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Getting Started

This page provides basic tutorials about the usage of MMAction2. For installation instructions, please see install.md.

Datasets

It is recommended to symlink the dataset root to $MMACTION2/data. If your folder structure is different, you may need to change the corresponding paths in config files.

mmaction2
├── mmaction
├── tools
├── configs
├── data
│   ├── kinetics400
│   │   ├── rawframes_train
│   │   ├── rawframes_val
│   │   ├── kinetics_train_list.txt
│   │   ├── kinetics_val_list.txt
│   ├── ucf101
│   │   ├── rawframes_train
│   │   ├── rawframes_val
│   │   ├── ucf101_train_list.txt
│   │   ├── ucf101_val_list.txt
│   ├── ...

For more information on data preparation, please see data_preparation.md

For using custom datasets, please refer to Tutorial 3: Adding New Dataset

Inference with Pre-Trained Models

We provide testing scripts to evaluate a whole dataset (Kinetics-400, Something-Something V1&V2, (Multi-)Moments in Time, etc.), and provide some high-level apis for easier integration to other projects.

MMAction2 also supports testing with CPU. However, it will be very slow and should only be used for debugging on a device without GPU. To test with CPU, one should first disable all GPUs (if exist) with export CUDA_VISIBLE_DEVICES=-1, and then call the testing scripts directly with python tools/test.py {OTHER_ARGS}.

Test a dataset

  • single GPU
  • single node multiple GPUs
  • multiple node

You can use the following commands to test a dataset.

# single-gpu testing
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] \
    [--gpu-collect] [--tmpdir ${TMPDIR}] [--options ${OPTIONS}] [--average-clips ${AVG_TYPE}] \
    [--launcher ${JOB_LAUNCHER}] [--local_rank ${LOCAL_RANK}] [--onnx] [--tensorrt]

# multi-gpu testing
./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] \
    [--gpu-collect] [--tmpdir ${TMPDIR}] [--options ${OPTIONS}] [--average-clips ${AVG_TYPE}] \
    [--launcher ${JOB_LAUNCHER}] [--local_rank ${LOCAL_RANK}]

Optional arguments:

  • RESULT_FILE: Filename of the output results. If not specified, the results will not be saved to a file.
  • EVAL_METRICS: Items to be evaluated on the results. Allowed values depend on the dataset, e.g., top_k_accuracy, mean_class_accuracy are available for all datasets in recognition, mmit_mean_average_precision for Multi-Moments in Time, mean_average_precision for Multi-Moments in Time and HVU single category. AR@AN for ActivityNet, etc.
  • --gpu-collect: If specified, recognition results will be collected using gpu communication. Otherwise, it will save the results on different gpus to TMPDIR and collect them by the rank 0 worker.
  • TMPDIR: Temporary directory used for collecting results from multiple workers, available when --gpu-collect is not specified.
  • OPTIONS: Custom options used for evaluation. Allowed values depend on the arguments of the evaluate function in dataset.
  • AVG_TYPE: Items to average the test clips. If set to prob, it will apply softmax before averaging the clip scores. Otherwise, it will directly average the clip scores.
  • JOB_LAUNCHER: Items for distributed job initialization launcher. Allowed choices are none, pytorch, slurm, mpi. Especially, if set to none, it will test in a non-distributed mode.
  • LOCAL_RANK: ID for local rank. If not specified, it will be set to 0.
  • --onnx: If specified, recognition results will be generated by onnx model and CHECKPOINT_FILE should be onnx model file path. Onnx model files are generated by /tools/deployment/pytorch2onnx.py. For now, multi-gpu mode and dynamic input shape mode are not supported. Please note that the output tensors of dataset and the input tensors of onnx model should share the same shape. And it is recommended to remove all test-time augmentation methods in test_pipeline(ThreeCrop, TenCrop, twice_sample, etc.)
  • --tensorrt: If specified, recognition results will be generated by TensorRT engine and CHECKPOINT_FILE should be TensorRT engine file path. TensorRT engines are generated by exported onnx models and TensorRT official conversion tools. For now, multi-gpu mode and dynamic input shape mode are not supported. Please note that the output tensors of dataset and the input tensors of TensorRT engine should share the same shape. And it is recommended to remove all test-time augmentation methods in test_pipeline(ThreeCrop, TenCrop, twice_sample, etc.)

