{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "toCy3v03Dwx7"
},
"source": [
"##### Copyright 2021 The TensorFlow Hub Authors.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\");"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:00:44.441823Z",
"iopub.status.busy": "2022-12-14T22:00:44.441604Z",
"iopub.status.idle": "2022-12-14T22:00:44.445520Z",
"shell.execute_reply": "2022-12-14T22:00:44.444952Z"
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"id": "QKe-ubNcDvgv"
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"outputs": [],
"source": [
"# Copyright 2021 The TensorFlow Hub Authors. All Rights Reserved.\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# http://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License.\n",
"# =============================================================================="
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qFdPvlXBOdUN"
},
"source": [
"# 用于流式动作识别的 MoViNet"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MfBg1C5NB3X0"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-vxk2Kbc_KSP"
},
"source": [
"本教程运行预训练的视频分类模型来对给定视频中的活动（例如跳舞、游泳、骑自行车等）进行分类。\n",
"\n",
"本教程中使用的模型架构称为 [MoViNet](https://arxiv.org/pdf/2103.11511.pdf)（移动视频网络）。MoVieNet 是一系列在庞大数据集 ([Kinetics 600](https://deepmind.com/research/open-source/kinetics)) 上训练的高效视频分类模型。\n",
"\n",
"与 TF Hub 上提供的 [i3d 模型](https://tfhub.dev/s?q=i3d-kinetics)相比，MoViNet 还支持流式视频的逐帧推断。\n",
"\n",
"预训练模型可从 [TF Hub](https://tfhub.dev/google/collections/movinet/1) 获得。TF Hub 集合还包括为 [TFLite](https://tensorflow.org/lite) 优化的量化模型。\n",
"\n",
"这些模型的源代码可在 [TensorFlow Model Garden](https://github.com/tensorflow/models/tree/master/official/projects/movinet) 中找到。包括[本教程的较长版本](https://colab.sandbox.google.com/github/tensorflow/models/blob/master/official/projects/movinet/movinet_tutorial.ipynb)，较长版本还介绍了如何构建和微调 MoViNet 模型。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3E96e1UKQ8uR"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8_oLnvJy7kz5"
},
"source": [
"## 安装\n",
"\n",
"对于较小模型 (A0-A2) 的推断，CPU 对于此 Colab 来说已经足够。"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:00:44.449061Z",
"iopub.status.busy": "2022-12-14T22:00:44.448626Z",
"iopub.status.idle": "2022-12-14T22:01:49.617055Z",
"shell.execute_reply": "2022-12-14T22:01:49.616041Z"
},
"id": "GUgUMGmY1yq-"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Reading package lists... 0%\r",
"\r",
"Reading package lists... 100%\r",
"\r",
"Reading package lists... Done\r",
"\r\n",
"\r",
"Building dependency tree... 0%\r",
"\r",
"Building dependency tree... 0%\r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Building dependency tree... 50%\r",
"\r",
"Building dependency tree... 50%\r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Building dependency tree \r",
"\r\n",
"\r",
"Reading state information... 0%\r",
"\r",
"Reading state information... 0%\r",
"\r",
"Reading state information... Done\r",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The following packages were automatically installed and are no longer required:\r\n",
" libatasmart4 libblockdev-fs2 libblockdev-loop2 libblockdev-part-err2\r\n",
" libblockdev-part2 libblockdev-swap2 libblockdev-utils2 libblockdev2\r\n",
" libparted-fs-resize0\r\n",
"Use 'sudo apt autoremove' to remove them.\r\n",
"The following additional packages will be installed:\r\n",
" i965-va-driver intel-media-va-driver libaacs0 libaom0 libass9 libavcodec58\r\n",
" libavdevice58 libavfilter7 libavformat58 libavresample4 libavutil56\r\n",
" libbdplus0 libbluray2 libbs2b0 libcdio-cdda2 libcdio-paranoia2 libcdio18\r\n",
" libchromaprint1 libcodec2-0.9 libdc1394-22 libfftw3-double3 libflite1\r\n",
" libgme0 libgsm1 libigdgmm11 liblilv-0-0 libmysofa1 libopenal-data libopenal1\r\n",
" libopenmpt0 libpostproc55 librubberband2 libsdl2-2.0-0 libserd-0-0 libshine3\r\n",
" libsnappy1v5 libsndio7.0 libsord-0-0 libsratom-0-0 libssh-gcrypt-4\r\n",
" libswresample3 libswscale5 libva-drm2 libva-x11-2 libva2 libvidstab1.1\r\n",
" libwebpmux3 libx264-155 libx265-179 libxvidcore4 libzvbi-common libzvbi0\r\n",
" mesa-va-drivers ocl-icd-libopencl1 va-driver-all\r\n",
"Suggested packages:\r\n",
" ffmpeg-doc i965-va-driver-shaders libbluray-bdj libfftw3-bin libfftw3-dev\r\n",
" libportaudio2 serdi sndiod sordi\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The following NEW packages will be installed:\r\n",
" ffmpeg i965-va-driver intel-media-va-driver libaacs0 libaom0 libass9\r\n",
" libavcodec58 libavdevice58 libavfilter7 libavformat58 libavresample4\r\n",
" libavutil56 libbdplus0 libbluray2 libbs2b0 libcdio-cdda2 libcdio-paranoia2\r\n",
" libcdio18 libchromaprint1 libcodec2-0.9 libdc1394-22 libfftw3-double3\r\n",
" libflite1 libgme0 libgsm1 libigdgmm11 liblilv-0-0 libmysofa1 libopenal-data\r\n",
" libopenal1 libopenmpt0 libpostproc55 librubberband2 libsdl2-2.0-0\r\n",
" libserd-0-0 libshine3 libsnappy1v5 libsndio7.0 libsord-0-0 libsratom-0-0\r\n",
" libssh-gcrypt-4 libswresample3 libswscale5 libva-drm2 libva-x11-2 libva2\r\n",
" libvidstab1.1 libwebpmux3 libx264-155 libx265-179 libxvidcore4\r\n",
" libzvbi-common libzvbi0 mesa-va-drivers ocl-icd-libopencl1 va-driver-all\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 upgraded, 56 newly installed, 0 to remove and 170 not upgraded.\r\n",
"Need to get 42.5 MB of archives.\r\n",
"After this operation, 156 MB of additional disk space will be used.\r\n",
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"Get:16 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/universe amd64 libzvbi-common all 0.2.35-17 [32.5 kB]\r\n",
"\u001b[33m\r",
"27% [16 libzvbi-common 2412 B/32.5 kB 7%] 614 kB/s 50s\u001b[0m"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" \r",
"Get:17 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/universe amd64 libzvbi0 amd64 0.2.35-17 [237 kB]\r\n",
"\u001b[33m\r",
"27% [17 libzvbi0 2413 B/237 kB 1%] 614 kB/s 50s\u001b[0m"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
"28% [Waiting for headers] 614 kB/s 50s\u001b[0m"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" \r",
"Get:18 http://us-central1.gce.archive.ubuntu.com/ubuntu focal-updates/universe amd64 libavcodec58 amd64 7:4.2.7-0ubuntu0.1 [4878 kB]\r\n",
"\u001b[33m\r",
"28% [18 libavcodec58 2409 B/4878 kB 0%] 614 kB/s 50s\u001b[0m"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
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]
},
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"name": "stdout",
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"text": [
"\u001b[33m\r",
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]
},
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"\u001b[33m\r",
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]
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"name": "stdout",
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"\u001b[33m\r",
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"\u001b[33m\r",
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},
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"\u001b[33m\r",
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"\u001b[33m\r",
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"\u001b[33m\r",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
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]
},
{
"name": "stdout",
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"text": [
"\u001b[33m\r",
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]
},
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"name": "stdout",
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"text": [
"\u001b[33m\r",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
"37% [Waiting for headers] 381 kB/s 1min 8s\u001b[0m\r",
" \r",
"Get:19 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/universe amd64 libass9 amd64 1:0.14.0-2 [88.0 kB]\r\n",
"\u001b[33m\r",
"37% [19 libass9 6635 B/88.0 kB 8%] 381 kB/s 1min 8s\u001b[0m"
]
},
{
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"output_type": "stream",
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"\u001b[33m\r",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" \r",
"Get:20 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/universe amd64 libbluray2 amd64 1:1.2.0-1 [138 kB]\r\n",
"\u001b[33m\r",
"38% [20 libbluray2 2410 B/138 kB 2%] 381 kB/s 1min 7s\u001b[0m"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
"39% [Waiting for headers] 381 kB/s 1min 7s\u001b[0m\r",
" \r",
"Get:21 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/universe amd64 libchromaprint1 amd64 1.4.3-3build1 [37.6 kB]\r\n",
"\u001b[33m\r",
"39% [21 libchromaprint1 2415 B/37.6 kB 6%] 381 kB/s 1min 7s\u001b[0m\u001b[33m\r",
"39% [Waiting for headers] 381 kB/s 1min 7s\u001b[0m"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" \r",
"Get:22 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/universe amd64 libgme0 amd64 0.6.2-1build1 [123 kB]\r\n",
"\u001b[33m\r",
