + " 0.52608\n", + " 0.486426\n", " \n", " \n", " 26\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.625514\n", - " 0.641622\n", - " 0.514204\n", - " 0.547718\n", - " 0.54766\n", + " bPLhV0PGR50.001.mp4\n", + " 0.623001\n", + " 0.642355\n", + " 0.513129\n", + " 0.548652\n", + " 0.543819\n", " \n", " \n", " 27\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.568821\n", - " 0.524382\n", - " 0.475687\n", - " 0.520644\n", - " 0.531275\n", + " bYXRyimxh7A.001.mp4\n", + " 0.568158\n", + " 0.529724\n", + " 0.476992\n", + " 0.523437\n", + " 0.528825\n", " \n", " \n", " 28\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.696397\n", - " 0.665074\n", - " 0.70902\n", - " 0.655993\n", - " 0.689747\n", + " ch2BcBv4SdQ.003.mp4\n", + " 0.698074\n", + " 0.670137\n", + " 0.713394\n", + " 0.658508\n", + " 0.693174\n", " \n", " \n", " 29\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.578405\n", - " 0.577321\n", - " 0.487293\n", - " 0.557221\n", - " 0.52153\n", + " cpch8WDydcM.004.mp4\n", + " 0.579871\n", + " 0.58113\n", + " 0.489795\n", + " 0.561388\n", + " 0.523101\n", " \n", " \n", " 30\n", - " E:\\Databases\\FirstImpressionsV2\\test\\test80_01...\n", - " 0.637576\n", - " 0.587702\n", - " 0.614512\n", - " 0.637398\n", - " 0.613861\n", + " De4i7-FX9Og.002.mp4\n", + " 0.639268\n", + " 0.58747\n", + " 0.61394\n", + " 0.636822\n", + " 0.613017\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Path Openness \\\n", - "ID \n", - "1 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.526971 \n", - "2 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.559385 \n", - "3 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.466969 \n", - "4 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.626113 \n", - "5 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.5925 \n", - "6 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.671855 \n", - "7 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.411555 \n", - "8 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.583696 \n", - "9 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.551353 \n", - "10 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.575084 \n", - "11 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.559182 \n", - "12 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.50948 \n", - "13 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.330026 \n", - "14 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.649351 \n", - "15 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.651914 \n", - "16 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.523986 \n", - "17 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.575113 \n", - "18 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.566349 \n", - "19 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.672282 \n", - "20 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.684442 \n", - "21 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.550788 \n", - "22 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.525446 \n", - "23 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.473489 \n", - "24 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.667829 \n", - "25 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.469207 \n", - "26 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.625514 \n", - "27 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.568821 \n", - "28 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.696397 \n", - "29 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.578405 \n", - "30 E:\\Databases\\FirstImpressionsV2\\test\\test80_01... 0.637576 \n", + " Path Openness Conscientiousness Extraversion \\\n", + "Person ID \n", + "1 13kjwEtSyXc.003.mp4 0.528491 0.461952 0.423844 \n", + "2 1Lv72Si4GnY.000.mp4 0.556622 0.432502 0.502193 \n", + "3 1uC-2TZqplE.003.mp4 0.465167 0.518381 0.331894 \n", + "4 2Z8Xi_DTlpI.000.mp4 0.62796 0.599573 0.564308 \n", + "5 3df_Uk9EmwU.002.mp4 0.582687 0.505265 0.497748 \n", + "6 3gmc2kLV4Bo.003.mp4 0.673677 0.64423 0.622223 \n", + "7 3hKgh9AB3tk.003.mp4 0.41655 0.394264 0.328741 \n", + "8 3S72dDIm1fM.005.mp4 0.582322 0.568466 0.501585 \n", + "9 3tPq9fNOXZQ.000.mp4 0.549069 0.451225 0.44713 \n", + "10 43tayteIFRk.001.mp4 0.571851 0.507518 0.456708 \n", + "11 4RKQGZzPClk.000.mp4 0.563701 0.402969 0.438301 \n", + "12 6zm71IHOCZA.005.mp4 0.508313 0.436114 0.333626 \n", + "13 7qGYGbIg45c.001.mp4 0.333581 0.329381 0.238896 \n", + "14 8YQKwMdiaAE.003.mp4 0.65042 0.551177 0.505542 \n", + "15 9Crw2RtrBcY.005.mp4 0.655932 0.834232 0.537374 \n", + "16 9eNHxfOV2Kg.005.mp4 0.523257 0.446606 0.387457 \n", + "17 9J-KIPMQmqk.002.mp4 0.57973 0.680401 0.474162 \n", + "18 9RfE2-aTvaM.002.mp4 0.572572 0.564212 0.468839 \n", + "19 9_6auSk_wkY.002.mp4 0.669528 0.661192 0.654868 \n", + "20 aaylz9A9K80.000.mp4 0.685854 0.600825 0.681677 \n", + "21 Af_F0IzHK6o.002.mp4 0.552855 0.491443 0.404893 \n", + "22 Ah5PEPT4xbo.000.mp4 0.530697 0.474127 0.433119 \n", + "23 AotbiNsU85A.003.mp4 0.469729 0.443068 0.352539 \n", + "24 BLc_GvsbI1U.001.mp4 0.669096 0.655879 0.604209 \n", + "25 bLOSPQ8MAC8.005.mp4 0.470269 0.6047 0.364384 \n", + "26 bPLhV0PGR50.001.mp4 0.623001 0.642355 0.513129 \n", + "27 bYXRyimxh7A.001.mp4 0.568158 0.529724 0.476992 \n", + "28 ch2BcBv4SdQ.003.mp4 0.698074 0.670137 0.713394 \n", + "29 cpch8WDydcM.004.mp4 0.579871 0.58113 0.489795 \n", + "30 De4i7-FX9Og.002.mp4 0.639268 0.58747 0.61394 \n", "\n", - " Conscientiousness Extraversion Agreeableness Non-Neuroticism \n", - "ID \n", - "1 0.460063 0.422793 0.502726 0.450519 \n", - "2 0.432843 0.504231 0.578673 0.513424 \n", - "3 0.51701 0.331863 0.451395 0.406188 \n", - "4 0.597363 0.564068 0.574056 0.589245 \n", - "5 0.507246 0.505394 0.585405 0.493066 \n", - "6 0.642559 0.614689 0.613508 0.619511 \n", - "7 0.394029 0.329323 0.488684 0.39105 \n", - "8 0.568682 0.505574 0.625314 0.587337 \n", - "9 0.450333 0.449763 0.495501 0.438009 \n", - "10 0.517972 0.46315 0.582468 0.537961 \n", - "11 0.398618 0.433806 0.480592 0.492383 \n", - "12 0.432549 0.3319 0.495221 0.486891 \n", - "13 0.322635 0.235595 0.369766 0.25056 \n", - "14 0.550074 0.502858 0.526621 0.566755 \n", - "15 0.83048 0.535514 0.695223 0.734383 \n", - "16 0.435594 0.382946 0.41001 0.466265 \n", - "17 0.678301 0.468646 0.602139 0.626021 \n", - "18 0.558975 0.462116 0.606252 0.569516 \n", - "19 0.6552 0.656699 0.627328 0.663199 \n", - "20 0.602593 0.680469 0.635343 0.652304 \n", - "21 0.492015 0.404885 0.562745 0.478233 \n", - "22 0.469039 0.428517 0.491442 0.45359 \n", - "23 0.442729 0.353017 0.447929 0.358706 \n", - "24 0.655159 0.603695 0.630121 0.614812 \n", - "25 0.594029 0.364701 0.522734 0.481228 \n", - "26 0.641622 0.514204 0.547718 0.54766 \n", - "27 0.524382 0.475687 0.520644 0.531275 \n", - "28 0.665074 0.70902 0.655993 0.689747 \n", - "29 0.577321 0.487293 0.557221 0.52153 \n", - "30 0.587702 0.614512 0.637398 0.613861 " + " Agreeableness Non-Neuroticism \n", + "Person ID \n", + "1 0.504062 0.451862 \n", + "2 0.578062 0.512365 \n", + "3 0.453452 0.407971 \n", + "4 0.574992 0.591495 \n", + "5 0.579352 0.484003 \n", + "6 0.617483 0.624848 \n", + "7 0.487896 0.392852 \n", + "8 0.624629 0.587418 \n", + "9 0.493269 0.43745 \n", + "10 0.57585 0.52462 \n", + "11 0.482963 0.497794 \n", + "12 0.497906 0.489626 \n", + "13 0.375451 0.254064 \n", + "14 0.531723 0.572215 \n", + "15 0.695053 0.73809 \n", + "16 0.417878 0.464942 \n", + "17 0.606024 0.633491 \n", + "18 0.611467 0.576528 \n", + "19 0.627607 0.662564 \n", + "20 0.63356 0.65243 \n", + "21 0.563796 0.482746 \n", + "22 0.493364 0.461316 \n", + "23 0.448632 0.355868 \n", + "24 0.630514 0.615415 \n", + "25 0.52608 0.486426 \n", + "26 0.548652 0.543819 \n", + "27 0.523437 0.528825 \n", + "28 0.658508 0.693174 \n", + "29 0.561388 0.523101 \n", + "30 0.636822 0.613017 " ] }, "metadata": {}, @@ -1205,7 +1193,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 22:24:55] Точность по отдельным персональным качествам личности человека ...