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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "VcjSRFELVbNk"
},
"source": [
"# MMAction2 Tutorial\n",
"\n",
"Welcome to MMAction2! This is the official colab tutorial for using MMAction2. In this tutorial, you will learn\n",
"- Perform inference with a MMAction2 recognizer.\n",
"- Train a new recognizer with a new dataset.\n",
"\n",
"\n",
"Let's start!"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7LqHGkGEVqpm"
},
"source": [
"## Install MMAction2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Bf8PpPXtVvmg",
"outputId": "9d3f4594-f151-4ee9-a19b-09f8a439ac04"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"nvcc: NVIDIA (R) Cuda compiler driver\n",
"Copyright (c) 2005-2022 NVIDIA Corporation\n",
"Built on Wed_Sep_21_10:33:58_PDT_2022\n",
"Cuda compilation tools, release 11.8, V11.8.89\n",
"Build cuda_11.8.r11.8/compiler.31833905_0\n",
"gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0\n",
"Copyright (C) 2019 Free Software Foundation, Inc.\n",
"This is free software; see the source for copying conditions. There is NO\n",
"warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n",
"\n"
]
}
],
"source": [
"# Check nvcc version\n",
"!nvcc -V\n",
"# Check GCC version\n",
"!gcc --version"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "ZPwKGzqydnb2",
"outputId": "27506fa7-48a2-4fe0-d377-56f940dafec4",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://download.pytorch.org/whl/cu118, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.0.0+cu118)\n",
"Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.15.1+cu118)\n",
"Requirement already satisfied: torchaudio in /usr/local/lib/python3.10/dist-packages (2.0.1+cu118)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.12.0)\n",
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
"Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.11.1)\n",
"Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.1)\n",
"Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n",
"Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.0.0)\n",
"Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (3.25.2)\n",
"Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (16.0.3)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.22.4)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision) (2.27.1)\n",
"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (8.4.0)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.2)\n",
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (1.26.15)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2022.12.7)\n",
"Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2.0.12)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.4)\n",
"Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n"
]
}
],
"source": [
"# install dependencies: (if your colab has CUDA 11.8)\n",
"%pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5PAJ4ArzV5Ry",
"outputId": "eb8539a0-9524-4c48-f3e1-0b013ce0d344"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting openmim\n",
" Downloading openmim-0.3.7-py2.py3-none-any.whl (51 kB)\n",
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" Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
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"Collecting ordered-set (from model-index->openmim)\n",
" Downloading ordered_set-4.1.0-py3-none-any.whl (7.6 kB)\n",
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"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->openmim) (1.16.0)\n",
"Installing collected packages: ordered-set, colorama, model-index, openmim\n",
"Successfully installed colorama-0.4.6 model-index-0.1.11 openmim-0.3.7 ordered-set-4.1.0\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Looking in links: https://download.openmmlab.com/mmcv/dist/cu118/torch2.0.0/index.html\n",
"Collecting mmengine\n",
" Downloading mmengine-0.7.3-py3-none-any.whl (372 kB)\n",
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"\u001B[?25hCollecting addict (from mmengine)\n",
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"Collecting yapf (from mmengine)\n",
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"Installing collected packages: addict, yapf, mmengine\n",
"Successfully installed addict-2.4.0 mmengine-0.7.3 yapf-0.33.0\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Looking in links: https://download.openmmlab.com/mmcv/dist/cu118/torch2.0.0/index.html\n",
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]
}
],
"source": [
"# install MMEngine, MMCV and MMDetection using MIM\n",
"%pip install -U openmim\n",
"!mim install mmengine\n",
"!mim install \"mmcv>=2.0.0\"\n",
"\n",
"# Install mmaction2\n",
"!rm -rf mmaction2\n",
"!git clone https://github.com/open-mmlab/mmaction2.git -b main\n",
"%cd mmaction2\n",
"\n",
"!pip install -e .\n",
"\n",
"# Install some optional requirements\n",
"!pip install -r requirements/optional.txt"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "No_zZAFpWC-a",
"outputId": "9386dd81-2308-4adb-d3cb-798de11c035e"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2.0.0+cu118 True\n",
"1.0.0\n",
"11.8\n",
"GCC 9.3\n",
"OrderedDict([('sys.platform', 'linux'), ('Python', '3.10.11 (main, Apr 5 2023, 14:15:10) [GCC 9.4.0]'), ('CUDA available', True), ('numpy_random_seed', 2147483648), ('GPU 0', 'Tesla T4'), ('CUDA_HOME', '/usr/local/cuda'), ('NVCC', 'Cuda compilation tools, release 11.8, V11.8.89'), ('GCC', 'x86_64-linux-gnu-gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0'), ('PyTorch', '2.0.0+cu118'), ('PyTorch compiling details', 'PyTorch built with:\\n - GCC 9.3\\n - C++ Version: 201703\\n - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications\\n - Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)\\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\\n - LAPACK is enabled (usually provided by MKL)\\n - NNPACK is enabled\\n - CPU capability usage: AVX2\\n - CUDA Runtime 11.8\\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90\\n - CuDNN 8.7\\n - Magma 2.6.1\\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \\n'), ('TorchVision', '0.15.1+cu118'), ('OpenCV', '4.7.0'), ('MMEngine', '0.7.3')])\n"
]
}
],
"source": [
"# Check Pytorch installation\n",
"import torch, torchvision\n",
"print(torch.__version__, torch.cuda.is_available())\n",
"\n",
"# Check MMAction2 installation\n",
"import mmaction\n",
"print(mmaction.__version__)\n",
"\n",
"# Check MMCV installation\n",