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]
},
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"name": "stdout",
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"text": [
"\u001b[33m\r",
"40% [Waiting for headers] 381 kB/s 1min 7s\u001b[0m\r",
" \r",
"Get:23 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/universe amd64 libopenmpt0 amd64 0.4.11-1build1 [599 kB]\r\n",
"\u001b[33m\r",
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]
},
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" \r",
"Get:24 http://us-central1.gce.archive.ubuntu.com/ubuntu focal-updates/main amd64 libssh-gcrypt-4 amd64 0.9.3-2ubuntu2.2 [202 kB]\r\n",
"\u001b[33m\r",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
"42% [Waiting for headers] 523 kB/s 47s\u001b[0m\r",
" \r",
"Get:25 http://us-central1.gce.archive.ubuntu.com/ubuntu focal-updates/universe amd64 libavformat58 amd64 7:4.2.7-0ubuntu0.1 [985 kB]\r\n",
"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
"44% [Waiting for headers] 523 kB/s 45s\u001b[0m\r",
" \r",
"Get:26 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/universe amd64 libbs2b0 amd64 3.1.0+dfsg-2.2build1 [10.2 kB]\r\n",
"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
"44% [Waiting for headers] 523 kB/s 45s\u001b[0m\r",
" \r",
"Get:27 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/universe amd64 libflite1 amd64 2.1-release-3 [12.8 MB]\r\n",
"\u001b[33m\r",
"44% [27 libflite1 2410 B/12.8 MB 0%] 523 kB/s 45s\u001b[0m"
]
},
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"text": [
"\u001b[33m\r",
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]
},
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]
},
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]
},
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"name": "stdout",
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"\u001b[33m\r",
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]
},
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]
},
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"name": "stdout",
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"text": [
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]
},
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
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"69% [Waiting for headers] 2161 kB/s 5s\u001b[0m\r",
" \r",
"Get:28 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/universe amd64 libserd-0-0 amd64 0.30.2-1 [46.6 kB]\r\n",
"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
"69% [Waiting for headers] 2161 kB/s 5s\u001b[0m\r",
" \r",
"Get:29 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/universe amd64 libsord-0-0 amd64 0.16.4-1 [19.5 kB]\r\n",
"\u001b[33m\r",
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"70% [Waiting for headers] 2161 kB/s 5s\u001b[0m"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" \r",
"Get:30 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/universe amd64 libsratom-0-0 amd64 0.6.4-1 [16.9 kB]\r\n",
"\u001b[33m\r",
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"70% [Waiting for headers] 2161 kB/s 5s\u001b[0m\r",
" \r",
"Get:31 http://us-central1.gce.archive.ubuntu.com/ubuntu focal-updates/universe amd64 liblilv-0-0 amd64 0.24.6-1ubuntu0.1 [40.6 kB]\r\n",
"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
"71% [Waiting for headers] 2161 kB/s 5s\u001b[0m\r",
" \r",
"Get:32 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/universe amd64 libmysofa1 amd64 1.0~dfsg0-1 [39.2 kB]\r\n",
"\u001b[33m\r",
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"71% [Waiting for headers] 2161 kB/s 5s\u001b[0m\r",
" \r",
"Get:33 http://us-central1.gce.archive.ubuntu.com/ubuntu focal-updates/universe amd64 libpostproc55 amd64 7:4.2.7-0ubuntu0.1 [55.0 kB]\r\n",
"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
"71% [Waiting for headers] 2161 kB/s 4s\u001b[0m\r",
" \r",
"Get:34 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/main amd64 libfftw3-double3 amd64 3.3.8-2ubuntu1 [728 kB]\r\n",
"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
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"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" \r",
"Get:35 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/universe amd64 librubberband2 amd64 1.8.2-1build1 [89.4 kB]\r\n",
"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
"74% [Waiting for headers] 2161 kB/s 4s\u001b[0m\r",
" \r",
"Get:36 http://us-central1.gce.archive.ubuntu.com/ubuntu focal-updates/universe amd64 libswscale5 amd64 7:4.2.7-0ubuntu0.1 [157 kB]\r\n",
"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
"74% [Waiting for headers] 2161 kB/s 4s\u001b[0m\r",
" \r",
"Get:37 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/universe amd64 libvidstab1.1 amd64 1.1.0-2 [35.0 kB]\r\n",
"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
"75% [Waiting for headers] 2161 kB/s 4s\u001b[0m\r",
" \r",
"Get:38 http://us-central1.gce.archive.ubuntu.com/ubuntu focal-updates/universe amd64 libavfilter7 amd64 7:4.2.7-0ubuntu0.1 [1085 kB]\r\n",
"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\r",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" \r",
"Get:39 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/main amd64 libcdio18 amd64 2.0.0-2 [58.6 kB]\r\n",
"\u001b[33m\r",
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]
},
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"output_type": "stream",
"text": [
"\u001b[33m\r",
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]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" \r",
"Get:40 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/main amd64 libcdio-cdda2 amd64 10.2+2.0.0-1 [17.6 kB]\r\n",
"\u001b[33m\r",
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"Get:41 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/main amd64 libcdio-paranoia2 amd64 10.2+2.0.0-1 [16.2 kB]\r\n",
"\u001b[33m\r",
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"Get:42 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/universe amd64 libdc1394-22 amd64 2.2.5-2.1 [79.6 kB]\r\n",
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"\u001b[33m\r",
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" \r",
"Get:43 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/universe amd64 libopenal-data all 1:1.19.1-1 [162 kB]\r\n",
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"Get:44 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/universe amd64 libsndio7.0 amd64 1.5.0-3 [24.5 kB]\r\n",
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"output_type": "stream",
"text": [
"\r",
" \r",
"Get:45 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/universe amd64 libopenal1 amd64 1:1.19.1-1 [492 kB]\r\n",
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"\u001b[33m\r",
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"Get:46 http://us-central1.gce.archive.ubuntu.com/ubuntu focal/universe amd64 libsdl2-2.0-0 amd64 2.0.10+dfsg1-3 [407 kB]\r\n",
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"Get:47 http://us-central1.gce.archive.ubuntu.com/ubuntu focal-updates/universe amd64 libavdevice58 amd64 7:4.2.7-0ubuntu0.1 [74.3 kB]\r\n",
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"\r",
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"\r",
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"\n",
"\u001b7\u001b[0;23r\u001b8\u001b[1A"
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"Selecting previously unselected package libaom0:amd64.\r\n",
"(Reading database ... \r",
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"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 0%]\u001b[49m\u001b[39m [..........................................................] \u001b8Unpacking libaom0:amd64 (1.0.0.errata1-3build1) ...\r\n"
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"Selecting previously unselected package libva2:amd64.\r\n"
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"Preparing to unpack .../01-libva2_2.7.0-2_amd64.deb ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 1%]\u001b[49m\u001b[39m [..........................................................] \u001b8Unpacking libva2:amd64 (2.7.0-2) ...\r\n",
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"text": [
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"Selecting previously unselected package ocl-icd-libopencl1:amd64.\r\n",
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"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 5%]\u001b[49m\u001b[39m [###.......................................................] \u001b8Selecting previously unselected package libcodec2-0.9:amd64.\r\n"
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"text": [
"Preparing to unpack .../06-libcodec2-0.9_0.9.2-2_amd64.deb ...\r\n",
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"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 6%]\u001b[49m\u001b[39m [###.......................................................] \u001b8Selecting previously unselected package libgsm1:amd64.\r\n"