** " + "**[2024-10-08 17:23:11] Точность по отдельным персональным качествам личности человека ...** " ], "text/plain": [ "" @@ -1255,8 +1243,8 @@ " \n", " \n", " MAE\n", - " 0.0873\n", - " 0.082\n", + " 0.0872\n", + " 0.0821\n", " 0.0805\n", " 0.087\n", " 0.0872\n", @@ -1264,8 +1252,8 @@ " \n", " \n", " Accuracy\n", - " 0.9127\n", - " 0.918\n", + " 0.9128\n", + " 0.9179\n", " 0.9195\n", " 0.913\n", " 0.9128\n", @@ -1278,8 +1266,8 @@ "text/plain": [ " Openness Conscientiousness Extraversion Agreeableness \\\n", "Metrics \n", - "MAE 0.0873 0.082 0.0805 0.087 \n", - "Accuracy 0.9127 0.918 0.9195 0.913 \n", + "MAE 0.0872 0.0821 0.0805 0.087 \n", + "Accuracy 0.9128 0.9179 0.9195 0.913 \n", "\n", " Non-Neuroticism Mean \n", "Metrics \n", @@ -1293,7 +1281,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 22:24:55] Средняя средних абсолютных ошибок: 0.0848, средняя точность: 0.9152 ...** " + "**[2024-10-08 17:23:11] Средняя средних абсолютных ошибок: 0.0848, средняя точность: 0.9152 ...** " ], "text/plain": [ "" @@ -1317,7 +1305,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 4762.254 сек. ---**" + "**--- Время выполнения: 2637.585 сек. ---**" ], "text/plain": [ "" @@ -1338,7 +1326,7 @@ "_b5.path_to_logs_ = './logs' # Директория для сохранения LOG файлов\n", "\n", "# Полный путь к файлу с верными предсказаниями для подсчета точности\n", - "url_accuracy = _b5.true_traits_['fi']['sberdisk']\n", + "url_accuracy = _b5.true_traits_['fi']['googledisk']\n", "\n", "res_get_video_union_predictions = _b5.get_video_union_predictions(\n", " depth = 1, # Глубина иерархии для получения аудио и видеоданных\n", @@ -1373,7 +1361,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.10.11" } }, "nbformat": 4, diff --git a/docs/source/user_guide/notebooks/Video-get_visual_features.ipynb b/docs/source/user_guide/notebooks/Video-get_visual_features.ipynb index a4dc2f5..9007951 100644 --- a/docs/source/user_guide/notebooks/Video-get_visual_features.ipynb +++ b/docs/source/user_guide/notebooks/Video-get_visual_features.ipynb @@ -40,15 +40,7 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n" - ] - } - ], + "outputs": [], "source": [ "from oceanai.modules.lab.build import Run" ] @@ -68,7 +60,7 @@ { "data": { "text/markdown": [ - "**[2024-03-28 21:50:44] OCEANAI - персональные качества личности человека:**
    Авторы:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
        Карпов Алексей [karpov@iias.spb.su]
    Сопровождающие:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
    Версия: 1.0.0a22
    Лицензия: BSD License

" + "**[2024-10-08 16:23:19] OCEANAI - персональные качества личности человека:**
    Авторы:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
        Карпов Алексей [karpov@iias.spb.su]
    Сопровождающие:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
    Версия: 1.0.0a40
    Лицензия: BSD License

" ], "text/plain": [ "" @@ -107,7 +99,7 @@ { "data": { "text/markdown": [ - "**[2024-03-28 21:50:46] Формирование нейросетевой архитектуры для получения нейросетевых признаков (видео модальность) ...** " + "**[2024-10-08 16:23:19] Формирование нейросетевой архитектуры для получения нейросетевых признаков (видео модальность) ...** " ], "text/plain": [ "" @@ -119,7 +111,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 1.001 сек. ---**" + "**--- Время выполнения: 0.105 сек. ---**" ], "text/plain": [ "" @@ -153,7 +145,7 @@ { "data": { "text/markdown": [ - "**[2024-03-28 21:50:50] Загрузка весов нейросетевой модели для получения нейросетевых признаков (видео модальность) ...** " + "**[2024-10-08 16:23:19] Загрузка весов нейросетевой модели для получения нейросетевых признаков (видео модальность) ...** " ], "text/plain": [ "" @@ -165,7 +157,7 @@ { "data": { "text/markdown": [ - "**[2024-03-28 21:50:56] Загрузка файла \"weights_2022-11-01_12-27-07.h5\" 100.0% ...** " + "**[2024-10-08 16:23:22] Загрузка файла \"weights_2022-11-01_12-27-07.pth\" 100.0% ...** " ], "text/plain": [ "" @@ -177,7 +169,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 6.461 сек. ---**" + "**--- Время выполнения: 3.994 сек. ---**" ], "text/plain": [ "" @@ -192,7 +184,7 @@ "_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_['video']['fi']['fe']['sberdisk']\n", + "url = _b5.weights_for_big5_['video']['fi']['fe']['googledisk']\n", "\n", "res_load_video_model_weights_deep_fe = _b5.load_video_model_weights_deep_fe(\n", " url = url, # Полный путь к файлу с весами нейросетевой модели\n", @@ -218,7 +210,7 @@ { "data": { "text/markdown": [ - "**[2024-03-28 21:50:58] Извлечение признаков (экспертных и нейросетевых) из визуального сигнала ...** " + "**[2024-10-08 16:23:23] Извлечение признаков (экспертных и нейросетевых) из визуального сигнала ...** " ], "text/plain": [ "" @@ -230,7 +222,7 @@ { "data": { "text/markdown": [ - "**[2024-03-28 21:51:22] Статистика извлеченных признаков из визуального сигнала:**
    Общее количество сегментов с:
        1. экспертными признаками: 16
        2. нейросетевыми признаками: 16
    Размерность матрицы экспертных признаков одного сегмента: 10 ✕ 115
    Размерность матрицы с нейросетевыми признаками одного сегмента: 10 ✕ 512
    Понижение кадровой частоты: с 30 до 5 " + "**[2024-10-08 16:23:24] Статистика извлеченных признаков из визуального сигнала:**
    Общее количество сегментов с:
        1. экспертными признаками: 16
        2. нейросетевыми признаками: 16
    Размерность матрицы экспертных признаков одного сегмента: 10 ✕ 115
    Размерность матрицы с нейросетевыми признаками одного сегмента: 10 ✕ 512
    Понижение кадровой частоты: с 30 до 5 " ], "text/plain": [ "" @@ -242,7 +234,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 23.465 сек. ---**" + "**--- Время выполнения: 1.331 сек. ---**" ], "text/plain": [ "" @@ -257,7 +249,7 @@ "# Путь к видеофайлу\n", "path = 'video_FI/test/_plk5k7PBEg.003.mp4'\n", "\n", - "hc_features, nn_features = _b5.get_visual_features(\n", + "hc_features, nn_features, emo_preds = _b5.get_visual_features(\n", " path = path, # Путь к видеофайлу\n", " reduction_fps = 5, # Понижение кадровой частоты\n", " window = 10, # Размер окна сегмента сигнала (в кадрах)\n", @@ -284,7 +276,7 @@ { "data": { "text/markdown": [ - "**[2024-03-28 21:51:25] Извлечение признаков (экспертных и нейросетевых) из визуального сигнала ...** " + "**[2024-10-08 16:23:24] Извлечение признаков (экспертных и нейросетевых) из визуального сигнала ...** " ], "text/plain": [ "" @@ -296,7 +288,7 @@ { "data": { "text/markdown": [ - "**[2024-03-28 21:51:43] Статистика извлеченных признаков из визуального сигнала:**