"from mmcv.ops import get_compiling_cuda_version, get_compiler_version\n",
"print(get_compiling_cuda_version())\n",
"print(get_compiler_version())\n",
"\n",
"# Check MMEngine installation\n",
"from mmengine.utils.dl_utils import collect_env\n",
"print(collect_env())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pXf7oV5DWdab"
},
"source": [
"## Perform inference with a MMAction2 recognizer\n",
"MMAction2 already provides high level APIs to do inference and training."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "64CW6d_AaT-Q",
"outputId": "ea330d8c-2e20-4dbd-d046-51d7c9ec4f7a"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2023-05-15 03:33:08-- https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth\n",
"Resolving download.openmmlab.com (download.openmmlab.com)... 163.181.82.216, 163.181.82.218, 163.181.82.213, ...\n",
"Connecting to download.openmmlab.com (download.openmmlab.com)|163.181.82.216|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 97579339 (93M) [application/octet-stream]\n",
"Saving to: ‘checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth’\n",
"\n",
"checkpoints/tsn_r50 100%[===================>] 93.06M 26.1MB/s in 3.6s \n",
"\n",
"2023-05-15 03:33:12 (26.2 MB/s) - ‘checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth’ saved [97579339/97579339]\n",
"\n"
]
}
],
"source": [
"!mkdir checkpoints\n",
"!wget -c https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth \\\n",
" -O checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HNZB7NoSabzj",
"outputId": "c0c2ba71-72ff-4cac-a5b8-65590f5a6bb0"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Loads checkpoint by local backend from path: checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth\n"
]
}
],
"source": [
"from mmaction.apis import inference_recognizer, init_recognizer\n",
"from mmengine import Config\n",
"\n",
"\n",
"# Choose to use a config and initialize the recognizer\n",
"config = 'configs/recognition/tsn/tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb.py'\n",
"config = Config.fromfile(config)\n",
"# Setup a checkpoint file to load\n",
"checkpoint = 'checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth'\n",
"# Initialize the recognizer\n",
"model = init_recognizer(config, checkpoint, device='cuda:0')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "rEMsBnpHapAn",
"outputId": "ec05049e-7289-4798-94fa-2b773cb23634",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"05/15 03:33:18 - mmengine - WARNING - \"FileClient\" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io\n",
"05/15 03:33:18 - mmengine - WARNING - \"HardDiskBackend\" is the alias of \"LocalBackend\" and the former will be deprecated in future.\n"
]
}
],
"source": [
"# Use the recognizer to do inference\n",
"from operator import itemgetter\n",
"video = 'demo/demo.mp4'\n",
"label = 'tools/data/kinetics/label_map_k400.txt'\n",
"results = inference_recognizer(model, video)\n",
"\n",
"pred_scores = results.pred_score.tolist()\n",
"score_tuples = tuple(zip(range(len(pred_scores)), pred_scores))\n",
"score_sorted = sorted(score_tuples, key=itemgetter(1), reverse=True)\n",
"top5_label = score_sorted[:5]\n",
"\n",
"labels = open(label).readlines()\n",
"labels = [x.strip() for x in labels]\n",
"results = [(labels[k[0]], k[1]) for k in top5_label]\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NIyJXqfWathq",
"outputId": "cb25aca9-e72d-4c54-f295-4c889713cb3a"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"The top-5 labels with corresponding scores are:\n",
"arm wrestling: 1.0\n",
"rock scissors paper: 6.434453414527752e-09\n",
"shaking hands: 2.7599860175087088e-09\n",
"clapping: 1.3454612979302283e-09\n",
"massaging feet: 5.555100823784187e-10\n"
]
}
],
"source": [
"print('The top-5 labels with corresponding scores are:')\n",
"for result in results:\n",
" print(f'{result[0]}: ', result[1])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QuZG8kZ2fJ5d"
},
"source": [
"## Train a recognizer on customized dataset\n",
"\n",
"To train a new recognizer, there are usually three things to do:\n",
"1. Support a new dataset\n",
"2. Modify the config\n",
"3. Train a new recognizer"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "neEFyxChfgiJ"
},
"source": [
"### Support a new dataset\n",
"\n",
"In this tutorial, we gives an example to convert the data into the format of existing datasets. Other methods and more advanced usages can be found in the [doc](/docs/tutorials/new_dataset.md)\n",
"\n",
"Firstly, let's download a tiny dataset obtained from [Kinetics-400](https://deepmind.com/research/open-source/open-source-datasets/kinetics/). We select 30 videos with their labels as train dataset and 10 videos with their labels as test dataset."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gjsUj9JzgUlJ",
"outputId": "96a0e6e9-0dd8-4c07-9fed-22b93d5c1318"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"rm: cannot remove 'kinetics400_tiny.zip*': No such file or directory\n",
"--2023-05-15 03:33:27-- https://download.openmmlab.com/mmaction/kinetics400_tiny.zip\n",
"Resolving download.openmmlab.com (download.openmmlab.com)... 163.181.82.216, 163.181.82.218, 163.181.82.213, ...\n",
"Connecting to download.openmmlab.com (download.openmmlab.com)|163.181.82.216|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 18308682 (17M) [application/zip]\n",
"Saving to: ‘kinetics400_tiny.zip’\n",
"\n",
"kinetics400_tiny.zi 100%[===================>] 17.46M 32.7MB/s in 0.5s \n",
"\n",
"2023-05-15 03:33:28 (32.7 MB/s) - ‘kinetics400_tiny.zip’ saved [18308682/18308682]\n",
"\n"
]
}
],
"source": [
"# download, decompress the data\n",
"!rm kinetics400_tiny.zip*\n",
"!rm -rf kinetics400_tiny\n",
"!wget https://download.openmmlab.com/mmaction/kinetics400_tiny.zip\n",
"!unzip kinetics400_tiny.zip > /dev/null"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AbZ-o7V6hNw4",
"outputId": "f229f352-1b43-41b7-a374-21404f618581"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Reading package lists...\n",
"Building dependency tree...\n",
"Reading state information...\n",
"The following NEW packages will be installed:\n",
" tree\n",
"0 upgraded, 1 newly installed, 0 to remove and 24 not upgraded.\n",
"Need to get 43.0 kB of archives.\n",
"After this operation, 115 kB of additional disk space will be used.\n",
"Get:1 http://archive.ubuntu.com/ubuntu focal/universe amd64 tree amd64 1.8.0-1 [43.0 kB]\n",
"Fetched 43.0 kB in 1s (48.9 kB/s)\n",
"Selecting previously unselected package tree.\n",