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"Preparing to unpack .../07-libgsm1_1.0.18-2_amd64.deb ...\r\n",
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"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 7%]\u001b[49m\u001b[39m [####......................................................] \u001b8Selecting previously unselected package libshine3:amd64.\r\n"
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"text": [
"Preparing to unpack .../08-libshine3_3.1.1-2_amd64.deb ...\r\n",
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"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 8%]\u001b[49m\u001b[39m [####......................................................] \u001b8"
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"Selecting previously unselected package libsnappy1v5:amd64.\r\n",
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"Selecting previously unselected package libwebpmux3:amd64.\r\n",
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"Selecting previously unselected package libx264-155:amd64.\r\n",
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"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 11%]\u001b[49m\u001b[39m [######....................................................] \u001b8Unpacking libx264-155:amd64 (2:0.155.2917+git0a84d98-2) ...\r\n"
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"Selecting previously unselected package libx265-179:amd64.\r\n",
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"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 12%]\u001b[49m\u001b[39m [######....................................................] \u001b8Unpacking libx265-179:amd64 (3.2.1-1build1) ...\r\n"
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"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 13%]\u001b[49m\u001b[39m [#######...................................................] \u001b8Selecting previously unselected package libzvbi-common.\r\n",
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"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 14%]\u001b[49m\u001b[39m [########..................................................] \u001b8Selecting previously unselected package libzvbi0:amd64.\r\n",
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"Unpacking libzvbi0:amd64 (0.2.35-17) ...\r\n"
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"Selecting previously unselected package libavcodec58:amd64.\r\n",
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"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 16%]\u001b[49m\u001b[39m [#########.................................................] \u001b8Selecting previously unselected package libass9:amd64.\r\n",
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"Unpacking libass9:amd64 (1:0.14.0-2) ...\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Selecting previously unselected package libbluray2:amd64.\r\n"
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preparing to unpack .../19-libbluray2_1%3a1.2.0-1_amd64.deb ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 17%]\u001b[49m\u001b[39m [##########................................................] \u001b8Unpacking libbluray2:amd64 (1:1.2.0-1) ...\r\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"Selecting previously unselected package libchromaprint1:amd64.\r\n",
"Preparing to unpack .../20-libchromaprint1_1.4.3-3build1_amd64.deb ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 18%]\u001b[49m\u001b[39m [##########................................................] \u001b8Unpacking libchromaprint1:amd64 (1.4.3-3build1) ...\r\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"Selecting previously unselected package libgme0:amd64.\r\n",
"Preparing to unpack .../21-libgme0_0.6.2-1build1_amd64.deb ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 19%]\u001b[49m\u001b[39m [###########...............................................] \u001b8Unpacking libgme0:amd64 (0.6.2-1build1) ...\r\n"
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"Selecting previously unselected package libopenmpt0:amd64.\r\n",
"Preparing to unpack .../22-libopenmpt0_0.4.11-1build1_amd64.deb ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 20%]\u001b[49m\u001b[39m [###########...............................................] \u001b8Unpacking libopenmpt0:amd64 (0.4.11-1build1) ...\r\n"
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"name": "stdout",
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"text": [
"Selecting previously unselected package libssh-gcrypt-4:amd64.\r\n"
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Preparing to unpack .../23-libssh-gcrypt-4_0.9.3-2ubuntu2.2_amd64.deb ...\r\n",
"Unpacking libssh-gcrypt-4:amd64 (0.9.3-2ubuntu2.2) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 21%]\u001b[49m\u001b[39m [############..............................................] \u001b8"
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"Selecting previously unselected package libavformat58:amd64.\r\n",
"Preparing to unpack .../24-libavformat58_7%3a4.2.7-0ubuntu0.1_amd64.deb ...\r\n",
"Unpacking libavformat58:amd64 (7:4.2.7-0ubuntu0.1) ...\r\n"
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"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 22%]\u001b[49m\u001b[39m [############..............................................] \u001b8"
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"Selecting previously unselected package libbs2b0:amd64.\r\n",
"Preparing to unpack .../25-libbs2b0_3.1.0+dfsg-2.2build1_amd64.deb ...\r\n",
"Unpacking libbs2b0:amd64 (3.1.0+dfsg-2.2build1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 23%]\u001b[49m\u001b[39m [#############.............................................] \u001b8"
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"Selecting previously unselected package libflite1:amd64.\r\n",
"Preparing to unpack .../26-libflite1_2.1-release-3_amd64.deb ...\r\n",
"Unpacking libflite1:amd64 (2.1-release-3) ...\r\n"
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"name": "stdout",
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"text": [
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 24%]\u001b[49m\u001b[39m [#############.............................................] \u001b8Selecting previously unselected package libserd-0-0:amd64.\r\n"
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"name": "stdout",
"output_type": "stream",
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"Preparing to unpack .../27-libserd-0-0_0.30.2-1_amd64.deb ...\r\n",
"Unpacking libserd-0-0:amd64 (0.30.2-1) ...\r\n"
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"name": "stdout",
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"Selecting previously unselected package libsord-0-0:amd64.\r\n",
"Preparing to unpack .../28-libsord-0-0_0.16.4-1_amd64.deb ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 25%]\u001b[49m\u001b[39m [##############............................................] \u001b8Unpacking libsord-0-0:amd64 (0.16.4-1) ...\r\n"
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"name": "stdout",
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"Selecting previously unselected package libsratom-0-0:amd64.\r\n",
"Preparing to unpack .../29-libsratom-0-0_0.6.4-1_amd64.deb ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 26%]\u001b[49m\u001b[39m [###############...........................................] \u001b8Unpacking libsratom-0-0:amd64 (0.6.4-1) ...\r\n"
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"Selecting previously unselected package liblilv-0-0:amd64.\r\n",
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"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 27%]\u001b[49m\u001b[39m [###############...........................................] \u001b8Unpacking liblilv-0-0:amd64 (0.24.6-1ubuntu0.1) ...\r\n"
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"name": "stdout",
"output_type": "stream",
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"Selecting previously unselected package libmysofa1:amd64.\r\n",
"Preparing to unpack .../31-libmysofa1_1.0~dfsg0-1_amd64.deb ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 28%]\u001b[49m\u001b[39m [################..........................................] \u001b8Unpacking libmysofa1:amd64 (1.0~dfsg0-1) ...\r\n"
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"Selecting previously unselected package libpostproc55:amd64.\r\n",
"Preparing to unpack .../32-libpostproc55_7%3a4.2.7-0ubuntu0.1_amd64.deb ...\r\n",
"Unpacking libpostproc55:amd64 (7:4.2.7-0ubuntu0.1) ...\r\n"
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{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 29%]\u001b[49m\u001b[39m [#################.........................................] \u001b8Selecting previously unselected package libfftw3-double3:amd64.\r\n",