    Общее количество сегментов с:
        1. экспертными признаками: 16
        2. нейросетевыми признаками: 16
    Размерность матрицы экспертных признаков одного сегмента: 10 ✕ 109
    Размерность матрицы с нейросетевыми признаками одного сегмента: 10 ✕ 512
    Понижение кадровой частоты: с 30 до 5 " + "**[2024-10-08 16:23:25] Статистика извлеченных признаков из визуального сигнала:**
    Общее количество сегментов с:
        1. экспертными признаками: 16
        2. нейросетевыми признаками: 16
    Размерность матрицы экспертных признаков одного сегмента: 10 ✕ 109
    Размерность матрицы с нейросетевыми признаками одного сегмента: 10 ✕ 512
    Понижение кадровой частоты: с 30 до 5 " ], "text/plain": [ "" @@ -308,7 +300,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 18.659 сек. ---**" + "**--- Время выполнения: 1.2 сек. ---**" ], "text/plain": [ "" @@ -323,7 +315,7 @@ "# Путь к видеофайлу\n", "path = 'video_FI/test/_plk5k7PBEg.003.mp4'\n", "\n", - "hc_features, nn_features = _b5.get_visual_features(\n", + "hc_features, nn_features, emo_preds = _b5.get_visual_features(\n", " path = path, # Путь к видеофайлу\n", " reduction_fps = 5, # Понижение кадровой частоты\n", " window = 10, # Размер окна сегмента сигнала (в кадрах)\n", @@ -334,13 +326,6 @@ " run = True # Блокировка выполнения\n", ")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -359,7 +344,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.10.11" } }, "nbformat": 4, diff --git a/docs/source/user_guide/notebooks/Video-load_video_model_deep_fe.ipynb b/docs/source/user_guide/notebooks/Video-load_video_model_deep_fe.ipynb index 3f8a9bf..25a9fc9 100644 --- a/docs/source/user_guide/notebooks/Video-load_video_model_deep_fe.ipynb +++ b/docs/source/user_guide/notebooks/Video-load_video_model_deep_fe.ipynb @@ -8,7 +8,7 @@ "\n", "
\n", "\n", - "> - `_b5.video_model_deep_fe_` - Нейросетевая модель **tf.keras.Model** для получения нейросетевых признаков" + "> - `_b5.video_model_deep_fe_` - Нейросетевая модель **nn.Module** для получения нейросетевых признаков" ] }, { @@ -60,7 +60,7 @@ { "data": { "text/markdown": [ - "**[2023-12-10 17:08:31] OCEANAI - персональные качества личности человека:**
    Авторы:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
        Карпов Алексей [karpov@iias.spb.su]
    Сопровождающие:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
    Версия: 1.0.0a5
    Лицензия: BSD License

" + "**[2024-10-08 16:26:05] OCEANAI - персональные качества личности человека:**
    Авторы:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
        Карпов Алексей [karpov@iias.spb.su]
    Сопровождающие:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
    Версия: 1.0.0a40
    Лицензия: BSD License

" ], "text/plain": [ "" @@ -99,7 +99,7 @@ { "data": { "text/markdown": [ - "**[2023-12-10 17:08:31] Формирование нейросетевой архитектуры для получения нейросетевых признаков (видео модальность) ...** " + "**[2024-10-08 16:26:05] Формирование нейросетевой архитектуры для получения нейросетевых признаков (видео модальность) ...** " ], "text/plain": [ "" @@ -111,7 +111,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 1.118 сек. ---**" + "**--- Время выполнения: 0.101 сек. ---**" ], "text/plain": [ "" @@ -145,7 +145,7 @@ { "data": { "text/markdown": [ - "**[2023-12-10 17:08:32] Загрузка весов нейросетевой модели для получения нейросетевых признаков (видео модальность) ...** " + "**[2024-10-08 16:26:06] Загрузка весов нейросетевой модели для получения нейросетевых признаков (видео модальность) ...** " ], "text/plain": [ "" @@ -157,7 +157,7 @@ { "data": { "text/markdown": [ - "**[2023-12-10 17:08:36] Загрузка файла \"weights_2022-11-01_12-27-07.h5\" 100.0% ...** " + "**[2024-10-08 16:26:09] Загрузка файла \"weights_2022-11-01_12-27-07.pth\" 100.0% ...** " ], "text/plain": [ "" @@ -169,7 +169,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 4.042 сек. ---**" + "**--- Время выполнения: 3.537 сек. ---**" ], "text/plain": [ "" @@ -184,7 +184,7 @@ "_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_['video']['fi']['fe']['sberdisk']\n", + "url = _b5.weights_for_big5_['video']['fi']['fe']['googledisk']\n", "\n", "res_load_video_model_weights_deep_fe = _b5.load_video_model_weights_deep_fe(\n", " url = url, # Полный путь к файлу с весами нейросетевой модели\n", @@ -208,464 +208,195 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"model_1\"\n", - "__________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3 0 [] \n", - " )] \n", - " \n", - " conv1/7x7_s2 (Conv2D) (None, 112, 112, 64 9408 ['input_1[0][0]'] \n", - " ) \n", - " \n", - " conv1/7x7_s2/bn (BatchNormaliz (None, 112, 112, 64 256 ['conv1/7x7_s2[0][0]'] \n", - " ation) ) \n", - " \n", - " activation (Activation) (None, 112, 112, 64 0 ['conv1/7x7_s2/bn[0][0]'] \n", - " ) \n", - " \n", - " max_pooling2d (MaxPooling2D) (None, 55, 55, 64) 0 ['activation[0][0]'] \n", - " \n", - " conv2_1_1x1_reduce (Conv2D) (None, 55, 55, 64) 4096 ['max_pooling2d[0][0]'] \n", - " \n", - " conv2_1_1x1_reduce/bn (BatchNo (None, 55, 55, 64) 256 ['conv2_1_1x1_reduce[0][0]'] \n", - " rmalization) \n", - " \n", - " activation_1 (Activation) (None, 55, 55, 64) 0 ['conv2_1_1x1_reduce/bn[0][0]'] \n", - " \n", - " conv2_1_3x3 (Conv2D) (None, 55, 55, 64) 36864 ['activation_1[0][0]'] \n", - " \n", - " conv2_1_3x3/bn (BatchNormaliza (None, 55, 55, 64) 256 ['conv2_1_3x3[0][0]'] \n", - " tion) \n", - " \n", - " activation_2 (Activation) (None, 55, 55, 64) 0 ['conv2_1_3x3/bn[0][0]'] \n", - " \n", - " conv2_1_1x1_increase (Conv2D) (None, 55, 55, 256) 16384 ['activation_2[0][0]'] \n", - " \n", - " conv2_1_1x1_proj (Conv2D) (None, 55, 55, 256) 16384 ['max_pooling2d[0][0]'] \n", - " \n", - " conv2_1_1x1_increase/bn (Batch (None, 55, 55, 256) 1024 ['conv2_1_1x1_increase[0][0]'] \n", - " Normalization) \n", - " \n", - " conv2_1_1x1_proj/bn (BatchNorm (None, 55, 55, 256) 1024 ['conv2_1_1x1_proj[0][0]'] \n", - " alization) \n", - " \n", - " add (Add) (None, 55, 55, 256) 0 ['conv2_1_1x1_increase/bn[0][0]',\n", - " 'conv2_1_1x1_proj/bn[0][0]'] \n", - " \n", - " activation_3 (Activation) (None, 55, 55, 256) 0 ['add[0][0]'] \n", - " \n", - " conv2_2_1x1_reduce (Conv2D) (None, 55, 55, 64) 16384 ['activation_3[0][0]'] \n", - " \n", - " conv2_2_1x1_reduce/bn (BatchNo (None, 55, 55, 64) 256 ['conv2_2_1x1_reduce[0][0]'] \n", - " rmalization) \n", - " \n", - " activation_4 (Activation) (None, 55, 55, 64) 0 ['conv2_2_1x1_reduce/bn[0][0]'] \n", - " \n", - " conv2_2_3x3 (Conv2D) (None, 55, 55, 64) 36864 ['activation_4[0][0]'] \n", - " \n", - " conv2_2_3x3/bn (BatchNormaliza (None, 55, 55, 64) 256 ['conv2_2_3x3[0][0]'] \n", - " tion) \n", - " \n", - " activation_5 (Activation) (None, 55, 55, 64) 0 ['conv2_2_3x3/bn[0][0]'] \n", - " \n", - " conv2_2_1x1_increase (Conv2D) (None, 55, 55, 256) 16384 ['activation_5[0][0]'] \n", - " \n", - " conv2_2_1x1_increase/bn (Batch (None, 55, 55, 256) 1024 ['conv2_2_1x1_increase[0][0]'] \n", - " Normalization) \n", - " \n", - " add_1 (Add) (None, 55, 55, 256) 0 ['conv2_2_1x1_increase/bn[0][0]',\n", - " 'activation_3[0][0]'] \n", - " \n", - " activation_6 (Activation) (None, 55, 55, 256) 0 ['add_1[0][0]'] \n", - " \n", - " conv2_3_1x1_reduce (Conv2D) (None, 55, 55, 64) 16384 ['activation_6[0][0]'] \n", - " \n", - " conv2_3_1x1_reduce/bn (BatchNo (None, 55, 55, 64) 256 ['conv2_3_1x1_reduce[0][0]'] \n", - " rmalization) \n", - " \n", - " activation_7 (Activation) (None, 55, 55, 64) 0 ['conv2_3_1x1_reduce/bn[0][0]'] \n", - " \n", - " conv2_3_3x3 (Conv2D) (None, 55, 55, 64) 36864 ['activation_7[0][0]'] \n", - " \n", - " conv2_3_3x3/bn (BatchNormaliza (None, 55, 55, 64) 256 ['conv2_3_3x3[0][0]'] \n", - " tion) \n", - " \n", - " activation_8 (Activation) (None, 55, 55, 64) 0 ['conv2_3_3x3/bn[0][0]'] \n", - " \n", - " conv2_3_1x1_increase (Conv2D) (None, 55, 55, 256) 16384 ['activation_8[0][0]'] \n", - " \n", - " conv2_3_1x1_increase/bn (Batch (None, 55, 55, 256) 1024 ['conv2_3_1x1_increase[0][0]'] \n", - " Normalization) \n", - " \n", - " add_2 (Add) (None, 55, 55, 256) 0 ['conv2_3_1x1_increase/bn[0][0]',\n", - " 'activation_6[0][0]'] \n", - " \n", - " activation_9 (Activation) (None, 55, 55, 256) 0 ['add_2[0][0]'] \n", - " \n", - " conv3_1_1x1_reduce (Conv2D) (None, 28, 28, 128) 32768 ['activation_9[0][0]'] \n", - " \n", - " conv3_1_1x1_reduce/bn (BatchNo (None, 28, 28, 128) 512 ['conv3_1_1x1_reduce[0][0]'] \n", - " rmalization) \n", - " \n", - " activation_10 (Activation) (None, 28, 28, 128) 0 ['conv3_1_1x1_reduce/bn[0][0]'] \n", - " \n", - " conv3_1_3x3 (Conv2D) (None, 28, 28, 128) 147456 ['activation_10[0][0]'] \n", - " \n", - " conv3_1_3x3/bn (BatchNormaliza (None, 28, 28, 128) 512 ['conv3_1_3x3[0][0]'] \n", - " tion) \n", - " \n", - " activation_11 (Activation) (None, 28, 28, 128) 0 ['conv3_1_3x3/bn[0][0]'] \n", - " \n", - " conv3_1_1x1_increase (Conv2D) (None, 28, 28, 512) 65536 ['activation_11[0][0]'] \n", - " \n", - " conv3_1_1x1_proj (Conv2D) (None, 28, 28, 512) 131072 ['activation_9[0][0]'] \n", - " \n", - " conv3_1_1x1_increase/bn (Batch (None, 28, 28, 512) 2048 ['conv3_1_1x1_increase[0][0]'] \n", - " Normalization) \n", - " \n", - " conv3_1_1x1_proj/bn (BatchNorm (None, 28, 28, 512) 2048 ['conv3_1_1x1_proj[0][0]'] \n", - " alization) \n", - " \n", - " add_3 (Add) (None, 28, 28, 512) 0 ['conv3_1_1x1_increase/bn[0][0]',\n", - " 'conv3_1_1x1_proj/bn[0][0]'] \n", - " \n", - " activation_12 (Activation) (None, 28, 28, 512) 0 ['add_3[0][0]'] \n", - " \n", - " conv3_2_1x1_reduce (Conv2D) (None, 28, 28, 128) 65536 ['activation_12[0][0]'] \n", - " \n", - " conv3_2_1x1_reduce/bn (BatchNo (None, 28, 28, 128) 512 ['conv3_2_1x1_reduce[0][0]'] \n", - " rmalization) \n", - " \n", - " activation_13 (Activation) (None, 28, 28, 128) 0 ['conv3_2_1x1_reduce/bn[0][0]'] \n", - " \n", - " conv3_2_3x3 (Conv2D) (None, 28, 28, 128) 147456 ['activation_13[0][0]'] \n", - " \n", - " conv3_2_3x3/bn (BatchNormaliza (None, 28, 28, 128) 512 ['conv3_2_3x3[0][0]'] \n", - " tion) \n", - " \n", - " activation_14 (Activation) (None, 28, 28, 128) 0 ['conv3_2_3x3/bn[0][0]'] \n", - " \n", - " conv3_2_1x1_increase (Conv2D) (None, 28, 28, 512) 65536 ['activation_14[0][0]'] \n", - " \n", - " conv3_2_1x1_increase/bn (Batch (None, 28, 28, 512) 2048 ['conv3_2_1x1_increase[0][0]'] \n", - " Normalization) \n", - " \n", - " add_4 (Add) (None, 28, 28, 512) 0 ['conv3_2_1x1_increase/bn[0][0]',\n", - " 'activation_12[0][0]'] \n", - " \n", - " activation_15 (Activation) (None, 28, 28, 512) 0 ['add_4[0][0]'] \n", - " \n", - " conv3_3_1x1_reduce (Conv2D) (None, 28, 28, 128) 65536 ['activation_15[0][0]'] \n", - " \n", - " conv3_3_1x1_reduce/bn (BatchNo (None, 28, 28, 128) 512 ['conv3_3_1x1_reduce[0][0]'] \n", - " rmalization) \n", - " \n", - " activation_16 (Activation) (None, 28, 28, 128) 0 ['conv3_3_1x1_reduce/bn[0][0]'] \n", - " \n", - " conv3_3_3x3 (Conv2D) (None, 28, 28, 128) 147456 ['activation_16[0][0]'] \n", - " \n", - " conv3_3_3x3/bn (BatchNormaliza (None, 28, 28, 128) 512 ['conv3_3_3x3[0][0]'] \n", - " tion) \n", - " \n", - " activation_17 (Activation) (None, 28, 28, 128) 0 ['conv3_3_3x3/bn[0][0]'] \n", - " \n", - " conv3_3_1x1_increase (Conv2D) (None, 28, 28, 512) 65536 ['activation_17[0][0]'] \n", - " \n", - " conv3_3_1x1_increase/bn (Batch (None, 28, 28, 512) 2048 ['conv3_3_1x1_increase[0][0]'] \n", - " Normalization) \n", - " \n", - " add_5 (Add) (None, 28, 28, 512) 0 ['conv3_3_1x1_increase/bn[0][0]',\n", - " 'activation_15[0][0]'] \n", - " \n", - " activation_18 (Activation) (None, 28, 28, 512) 0 ['add_5[0][0]'] \n", - " \n", - " conv3_4_1x1_reduce (Conv2D) (None, 28, 28, 128) 65536 ['activation_18[0][0]'] \n", - " \n", - " conv3_4_1x1_reduce/bn (BatchNo (None, 28, 28, 128) 512 ['conv3_4_1x1_reduce[0][0]'] \n", - " rmalization) \n", - " \n", - " activation_19 (Activation) (None, 28, 28, 128) 0 ['conv3_4_1x1_reduce/bn[0][0]'] \n", - " \n", - " conv3_4_3x3 (Conv2D) (None, 28, 28, 128) 147456 ['activation_19[0][0]'] \n", - " \n", - " conv3_4_3x3/bn (BatchNormaliza (None, 28, 28, 128) 512 ['conv3_4_3x3[0][0]'] \n", - " tion) \n", - " \n", - " activation_20 (Activation) (None, 28, 28, 128) 0 ['conv3_4_3x3/bn[0][0]'] \n", - " \n", - " conv3_4_1x1_increase (Conv2D) (None, 28, 28, 512) 65536 ['activation_20[0][0]'] \n", - " \n", - " conv3_4_1x1_increase/bn (Batch (None, 28, 28, 512) 2048 ['conv3_4_1x1_increase[0][0]'] \n", - " Normalization) \n", - " \n", - " add_6 (Add) (None, 28, 28, 512) 0 ['conv3_4_1x1_increase/bn[0][0]',\n", - " 'activation_18[0][0]'] \n", - " \n", - " activation_21 (Activation) (None, 28, 28, 512) 0 ['add_6[0][0]'] \n", - " \n", - " conv4_1_1x1_reduce (Conv2D) (None, 14, 14, 256) 131072 ['activation_21[0][0]'] \n", - " \n", - " conv4_1_1x1_reduce/bn (BatchNo (None, 14, 14, 256) 1024 ['conv4_1_1x1_reduce[0][0]'] \n", - " rmalization) \n", - " \n", - " activation_22 (Activation) (None, 14, 14, 256) 0 ['conv4_1_1x1_reduce/bn[0][0]'] \n", - " \n", - " conv4_1_3x3 (Conv2D) (None, 14, 14, 256) 589824 ['activation_22[0][0]'] \n", - " \n", - " conv4_1_3x3/bn (BatchNormaliza (None, 14, 14, 256) 1024 ['conv4_1_3x3[0][0]'] \n", - " tion) \n", - " \n", - " activation_23 (Activation) (None, 14, 14, 256) 0 ['conv4_1_3x3/bn[0][0]'] \n", - " \n", - " conv4_1_1x1_increase (Conv2D) (None, 14, 14, 1024 262144 ['activation_23[0][0]'] \n", - " ) \n", - " \n", - " conv4_1_1x1_proj (Conv2D) (None, 14, 14, 1024 524288 ['activation_21[0][0]'] \n", - " ) \n", - " \n", - " conv4_1_1x1_increase/bn (Batch (None, 14, 14, 1024 4096 ['conv4_1_1x1_increase[0][0]'] \n", - " Normalization) ) \n", - " \n", - " conv4_1_1x1_proj/bn (BatchNorm (None, 14, 14, 1024 4096 ['conv4_1_1x1_proj[0][0]'] \n", - " alization) ) \n", - " \n", - " add_7 (Add) (None, 14, 14, 1024 0 ['conv4_1_1x1_increase/bn[0][0]',\n", - " ) 'conv4_1_1x1_proj/bn[0][0]'] \n", - " \n", - " activation_24 (Activation) (None, 14, 14, 1024 0 ['add_7[0][0]'] \n", - " ) \n", - " \n", - " conv4_2_1x1_reduce (Conv2D) (None, 14, 14, 256) 262144 ['activation_24[0][0]'] \n", - " \n", - " conv4_2_1x1_reduce/bn (BatchNo (None, 14, 14, 256) 1024 ['conv4_2_1x1_reduce[0][0]'] \n", - " rmalization) \n", - " \n", - " activation_25 (Activation) (None, 14, 14, 256) 0 ['conv4_2_1x1_reduce/bn[0][0]'] \n", - " \n", - " conv4_2_3x3 (Conv2D) (None, 14, 14, 256) 589824 ['activation_25[0][0]'] \n", - " \n", - " conv4_2_3x3/bn (BatchNormaliza (None, 14, 14, 256) 1024 ['conv4_2_3x3[0][0]'] \n", - " tion) \n", - " \n", - " activation_26 (Activation) (None, 14, 14, 256) 0 ['conv4_2_3x3/bn[0][0]'] \n", - " \n", - " conv4_2_1x1_increase (Conv2D) (None, 14, 14, 1024 262144 ['activation_26[0][0]'] \n", - " ) \n", - " \n", - " conv4_2_1x1_increase/bn (Batch (None, 14, 14, 1024 4096 ['conv4_2_1x1_increase[0][0]'] \n", - " Normalization) ) \n", - " \n", - " add_8 (Add) (None, 14, 14, 1024 0 ['conv4_2_1x1_increase/bn[0][0]',\n", - " ) 'activation_24[0][0]'] \n", - " \n", - " activation_27 (Activation) (None, 14, 14, 1024 0 ['add_8[0][0]'] \n", - " ) \n", - " \n", - " conv4_3_1x1_reduce (Conv2D) (None, 14, 14, 256) 262144 ['activation_27[0][0]'] \n", - " \n", - " conv4_3_1x1_reduce/bn (BatchNo (None, 14, 14, 256) 1024 ['conv4_3_1x1_reduce[0][0]'] \n", - " rmalization) \n", - " \n", - " activation_28 (Activation) (None, 14, 14, 256) 0 ['conv4_3_1x1_reduce/bn[0][0]'] \n", - " \n", - " conv4_3_3x3 (Conv2D) (None, 14, 14, 256) 589824 ['activation_28[0][0]'] \n", - " \n", - " conv4_3_3x3/bn (BatchNormaliza (None, 14, 14, 256) 1024 ['conv4_3_3x3[0][0]'] \n", - " tion) \n", - " \n", - " activation_29 (Activation) (None, 14, 14, 256) 0 ['conv4_3_3x3/bn[0][0]'] \n", - " \n", - " conv4_3_1x1_increase (Conv2D) (None, 14, 14, 1024 262144 ['activation_29[0][0]'] \n", - " ) \n", - " \n", - " conv4_3_1x1_increase/bn (Batch (None, 14, 14, 1024 4096 ['conv4_3_1x1_increase[0][0]'] \n", - " Normalization) ) \n", - " \n", - " add_9 (Add) (None, 14, 14, 1024 0 ['conv4_3_1x1_increase/bn[0][0]',\n", - " ) 'activation_27[0][0]'] \n", - " \n", - " activation_30 (Activation) (None, 14, 14, 1024 0 ['add_9[0][0]'] \n", - " ) \n", - " \n", - " conv4_4_1x1_reduce (Conv2D) (None, 14, 14, 256) 262144 ['activation_30[0][0]'] \n", - " \n", - " conv4_4_1x1_reduce/bn (BatchNo (None, 14, 14, 256) 1024 ['conv4_4_1x1_reduce[0][0]'] \n", - " rmalization) \n", - " \n", - " activation_31 (Activation) (None, 14, 14, 256) 0 ['conv4_4_1x1_reduce/bn[0][0]'] \n", - " \n", - " conv4_4_3x3 (Conv2D) (None, 14, 14, 256) 589824 ['activation_31[0][0]'] \n", - " \n", - " conv4_4_3x3/bn (BatchNormaliza (None, 14, 14, 256) 1024 ['conv4_4_3x3[0][0]'] \n", - " tion) \n", - " \n", - " activation_32 (Activation) (None, 14, 14, 256) 0 ['conv4_4_3x3/bn[0][0]'] \n", - " \n", - " conv4_4_1x1_increase (Conv2D) (None, 14, 14, 1024 262144 ['activation_32[0][0]'] \n", - " ) \n", - " \n", - " conv4_4_1x1_increase/bn (Batch (None, 14, 14, 1024 4096 ['conv4_4_1x1_increase[0][0]'] \n", - " Normalization) ) \n", - " \n", - " add_10 (Add) (None, 14, 14, 1024 0 ['conv4_4_1x1_increase/bn[0][0]',\n", - " ) 'activation_30[0][0]'] \n", - " \n", - " activation_33 (Activation) (None, 14, 14, 1024 0 ['add_10[0][0]'] \n", - " ) \n", - " \n", - " conv4_5_1x1_reduce (Conv2D) (None, 14, 14, 256) 262144 ['activation_33[0][0]'] \n", - " \n", - " conv4_5_1x1_reduce/bn (BatchNo (None, 14, 14, 256) 1024 ['conv4_5_1x1_reduce[0][0]'] \n", - " rmalization) \n", - " \n", - " activation_34 (Activation) (None, 14, 14, 256) 0 ['conv4_5_1x1_reduce/bn[0][0]'] \n", - " \n", - " conv4_5_3x3 (Conv2D) (None, 14, 14, 256) 589824 ['activation_34[0][0]'] \n", - " \n", - " conv4_5_3x3/bn (BatchNormaliza (None, 14, 14, 256) 1024 ['conv4_5_3x3[0][0]'] \n", - " tion) \n", - " \n", - " activation_35 (Activation) (None, 14, 14, 256) 0 ['conv4_5_3x3/bn[0][0]'] \n", - " \n", - " conv4_5_1x1_increase (Conv2D) (None, 14, 14, 1024 262144 ['activation_35[0][0]'] \n", - " ) \n", - " \n", - " conv4_5_1x1_increase/bn (Batch (None, 14, 14, 1024 4096 ['conv4_5_1x1_increase[0][0]'] \n", - " Normalization) ) \n", - " \n", - " add_11 (Add) (None, 14, 14, 1024 0 ['conv4_5_1x1_increase/bn[0][0]',\n", - " ) 'activation_33[0][0]'] \n", - " \n", - " activation_36 (Activation) (None, 14, 14, 1024 0 ['add_11[0][0]'] \n", - " ) \n", - " \n", - " conv4_6_1x1_reduce (Conv2D) (None, 14, 14, 256) 262144 ['activation_36[0][0]'] \n", - " \n", - " conv4_6_1x1_reduce/bn (BatchNo (None, 14, 14, 256) 1024 ['conv4_6_1x1_reduce[0][0]'] \n", - " rmalization) \n", - " \n", - " activation_37 (Activation) (None, 14, 14, 256) 0 ['conv4_6_1x1_reduce/bn[0][0]'] \n", - " \n", - " conv4_6_3x3 (Conv2D) (None, 14, 14, 256) 589824 ['activation_37[0][0]'] \n", - " \n", - " conv4_6_3x3/bn (BatchNormaliza (None, 14, 14, 256) 1024 ['conv4_6_3x3[0][0]'] \n", - " tion) \n", - " \n", - " activation_38 (Activation) (None, 14, 14, 256) 0 ['conv4_6_3x3/bn[0][0]'] \n", - " \n", - " conv4_6_1x1_increase (Conv2D) (None, 14, 14, 1024 262144 ['activation_38[0][0]'] \n", - " ) \n", - " \n", - " conv4_6_1x1_increase/bn (Batch (None, 14, 14, 1024 4096 ['conv4_6_1x1_increase[0][0]'] \n", - " Normalization) ) \n", - " \n", - " add_12 (Add) (None, 14, 14, 1024 0 ['conv4_6_1x1_increase/bn[0][0]',\n", - " ) 'activation_36[0][0]'] \n", - " \n", - " activation_39 (Activation) (None, 14, 14, 1024 0 ['add_12[0][0]'] \n", - " ) \n", - " \n", - " conv5_1_1x1_reduce (Conv2D) (None, 7, 7, 512) 524288 ['activation_39[0][0]'] \n", - " \n", - " conv5_1_1x1_reduce/bn (BatchNo (None, 7, 7, 512) 2048 ['conv5_1_1x1_reduce[0][0]'] \n", - " rmalization) \n", - " \n", - " activation_40 (Activation) (None, 7, 7, 512) 0 ['conv5_1_1x1_reduce/bn[0][0]'] \n", - " \n", - " conv5_1_3x3 (Conv2D) (None, 7, 7, 512) 2359296 ['activation_40[0][0]'] \n", - " \n", - " conv5_1_3x3/bn (BatchNormaliza (None, 7, 7, 512) 2048 ['conv5_1_3x3[0][0]'] \n", - " tion) \n", - " \n", - " activation_41 (Activation) (None, 7, 7, 512) 0 ['conv5_1_3x3/bn[0][0]'] \n", - " \n", - " conv5_1_1x1_increase (Conv2D) (None, 7, 7, 2048) 1048576 ['activation_41[0][0]'] \n", - " \n", - " conv5_1_1x1_proj (Conv2D) (None, 7, 7, 2048) 2097152 ['activation_39[0][0]'] \n", - " \n", - " conv5_1_1x1_increase/bn (Batch (None, 7, 7, 2048) 8192 ['conv5_1_1x1_increase[0][0]'] \n", - " Normalization) \n", - " \n", - " conv5_1_1x1_proj/bn (BatchNorm (None, 7, 7, 2048) 8192 ['conv5_1_1x1_proj[0][0]'] \n", - " alization) \n", - " \n", - " add_13 (Add) (None, 7, 7, 2048) 0 ['conv5_1_1x1_increase/bn[0][0]',\n", - " 'conv5_1_1x1_proj/bn[0][0]'] \n", - " \n", - " activation_42 (Activation) (None, 7, 7, 2048) 0 ['add_13[0][0]'] \n", - " \n", - " conv5_2_1x1_reduce (Conv2D) (None, 7, 7, 512) 1048576 ['activation_42[0][0]'] \n", - " \n", - " conv5_2_1x1_reduce/bn (BatchNo (None, 7, 7, 512) 2048 ['conv5_2_1x1_reduce[0][0]'] \n", - " rmalization) \n", - " \n", - " activation_43 (Activation) (None, 7, 7, 512) 0 ['conv5_2_1x1_reduce/bn[0][0]'] \n", - " \n", - " conv5_2_3x3 (Conv2D) (None, 7, 7, 512) 2359296 ['activation_43[0][0]'] \n", - " \n", - " conv5_2_3x3/bn (BatchNormaliza (None, 7, 7, 512) 2048 ['conv5_2_3x3[0][0]'] \n", - " tion) \n", - " \n", - " activation_44 (Activation) (None, 7, 7, 512) 0 ['conv5_2_3x3/bn[0][0]'] \n", - " \n", - " conv5_2_1x1_increase (Conv2D) (None, 7, 7, 2048) 1048576 ['activation_44[0][0]'] \n", - " \n", - " conv5_2_1x1_increase/bn (Batch (None, 7, 7, 2048) 8192 ['conv5_2_1x1_increase[0][0]'] \n", - " Normalization) \n", - " \n", - " add_14 (Add) (None, 7, 7, 2048) 0 ['conv5_2_1x1_increase/bn[0][0]',\n", - " 'activation_42[0][0]'] \n", - " \n", - " activation_45 (Activation) (None, 7, 7, 2048) 0 ['add_14[0][0]'] \n", - " \n", - " conv5_3_1x1_reduce (Conv2D) (None, 7, 7, 512) 1048576 ['activation_45[0][0]'] \n", - " \n", - " conv5_3_1x1_reduce/bn (BatchNo (None, 7, 7, 512) 2048 ['conv5_3_1x1_reduce[0][0]'] \n", - " rmalization) \n", - " \n", - " activation_46 (Activation) (None, 7, 7, 512) 0 ['conv5_3_1x1_reduce/bn[0][0]'] \n", - " \n", - " conv5_3_3x3 (Conv2D) (None, 7, 7, 512) 2359296 ['activation_46[0][0]'] \n", - " \n", - " conv5_3_3x3/bn (BatchNormaliza (None, 7, 7, 512) 2048 ['conv5_3_3x3[0][0]'] \n", - " tion) \n", - " \n", - " activation_47 (Activation) (None, 7, 7, 512) 0 ['conv5_3_3x3/bn[0][0]'] \n", - " \n", - " conv5_3_1x1_increase (Conv2D) (None, 7, 7, 2048) 1048576 ['activation_47[0][0]'] \n", - " \n", - " conv5_3_1x1_increase/bn (Batch (None, 7, 7, 2048) 8192 ['conv5_3_1x1_increase[0][0]'] \n", - " Normalization) \n", - " \n", - " add_15 (Add) (None, 7, 7, 2048) 0 ['conv5_3_1x1_increase/bn[0][0]',\n", - " 'activation_45[0][0]'] \n", - " \n", - " activation_48 (Activation) (None, 7, 7, 2048) 0 ['add_15[0][0]'] \n", - " \n", - " avg_pool (AveragePooling2D) (None, 1, 1, 2048) 0 ['activation_48[0][0]'] \n", - " \n", - " global_average_pooling2d (Glob (None, 2048) 0 ['avg_pool[0][0]'] \n", - " alAveragePooling2D) \n", - " \n", - " gaussian_noise (GaussianNoise) (None, 2048) 0 ['global_average_pooling2d[0][0]'\n", - " ] \n", - " \n", - " dense_x (Dense) (None, 512) 1049088 ['gaussian_noise[0][0]'] \n", - " \n", - "==================================================================================================\n", - "Total params: 24,610,240\n", - "Trainable params: 24,557,120\n", - "Non-trainable params: 53,120\n", - "__________________________________________________________________________________________________\n" - ] + "data": { + "text/plain": [ + "ResNet(\n", + " (conv_layer_s2_same): Conv2dSame(3, 64, kernel_size=(7, 7), stride=(2, 2), bias=False)\n", + " (batch_norm1): BatchNorm2d(64, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (relu): ReLU()\n", + " (max_pool): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (layer1): Sequential(\n", + " (0): Bottleneck(\n", + " (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (batch_norm1): BatchNorm2d(64, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)\n", + " (batch_norm2): BatchNorm2d(64, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " 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" (batch_norm3): BatchNorm2d(2048, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (i_downsample): Sequential(\n", + " (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + " (1): BatchNorm2d(2048, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " )\n", + " (relu): ReLU()\n", + " )\n", + " (1): Bottleneck(\n", + " (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (batch_norm1): BatchNorm2d(512, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)\n", + " (batch_norm2): BatchNorm2d(512, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (batch_norm3): BatchNorm2d(2048, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (relu): ReLU()\n", + " )\n", + " (2): Bottleneck(\n", + " (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (batch_norm1): BatchNorm2d(512, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)\n", + " (batch_norm2): BatchNorm2d(512, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (batch_norm3): BatchNorm2d(2048, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (relu): ReLU()\n", + " )\n", + " )\n", + " (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n", + " (fc1): Linear(in_features=2048, out_features=512, bias=True)\n", + " (relu1): ReLU()\n", + " (fc2): Linear(in_features=512, out_features=7, bias=True)\n", + ")" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "_b5.video_model_deep_fe_.summary()" + "_b5.video_model_deep_fe_" ] } ], @@ -685,7 +416,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.10.11" } }, "nbformat": 4, diff --git a/docs/source/user_guide/notebooks/Video-load_video_model_hc.ipynb b/docs/source/user_guide/notebooks/Video-load_video_model_hc.ipynb index 15f5958..3c2f438 100644 --- a/docs/source/user_guide/notebooks/Video-load_video_model_hc.ipynb +++ b/docs/source/user_guide/notebooks/Video-load_video_model_hc.ipynb @@ -8,7 +8,7 @@ "\n", "
\n", "\n", - "> - `_b5.video_model_hc_` - Нейросетевая модель **tf.keras.Model** для получения признаков / оценок на базе экспертных признаков" + "> - `_b5.video_model_hc_` - Нейросетевая модель **nn.Module** для получения признаков / оценок на базе экспертных признаков" ] }, { @@ -60,7 +60,7 @@ { "data": { "text/markdown": [ - "**[2023-12-10 17:11:13] OCEANAI - персональные качества личности человека:**
    Авторы:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
        Карпов Алексей [karpov@iias.spb.su]
    Сопровождающие:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
    Версия: 1.0.0a5
    Лицензия: BSD License

" + "**[2024-10-08 16:27:50] OCEANAI - персональные качества личности человека:**
    Авторы:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
        Карпов Алексей [karpov@iias.spb.su]
    Сопровождающие:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
    Версия: 1.0.0a40
    Лицензия: BSD License

" ], "text/plain": [ "" @@ -99,7 +99,7 @@ { "data": { "text/markdown": [ - "**[2023-12-10 17:11:13] Формирование нейросетевой архитектуры модели для получения оценок по экспертным признакам (видео модальность) ...** " + "**[2024-10-08 16:27:51] Формирование нейросетевой архитектуры модели для получения оценок по экспертным признакам (видео модальность) ...** " ], "text/plain": [ "" @@ -111,7 +111,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 0.789 сек. ---**" + "**--- Время выполнения: 0.002 сек. ---**" ], "text/plain": [ "" @@ -146,7 +146,7 @@ { "data": { "text/markdown": [ - "**[2023-12-10 17:11:14] Загрузка весов нейросетевой модели для получения оценок по экспертным признакам (видео модальность) ...** " + "**[2024-10-08 16:28:02] Загрузка весов нейросетевой модели для получения оценок по экспертным признакам (видео модальность) ...** " ], "text/plain": [ "" @@ -158,7 +158,7 @@ { "data": { "text/markdown": [ - "**[2023-12-10 17:11:14] Загрузка файла \"weights_2022-08-27_18-53-35.h5\" 100.0% ...** " + "**[2024-10-08 16:28:04] Загрузка файла \"weights_2022-08-27_18-53-35.pth\" 100.0% ...