"(Reading database ... 122519 files and directories currently installed.)\n",
"Preparing to unpack .../tree_1.8.0-1_amd64.deb ...\n",
"Unpacking tree (1.8.0-1) ...\n",
"Setting up tree (1.8.0-1) ...\n",
"Processing triggers for man-db (2.9.1-1) ...\n",
"\u001B[01;34mkinetics400_tiny\u001B[00m\n",
"├── kinetics_tiny_train_video.txt\n",
"├── kinetics_tiny_val_video.txt\n",
"├── \u001B[01;34mtrain\u001B[00m\n",
"│ ├── 27_CSXByd3s.mp4\n",
"│ ├── 34XczvTaRiI.mp4\n",
"│ ├── A-wiliK50Zw.mp4\n",
"│ ├── D32_1gwq35E.mp4\n",
"│ ├── D92m0HsHjcQ.mp4\n",
"│ ├── DbX8mPslRXg.mp4\n",
"│ ├── FMlSTTpN3VY.mp4\n",
"│ ├── h10B9SVE-nk.mp4\n",
"│ ├── h2YqqUhnR34.mp4\n",
"│ ├── iRuyZSKhHRg.mp4\n",
"│ ├── IyfILH9lBRo.mp4\n",
"│ ├── kFC3KY2bOP8.mp4\n",
"│ ├── LvcFDgCAXQs.mp4\n",
"│ ├── O46YA8tI530.mp4\n",
"│ ├── oMrZaozOvdQ.mp4\n",
"│ ├── oXy-e_P_cAI.mp4\n",
"│ ├── P5M-hAts7MQ.mp4\n",
"│ ├── phDqGd0NKoo.mp4\n",
"│ ├── PnOe3GZRVX8.mp4\n",
"│ ├── R8HXQkdgKWA.mp4\n",
"│ ├── RqnKtCEoEcA.mp4\n",
"│ ├── soEcZZsBmDs.mp4\n",
"│ ├── TkkZPZHbAKA.mp4\n",
"│ ├── T_TMNGzVrDk.mp4\n",
"│ ├── WaS0qwP46Us.mp4\n",
"│ ├── Wh_YPQdH1Zg.mp4\n",
"│ ├── WWP5HZJsg-o.mp4\n",
"│ ├── xGY2dP0YUjA.mp4\n",
"│ ├── yLC9CtWU5ws.mp4\n",
"│ └── ZQV4U2KQ370.mp4\n",
"└── \u001B[01;34mval\u001B[00m\n",
" ├── 0pVGiAU6XEA.mp4\n",
" ├── AQrbRSnRt8M.mp4\n",
" ├── b6Q_b7vgc7Q.mp4\n",
" ├── ddvJ6-faICE.mp4\n",
" ├── IcLztCtvhb8.mp4\n",
" ├── ik4BW3-SCts.mp4\n",
" ├── jqRrH30V0k4.mp4\n",
" ├── SU_x2LQqSLs.mp4\n",
" ├── u4Rm6srmIS8.mp4\n",
" └── y5Iu7XkTqV0.mp4\n",
"\n",
"2 directories, 42 files\n"
]
}
],
"source": [
"# Check the directory structure of the tiny data\n",
"\n",
"# Install tree first\n",
"!apt-get -q install tree\n",
"!tree kinetics400_tiny"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fTdi6dI0hY3g",
"outputId": "95f22438-566c-4496-fe0c-50e128b47b5e"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"D32_1gwq35E.mp4 0\n",
"iRuyZSKhHRg.mp4 1\n",
"oXy-e_P_cAI.mp4 0\n",
"34XczvTaRiI.mp4 1\n",
"h2YqqUhnR34.mp4 0\n",
"O46YA8tI530.mp4 0\n",
"kFC3KY2bOP8.mp4 1\n",
"WWP5HZJsg-o.mp4 1\n",
"phDqGd0NKoo.mp4 1\n",
"yLC9CtWU5ws.mp4 0\n",
"27_CSXByd3s.mp4 1\n",
"IyfILH9lBRo.mp4 1\n",
"T_TMNGzVrDk.mp4 1\n",
"TkkZPZHbAKA.mp4 0\n",
"PnOe3GZRVX8.mp4 1\n",
"soEcZZsBmDs.mp4 1\n",
"FMlSTTpN3VY.mp4 1\n",
"WaS0qwP46Us.mp4 0\n",
"A-wiliK50Zw.mp4 1\n",
"oMrZaozOvdQ.mp4 1\n",
"ZQV4U2KQ370.mp4 0\n",
"DbX8mPslRXg.mp4 1\n",
"h10B9SVE-nk.mp4 1\n",
"P5M-hAts7MQ.mp4 0\n",
"R8HXQkdgKWA.mp4 0\n",
"D92m0HsHjcQ.mp4 0\n",
"RqnKtCEoEcA.mp4 0\n",
"LvcFDgCAXQs.mp4 0\n",
"xGY2dP0YUjA.mp4 0\n",
"Wh_YPQdH1Zg.mp4 0\n"
]
}
],
"source": [
"# After downloading the data, we need to check the annotation format\n",
"!cat kinetics400_tiny/kinetics_tiny_train_video.txt"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0bq0mxmEi29H"
},
"source": [
"According to the format defined in [`VideoDataset`](./datasets/video_dataset.py), each line indicates a sample video with the filepath and label, which are split with a whitespace."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ht_DGJA9jQar"
},
"source": [
"### Modify the config\n",
"\n",
"In the next step, we need to modify the config for the training.\n",
"To accelerate the process, we finetune a recognizer using a pre-trained recognizer."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"id": "LjCcmCKOjktc"
},
"outputs": [],
"source": [
"cfg = Config.fromfile('./configs/recognition/tsn/tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb.py')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tc8YhFFGjp3e"
},
"source": [
"Given a config that trains a TSN model on kinetics400-full dataset, we need to modify some values to use it for training TSN on Kinetics400-tiny dataset.\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tlhu9byjjt-K",
"outputId": "2d984a1d-93f7-493f-fd77-e19af8285f38"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Config:\n",
"model = dict(\n",
" type='Recognizer2D',\n",
" backbone=dict(\n",
" type='ResNet',\n",
" pretrained='https://download.pytorch.org/models/resnet50-11ad3fa6.pth',\n",
" depth=50,\n",
" norm_eval=False),\n",
" cls_head=dict(\n",
" type='TSNHead',\n",
" num_classes=2,\n",
" in_channels=2048,\n",
" spatial_type='avg',\n",
" consensus=dict(type='AvgConsensus', dim=1),\n",
" dropout_ratio=0.4,\n",
" init_std=0.01,\n",
" average_clips='prob'),\n",
" data_preprocessor=dict(\n",
" type='ActionDataPreprocessor',\n",
" mean=[123.675, 116.28, 103.53],\n",
" std=[58.395, 57.12, 57.375],\n",
" format_shape='NCHW'),\n",
" train_cfg=None,\n",
" test_cfg=None)\n",
"train_cfg = dict(\n",
" type='EpochBasedTrainLoop', max_epochs=10, val_begin=1, val_interval=1)\n",
"val_cfg = dict(type='ValLoop')\n",
"test_cfg = dict(type='TestLoop')\n",
"param_scheduler = [\n",
" dict(\n",
" type='MultiStepLR',\n",
" begin=0,\n",
" end=100,\n",
" by_epoch=True,\n",
" milestones=[40, 80],\n",
" gamma=0.1)\n",
"]\n",
"optim_wrapper = dict(\n",
" optimizer=dict(\n",
" type='SGD', lr=7.8125e-05, momentum=0.9, weight_decay=0.0001),\n",
" clip_grad=dict(max_norm=40, norm_type=2))\n",
"default_scope = 'mmaction'\n",
"default_hooks = dict(\n",
" runtime_info=dict(type='RuntimeInfoHook'),\n",
" timer=dict(type='IterTimerHook'),\n",
" logger=dict(type='LoggerHook', interval=20, ignore_last=False),\n",
" param_scheduler=dict(type='ParamSchedulerHook'),\n",
" checkpoint=dict(\n",
" type='CheckpointHook', interval=3, save_best='auto', max_keep_ckpts=3),\n",
" sampler_seed=dict(type='DistSamplerSeedHook'),\n",
" sync_buffers=dict(type='SyncBuffersHook'))\n",
"env_cfg = dict(\n",
" cudnn_benchmark=False,\n",
" mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n",
" dist_cfg=dict(backend='nccl'))\n",
"log_processor = dict(type='LogProcessor', window_size=20, by_epoch=True)\n",
"vis_backends = [dict(type='LocalVisBackend')]\n",
"visualizer = dict(\n",
" type='ActionVisualizer', vis_backends=[dict(type='LocalVisBackend')])\n",
"log_level = 'INFO'\n",
"load_from = './checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth'\n",
"resume = False\n",
"dataset_type = 'VideoDataset'\n",
"data_root = 'kinetics400_tiny/train/'\n",
"data_root_val = 'kinetics400_tiny/val/'\n",
"ann_file_train = 'kinetics400_tiny/kinetics_tiny_train_video.txt'\n",
"ann_file_val = 'kinetics400_tiny/kinetics_tiny_val_video.txt'\n",
"file_client_args = dict(io_backend='disk')\n",
"train_pipeline = [\n",
" dict(type='DecordInit', io_backend='disk'),\n",
" dict(type='SampleFrames', clip_len=1, frame_interval=1, num_clips=3),\n",