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"Unpacking libfftw3-double3:amd64 (3.3.8-2ubuntu1) ...\r\n"
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{
"name": "stdout",
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"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 30%]\u001b[49m\u001b[39m [#################.........................................] \u001b8Selecting previously unselected package librubberband2:amd64.\r\n"
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"Preparing to unpack .../34-librubberband2_1.8.2-1build1_amd64.deb ...\r\n",
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"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 31%]\u001b[49m\u001b[39m [##################........................................] \u001b8"
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"Selecting previously unselected package libswscale5:amd64.\r\n",
"Preparing to unpack .../35-libswscale5_7%3a4.2.7-0ubuntu0.1_amd64.deb ...\r\n",
"Unpacking libswscale5:amd64 (7:4.2.7-0ubuntu0.1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 32%]\u001b[49m\u001b[39m [##################........................................] \u001b8"
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"Selecting previously unselected package libvidstab1.1:amd64.\r\n",
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"Unpacking libvidstab1.1:amd64 (1.1.0-2) ...\r\n"
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"Selecting previously unselected package libavfilter7:amd64.\r\n",
"Preparing to unpack .../37-libavfilter7_7%3a4.2.7-0ubuntu0.1_amd64.deb ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 33%]\u001b[49m\u001b[39m [###################.......................................] \u001b8Unpacking libavfilter7:amd64 (7:4.2.7-0ubuntu0.1) ...\r\n"
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"Selecting previously unselected package libcdio18:amd64.\r\n",
"Preparing to unpack .../38-libcdio18_2.0.0-2_amd64.deb ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 34%]\u001b[49m\u001b[39m [###################.......................................] \u001b8Unpacking libcdio18:amd64 (2.0.0-2) ...\r\n"
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"Selecting previously unselected package libcdio-cdda2:amd64.\r\n",
"Preparing to unpack .../39-libcdio-cdda2_10.2+2.0.0-1_amd64.deb ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 35%]\u001b[49m\u001b[39m [####################......................................] \u001b8Unpacking libcdio-cdda2:amd64 (10.2+2.0.0-1) ...\r\n"
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"Selecting previously unselected package libcdio-paranoia2:amd64.\r\n"
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"output_type": "stream",
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"Preparing to unpack .../40-libcdio-paranoia2_10.2+2.0.0-1_amd64.deb ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 36%]\u001b[49m\u001b[39m [####################......................................] \u001b8Unpacking libcdio-paranoia2:amd64 (10.2+2.0.0-1) ...\r\n",
"Selecting previously unselected package libdc1394-22:amd64.\r\n"
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"output_type": "stream",
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"Preparing to unpack .../41-libdc1394-22_2.2.5-2.1_amd64.deb ...\r\n",
"Unpacking libdc1394-22:amd64 (2.2.5-2.1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 37%]\u001b[49m\u001b[39m [#####################.....................................] \u001b8Selecting previously unselected package libopenal-data.\r\n"
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"Preparing to unpack .../42-libopenal-data_1%3a1.19.1-1_all.deb ...\r\n",
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"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 38%]\u001b[49m\u001b[39m [######################....................................] \u001b8"
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"Selecting previously unselected package libsndio7.0:amd64.\r\n",
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"Unpacking libsndio7.0:amd64 (1.5.0-3) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 39%]\u001b[49m\u001b[39m [######################....................................] \u001b8"
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"Selecting previously unselected package libopenal1:amd64.\r\n",
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"Unpacking libopenal1:amd64 (1:1.19.1-1) ...\r\n"
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"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 40%]\u001b[49m\u001b[39m [#######################...................................] \u001b8Selecting previously unselected package libsdl2-2.0-0:amd64.\r\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"Preparing to unpack .../45-libsdl2-2.0-0_2.0.10+dfsg1-3_amd64.deb ...\r\n",
"Unpacking libsdl2-2.0-0:amd64 (2.0.10+dfsg1-3) ...\r\n"
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"text": [
"Selecting previously unselected package libavdevice58:amd64.\r\n"
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{
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"output_type": "stream",
"text": [
"Preparing to unpack .../46-libavdevice58_7%3a4.2.7-0ubuntu0.1_amd64.deb ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 41%]\u001b[49m\u001b[39m [#######################...................................] \u001b8Unpacking libavdevice58:amd64 (7:4.2.7-0ubuntu0.1) ...\r\n",
"Selecting previously unselected package libavresample4:amd64.\r\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"Preparing to unpack .../47-libavresample4_7%3a4.2.7-0ubuntu0.1_amd64.deb ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 42%]\u001b[49m\u001b[39m [########################..................................] \u001b8Unpacking libavresample4:amd64 (7:4.2.7-0ubuntu0.1) ...\r\n"
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"Selecting previously unselected package ffmpeg.\r\n",
"Preparing to unpack .../48-ffmpeg_7%3a4.2.7-0ubuntu0.1_amd64.deb ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 43%]\u001b[49m\u001b[39m [#########################.................................] \u001b8Unpacking ffmpeg (7:4.2.7-0ubuntu0.1) ...\r\n"
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"Selecting previously unselected package libigdgmm11:amd64.\r\n",
"Preparing to unpack .../49-libigdgmm11_20.1.1+ds1-1_amd64.deb ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 44%]\u001b[49m\u001b[39m [#########################.................................] \u001b8Unpacking libigdgmm11:amd64 (20.1.1+ds1-1) ...\r\n"
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"Selecting previously unselected package intel-media-va-driver:amd64.\r\n",
"Preparing to unpack .../50-intel-media-va-driver_20.1.1+dfsg1-1_amd64.deb ...\r\n",
"Unpacking intel-media-va-driver:amd64 (20.1.1+dfsg1-1) ...\r\n"
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"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 45%]\u001b[49m\u001b[39m [##########################................................] \u001b8Selecting previously unselected package libaacs0:amd64.\r\n"
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"Preparing to unpack .../51-libaacs0_0.9.0-2_amd64.deb ...\r\n",
"Unpacking libaacs0:amd64 (0.9.0-2) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 46%]\u001b[49m\u001b[39m [##########################................................] \u001b8Selecting previously unselected package libbdplus0:amd64.\r\n"
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"Preparing to unpack .../52-libbdplus0_0.1.2-3_amd64.deb ...\r\n",
"Unpacking libbdplus0:amd64 (0.1.2-3) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 47%]\u001b[49m\u001b[39m [###########################...............................] \u001b8Selecting previously unselected package mesa-va-drivers:amd64.\r\n"
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"text": [
"Preparing to unpack .../53-mesa-va-drivers_21.2.6-0ubuntu0.1~20.04.2_amd64.deb ...\r\n",
"Unpacking mesa-va-drivers:amd64 (21.2.6-0ubuntu0.1~20.04.2) ...\r\n"
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"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 48%]\u001b[49m\u001b[39m [###########################...............................] \u001b8"
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"Selecting previously unselected package i965-va-driver:amd64.\r\n",
"Preparing to unpack .../54-i965-va-driver_2.4.0-0ubuntu1_amd64.deb ...\r\n",
"Unpacking i965-va-driver:amd64 (2.4.0-0ubuntu1) ...\r\n"
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"Selecting previously unselected package va-driver-all:amd64.\r\n",