Examples:

Assume that you have already downloaded the checkpoints to the directory checkpoints/.

  1. Test TSN on Kinetics-400 (without saving the test results) and evaluate the top-k accuracy and mean class accuracy.

    python tools/test.py configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py \
    checkpoints/SOME_CHECKPOINT.pth \
    --eval top_k_accuracy mean_class_accuracy

  2. Test TSN on Something-Something V1 with 8 GPUS, and evaluate the top-k accuracy.

    ./tools/dist_test.sh configs/recognition/tsn/tsn_r50_1x1x8_50e_sthv1_rgb.py \
    checkpoints/SOME_CHECKPOINT.pth \
    8 --out results.pkl --eval top_k_accuracy

  3. Test TSN on Kinetics-400 in slurm environment and evaluate the top-k accuracy

    python tools/test.py configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py \
    checkpoints/SOME_CHECKPOINT.pth \
    --launcher slurm --eval top_k_accuracy

  4. Test TSN on Something-Something V1 with onnx model and evaluate the top-k accuracy

    python tools/test.py configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py \
    checkpoints/SOME_CHECKPOINT.onnx \
    --eval top_k_accuracy --onnx

High-level APIs for testing a video and rawframes

Here is an example of building the model and testing a given video.

import torch

from mmaction.apis import init_recognizer, inference_recognizer

config_file = 'configs/recognition/tsn/tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py'
# download the checkpoint from model zoo and put it in `checkpoints/`
checkpoint_file = 'checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth'

# assign the desired device.
device = 'cuda:0' # or 'cpu'
device = torch.device(device)

 # build the model from a config file and a checkpoint file
model = init_recognizer(config_file, checkpoint_file, device=device)

# test a single video and show the result:
video = 'demo/demo.mp4'
labels = 'tools/data/kinetics/label_map_k400.txt'
results = inference_recognizer(model, video)

# show the results
labels = open('tools/data/kinetics/label_map_k400.txt').readlines()
labels = [x.strip() for x in labels]
results = [(labels[k[0]], k[1]) for k in results]

print(f'The top-5 labels with corresponding scores are:')
for result in results:
    print(f'{result[0]}: ', result[1])

Here is an example of building the model and testing with a given rawframes directory.

import torch

from mmaction.apis import init_recognizer, inference_recognizer

config_file = 'configs/recognition/tsn/tsn_r50_inference_1x1x3_100e_kinetics400_rgb.py'
# download the checkpoint from model zoo and put it in `checkpoints/`
checkpoint_file = 'checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth'

# assign the desired device.
device = 'cuda:0' # or 'cpu'
device = torch.device(device)

 # build the model from a config file and a checkpoint file
model = init_recognizer(config_file, checkpoint_file, device=device)

# test rawframe directory of a single video and show the result:
video = 'SOME_DIR_PATH/'
labels = 'tools/data/kinetics/label_map_k400.txt'
results = inference_recognizer(model, video)

# show the results
labels = open('tools/data/kinetics/label_map_k400.txt').readlines()
labels = [x.strip() for x in labels]
results = [(labels[k[0]], k[1]) for k in results]

print(f'The top-5 labels with corresponding scores are:')
for result in results:
    print(f'{result[0]}: ', result[1])

Here is an example of building the model and testing with a given video url.

import torch

from mmaction.apis import init_recognizer, inference_recognizer

config_file = 'configs/recognition/tsn/tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py'
# download the checkpoint from model zoo and put it in `checkpoints/`
checkpoint_file = 'checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth'

# assign the desired device.
device = 'cuda:0' # or 'cpu'
device = torch.device(device)

 # build the model from a config file and a checkpoint file
model = init_recognizer(config_file, checkpoint_file, device=device)

# test url of a single video and show the result:
video = 'https://www.learningcontainer.com/wp-content/uploads/2020/05/sample-mp4-file.mp4'
labels = 'tools/data/kinetics/label_map_k400.txt'
results = inference_recognizer(model, video)

# show the results
labels = open('tools/data/kinetics/label_map_k400.txt').readlines()
labels = [x.strip() for x in labels]
results = [(labels[k[0]], k[1]) for k in results]

print(f'The top-5 labels with corresponding scores are:')
for result in results:
    print(f'{result[0]}: ', result[1])