" dict(type='DecordDecode'),\n",
" dict(type='Resize', scale=(-1, 256)),\n",
" dict(\n",
" type='MultiScaleCrop',\n",
" input_size=224,\n",
" scales=(1, 0.875, 0.75, 0.66),\n",
" random_crop=False,\n",
" max_wh_scale_gap=1),\n",
" dict(type='Resize', scale=(224, 224), keep_ratio=False),\n",
" dict(type='Flip', flip_ratio=0.5),\n",
" dict(type='FormatShape', input_format='NCHW'),\n",
" dict(type='PackActionInputs')\n",
"]\n",
"val_pipeline = [\n",
" dict(type='DecordInit', io_backend='disk'),\n",
" dict(\n",
" type='SampleFrames',\n",
" clip_len=1,\n",
" frame_interval=1,\n",
" num_clips=3,\n",
" test_mode=True),\n",
" dict(type='DecordDecode'),\n",
" dict(type='Resize', scale=(-1, 256)),\n",
" dict(type='CenterCrop', crop_size=224),\n",
" dict(type='FormatShape', input_format='NCHW'),\n",
" dict(type='PackActionInputs')\n",
"]\n",
"test_pipeline = [\n",
" dict(type='DecordInit', io_backend='disk'),\n",
" dict(\n",
" type='SampleFrames',\n",
" clip_len=1,\n",
" frame_interval=1,\n",
" num_clips=25,\n",
" test_mode=True),\n",
" dict(type='DecordDecode'),\n",
" dict(type='Resize', scale=(-1, 256)),\n",
" dict(type='TenCrop', crop_size=224),\n",
" dict(type='FormatShape', input_format='NCHW'),\n",
" dict(type='PackActionInputs')\n",
"]\n",
"train_dataloader = dict(\n",
" batch_size=2,\n",
" num_workers=2,\n",
" persistent_workers=True,\n",
" sampler=dict(type='DefaultSampler', shuffle=True),\n",
" dataset=dict(\n",
" type='VideoDataset',\n",
" ann_file='kinetics400_tiny/kinetics_tiny_train_video.txt',\n",
" data_prefix=dict(video='kinetics400_tiny/train/'),\n",
" pipeline=[\n",
" dict(type='DecordInit', io_backend='disk'),\n",
" dict(\n",
" type='SampleFrames', clip_len=1, frame_interval=1,\n",
" num_clips=3),\n",
" dict(type='DecordDecode'),\n",
" dict(type='Resize', scale=(-1, 256)),\n",
" dict(\n",
" type='MultiScaleCrop',\n",
" input_size=224,\n",
" scales=(1, 0.875, 0.75, 0.66),\n",
" random_crop=False,\n",
" max_wh_scale_gap=1),\n",
" dict(type='Resize', scale=(224, 224), keep_ratio=False),\n",
" dict(type='Flip', flip_ratio=0.5),\n",
" dict(type='FormatShape', input_format='NCHW'),\n",
" dict(type='PackActionInputs')\n",
" ]))\n",
"val_dataloader = dict(\n",
" batch_size=2,\n",
" num_workers=2,\n",
" persistent_workers=True,\n",
" sampler=dict(type='DefaultSampler', shuffle=False),\n",
" dataset=dict(\n",
" type='VideoDataset',\n",
" ann_file='kinetics400_tiny/kinetics_tiny_val_video.txt',\n",
" data_prefix=dict(video='kinetics400_tiny/val/'),\n",
" pipeline=[\n",
" dict(type='DecordInit', io_backend='disk'),\n",
" dict(\n",
" type='SampleFrames',\n",
" clip_len=1,\n",
" frame_interval=1,\n",
" num_clips=3,\n",
" test_mode=True),\n",
" dict(type='DecordDecode'),\n",
" dict(type='Resize', scale=(-1, 256)),\n",
" dict(type='CenterCrop', crop_size=224),\n",
" dict(type='FormatShape', input_format='NCHW'),\n",
" dict(type='PackActionInputs')\n",
" ],\n",
" test_mode=True))\n",
"test_dataloader = dict(\n",
" batch_size=1,\n",
" num_workers=2,\n",
" persistent_workers=True,\n",
" sampler=dict(type='DefaultSampler', shuffle=False),\n",
" dataset=dict(\n",
" type='VideoDataset',\n",
" ann_file='kinetics400_tiny/kinetics_tiny_val_video.txt',\n",
" data_prefix=dict(video='kinetics400_tiny/val/'),\n",
" pipeline=[\n",
" dict(type='DecordInit', io_backend='disk'),\n",
" dict(\n",
" type='SampleFrames',\n",
" clip_len=1,\n",
" frame_interval=1,\n",
" num_clips=25,\n",
" test_mode=True),\n",
" dict(type='DecordDecode'),\n",
" dict(type='Resize', scale=(-1, 256)),\n",
" dict(type='TenCrop', crop_size=224),\n",
" dict(type='FormatShape', input_format='NCHW'),\n",
" dict(type='PackActionInputs')\n",
" ],\n",
" test_mode=True))\n",
"val_evaluator = dict(type='AccMetric')\n",
"test_evaluator = dict(type='AccMetric')\n",
"auto_scale_lr = dict(enable=False, base_batch_size=256)\n",
"work_dir = './tutorial_exps'\n",
"\n"
]
}
],
"source": [
"from mmengine.runner import set_random_seed\n",
"\n",
"# Modify dataset type and path\n",
"cfg.data_root = 'kinetics400_tiny/train/'\n",
"cfg.data_root_val = 'kinetics400_tiny/val/'\n",
"cfg.ann_file_train = 'kinetics400_tiny/kinetics_tiny_train_video.txt'\n",
"cfg.ann_file_val = 'kinetics400_tiny/kinetics_tiny_val_video.txt'\n",
"\n",
"\n",
"cfg.test_dataloader.dataset.ann_file = 'kinetics400_tiny/kinetics_tiny_val_video.txt'\n",
"cfg.test_dataloader.dataset.data_prefix.video = 'kinetics400_tiny/val/'\n",
"\n",
"cfg.train_dataloader.dataset.ann_file = 'kinetics400_tiny/kinetics_tiny_train_video.txt'\n",
"cfg.train_dataloader.dataset.data_prefix.video = 'kinetics400_tiny/train/'\n",
"\n",
"cfg.val_dataloader.dataset.ann_file = 'kinetics400_tiny/kinetics_tiny_val_video.txt'\n",
"cfg.val_dataloader.dataset.data_prefix.video = 'kinetics400_tiny/val/'\n",
"\n",
"\n",
"# Modify num classes of the model in cls_head\n",
"cfg.model.cls_head.num_classes = 2\n",
"# We can use the pre-trained TSN model\n",
"cfg.load_from = './checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth'\n",
"\n",
"# Set up working dir to save files and logs.\n",
"cfg.work_dir = './tutorial_exps'\n",
"\n",
"# The original learning rate (LR) is set for 8-GPU training.\n",
"# We divide it by 8 since we only use one GPU.\n",
"cfg.train_dataloader.batch_size = cfg.train_dataloader.batch_size // 16\n",
"cfg.val_dataloader.batch_size = cfg.val_dataloader.batch_size // 16\n",
"cfg.optim_wrapper.optimizer.lr = cfg.optim_wrapper.optimizer.lr / 8 / 16\n",
"cfg.train_cfg.max_epochs = 10\n",
"\n",
"cfg.train_dataloader.num_workers = 2\n",
"cfg.val_dataloader.num_workers = 2\n",
"cfg.test_dataloader.num_workers = 2\n",
"\n",
"# We can initialize the logger for training and have a look\n",
"# at the final config used for training\n",
"print(f'Config:\\n{cfg.pretty_text}')\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tES-qnZ3k38Z"
},
"source": [
"### Train a new recognizer\n",
"\n",
"Finally, lets initialize the dataset and recognizer, then train a new recognizer!"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dDBWkdDRk6oz",
"outputId": "044b9e09-2038-41c9-d5a3-8a74ae11ade2"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"05/15 03:33:34 - mmengine - INFO - \n",
"------------------------------------------------------------\n",
"System environment:\n",
" sys.platform: linux\n",
" Python: 3.10.11 (main, Apr 5 2023, 14:15:10) [GCC 9.4.0]\n",
" CUDA available: True\n",
" numpy_random_seed: 1853452922\n",
" GPU 0: Tesla T4\n",