"Preparing to unpack .../55-va-driver-all_2.7.0-2_amd64.deb ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 49%]\u001b[49m\u001b[39m [############################..............................] \u001b8Unpacking va-driver-all:amd64 (2.7.0-2) ...\r\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"Setting up libgme0:amd64 (0.6.2-1build1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 50%]\u001b[49m\u001b[39m [#############################.............................] \u001b8Setting up libssh-gcrypt-4:amd64 (0.9.3-2ubuntu2.2) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 51%]\u001b[49m\u001b[39m [#############################.............................] \u001b8Setting up libshine3:amd64 (3.1.1-2) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 52%]\u001b[49m\u001b[39m [##############################............................] \u001b8Setting up libgsm1:amd64 (1.0.18-2) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 53%]\u001b[49m\u001b[39m [##############################............................] \u001b8Setting up libx264-155:amd64 (2:0.155.2917+git0a84d98-2) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 54%]\u001b[49m\u001b[39m [###############################...........................] \u001b8Setting up libx265-179:amd64 (3.2.1-1build1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 55%]\u001b[49m\u001b[39m [###############################...........................] \u001b8Setting up libaom0:amd64 (1.0.0.errata1-3build1) ...\r\n"
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"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 56%]\u001b[49m\u001b[39m [################################..........................] \u001b8Setting up libmysofa1:amd64 (1.0~dfsg0-1) ...\r\n",
"Setting up libdc1394-22:amd64 (2.2.5-2.1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 57%]\u001b[49m\u001b[39m [#################################.........................] \u001b8Setting up libcdio18:amd64 (2.0.0-2) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 58%]\u001b[49m\u001b[39m [#################################.........................] \u001b8Setting up libxvidcore4:amd64 (2:1.3.7-1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 59%]\u001b[49m\u001b[39m [##################################........................] \u001b8Setting up libsnappy1v5:amd64 (1.1.8-1build1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 60%]\u001b[49m\u001b[39m [##################################........................] \u001b8Setting up libass9:amd64 (1:0.14.0-2) ...\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 61%]\u001b[49m\u001b[39m [###################################.......................] \u001b8Setting up libva2:amd64 (2.7.0-2) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 62%]\u001b[49m\u001b[39m [####################################......................] \u001b8Setting up libigdgmm11:amd64 (20.1.1+ds1-1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 63%]\u001b[49m\u001b[39m [####################################......................] \u001b8Setting up libcodec2-0.9:amd64 (0.9.2-2) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 64%]\u001b[49m\u001b[39m [#####################################.....................] \u001b8Setting up intel-media-va-driver:amd64 (20.1.1+dfsg1-1) ...\r\n",
"Setting up libaacs0:amd64 (0.9.0-2) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 65%]\u001b[49m\u001b[39m [#####################################.....................] \u001b8Setting up libfftw3-double3:amd64 (3.3.8-2ubuntu1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 66%]\u001b[49m\u001b[39m [######################################....................] \u001b8Setting up librubberband2:amd64 (1.8.2-1build1) ...\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 67%]\u001b[49m\u001b[39m [######################################....................] \u001b8Setting up libsndio7.0:amd64 (1.5.0-3) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 68%]\u001b[49m\u001b[39m [#######################################...................] \u001b8Setting up libbdplus0:amd64 (0.1.2-3) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 69%]\u001b[49m\u001b[39m [########################################..................] \u001b8Setting up libvidstab1.1:amd64 (1.1.0-2) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 70%]\u001b[49m\u001b[39m [########################################..................] \u001b8Setting up libflite1:amd64 (2.1-release-3) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 71%]\u001b[49m\u001b[39m [#########################################.................] \u001b8Setting up libva-drm2:amd64 (2.7.0-2) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 72%]\u001b[49m\u001b[39m [#########################################.................] \u001b8Setting up ocl-icd-libopencl1:amd64 (2.2.11-1ubuntu1) ...\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Setting up libbs2b0:amd64 (3.1.0+dfsg-2.2build1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 73%]\u001b[49m\u001b[39m [##########################################................] \u001b8Setting up libopenal-data (1:1.19.1-1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 74%]\u001b[49m\u001b[39m [###########################################...............] \u001b8Setting up mesa-va-drivers:amd64 (21.2.6-0ubuntu0.1~20.04.2) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 75%]\u001b[49m\u001b[39m [###########################################...............] \u001b8Setting up libbluray2:amd64 (1:1.2.0-1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 76%]\u001b[49m\u001b[39m [############################################..............] \u001b8Setting up libsdl2-2.0-0:amd64 (2.0.10+dfsg1-3) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 77%]\u001b[49m\u001b[39m [############################################..............] \u001b8Setting up libva-x11-2:amd64 (2.7.0-2) ...\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 78%]\u001b[49m\u001b[39m [#############################################.............] \u001b8Setting up libwebpmux3:amd64 (0.6.1-2ubuntu0.20.04.1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 79%]\u001b[49m\u001b[39m [#############################################.............] \u001b8Setting up libopenmpt0:amd64 (0.4.11-1build1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 80%]\u001b[49m\u001b[39m [##############################################............] \u001b8Setting up libzvbi-common (0.2.35-17) ...\r\n",
"Setting up i965-va-driver:amd64 (2.4.0-0ubuntu1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 81%]\u001b[49m\u001b[39m [###############################################...........] \u001b8Setting up libserd-0-0:amd64 (0.30.2-1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 82%]\u001b[49m\u001b[39m [###############################################...........] \u001b8Setting up libzvbi0:amd64 (0.2.35-17) ...\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 83%]\u001b[49m\u001b[39m [################################################..........] \u001b8Setting up libcdio-cdda2:amd64 (10.2+2.0.0-1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 84%]\u001b[49m\u001b[39m [################################################..........] \u001b8Setting up libcdio-paranoia2:amd64 (10.2+2.0.0-1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 85%]\u001b[49m\u001b[39m [#################################################.........] \u001b8Setting up libopenal1:amd64 (1:1.19.1-1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 86%]\u001b[49m\u001b[39m [##################################################........] \u001b8Setting up libavutil56:amd64 (7:4.2.7-0ubuntu0.1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 87%]\u001b[49m\u001b[39m [##################################################........] \u001b8Setting up va-driver-all:amd64 (2.7.0-2) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 88%]\u001b[49m\u001b[39m [###################################################.......] \u001b8Setting up libpostproc55:amd64 (7:4.2.7-0ubuntu0.1) ...\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Setting up libsord-0-0:amd64 (0.16.4-1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 89%]\u001b[49m\u001b[39m [###################################################.......] \u001b8Setting up libsratom-0-0:amd64 (0.6.4-1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 90%]\u001b[49m\u001b[39m [####################################################......] \u001b8Setting up libswscale5:amd64 (7:4.2.7-0ubuntu0.1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 91%]\u001b[49m\u001b[39m [####################################################......] \u001b8Setting up liblilv-0-0:amd64 (0.24.6-1ubuntu0.1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 92%]\u001b[49m\u001b[39m [#####################################################.....] \u001b8Setting up libswresample3:amd64 (7:4.2.7-0ubuntu0.1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 93%]\u001b[49m\u001b[39m [######################################################....] \u001b8Setting up libavresample4:amd64 (7:4.2.7-0ubuntu0.1) ...\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 94%]\u001b[49m\u001b[39m [######################################################....] \u001b8Setting up libavcodec58:amd64 (7:4.2.7-0ubuntu0.1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 95%]\u001b[49m\u001b[39m [#######################################################...] \u001b8Setting up libchromaprint1:amd64 (1.4.3-3build1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 96%]\u001b[49m\u001b[39m [#######################################################...] \u001b8Setting up libavformat58:amd64 (7:4.2.7-0ubuntu0.1) ...\r\n",