:::{note} We define data_prefix in config files and set it None as default for our provided inference configs. If the data_prefix is not None, the path for the video file (or rawframe directory) to get will be data_prefix/video. Here, the video is the param in the demo scripts above. This detail can be found in rawframe_dataset.py and video_dataset.py. For example,

  • When video (rawframes) path is SOME_DIR_PATH/VIDEO.mp4 (SOME_DIR_PATH/VIDEO_NAME/img_xxxxx.jpg), and data_prefix is None in the config file, the param video should be SOME_DIR_PATH/VIDEO.mp4 (SOME_DIR_PATH/VIDEO_NAME).
  • When video (rawframes) path is SOME_DIR_PATH/VIDEO.mp4 (SOME_DIR_PATH/VIDEO_NAME/img_xxxxx.jpg), and data_prefix is SOME_DIR_PATH in the config file, the param video should be VIDEO.mp4 (VIDEO_NAME).
  • When rawframes path is VIDEO_NAME/img_xxxxx.jpg, and data_prefix is None in the config file, the param video should be VIDEO_NAME.
  • When passing a url instead of a local video file, you need to use OpenCV as the video decoding backend.

:::

A notebook demo can be found in demo/demo.ipynb

Build a Model

Build a model with basic components

In MMAction2, model components are basically categorized as 4 types.

  • recognizer: the whole recognizer model pipeline, usually contains a backbone and cls_head.
  • backbone: usually an FCN network to extract feature maps, e.g., ResNet, BNInception.
  • cls_head: the component for classification task, usually contains an FC layer with some pooling layers.
  • localizer: the model for localization task, currently available: BSN, BMN.

Following some basic pipelines (e.g., Recognizer2D), the model structure can be customized through config files with no pains.

If we want to implement some new components, e.g., the temporal shift backbone structure as in TSM: Temporal Shift Module for Efficient Video Understanding, there are several things to do.

  1. create a new file in mmaction/models/backbones/resnet_tsm.py.

    from ..builder import BACKBONES
from .resnet import ResNet

@BACKBONES.register_module()
class ResNetTSM(ResNet):

  def __init__(self,
               depth,
               num_segments=8,
               is_shift=True,
               shift_div=8,
               shift_place='blockres',
               temporal_pool=False,
               **kwargs):
      pass

  def forward(self, x):
      # implementation is ignored
      pass

  2. Import the module in mmaction/models/backbones/__init__.py

    from .resnet_tsm import ResNetTSM

  3. modify the config file from

    backbone=dict(
  type='ResNet',
  pretrained='torchvision://resnet50',
  depth=50,
  norm_eval=False)

    to

    backbone=dict(
    type='ResNetTSM',
    pretrained='torchvision://resnet50',
    depth=50,
    norm_eval=False,
    shift_div=8)

Write a new model

To write a new recognition pipeline, you need to inherit from BaseRecognizer, which defines the following abstract methods.

  • forward_train(): forward method of the training mode.
  • forward_test(): forward method of the testing mode.

Recognizer2D and Recognizer3D are good examples which show how to do that.

Train a Model

Iteration pipeline

MMAction2 implements distributed training and non-distributed training, which uses MMDistributedDataParallel and MMDataParallel respectively.

We adopt distributed training for both single machine and multiple machines. Supposing that the server has 8 GPUs, 8 processes will be started and each process runs on a single GPU.

Each process keeps an isolated model, data loader, and optimizer. Model parameters are only synchronized once at the beginning. After a forward and backward pass, gradients will be allreduced among all GPUs, and the optimizer will update model parameters. Since the gradients are allreduced, the model parameter stays the same for all processes after the iteration.

Training setting

All outputs (log files and checkpoints) will be saved to the working directory, which is specified by work_dir in the config file.

By default we evaluate the model on the validation set after each epoch, you can change the evaluation interval by modifying the interval argument in the training config

evaluation = dict(interval=5)  # This evaluate the model per 5 epoch.

According to the Linear Scaling Rule, you need to set the learning rate proportional to the batch size if you use different GPUs or videos per GPU, e.g., lr=0.01 for 4 GPUs x 2 video/gpu and lr=0.08 for 16 GPUs x 4 video/gpu.