" CUDA_HOME: /usr/local/cuda\n",
" NVCC: Cuda compilation tools, release 11.8, V11.8.89\n",
" GCC: x86_64-linux-gnu-gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0\n",
" PyTorch: 2.0.0+cu118\n",
" PyTorch compiling details: PyTorch built with:\n",
" - GCC 9.3\n",
" - C++ Version: 201703\n",
" - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications\n",
" - Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)\n",
" - OpenMP 201511 (a.k.a. OpenMP 4.5)\n",
" - LAPACK is enabled (usually provided by MKL)\n",
" - NNPACK is enabled\n",
" - CPU capability usage: AVX2\n",
" - CUDA Runtime 11.8\n",
" - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90\n",
" - CuDNN 8.7\n",
" - Magma 2.6.1\n",
" - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n",
"\n",
" TorchVision: 0.15.1+cu118\n",
" OpenCV: 4.7.0\n",
" MMEngine: 0.7.3\n",
"\n",
"Runtime environment:\n",
" cudnn_benchmark: False\n",
" mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}\n",
" dist_cfg: {'backend': 'nccl'}\n",
" seed: None\n",
" Distributed launcher: none\n",
" Distributed training: False\n",
" GPU number: 1\n",
"------------------------------------------------------------\n",
"\n",
"05/15 03:33:34 - mmengine - INFO - Config:\n",
"model = dict(\n",
" type='Recognizer2D',\n",
" backbone=dict(\n",
" type='ResNet',\n",
" pretrained='https://download.pytorch.org/models/resnet50-11ad3fa6.pth',\n",
" depth=50,\n",
" norm_eval=False),\n",
" cls_head=dict(\n",
" type='TSNHead',\n",
" num_classes=2,\n",
" in_channels=2048,\n",
" spatial_type='avg',\n",
" consensus=dict(type='AvgConsensus', dim=1),\n",
" dropout_ratio=0.4,\n",
" init_std=0.01,\n",
" average_clips='prob'),\n",
" data_preprocessor=dict(\n",
" type='ActionDataPreprocessor',\n",
" mean=[123.675, 116.28, 103.53],\n",
" std=[58.395, 57.12, 57.375],\n",
" format_shape='NCHW'),\n",
" train_cfg=None,\n",
" test_cfg=None)\n",
"train_cfg = dict(\n",
" type='EpochBasedTrainLoop', max_epochs=10, val_begin=1, val_interval=1)\n",
"val_cfg = dict(type='ValLoop')\n",
"test_cfg = dict(type='TestLoop')\n",
"param_scheduler = [\n",
" dict(\n",
" type='MultiStepLR',\n",
" begin=0,\n",
" end=100,\n",
" by_epoch=True,\n",
" milestones=[40, 80],\n",
" gamma=0.1)\n",
"]\n",
"optim_wrapper = dict(\n",
" optimizer=dict(\n",
" type='SGD', lr=7.8125e-05, momentum=0.9, weight_decay=0.0001),\n",
" clip_grad=dict(max_norm=40, norm_type=2))\n",
"default_scope = 'mmaction'\n",
"default_hooks = dict(\n",
" runtime_info=dict(type='RuntimeInfoHook'),\n",
" timer=dict(type='IterTimerHook'),\n",
" logger=dict(type='LoggerHook', interval=20, ignore_last=False),\n",
" param_scheduler=dict(type='ParamSchedulerHook'),\n",
" checkpoint=dict(\n",
" type='CheckpointHook', interval=3, save_best='auto', max_keep_ckpts=3),\n",
" sampler_seed=dict(type='DistSamplerSeedHook'),\n",
" sync_buffers=dict(type='SyncBuffersHook'))\n",
"env_cfg = dict(\n",
" cudnn_benchmark=False,\n",
" mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n",
" dist_cfg=dict(backend='nccl'))\n",
"log_processor = dict(type='LogProcessor', window_size=20, by_epoch=True)\n",
"vis_backends = [dict(type='LocalVisBackend')]\n",
"visualizer = dict(\n",
" type='ActionVisualizer', vis_backends=[dict(type='LocalVisBackend')])\n",
"log_level = 'INFO'\n",
"load_from = './checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth'\n",
"resume = False\n",
"dataset_type = 'VideoDataset'\n",
"data_root = 'kinetics400_tiny/train/'\n",
"data_root_val = 'kinetics400_tiny/val/'\n",
"ann_file_train = 'kinetics400_tiny/kinetics_tiny_train_video.txt'\n",
"ann_file_val = 'kinetics400_tiny/kinetics_tiny_val_video.txt'\n",
"file_client_args = dict(io_backend='disk')\n",
"train_pipeline = [\n",
" dict(type='DecordInit', io_backend='disk'),\n",
" dict(type='SampleFrames', clip_len=1, frame_interval=1, num_clips=3),\n",
" dict(type='DecordDecode'),\n",
" dict(type='Resize', scale=(-1, 256)),\n",
" dict(\n",
" type='MultiScaleCrop',\n",
" input_size=224,\n",
" scales=(1, 0.875, 0.75, 0.66),\n",
" random_crop=False,\n",
" max_wh_scale_gap=1),\n",
" dict(type='Resize', scale=(224, 224), keep_ratio=False),\n",
" dict(type='Flip', flip_ratio=0.5),\n",
" dict(type='FormatShape', input_format='NCHW'),\n",
" dict(type='PackActionInputs')\n",
"]\n",
"val_pipeline = [\n",
" dict(type='DecordInit', io_backend='disk'),\n",
" dict(\n",
" type='SampleFrames',\n",
" clip_len=1,\n",
" frame_interval=1,\n",
" num_clips=3,\n",
" test_mode=True),\n",
" dict(type='DecordDecode'),\n",
" dict(type='Resize', scale=(-1, 256)),\n",
" dict(type='CenterCrop', crop_size=224),\n",
" dict(type='FormatShape', input_format='NCHW'),\n",
" dict(type='PackActionInputs')\n",
"]\n",
"test_pipeline = [\n",
" dict(type='DecordInit', io_backend='disk'),\n",
" dict(\n",
" type='SampleFrames',\n",
" clip_len=1,\n",
" frame_interval=1,\n",
" num_clips=25,\n",
" test_mode=True),\n",
" dict(type='DecordDecode'),\n",
" dict(type='Resize', scale=(-1, 256)),\n",
" dict(type='TenCrop', crop_size=224),\n",
" dict(type='FormatShape', input_format='NCHW'),\n",
" dict(type='PackActionInputs')\n",
"]\n",
"train_dataloader = dict(\n",
" batch_size=2,\n",
" num_workers=2,\n",
" persistent_workers=True,\n",
" sampler=dict(type='DefaultSampler', shuffle=True),\n",
" dataset=dict(\n",
" type='VideoDataset',\n",
" ann_file='kinetics400_tiny/kinetics_tiny_train_video.txt',\n",
" data_prefix=dict(video='kinetics400_tiny/train/'),\n",
" pipeline=[\n",
" dict(type='DecordInit', io_backend='disk'),\n",
" dict(\n",
" type='SampleFrames', clip_len=1, frame_interval=1,\n",
" num_clips=3),\n",
" dict(type='DecordDecode'),\n",
" dict(type='Resize', scale=(-1, 256)),\n",
" dict(\n",
" type='MultiScaleCrop',\n",
" input_size=224,\n",
" scales=(1, 0.875, 0.75, 0.66),\n",
" random_crop=False,\n",
" max_wh_scale_gap=1),\n",
" dict(type='Resize', scale=(224, 224), keep_ratio=False),\n",
" dict(type='Flip', flip_ratio=0.5),\n",
" dict(type='FormatShape', input_format='NCHW'),\n",
" dict(type='PackActionInputs')\n",
" ]))\n",
"val_dataloader = dict(\n",
" batch_size=2,\n",
" num_workers=2,\n",
" persistent_workers=True,\n",
" sampler=dict(type='DefaultSampler', shuffle=False),\n",
" dataset=dict(\n",
" type='VideoDataset',\n",
" ann_file='kinetics400_tiny/kinetics_tiny_val_video.txt',\n",
" data_prefix=dict(video='kinetics400_tiny/val/'),\n",
" pipeline=[\n",
" dict(type='DecordInit', io_backend='disk'),\n",
" dict(\n",
" type='SampleFrames',\n",
" clip_len=1,\n",
" frame_interval=1,\n",