"Setting up libavfilter7:amd64 (7:4.2.7-0ubuntu0.1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 97%]\u001b[49m\u001b[39m [########################################################..] \u001b8Setting up libavdevice58:amd64 (7:4.2.7-0ubuntu0.1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 98%]\u001b[49m\u001b[39m [########################################################..] \u001b8Setting up ffmpeg (7:4.2.7-0ubuntu0.1) ...\r\n",
"\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 99%]\u001b[49m\u001b[39m [#########################################################.] \u001b8"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing triggers for man-db (2.9.1-1) ...\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing triggers for libc-bin (2.31-0ubuntu9.9) ...\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r\n",
"\u001b7\u001b[0;24r\u001b8\u001b[1A\u001b[J"
]
}
],
"source": [
"!sudo apt install -y ffmpeg\n",
"!pip install -q mediapy"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:01:49.621156Z",
"iopub.status.busy": "2022-12-14T22:01:49.620895Z",
"iopub.status.idle": "2022-12-14T22:01:53.546345Z",
"shell.execute_reply": "2022-12-14T22:01:53.545234Z"
},
"id": "s3khsunT7kWa"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33mWARNING: Skipping opencv-python-headless as it is not installed.\u001b[0m\u001b[33m\r\n",
"\u001b[0m"
]
}
],
"source": [
"!pip uninstall -q -y opencv-python-headless\n",
"!pip install -q \"opencv-python-headless<4.3\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:01:53.550855Z",
"iopub.status.busy": "2022-12-14T22:01:53.550091Z",
"iopub.status.idle": "2022-12-14T22:01:55.906037Z",
"shell.execute_reply": "2022-12-14T22:01:55.904987Z"
},
"id": "dI_1csl6Q-gH"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-12-14 22:01:54.870835: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
"2022-12-14 22:01:54.870941: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
"2022-12-14 22:01:54.870971: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
]
}
],
"source": [
"# Import libraries\n",
"import pathlib\n",
"\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import mediapy as media\n",
"import numpy as np\n",
"import PIL\n",
"\n",
"import tensorflow as tf\n",
"import tensorflow_hub as hub\n",
"import tqdm\n",
"\n",
"mpl.rcParams.update({\n",
" 'font.size': 10,\n",
"})"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Pn8K9oWbmREi"
},
"source": [
"获取 kinetics 600 标签列表，并打印前几个标签："
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:01:55.911154Z",
"iopub.status.busy": "2022-12-14T22:01:55.910347Z",
"iopub.status.idle": "2022-12-14T22:01:56.150236Z",
"shell.execute_reply": "2022-12-14T22:01:56.149640Z"
},
"id": "2VJUAcjhkfb3"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading data from https://raw.githubusercontent.com/tensorflow/models/f8af2291cced43fc9f1d9b41ddbf772ae7b0d7d2/official/projects/movinet/files/kinetics_600_labels.txt\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"8192/9209 [=========================>....] - ETA: 0s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"9209/9209 [==============================] - 0s 0us/step\n"
]
},
{
"data": {
"text/plain": [
"array(['abseiling', 'acting in play', 'adjusting glasses', 'air drumming',\n",
" 'alligator wrestling', 'answering questions', 'applauding',\n",
" 'applying cream', 'archaeological excavation', 'archery',\n",
" 'arguing', 'arm wrestling', 'arranging flowers',\n",
" 'assembling bicycle', 'assembling computer',\n",
" 'attending conference', 'auctioning', 'backflip (human)',\n",
" 'baking cookies', 'bandaging'], dtype='}\n",
" <1>: float32 Tensor, shape=(None, 600)\n"
]
}
],
"source": [
"sig = model.signatures['serving_default']\n",
"print(sig.pretty_printed_signature())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "M4Xny1ANomi4"
},
"source": [
"要在视频上运行此签名，您需要先将外部 `batch` 维度添加到视频中。"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:02:15.714357Z",
"iopub.status.busy": "2022-12-14T22:02:15.713796Z",
"iopub.status.idle": "2022-12-14T22:02:26.352514Z",
"shell.execute_reply": "2022-12-14T22:02:26.351662Z"
},
"id": "LBOFEDG1XvZE"
},
"outputs": [],
"source": [
"#warmup\n",
"sig(image = jumpingjack[tf.newaxis, :1]);"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:02:26.356766Z",
"iopub.status.busy": "2022-12-14T22:02:26.356178Z",
"iopub.status.idle": "2022-12-14T22:02:32.699062Z",
"shell.execute_reply": "2022-12-14T22:02:32.698280Z"
},
"id": "jCeW3KycVbGn"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(600,)\n",
"\n",
"CPU times: user 4.03 s, sys: 33.9 ms, total: 4.07 s\n",
"Wall time: 6.34 s\n"
]
}
],
"source": [
"%%time\n",
"logits = sig(image = jumpingjack[tf.newaxis, ...])\n",
"logits = logits['classifier_head'][0]\n",
"\n",
"print(logits.shape)\n",
"print()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AE8doqkPpxED"
},
"source": [
"定义一个 `get_top_k` 函数，将上述输出处理打包以备后用。"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:02:32.702743Z",
"iopub.status.busy": "2022-12-14T22:02:32.702009Z",
"iopub.status.idle": "2022-12-14T22:02:32.707358Z",
"shell.execute_reply": "2022-12-14T22:02:32.706653Z"
},
"id": "OozPNO6LvZ00"
},
"outputs": [],
"source": [
"#@title\n",
"# Get top_k labels and probabilities\n",
"def get_top_k(probs, k=5, label_map=KINETICS_600_LABELS):\n",
" \"\"\"Outputs the top k model labels and probabilities on the given video.\n",
"\n",
" Args:\n",
" probs: probability tensor of shape (num_frames, num_classes) that represents\n",
" the probability of each class on each frame.\n",
" k: the number of top predictions to select.\n",
" label_map: a list of labels to map logit indices to label strings.\n",
"\n",
" Returns:\n",
" a tuple of the top-k labels and probabilities.\n",
" \"\"\"\n",
" # Sort predictions to find top_k\n",
" top_predictions = tf.argsort(probs, axis=-1, direction='DESCENDING')[:k]\n",
" # collect the labels of top_k predictions\n",
" top_labels = tf.gather(label_map, top_predictions, axis=-1)\n",
" # decode lablels\n",
" top_labels = [label.decode('utf8') for label in top_labels.numpy()]\n",
" # top_k probabilities of the predictions\n",
" top_probs = tf.gather(probs, top_predictions, axis=-1).numpy()\n",
" return tuple(zip(top_labels, top_probs))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kTfKMT29pP_Z"
},
"source": [
"将 `logits` 转换为概率，并查找视频的前 5 个类。模型确认该视频可能是 `jumping jacks`。"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:02:32.710324Z",
"iopub.status.busy": "2022-12-14T22:02:32.709877Z",
"iopub.status.idle": "2022-12-14T22:02:32.724105Z",
"shell.execute_reply": "2022-12-14T22:02:32.723520Z"
},
"id": "Z-SrNGsGV5Mt"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"jumping jacks : 0.834\n",
"zumba : 0.008\n",
"lunge : 0.003\n",
"doing aerobics : 0.003\n",
"polishing metal : 0.002\n"
]
}
],
"source": [
"probs = tf.nn.softmax(logits, axis=-1)\n",
"for label, p in get_top_k(probs):\n",
" print(f'{label:20s}: {p:.3f}')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ltdijoQpqjxZ"
},
"source": [
"### 流式模型"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9dqdUPQXq45b"
},
"source": [
"上一部分使用了一个贯穿整个视频的模型。通常在处理视频时，您不希望在最后进行单次预测，而是希望逐帧更新预测。`stream` 版本的模型可让您实现此目的。\n",
"\n",
"加载 `stream` 版本的模型。"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:02:32.727179Z",
"iopub.status.busy": "2022-12-14T22:02:32.726675Z",
"iopub.status.idle": "2022-12-14T22:03:17.976845Z",
"shell.execute_reply": "2022-12-14T22:03:17.976058Z"
},
"id": "mxt0hRXFZkAM"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 43 s, sys: 2.22 s, total: 45.3 s\n",
"Wall time: 45.2 s\n"
]
}
],
"source": [
"%%time\n",
"id = 'a2'\n",
"mode = 'stream'\n",
"version = '3'\n",
"hub_url = f'https://tfhub.dev/tensorflow/movinet/{id}/{mode}/kinetics-600/classification/{version}'\n",
"model = hub.load(hub_url)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pDswtsGgsYGS"
},
"source": [
"此模型的用法比 `base` 模型略微复杂一些。您必须跟踪模型 RNN 的内部状态。"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:03:17.981381Z",
"iopub.status.busy": "2022-12-14T22:03:17.980810Z",
"iopub.status.idle": "2022-12-14T22:03:17.985558Z",
"shell.execute_reply": "2022-12-14T22:03:17.984927Z"
},
"id": "0fM_Vb1VsbDm"
},
"outputs": [
{
"data": {
"text/plain": [
"['call', 'init_states']"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(model.signatures.keys())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ojr1_iYCtPvp"