MMAction2 also supports training with CPU. However, it will be very slow and should only be used for debugging on a device without GPU. To train with CPU, one should first disable all GPUs (if exist) with export CUDA_VISIBLE_DEVICES=-1, and then call the training scripts directly with python tools/train.py {OTHER_ARGS}.

Train with a single GPU

python tools/train.py ${CONFIG_FILE} [optional arguments]

If you want to specify the working directory in the command, you can add an argument --work-dir ${YOUR_WORK_DIR}.

Train with multiple GPUs

./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]

Optional arguments are:

  • --validate (strongly recommended): Perform evaluation at every k (default value is 5, which can be modified by changing the interval value in evaluation dict in each config file) epochs during the training.
  • --test-last: Test the final checkpoint when training is over, save the prediction to ${WORK_DIR}/last_pred.pkl.
  • --test-best: Test the best checkpoint when training is over, save the prediction to ${WORK_DIR}/best_pred.pkl.
  • --work-dir ${WORK_DIR}: Override the working directory specified in the config file.
  • --resume-from ${CHECKPOINT_FILE}: Resume from a previous checkpoint file.
  • --gpus ${GPU_NUM}: Number of gpus to use, which is only applicable to non-distributed training.
  • --gpu-ids ${GPU_IDS}: IDs of gpus to use, which is only applicable to non-distributed training.
  • --seed ${SEED}: Seed id for random state in python, numpy and pytorch to generate random numbers.
  • --deterministic: If specified, it will set deterministic options for CUDNN backend.
  • JOB_LAUNCHER: Items for distributed job initialization launcher. Allowed choices are none, pytorch, slurm, mpi. Especially, if set to none, it will test in a non-distributed mode.
  • LOCAL_RANK: ID for local rank. If not specified, it will be set to 0.

Difference between resume-from and load-from: resume-from loads both the model weights and optimizer status, and the epoch is also inherited from the specified checkpoint. It is usually used for resuming the training process that is interrupted accidentally. load-from only loads the model weights and the training epoch starts from 0. It is usually used for finetuning.

Here is an example of using 8 GPUs to load TSN checkpoint.

./tools/dist_train.sh configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py 8 --resume-from work_dirs/tsn_r50_1x1x3_100e_kinetics400_rgb/latest.pth

Train with multiple machines

If you can run MMAction2 on a cluster managed with slurm, you can use the script slurm_train.sh. (This script also supports single machine training.)

[GPUS=${GPUS}] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} [--work-dir ${WORK_DIR}]

Here is an example of using 16 GPUs to train TSN on the dev partition in a slurm cluster. (use GPUS_PER_NODE=8 to specify a single slurm cluster node with 8 GPUs.)

GPUS=16 ./tools/slurm_train.sh dev tsn_r50_k400 configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py --work-dir work_dirs/tsn_r50_1x1x3_100e_kinetics400_rgb

You can check slurm_train.sh for full arguments and environment variables.

If you have just multiple machines connected with ethernet, you can simply run the following commands:

On the first machine:

NNODES=2 NODE_RANK=0 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR sh tools/dist_train.sh $CONFIG $GPUS

On the second machine:

NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR sh tools/dist_train.sh $CONFIG $GPUS

It can be extremely slow if you do not have high-speed networking like InfiniBand.

Launch multiple jobs on a single machine

If you launch multiple jobs on a single machine, e.g., 2 jobs of 4-GPU training on a machine with 8 GPUs, you need to specify different ports (29500 by default) for each job to avoid communication conflict.

If you use dist_train.sh to launch training jobs, you can set the port in commands.

CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 ./tools/dist_train.sh ${CONFIG_FILE} 4
CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 ./tools/dist_train.sh ${CONFIG_FILE} 4

If you use launch training jobs with slurm, you need to modify dist_params in the config files (usually the 6th line from the bottom in config files) to set different communication ports.

In config1.py,

dist_params = dict(backend='nccl', port=29500)

In config2.py,

dist_params = dict(backend='nccl', port=29501)

Then you can launch two jobs with config1.py ang config2.py.

CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py [--work-dir ${WORK_DIR}]
CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py [--work-dir ${WORK_DIR}]

Tutorials

Currently, we provide some tutorials for users to learn about configs, finetune model, add new dataset, customize data pipelines, add new modules, export a model to ONNX and customize runtime settings.