** " ], "text/plain": [ "" @@ -170,7 +170,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 0.226 сек. ---**" + "**--- Время выполнения: 2.825 сек. ---**" ], "text/plain": [ "" @@ -185,7 +185,7 @@ "_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_['video']['fi']['hc']['sberdisk']\n", + "url = _b5.weights_for_big5_['video']['fi']['hc']['googledisk']\n", "\n", "res_load_video_model_weights_hc = _b5.load_video_model_weights_hc(\n", " url = url, # Полный путь к файлу с весами нейросетевой модели\n", @@ -209,35 +209,24 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"model_1\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " input_1 (InputLayer) [(None, 10, 115)] 0 \n", - " \n", - " lstm (LSTM) (None, 10, 64) 46080 \n", - " \n", - " dropout (Dropout) (None, 10, 64) 0 \n", - " \n", - " lstm_128_v_hc (LSTM) (None, 128) 98816 \n", - " \n", - " dropout_1 (Dropout) (None, 128) 0 \n", - " \n", - " dense (Dense) (None, 5) 645 \n", - " \n", - "=================================================================\n", - "Total params: 145,541\n", - "Trainable params: 145,541\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] + "data": { + "text/plain": [ + "video_model_hc(\n", + " (lstm1): LSTM(115, 64, batch_first=True)\n", + " (dropout1): Dropout(p=0.2, inplace=False)\n", + " (lstm2): LSTM(64, 128, batch_first=True)\n", + " (dropout2): Dropout(p=0.2, inplace=False)\n", + " (fc): Linear(in_features=128, out_features=5, bias=True)\n", + ")" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "_b5.video_model_hc_.summary()" + "_b5.video_model_hc_" ] }, { @@ -255,7 +244,7 @@ { "data": { "text/markdown": [ - "**[2023-12-10 17:11:14] Формирование нейросетевой архитектуры модели для получения оценок по экспертным признакам (видео модальность) ...** " + "**[2024-10-08 16:28:14] Формирование нейросетевой архитектуры модели для получения оценок по экспертным признакам (видео модальность) ...** " ], "text/plain": [ "" @@ -267,7 +256,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 0.25 сек. ---**" + "**--- Время выполнения: 0.002 сек. ---**" ], "text/plain": [ "" @@ -302,7 +291,7 @@ { "data": { "text/markdown": [ - "**[2023-12-10 17:11:14] Загрузка весов нейросетевой модели для получения оценок по экспертным признакам (видео модальность) ...** " + "**[2024-10-08 16:28:23] Загрузка весов нейросетевой модели для получения оценок по экспертным признакам (видео модальность) ...** " ], "text/plain": [ "" @@ -314,7 +303,7 @@ { "data": { "text/markdown": [ - "**[2023-12-10 17:11:15] Загрузка файла \"vhc_mupta_2022-07-22_10-02-37.h5\" 100.0% ...** " + "**[2024-10-08 16:28:27] Загрузка файла \"vhc_mupta_2022-07-22_10-02-37.pth\" 100.0% ...** " ], "text/plain": [ "" @@ -326,7 +315,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 0.307 сек. ---**" + "**--- Время выполнения: 4.138 сек. ---**" ], "text/plain": [ "" @@ -341,7 +330,7 @@ "_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_['video']['mupta']['hc']['sberdisk']\n", + "url = _b5.weights_for_big5_['video']['mupta']['hc']['googledisk']\n", "\n", "res_load_video_model_weights_hc = _b5.load_video_model_weights_hc(\n", " url = url, # Полный путь к файлу с весами нейросетевой модели\n", @@ -365,35 +354,24 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"model_3\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " input_2 (InputLayer) [(None, 10, 109)] 0 \n", - " \n", - " lstm_1 (LSTM) (None, 10, 64) 44544 \n", - " \n", - " dropout_2 (Dropout) (None, 10, 64) 0 \n", - " \n", - " lstm_128_v_hc (LSTM) (None, 128) 98816 \n", - " \n", - " dropout_3 (Dropout) (None, 128) 0 \n", - " \n", - " dense_1 (Dense) (None, 5) 645 \n", - " \n", - "=================================================================\n", - "Total params: 144,005\n", - "Trainable params: 144,005\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] + "data": { + "text/plain": [ + "video_model_hc(\n", + " (lstm1): LSTM(109, 64, batch_first=True)\n", + " (dropout1): Dropout(p=0.2, inplace=False)\n", + " (lstm2): LSTM(64, 128, batch_first=True)\n", + " (dropout2): Dropout(p=0.2, inplace=False)\n", + " (fc): Linear(in_features=128, out_features=5, bias=True)\n", + ")" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "_b5.video_model_hc_.summary()" + "_b5.video_model_hc_" ] } ], @@ -413,7 +391,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.10.11" } }, "nbformat": 4, diff --git a/docs/source/user_guide/notebooks/Video-load_video_model_nn.ipynb b/docs/source/user_guide/notebooks/Video-load_video_model_nn.ipynb index 4e97a3e..94b6782 100644 --- a/docs/source/user_guide/notebooks/Video-load_video_model_nn.ipynb +++ b/docs/source/user_guide/notebooks/Video-load_video_model_nn.ipynb @@ -8,7 +8,7 @@ "\n", "
\n", "\n", - "> - `_b5.video_model_nn_` - Нейросетевая модель **tf.keras.Model** для получения оценок по нейросетевым признакам" + "> - `_b5.video_model_nn_` - Нейросетевая модель **nn.Module** для получения оценок по нейросетевым признакам" ] }, { @@ -60,7 +60,7 @@ { "data": { "text/markdown": [ - "**[2023-12-10 17:12:11] OCEANAI - персональные качества личности человека:**
    Авторы:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
        Карпов Алексей [karpov@iias.spb.su]
    Сопровождающие:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
    Версия: 1.0.0a5
    Лицензия: BSD License

" + "**[2024-10-08 16:30:08] OCEANAI - персональные качества личности человека:**
    Авторы:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
        Карпов Алексей [karpov@iias.spb.su]
    Сопровождающие:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
    Версия: 1.0.0a40
    Лицензия: BSD License

" ], "text/plain": [ "" @@ -99,7 +99,7 @@ { "data": { "text/markdown": [ - "**[2023-12-10 17:12:11] Формирование нейросетевой архитектуры для получения оценок по нейросетевым признакам (видео модальность) ...** " + "**[2024-10-08 16:30:08] Формирование нейросетевой архитектуры для получения оценок по нейросетевым признакам (видео модальность) ...** " ], "text/plain": [ "" @@ -111,7 +111,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 1.559 сек. ---**" + "**--- Время выполнения: 0.029 сек. ---**" ], "text/plain": [ "" @@ -145,7 +145,7 @@ { "data": { "text/markdown": [ - "**[2023-12-10 17:12:13] Загрузка весов нейросетевой модели для получения оценок по нейросетевым признакам (видео модальность) ...** " + "**[2024-10-08 16:30:12] Загрузка весов нейросетевой модели для получения оценок по нейросетевым признакам (видео модальность) ...** " ], "text/plain": [ "" @@ -157,7 +157,7 @@ { "data": { "text/markdown": [ - "**[2023-12-10 17:12:14] Загрузка файла \"weights_2022-03-22_16-31-48.h5\" 100.0% ...** " + "**[2024-10-08 16:30:18] Загрузка файла \"weights_2022-03-22_16-31-48.pth\" 100.0% ...** " ], "text/plain": [ "" @@ -169,7 +169,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 1.053 сек. ---**" + "**--- Время выполнения: 5.676 сек. ---**" ], "text/plain": [ "" @@ -184,7 +184,7 @@ "_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_['video']['fi']['nn']['sberdisk']\n", + "url = _b5.weights_for_big5_['video']['fi']['nn']['googledisk']\n", "\n", "res_load_video_model_weights_nn = _b5.load_video_model_weights_nn(\n", " url = url, # Полный путь к файлу с весами нейросетевой модели\n", @@ -208,33 +208,22 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"model_1\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " input_1 (InputLayer) [(None, 10, 512)] 0 \n", - " \n", - " lstm_1024_v_nn (LSTM) (None, 1024) 6295552 \n", - " \n", - " dropout (Dropout) (None, 1024) 0 \n", - " \n", - " dense (Dense) (None, 5) 5125 \n", - " \n", - " activation (Activation) (None, 5) 0 \n", - " \n", - "=================================================================\n", - "Total params: 6,300,677\n", - "Trainable params: 6,300,677\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] + "data": { + "text/plain": [ + "video_model_nn(\n", + " (lstm1): LSTM(512, 1024, batch_first=True)\n", + " (dropout1): Dropout(p=0.2, inplace=False)\n", + " (fc): Linear(in_features=1024, out_features=5, bias=True)\n", + ")" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "_b5.video_model_nn_.summary()" + "_b5.video_model_nn_" ] } ], @@ -254,7 +243,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.10.11" } }, "nbformat": 4, diff --git a/docs/source/user_guide/notebooks/Video-load_video_models_oceanai.ipynb b/docs/source/user_guide/notebooks/Video-load_video_models_oceanai.ipynb index 77018c2..d4bee40 100644 --- a/docs/source/user_guide/notebooks/Video-load_video_models_oceanai.ipynb +++ b/docs/source/user_guide/notebooks/Video-load_video_models_oceanai.ipynb @@ -8,7 +8,7 @@ "\n", "