},
"source": [
"`init_states` 签名将视频的**形状** `(batch, frames, height, width, colors)` 作为输入，并返回包含初始 RNN 状态的大型张量字典： "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:03:17.988784Z",
"iopub.status.busy": "2022-12-14T22:03:17.988222Z",
"iopub.status.idle": "2022-12-14T22:03:17.993434Z",
"shell.execute_reply": "2022-12-14T22:03:17.992854Z"
},
"id": "67loYFGpo_RP"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"signature_wrapper(*, input_shape).\n",
" Args:.\n",
" input_shape: int32 Tensor, shape=(5,).\n",
" Returns:.\n",
" {'state/b0/l0/pool_buffer': <1>, 'state/b0/l0/pool_frame_count': <2>, 'state/b0/l1/pool_buffer': <3>, 'state/b0/l1/pool_frame_count': <4>, 'state/b0/l1/stream_buffer': <5>, 'state/b0/l2/pool_buffer': <6>, 'state/b0/l2/pool_frame_count': <7>, 'state/b0/l2/stream_buffer': <8>, 'state/b1/l0/pool_buffer': <9>, 'state/b1/l0/pool_frame_count': <10>, 'state/b1/l0/stream_buffer': <11>, 'state/b1/l1/pool_buffer': <12>, 'state/b1/l1/pool_frame_count': <13>, 'state/b1/l1/stream_buffer': <14>, 'state/b1/l2/pool_buffer': <15>, 'state/b1/l2/pool_frame_count': <16>, 'state/b1/l2/stream_buffer': <17>, 'state/b1/l3/pool_buffer': <18>, 'state/b1/l3/pool_frame_count': <19>, 'state/b1/l3/stream_buffer': <20>, 'state/b1/l4/pool_buffer': <21>, 'state/b1/l4/pool_frame_count': <22>, 'state/b1/l4/stream_buffer': <23>, 'state/b2/l0/pool_buffer': <24>, 'state/b2/l0/pool_frame_count': <25>, 'state/b2/l0/stream_buffer': <26>, 'state/b2/l1/pool_buffer': <27>, 'state/b2/l1/pool_frame_count': <28>, 'state/b2/l1/stream_buffer': <29>, 'state/b2/l2/pool_buffer': <30>, 'state/b2/l2/pool_frame_count': <31>, 'state/b2/l2/stream_buffer': <32>, 'state/b2/l3/pool_buffer': <33>, 'state/b2/l3/pool_frame_count': <34>, 'state/b2/l3/stream_buffer': <35>, 'state/b2/l4/pool_buffer': <36>, 'state/b2/l4/pool_frame_count': <37>, 'state/b2/l4/stream_buffer': <38>, 'state/b3/l0/pool_buffer': <39>, 'state/b3/l0/pool_frame_count': <40>, 'state/b3/l0/stream_buffer': <41>, 'state/b3/l1/pool_buffer': <42>, 'state/b3/l1/pool_frame_count': <43>, 'state/b3/l1/stream_buffer': <44>, 'state/b3/l2/pool_buffer': <45>, 'state/b3/l2/pool_frame_count': <46>, 'state/b3/l2/stream_buffer': <47>, 'state/b3/l3/pool_buffer': <48>, 'state/b3/l3/pool_frame_count': <49>, 'state/b3/l3/stream_buffer': <50>, 'state/b3/l4/pool_buffer': <51>, 'state/b3/l4/pool_frame_count': <52>, 'state/b3/l5/pool_buffer': <53>, 'state/b3/l5/pool_frame_count': <54>, 'state/b3/l5/stream_buffer': <55>, 'state/b4/l0/pool_buffer': <56>, 'state/b4/l0/pool_frame_count': <57>, 'state/b4/l0/stream_buffer': <58>, 'state/b4/l1/pool_buffer': <59>, 'state/b4/l1/pool_frame_count': <60>, 'state/b4/l2/pool_buffer': <61>, 'state/b4/l2/pool_frame_count': <62>, 'state/b4/l3/pool_buffer': <63>, 'state/b4/l3/pool_frame_count': <64>, 'state/b4/l4/pool_buffer': <65>, 'state/b4/l4/pool_frame_count': <66>, 'state/b4/l5/pool_buffer': <67>, 'state/b4/l5/pool_frame_count': <68>, 'state/b4/l5/stream_buffer': <69>, 'state/b4/l6/pool_buffer': <70>, 'state/b4/l6/pool_frame_count': <71>, 'state/head/pool_buffer': <72>, 'state/head/pool_frame_count': <73>}.\n",
" <1>: float32 Tensor, shape=(None, 1, 1, 1, 40).\n",
" <2>: int32 Tensor, shape=(1,).\n",
" <3>: float32 Tensor, shape=(None, 1, 1, 1, 40).\n",
" <4>: int32 Tensor, shape=(1,).\n",
" <5>: float32 Tensor, shape=(None, 2, None, None, 40).\n",
" ...\n"
]
}
],
"source": [
"lines = model.signatures['init_states'].pretty_printed_signature().splitlines()\n",
"lines = lines[:10]\n",
"lines.append(' ...')\n",
"print('.\\n'.join(lines))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:03:17.996420Z",
"iopub.status.busy": "2022-12-14T22:03:17.995850Z",
"iopub.status.idle": "2022-12-14T22:03:18.225334Z",
"shell.execute_reply": "2022-12-14T22:03:18.224581Z"
},
"id": "v5lG3vejn5df"
},
"outputs": [],
"source": [
"initial_state = model.init_states(jumpingjack[tf.newaxis, ...].shape)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:03:18.229367Z",
"iopub.status.busy": "2022-12-14T22:03:18.228877Z",
"iopub.status.idle": "2022-12-14T22:03:18.233334Z",
"shell.execute_reply": "2022-12-14T22:03:18.232778Z"
},
"id": "J3DwmyHnuhH_"
},
"outputs": [
{
"data": {
"text/plain": [
"dict"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(initial_state)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:03:18.236196Z",
"iopub.status.busy": "2022-12-14T22:03:18.235681Z",
"iopub.status.idle": "2022-12-14T22:03:18.239780Z",
"shell.execute_reply": "2022-12-14T22:03:18.239226Z"
},
"id": "K8SyiEU6tB-e"
},
"outputs": [
{
"data": {
"text/plain": [
"['state/b0/l0/pool_buffer',\n",
" 'state/b0/l0/pool_frame_count',\n",
" 'state/b0/l1/pool_buffer',\n",
" 'state/b0/l1/pool_frame_count',\n",
" 'state/b0/l1/stream_buffer']"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(sorted(initial_state.keys()))[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xeMCzJMBvwRF"
},
"source": [
"获得 RNN 的初始状态后，您可以传递状态和视频帧作为输入（保持视频帧的 `(batch, frames, height, width, colors)` 形状）。该模型会返回一个 `(logits, state)` 对。\n",
"\n",
"刚看到第一帧后，模型不相信视频是“跳跃运动”："
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:03:18.242838Z",
"iopub.status.busy": "2022-12-14T22:03:18.242314Z",
"iopub.status.idle": "2022-12-14T22:03:18.247502Z",
"shell.execute_reply": "2022-12-14T22:03:18.246944Z"
},
"id": "McSLdIgtsI3d"
},
"outputs": [],
"source": [
"inputs = initial_state.copy()\n",
"\n",
"# Add the batch axis, take the first frme, but keep the frame-axis.\n",
"inputs['image'] = jumpingjack[tf.newaxis, 0:1, ...] "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:03:18.250305Z",
"iopub.status.busy": "2022-12-14T22:03:18.249797Z",
"iopub.status.idle": "2022-12-14T22:03:25.171874Z",
"shell.execute_reply": "2022-12-14T22:03:25.170985Z"
},
"id": "WlH7PqLPX664"
},
"outputs": [],
"source": [
"# warmup\n",
"model(inputs);"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:03:25.176083Z",
"iopub.status.busy": "2022-12-14T22:03:25.175447Z",
"iopub.status.idle": "2022-12-14T22:03:25.220492Z",
"shell.execute_reply": "2022-12-14T22:03:25.219835Z"
},
"id": "7uzNXtu7X5sr"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"golf chipping : 0.427\n",
"tackling : 0.134\n",
"lunge : 0.056\n",
"stretching arm : 0.053\n",
"passing american football (not in game): 0.039\n",
"\n"
]
}
],
"source": [
"logits, new_state = model(inputs)\n",
"logits = logits[0]\n",
"probs = tf.nn.softmax(logits, axis=-1)\n",
"\n",
"for label, p in get_top_k(probs):\n",
" print(f'{label:20s}: {p:.3f}')\n",
"\n",
"print()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oLU644FQwXSb"
},
"source": [
"如果您在循环中运行模型，并在每一帧中传递更新的状态，模型会迅速收敛到正确的结果："
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:03:25.223986Z",
"iopub.status.busy": "2022-12-14T22:03:25.223426Z",
"iopub.status.idle": "2022-12-14T22:03:25.701045Z",
"shell.execute_reply": "2022-12-14T22:03:25.700372Z"
},
"id": "Fzm7T4ImmIEg"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 516 ms, sys: 17.5 ms, total: 533 ms\n",
"Wall time: 473 ms\n"
]
}
],
"source": [
"%%time\n",
"state = initial_state.copy()\n",
"all_logits = []\n",
"\n",
"for n in range(len(jumpingjack)):\n",
" inputs = state\n",
" inputs['image'] = jumpingjack[tf.newaxis, n:n+1, ...]\n",
" result, state = model(inputs)\n",
" all_logits.append(logits)\n",
"\n",
"probabilities = tf.nn.softmax(all_logits, axis=-1)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:03:25.704441Z",
"iopub.status.busy": "2022-12-14T22:03:25.703959Z",
"iopub.status.idle": "2022-12-14T22:03:25.710210Z",
"shell.execute_reply": "2022-12-14T22:03:25.709590Z"
},
"id": "B7UtHoSWcOT2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"golf chipping : 0.427\n",
"tackling : 0.134\n",
"lunge : 0.056\n",
"stretching arm : 0.053\n",
"passing american football (not in game): 0.039\n"
]
}
],
"source": [
"for label, p in get_top_k(probabilities[-1]):\n",
" print(f'{label:20s}: {p:.3f}')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:03:25.713437Z",
"iopub.status.busy": "2022-12-14T22:03:25.712865Z",
"iopub.status.idle": "2022-12-14T22:03:25.851643Z",
"shell.execute_reply": "2022-12-14T22:03:25.850948Z"
},
"id": "6ffV3NhZcsrv"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"id = tf.argmax(probabilities[-1])\n",
"plt.plot(probabilities[:, id])\n",
"plt.xlabel('Frame #')\n",
"plt.ylabel(f\"p('{KINETICS_600_LABELS[id]}')\");"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "d7MZ_AfRW845"
},
"source": [
"您可能会注意到，最终概率比上一部分中运行 `base` 模型的确定性要高得多。`base` 模型返回帧上预测的平均值。"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:03:25.854874Z",
"iopub.status.busy": "2022-12-14T22:03:25.854640Z",
"iopub.status.idle": "2022-12-14T22:03:25.862071Z",
"shell.execute_reply": "2022-12-14T22:03:25.861357Z"
},
"id": "0Wij4tsyW8dR"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"golf chipping : 0.427\n",
"tackling : 0.134\n",
"lunge : 0.056\n",
"stretching arm : 0.053\n",
"passing american football (not in game): 0.039\n"