" num_clips=3,\n",
" test_mode=True),\n",
" dict(type='DecordDecode'),\n",
" dict(type='Resize', scale=(-1, 256)),\n",
" dict(type='CenterCrop', crop_size=224),\n",
" dict(type='FormatShape', input_format='NCHW'),\n",
" dict(type='PackActionInputs')\n",
" ],\n",
" test_mode=True))\n",
"test_dataloader = dict(\n",
" batch_size=1,\n",
" num_workers=2,\n",
" persistent_workers=True,\n",
" sampler=dict(type='DefaultSampler', shuffle=False),\n",
" dataset=dict(\n",
" type='VideoDataset',\n",
" ann_file='kinetics400_tiny/kinetics_tiny_val_video.txt',\n",
" data_prefix=dict(video='kinetics400_tiny/val/'),\n",
" pipeline=[\n",
" dict(type='DecordInit', io_backend='disk'),\n",
" dict(\n",
" type='SampleFrames',\n",
" clip_len=1,\n",
" frame_interval=1,\n",
" num_clips=25,\n",
" test_mode=True),\n",
" dict(type='DecordDecode'),\n",
" dict(type='Resize', scale=(-1, 256)),\n",
" dict(type='TenCrop', crop_size=224),\n",
" dict(type='FormatShape', input_format='NCHW'),\n",
" dict(type='PackActionInputs')\n",
" ],\n",
" test_mode=True))\n",
"val_evaluator = dict(type='AccMetric')\n",
"test_evaluator = dict(type='AccMetric')\n",
"auto_scale_lr = dict(enable=False, base_batch_size=256)\n",
"work_dir = './tutorial_exps'\n",
"\n",
"05/15 03:33:35 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.\n",
"05/15 03:33:35 - mmengine - INFO - Hooks will be executed in the following order:\n",
"before_run:\n",
"(VERY_HIGH ) RuntimeInfoHook \n",
"(BELOW_NORMAL) LoggerHook \n",
" -------------------- \n",
"before_train:\n",
"(VERY_HIGH ) RuntimeInfoHook \n",
"(NORMAL ) IterTimerHook \n",
"(VERY_LOW ) CheckpointHook \n",
" -------------------- \n",
"before_train_epoch:\n",
"(VERY_HIGH ) RuntimeInfoHook \n",
"(NORMAL ) IterTimerHook \n",
"(NORMAL ) DistSamplerSeedHook \n",
" -------------------- \n",
"before_train_iter:\n",
"(VERY_HIGH ) RuntimeInfoHook \n",
"(NORMAL ) IterTimerHook \n",
" -------------------- \n",
"after_train_iter:\n",
"(VERY_HIGH ) RuntimeInfoHook \n",
"(NORMAL ) IterTimerHook \n",
"(BELOW_NORMAL) LoggerHook \n",
"(LOW ) ParamSchedulerHook \n",
"(VERY_LOW ) CheckpointHook \n",
" -------------------- \n",
"after_train_epoch:\n",
"(NORMAL ) IterTimerHook \n",
"(NORMAL ) SyncBuffersHook \n",
"(LOW ) ParamSchedulerHook \n",
"(VERY_LOW ) CheckpointHook \n",
" -------------------- \n",
"before_val_epoch:\n",
"(NORMAL ) IterTimerHook \n",
"(NORMAL ) SyncBuffersHook \n",
" -------------------- \n",
"before_val_iter:\n",
"(NORMAL ) IterTimerHook \n",
" -------------------- \n",
"after_val_iter:\n",
"(NORMAL ) IterTimerHook \n",
"(BELOW_NORMAL) LoggerHook \n",
" -------------------- \n",
"after_val_epoch:\n",
"(VERY_HIGH ) RuntimeInfoHook \n",
"(NORMAL ) IterTimerHook \n",
"(BELOW_NORMAL) LoggerHook \n",
"(LOW ) ParamSchedulerHook \n",
"(VERY_LOW ) CheckpointHook \n",
" -------------------- \n",
"after_train:\n",
"(VERY_LOW ) CheckpointHook \n",
" -------------------- \n",
"before_test_epoch:\n",
"(NORMAL ) IterTimerHook \n",
" -------------------- \n",
"before_test_iter:\n",
"(NORMAL ) IterTimerHook \n",
" -------------------- \n",
"after_test_iter:\n",
"(NORMAL ) IterTimerHook \n",
"(BELOW_NORMAL) LoggerHook \n",
" -------------------- \n",
"after_test_epoch:\n",
"(VERY_HIGH ) RuntimeInfoHook \n",
"(NORMAL ) IterTimerHook \n",
"(BELOW_NORMAL) LoggerHook \n",
" -------------------- \n",
"after_run:\n",
"(BELOW_NORMAL) LoggerHook \n",
" -------------------- \n",
"Loads checkpoint by http backend from path: https://download.pytorch.org/models/resnet50-11ad3fa6.pth\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Downloading: \"https://download.pytorch.org/models/resnet50-11ad3fa6.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-11ad3fa6.pth\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"05/15 03:33:37 - mmengine - INFO - These parameters in pretrained checkpoint are not loaded: {'fc.weight', 'fc.bias'}\n",
"Loads checkpoint by local backend from path: ./checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth\n",
"The model and loaded state dict do not match exactly\n",
"\n",
"size mismatch for cls_head.fc_cls.weight: copying a param with shape torch.Size([400, 2048]) from checkpoint, the shape in current model is torch.Size([2, 2048]).\n",
"size mismatch for cls_head.fc_cls.bias: copying a param with shape torch.Size([400]) from checkpoint, the shape in current model is torch.Size([2]).\n",
"05/15 03:33:37 - mmengine - INFO - Load checkpoint from ./checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth\n",
"05/15 03:33:37 - mmengine - WARNING - \"FileClient\" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io\n",
"05/15 03:33:37 - mmengine - INFO - Checkpoints will be saved to /content/mmaction2/tutorial_exps.\n",
"05/15 03:33:41 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb_20230515_033334\n",
"05/15 03:33:41 - mmengine - INFO - Epoch(train) [1][15/15] lr: 7.8125e-05 eta: 0:00:31 time: 0.2334 data_time: 0.0793 memory: 2917 grad_norm: 11.9900 loss: 0.6971 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6971\n",
"05/15 03:33:42 - mmengine - INFO - Epoch(val) [1][5/5] acc/top1: 0.3000 acc/top5: 1.0000 acc/mean1: 0.3000 data_time: 0.1994 time: 0.2254\n",
"05/15 03:33:42 - mmengine - INFO - The best checkpoint with 0.3000 acc/top1 at 1 epoch is saved to best_acc_top1_epoch_1.pth.\n",
"05/15 03:33:46 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb_20230515_033334\n",
"05/15 03:33:46 - mmengine - INFO - Epoch(train) [2][15/15] lr: 7.8125e-05 eta: 0:00:29 time: 0.2373 data_time: 0.1369 memory: 961 grad_norm: 12.4935 loss: 0.7158 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 0.7158\n",
"05/15 03:33:48 - mmengine - INFO - Epoch(val) [2][5/5] acc/top1: 0.7000 acc/top5: 1.0000 acc/mean1: 0.7000 data_time: 0.2692 time: 0.3006\n",
"05/15 03:33:48 - mmengine - INFO - The previous best checkpoint /content/mmaction2/tutorial_exps/best_acc_top1_epoch_1.pth is removed\n",
"05/15 03:33:48 - mmengine - INFO - The best checkpoint with 0.7000 acc/top1 at 2 epoch is saved to best_acc_top1_epoch_2.pth.\n",
"05/15 03:33:51 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb_20230515_033334\n",
"05/15 03:33:51 - mmengine - INFO - Epoch(train) [3][15/15] lr: 7.8125e-05 eta: 0:00:24 time: 0.2112 data_time: 0.1163 memory: 961 grad_norm: 13.4063 loss: 0.7338 top1_acc: 0.0000 top5_acc: 1.0000 loss_cls: 0.7338\n",
"05/15 03:33:51 - mmengine - INFO - Saving checkpoint at 3 epochs\n",