\n", "\n", - "> - `_b5.video_models_b5_` - Нейросетевые модели **tf.keras.Model** для получения результатов оценки персональных качеств" + "> - `_b5.video_models_b5_` - Нейросетевые модели **nn.Module** для получения результатов оценки персональных качеств" ] }, { @@ -60,7 +60,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:04:19] OCEANAI - персональные качества личности человека:**
    Авторы:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
        Карпов Алексей [karpov@iias.spb.su]
    Сопровождающие:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
    Версия: 1.0.0a16
    Лицензия: BSD License

" + "**[2024-10-08 16:31:14] OCEANAI - персональные качества личности человека:**
    Авторы:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
        Карпов Алексей [karpov@iias.spb.su]
    Сопровождающие:
        Рюмина Елена [ryumina_ev@mail.ru]
        Рюмин Дмитрий [dl_03.03.1991@mail.ru]
    Версия: 1.0.0a40
    Лицензия: BSD License

" ], "text/plain": [ "" @@ -99,7 +99,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:04:19] Формирование нейросетевых архитектур моделей для получения результатов оценки персональных качеств (видео модальность) ...** " + "**[2024-10-08 16:31:15] Формирование нейросетевых архитектур моделей для получения результатов оценки персональных качеств (видео модальность) ...** " ], "text/plain": [ "" @@ -111,7 +111,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 0.094 сек. ---**" + "**--- Время выполнения: 0.002 сек. ---**" ], "text/plain": [ "" @@ -145,7 +145,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:04:19] Загрузка весов нейросетевых моделей для получения результатов оценки персональных качеств (видео модальность) ...** " + "**[2024-10-08 16:31:31] Загрузка весов нейросетевых моделей для получения результатов оценки персональных качеств (видео модальность) ...** " ], "text/plain": [ "" @@ -157,7 +157,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:04:19] Загрузка файла \"weights_2022-06-15_16-46-30.h5\" 100.0% ...** **Открытость опыту**" + "**[2024-10-08 16:31:33] Загрузка файла \"weights_2022-06-15_16-46-30.pth\" 100.0% ...** **Открытость опыту**" ], "text/plain": [ "" @@ -169,7 +169,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:04:20] Загрузка файла \"weights_2022-06-15_16-48-50.h5\" 100.0% ...** **Добросовестность**" + "**[2024-10-08 16:31:36] Загрузка файла \"weights_2022-06-15_16-48-50.pth\" 100.0% ...** **Добросовестность**" ], "text/plain": [ "" @@ -181,7 +181,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:04:20] Загрузка файла \"weights_2022-06-15_16-54-06.h5\" 100.0% ...** **Экстраверсия**" + "**[2024-10-08 16:31:38] Загрузка файла \"weights_2022-06-15_16-54-06.pth\" 100.0% ...** **Экстраверсия**" ], "text/plain": [ "" @@ -193,7 +193,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:04:20] Загрузка файла \"weights_2022-06-15_17-02-03.h5\" 100.0% ...** **Доброжелательность**" + "**[2024-10-08 16:31:41] Загрузка файла \"weights_2022-06-15_17-02-03.pth\" 100.0% ...** **Доброжелательность**" ], "text/plain": [ "" @@ -205,7 +205,7 @@ { "data": { "text/markdown": [ - "**[2023-12-14 21:04:20] Загрузка файла \"weights_2022-06-15_17-06-15.h5\" 100.0% ...** **Эмоциональная стабильность**" + "**[2024-10-08 16:31:44] Загрузка файла \"weights_2022-06-15_17-06-15.pth\" 100.0% ...** **Эмоциональная стабильность**" ], "text/plain": [ "" @@ -217,7 +217,7 @@ { "data": { "text/markdown": [ - "**--- Время выполнения: 0.998 сек. ---**" + "**--- Время выполнения: 12.981 сек. ---**" ], "text/plain": [ "" @@ -232,11 +232,11 @@ "_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_openness = _b5.weights_for_big5_['video']['fi']['b5']['openness']['sberdisk']\n", - "url_conscientiousness = _b5.weights_for_big5_['video']['fi']['b5']['conscientiousness']['sberdisk']\n", - "url_extraversion = _b5.weights_for_big5_['video']['fi']['b5']['extraversion']['sberdisk']\n", - "url_agreeableness = _b5.weights_for_big5_['video']['fi']['b5']['agreeableness']['sberdisk']\n", - "url_non_neuroticism = _b5.weights_for_big5_['video']['fi']['b5']['non_neuroticism']['sberdisk']\n", + "url_openness = _b5.weights_for_big5_['video']['fi']['b5']['openness']['googledisk']\n", + "url_conscientiousness = _b5.weights_for_big5_['video']['fi']['b5']['conscientiousness']['googledisk']\n", + "url_extraversion = _b5.weights_for_big5_['video']['fi']['b5']['extraversion']['googledisk']\n", + "url_agreeableness = _b5.weights_for_big5_['video']['fi']['b5']['agreeableness']['googledisk']\n", + "url_non_neuroticism = _b5.weights_for_big5_['video']['fi']['b5']['non_neuroticism']['googledisk']\n", "\n", "res_load_video_models_weights_b5 = _b5.load_video_models_weights_b5(\n", " url_openness = url_openness, # Открытость опыту\n", @@ -270,29 +270,21 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"model\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " input_1 (InputLayer) [(None, 32)] 0 \n", - " \n", - " dense_1 (Dense) (None, 1) 33 \n", - " \n", - " activ_1 (Activation) (None, 1) 0 \n", - " \n", - "=================================================================\n", - "Total params: 33 (132.00 Byte)\n", - "Trainable params: 33 (132.00 Byte)\n", - "Non-trainable params: 0 (0.00 Byte)\n", - "_________________________________________________________________\n" - ] + "data": { + "text/plain": [ + "video_model_b5(\n", + " (fc): Linear(in_features=32, out_features=1, bias=True)\n", + " (sigmoid): Sigmoid()\n", + ")" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "_b5.video_models_b5_['openness'].summary()" + "_b5.video_models_b5_['openness']" ] } ], @@ -312,7 +304,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.10.11" } }, "nbformat": 4, diff --git a/oceanai/modules/lab/video.py b/oceanai/modules/lab/video.py index 4507282..97a0bfd 100644 --- a/oceanai/modules/lab/video.py +++ b/oceanai/modules/lab/video.py @@ -1756,7 +1756,7 @@ def alignment_procedure(left_eye: List[int], right_eye: List[int]) -> float: # Коды ошибок нейросетевой модели code_error_pred_deep_fe = -1 - batch_size_limit = 100 + batch_size_limit = 50 try: # Отправка областей с лицами в нейросетевую модель для получения нейросетевых признаков