]
}
],
"source": [
"for label, p in get_top_k(tf.reduce_mean(probabilities, axis=0)):\n",
" print(f'{label:20s}: {p:.3f}')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qLUoC9ejggGo"
},
"source": [
"## 让预测变成随时间变化的动画\n",
"\n",
"上一部分详细介绍了如何使用这些模型。本部分在此基础上生成一些不错的推断动画。 "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OnFqOXazoWgy"
},
"source": [
"下面的隐藏单元定义了本部分中使用的辅助函数。"
]
},
{
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"#@title\n",
"# Get top_k labels and probabilities predicted using MoViNets streaming model\n",
"def get_top_k_streaming_labels(probs, k=5, label_map=KINETICS_600_LABELS):\n",
" \"\"\"Returns the top-k labels over an entire video sequence.\n",
"\n",
" Args:\n",
" probs: probability tensor of shape (num_frames, num_classes) that represents\n",
" the probability of each class on each frame.\n",
" k: the number of top predictions to select.\n",
" label_map: a list of labels to map logit indices to label strings.\n",
"\n",
" Returns:\n",
" a tuple of the top-k probabilities, labels, and logit indices\n",
" \"\"\"\n",
" top_categories_last = tf.argsort(probs, -1, 'DESCENDING')[-1, :1]\n",
" # Sort predictions to find top_k\n",
" categories = tf.argsort(probs, -1, 'DESCENDING')[:, :k]\n",
" categories = tf.reshape(categories, [-1])\n",
"\n",
" counts = sorted([\n",
" (i.numpy(), tf.reduce_sum(tf.cast(categories == i, tf.int32)).numpy())\n",
" for i in tf.unique(categories)[0]\n",
" ], key=lambda x: x[1], reverse=True)\n",
"\n",
" top_probs_idx = tf.constant([i for i, _ in counts[:k]])\n",
" top_probs_idx = tf.concat([top_categories_last, top_probs_idx], 0)\n",
" # find unique indices of categories\n",
" top_probs_idx = tf.unique(top_probs_idx)[0][:k+1]\n",
" # top_k probabilities of the predictions\n",
" top_probs = tf.gather(probs, top_probs_idx, axis=-1)\n",
" top_probs = tf.transpose(top_probs, perm=(1, 0))\n",
" # collect the labels of top_k predictions\n",
" top_labels = tf.gather(label_map, top_probs_idx, axis=0)\n",
" # decode the top_k labels\n",
" top_labels = [label.decode('utf8') for label in top_labels.numpy()]\n",
"\n",
" return top_probs, top_labels, top_probs_idx\n",
"\n",
"# Plot top_k predictions at a given time step\n",
"def plot_streaming_top_preds_at_step(\n",
" top_probs,\n",
" top_labels,\n",
" step=None,\n",
" image=None,\n",
" legend_loc='lower left',\n",
" duration_seconds=10,\n",
" figure_height=500,\n",
" playhead_scale=0.8,\n",
" grid_alpha=0.3):\n",
" \"\"\"Generates a plot of the top video model predictions at a given time step.\n",
"\n",
" Args:\n",
" top_probs: a tensor of shape (k, num_frames) representing the top-k\n",
" probabilities over all frames.\n",
" top_labels: a list of length k that represents the top-k label strings.\n",
" step: the current time step in the range [0, num_frames].\n",
" image: the image frame to display at the current time step.\n",
" legend_loc: the placement location of the legend.\n",
" duration_seconds: the total duration of the video.\n",
" figure_height: the output figure height.\n",
" playhead_scale: scale value for the playhead.\n",
" grid_alpha: alpha value for the gridlines.\n",
"\n",
" Returns:\n",
" A tuple of the output numpy image, figure, and axes.\n",
" \"\"\"\n",
" # find number of top_k labels and frames in the video\n",
" num_labels, num_frames = top_probs.shape\n",
" if step is None:\n",
" step = num_frames\n",
" # Visualize frames and top_k probabilities of streaming video\n",
" fig = plt.figure(figsize=(6.5, 7), dpi=300)\n",
" gs = mpl.gridspec.GridSpec(8, 1)\n",
" ax2 = plt.subplot(gs[:-3, :])\n",
" ax = plt.subplot(gs[-3:, :])\n",
" # display the frame\n",
" if image is not None:\n",
" ax2.imshow(image, interpolation='nearest')\n",
" ax2.axis('off')\n",
" # x-axis (frame number)\n",
" preview_line_x = tf.linspace(0., duration_seconds, num_frames)\n",
" # y-axis (top_k probabilities)\n",
" preview_line_y = top_probs\n",
"\n",
" line_x = preview_line_x[:step+1]\n",
" line_y = preview_line_y[:, :step+1]\n",
"\n",
" for i in range(num_labels):\n",
" ax.plot(preview_line_x, preview_line_y[i], label=None, linewidth='1.5',\n",
" linestyle=':', color='gray')\n",
" ax.plot(line_x, line_y[i], label=top_labels[i], linewidth='2.0')\n",
"\n",
"\n",
" ax.grid(which='major', linestyle=':', linewidth='1.0', alpha=grid_alpha)\n",
" ax.grid(which='minor', linestyle=':', linewidth='0.5', alpha=grid_alpha)\n",
"\n",
" min_height = tf.reduce_min(top_probs) * playhead_scale\n",
" max_height = tf.reduce_max(top_probs)\n",
" ax.vlines(preview_line_x[step], min_height, max_height, colors='red')\n",
" ax.scatter(preview_line_x[step], max_height, color='red')\n",
"\n",
" ax.legend(loc=legend_loc)\n",
"\n",
" plt.xlim(0, duration_seconds)\n",
" plt.ylabel('Probability')\n",
" plt.xlabel('Time (s)')\n",
" plt.yscale('log')\n",
"\n",
" fig.tight_layout()\n",
" fig.canvas.draw()\n",
"\n",
" data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)\n",
" data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))\n",
" plt.close()\n",
"\n",
" figure_width = int(figure_height * data.shape[1] / data.shape[0])\n",
" image = PIL.Image.fromarray(data).resize([figure_width, figure_height])\n",
" image = np.array(image)\n",
"\n",
" return image\n",
"\n",
"# Plotting top_k predictions from MoViNets streaming model\n",
"def plot_streaming_top_preds(\n",
" probs,\n",
" video,\n",
" top_k=5,\n",
" video_fps=25.,\n",
" figure_height=500,\n",
" use_progbar=True):\n",
" \"\"\"Generates a video plot of the top video model predictions.\n",
"\n",
" Args:\n",
" probs: probability tensor of shape (num_frames, num_classes) that represents\n",
" the probability of each class on each frame.\n",
" video: the video to display in the plot.\n",
" top_k: the number of top predictions to select.\n",
" video_fps: the input video fps.\n",
" figure_fps: the output video fps.\n",
" figure_height: the height of the output video.\n",
" use_progbar: display a progress bar.\n",
"\n",
" Returns:\n",
" A numpy array representing the output video.\n",
" \"\"\"\n",
" # select number of frames per second\n",
" video_fps = 8.\n",
" # select height of the image\n",
" figure_height = 500\n",
" # number of time steps of the given video\n",
" steps = video.shape[0]\n",
" # estimate duration of the video (in seconds)\n",
" duration = steps / video_fps\n",
" # estiamte top_k probabilities and corresponding labels\n",
" top_probs, top_labels, _ = get_top_k_streaming_labels(probs, k=top_k)\n",
"\n",
" images = []\n",
" step_generator = tqdm.trange(steps) if use_progbar else range(steps)\n",
" for i in step_generator:\n",
" image = plot_streaming_top_preds_at_step(\n",
" top_probs=top_probs,\n",
" top_labels=top_labels,\n",
" step=i,\n",
" image=video[i],\n",
" duration_seconds=duration,\n",
" figure_height=figure_height,\n",
" )\n",
" images.append(image)\n",
"\n",
" return np.array(images)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eLgFBslcZOQO"
},
"source": [
"首先，在视频的帧上运行流式模型，然后收集 logit："
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"execution": {
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"shell.execute_reply": "2022-12-14T22:03:26.133918Z"
},
"id": "tXWR13wthnK5"
},
"outputs": [],
"source": [
"init_states = model.init_states(jumpingjack[tf.newaxis].shape)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T22:03:26.138746Z",
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"shell.execute_reply": "2022-12-14T22:03:26.624405Z"
},
"id": "YqSkt7l8ltwt"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
" 0%| | 0/13 [00:00\n",
" \n",
" This browser does not support the video tag.\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# For gif format, set codec='gif'\n",
"media.show_video(plot_video, fps=3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LCImgZ3OdJw7"
},
"source": [
"## 资源\n",
"\n",
"预训练模型可从 [TF Hub](https://tfhub.dev/google/collections/movinet/1) 获得。TF Hub 集合还包括为 [TFLite](https://tensorflow.org/lite) 优化的量化模型。\n",
"\n",
"这些模型的源代码可在 [TensorFlow Model Garden](https://github.com/tensorflow/models/tree/master/official/projects/movinet) 中找到。包括[本教程的较长版本](https://colab.sandbox.google.com/github/tensorflow/models/blob/master/official/projects/movinet/movinet_tutorial.ipynb)，较长版本还介绍了如何构建和微调 MoViNet 模型。 "
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "movinet.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"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": "3.9.16"
}
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"nbformat": 4,
"nbformat_minor": 0
}
![](\"https://tensorflow.google.cn/images/tf_logo_32px.png\"){kind=logo}
在 TensorFlow.org 上查看\n",
"![](\"https://tensorflow.google.cn/images/colab_logo_32px.png\"){kind=logo}
在 Google Colab 中运行![](\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\")
在 GitHub 上查看源代码![](\"https://tensorflow.google.cn/images/download_logo_32px.png\"){kind=logo}
下载笔记本 ![](\"https://tensorflow.google.cn/images/hub_logo_32px.png\"){kind=logo}
查看 TF Hub 模型 \n",
"