"05/15 03:33:53 - mmengine - INFO - Epoch(val) [3][5/5] acc/top1: 0.4000 acc/top5: 1.0000 acc/mean1: 0.4000 data_time: 0.1669 time: 0.1906\n",
"05/15 03:33:56 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb_20230515_033334\n",
"05/15 03:33:56 - mmengine - INFO - Epoch(train) [4][15/15] lr: 7.8125e-05 eta: 0:00:19 time: 0.1750 data_time: 0.0907 memory: 961 grad_norm: 12.4322 loss: 0.6894 top1_acc: 0.0000 top5_acc: 1.0000 loss_cls: 0.6894\n",
"05/15 03:33:57 - mmengine - INFO - Epoch(val) [4][5/5] acc/top1: 0.7000 acc/top5: 1.0000 acc/mean1: 0.7000 data_time: 0.1791 time: 0.2030\n",
"05/15 03:34:00 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb_20230515_033334\n",
"05/15 03:34:00 - mmengine - INFO - Epoch(train) [5][15/15] lr: 7.8125e-05 eta: 0:00:16 time: 0.2016 data_time: 0.1155 memory: 961 grad_norm: 11.5982 loss: 0.6940 top1_acc: 0.0000 top5_acc: 1.0000 loss_cls: 0.6940\n",
"05/15 03:34:02 - mmengine - INFO - Epoch(val) [5][5/5] acc/top1: 0.7000 acc/top5: 1.0000 acc/mean1: 0.7000 data_time: 0.3145 time: 0.3455\n",
"05/15 03:34:05 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb_20230515_033334\n",
"05/15 03:34:05 - mmengine - INFO - Epoch(train) [6][15/15] lr: 7.8125e-05 eta: 0:00:13 time: 0.2366 data_time: 0.1440 memory: 961 grad_norm: 12.0952 loss: 0.6667 top1_acc: 0.0000 top5_acc: 1.0000 loss_cls: 0.6667\n",
"05/15 03:34:05 - mmengine - INFO - Saving checkpoint at 6 epochs\n",
"05/15 03:34:08 - mmengine - INFO - Epoch(val) [6][5/5] acc/top1: 0.6000 acc/top5: 1.0000 acc/mean1: 0.6000 data_time: 0.2172 time: 0.2403\n",
"05/15 03:34:10 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb_20230515_033334\n",
"05/15 03:34:10 - mmengine - INFO - Epoch(train) [7][15/15] lr: 7.8125e-05 eta: 0:00:09 time: 0.1784 data_time: 0.0942 memory: 961 grad_norm: 12.4209 loss: 0.6570 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6570\n",
"05/15 03:34:11 - mmengine - INFO - Epoch(val) [7][5/5] acc/top1: 0.9000 acc/top5: 1.0000 acc/mean1: 0.9000 data_time: 0.1898 time: 0.2118\n",
"05/15 03:34:11 - mmengine - INFO - The previous best checkpoint /content/mmaction2/tutorial_exps/best_acc_top1_epoch_2.pth is removed\n",
"05/15 03:34:12 - mmengine - INFO - The best checkpoint with 0.9000 acc/top1 at 7 epoch is saved to best_acc_top1_epoch_7.pth.\n",
"05/15 03:34:15 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb_20230515_033334\n",
"05/15 03:34:15 - mmengine - INFO - Epoch(train) [8][15/15] lr: 7.8125e-05 eta: 0:00:06 time: 0.2073 data_time: 0.1220 memory: 961 grad_norm: 11.4271 loss: 0.6241 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6241\n",
"05/15 03:34:17 - mmengine - INFO - Epoch(val) [8][5/5] acc/top1: 1.0000 acc/top5: 1.0000 acc/mean1: 1.0000 data_time: 0.3497 time: 0.3890\n",
"05/15 03:34:17 - mmengine - INFO - The previous best checkpoint /content/mmaction2/tutorial_exps/best_acc_top1_epoch_7.pth is removed\n",
"05/15 03:34:18 - mmengine - INFO - The best checkpoint with 1.0000 acc/top1 at 8 epoch is saved to best_acc_top1_epoch_8.pth.\n",
"05/15 03:34:21 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb_20230515_033334\n",
"05/15 03:34:21 - mmengine - INFO - Epoch(train) [9][15/15] lr: 7.8125e-05 eta: 0:00:03 time: 0.2309 data_time: 0.1390 memory: 961 grad_norm: 12.3066 loss: 0.6451 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 0.6451\n",
"05/15 03:34:21 - mmengine - INFO - Saving checkpoint at 9 epochs\n",
"05/15 03:34:23 - mmengine - INFO - Epoch(val) [9][5/5] acc/top1: 1.0000 acc/top5: 1.0000 acc/mean1: 1.0000 data_time: 0.2023 time: 0.2256\n",
"05/15 03:34:26 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb_20230515_033334\n",
"05/15 03:34:26 - mmengine - INFO - Epoch(train) [10][15/15] lr: 7.8125e-05 eta: 0:00:00 time: 0.1733 data_time: 0.0951 memory: 961 grad_norm: 11.1461 loss: 0.5931 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.5931\n",
"05/15 03:34:26 - mmengine - INFO - Saving checkpoint at 10 epochs\n",
"05/15 03:34:27 - mmengine - INFO - Epoch(val) [10][5/5] acc/top1: 1.0000 acc/top5: 1.0000 acc/mean1: 1.0000 data_time: 0.1836 time: 0.2048\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Recognizer2D(\n",
" (data_preprocessor): ActionDataPreprocessor()\n",
" (backbone): ResNet(\n",
" (conv1): ConvModule(\n",
" (conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
" (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (activate): ReLU(inplace=True)\n",
" )\n",
" (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
" (layer1): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): ConvModule(\n",
" (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (activate): ReLU(inplace=True)\n",
" )\n",
" (conv2): ConvModule(\n",
" (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (activate): ReLU(inplace=True)\n",
" )\n",
" (conv3): ConvModule(\n",
" (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (relu): ReLU(inplace=True)\n",
" (downsample): ConvModule(\n",
" (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): ConvModule(\n",
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" (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (activate): ReLU(inplace=True)\n",
" )\n",
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" (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (activate): ReLU(inplace=True)\n",
" )\n",
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" (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
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" )\n",
" (2): Bottleneck(\n",
" (conv1): ConvModule(\n",
" (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (activate): ReLU(inplace=True)\n",
" )\n",
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" (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (activate): ReLU(inplace=True)\n",
" )\n",
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" (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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" )\n",
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" )\n",
" )\n",
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" )\n",
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" )\n",
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" (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
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" )\n",
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" )\n",
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" )\n",
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" )\n",
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" (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
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" )\n",
" (3): Bottleneck(\n",
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" )\n",
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" (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (activate): ReLU(inplace=True)\n",
" )\n",
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" (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
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" )\n",
" )\n",
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" )\n",
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" )\n",
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" (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
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" (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (activate): ReLU(inplace=True)\n",
" )\n",
" (conv2): ConvModule(\n",
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" (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (activate): ReLU(inplace=True)\n",
" )\n",
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" )\n",
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" )\n",
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" )\n",
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" )\n",
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" )\n",
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" )\n",
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" )\n",
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" (activate): ReLU(inplace=True)\n",
" )\n",
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" (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (4): Bottleneck(\n",
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" (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (activate): ReLU(inplace=True)\n",
" )\n",
" (conv2): ConvModule(\n",
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" (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (activate): ReLU(inplace=True)\n",
" )\n",
" (conv3): ConvModule(\n",
" (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (5): Bottleneck(\n",
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" (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (activate): ReLU(inplace=True)\n",
" )\n",
" (conv2): ConvModule(\n",
" (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (activate): ReLU(inplace=True)\n",
" )\n",
" (conv3): ConvModule(\n",
" (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (layer4): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): ConvModule(\n",
" (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (activate): ReLU(inplace=True)\n",
" )\n",
" (conv2): ConvModule(\n",
" (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (activate): ReLU(inplace=True)\n",
" )\n",
" (conv3): ConvModule(\n",
" (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (relu): ReLU(inplace=True)\n",
" (downsample): ConvModule(\n",
" (conv): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): ConvModule(\n",
" (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (activate): ReLU(inplace=True)\n",
" )\n",
" (conv2): ConvModule(\n",
" (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (activate): ReLU(inplace=True)\n",
" )\n",
" (conv3): ConvModule(\n",
" (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): ConvModule(\n",
" (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (activate): ReLU(inplace=True)\n",
" )\n",
" (conv2): ConvModule(\n",
" (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (activate): ReLU(inplace=True)\n",
" )\n",
" (conv3): ConvModule(\n",
" (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" )\n",
" )\n",
" (cls_head): TSNHead(\n",
" (loss_cls): CrossEntropyLoss()\n",
" (consensus): AvgConsensus()\n",
" (avg_pool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
" (dropout): Dropout(p=0.4, inplace=False)\n",
" (fc_cls): Linear(in_features=2048, out_features=2, bias=True)\n",
" )\n",
")"
]
},
"metadata": {},
"execution_count": 15
}
],
"source": [
"import os.path as osp\n",
"import mmengine\n",
"from mmengine.runner import Runner\n",
"\n",
"# Create work_dir\n",
"mmengine.mkdir_or_exist(osp.abspath(cfg.work_dir))\n",
"\n",
"# build the runner from config\n",
"runner = Runner.from_cfg(cfg)\n",
"\n",
"# start training\n",
"runner.train()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zdSd7oTLlxIf"
},
"source": [
"### Understand the log\n",
"From the log, we can have a basic understanding the training process and know how well the recognizer is trained.\n",
"\n",
"Firstly, the ResNet-50 backbone pre-trained on ImageNet is loaded, this is a common practice since training from scratch is more cost. The log shows that all the weights of the ResNet-50 backbone are loaded except the `fc.bias` and `fc.weight`.\n",
"\n",
"Second, since the dataset we are using is small, we loaded a TSN model and finetune it for action recognition.\n",
"The original TSN is trained on original Kinetics-400 dataset which contains 400 classes but Kinetics-400 Tiny dataset only have 2 classes. Therefore, the last FC layer of the pre-trained TSN for classification has different weight shape and is not used.\n",
"\n",
"Third, after training, the recognizer is evaluated by the default evaluation. The results show that the recognizer achieves 100% top1 accuracy and 100% top5 accuracy on the val dataset,\n",
" \n",
"Not bad!"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ryVoSfZVmogw"
},
"source": [
"## Test the trained recognizer\n",
"\n",
"After finetuning the recognizer, let's check the prediction results!"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "eyY3hCMwyTct",
"outputId": "34fbbdc5-b9fd-4fd2-8030-3ba56b10adbf"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"05/15 03:34:36 - mmengine - INFO - Epoch(test) [10/10] acc/top1: 0.9000 acc/top5: 1.0000 acc/mean1: 0.9000 data_time: 0.0586 time: 0.7817\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'acc/top1': 0.9, 'acc/top5': 1.0, 'acc/mean1': 0.9}"
]
},
"metadata": {},
"execution_count": 16
}
],
"source": [
"runner.test()"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "mmact_dev",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.12"
},
"vscode": {
"interpreter": {
"hash": "189c342a4747645665e89db23000ac4d4edb7a87c4cd0b2f881610f468fb778d"
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|