README.md

TSM

Introduction

@inproceedings{lin2019tsm,
  title={TSM: Temporal Shift Module for Efficient Video Understanding},
  author={Lin, Ji and Gan, Chuang and Han, Song},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  year={2019}
}

@article{NonLocal2018,
  author =   {Xiaolong Wang and Ross Girshick and Abhinav Gupta and Kaiming He},
  title =    {Non-local Neural Networks},
  journal =  {CVPR},
  year =     {2018}
}

Model Zoo

Kinetics-400

config	resolution	gpus	backbone	pretrain	top1 acc	top5 acc	reference top1 acc	reference top5 acc	inference_time(video/s)	gpu_mem(M)	ckpt	log	json
tsm_r50_1x1x8_50e_kinetics400_rgb	340x256	8	ResNet50	ImageNet	70.24	89.56	70.36	89.49	74.0 (8x1 frames)	7079	ckpt	log	json
tsm_r50_1x1x8_50e_kinetics400_rgb	short-side 256	8	ResNet50	ImageNet	70.59	89.52	x	x	x	7079	ckpt	log	json
tsm_r50_1x1x8_50e_kinetics400_rgb	short-side 320	8	ResNet50	ImageNet	70.73	89.81	x	x	x	7079	ckpt	log	json
tsm_r50_1x1x8_100e_kinetics400_rgb	short-side 320	8	ResNet50	ImageNet	71.90	90.03	x	x	x	7079	ckpt	log	json
tsm_r50_gpu_normalize_1x1x8_50e_kinetics400_rgb.py	short-side 256	8	ResNet50	ImageNet	70.48	89.40	x	x	x	7076	ckpt	log	json
tsm_r50_video_1x1x8_50e_kinetics400_rgb	short-side 256	8	ResNet50	ImageNet	70.25	89.66	70.36	89.49	74.0 (8x1 frames)	7077	ckpt	log	json
tsm_r50_dense_1x1x8_50e_kinetics400_rgb	short-side 320	8	ResNet50	ImageNet	73.46	90.84	x	x	x	7079	ckpt	log	json
tsm_r50_dense_1x1x8_100e_kinetics400_rgb	short-side 320	8	ResNet50	ImageNet	74.55	91.74	x	x	x	7079	ckpt	log	json
tsm_r50_1x1x16_50e_kinetics400_rgb	340x256	8	ResNet50	ImageNet	72.09	90.37	70.67	89.98	47.0 (16x1 frames)	10404	ckpt	log	json
tsm_r50_1x1x16_50e_kinetics400_rgb	short-side 256	8x4	ResNet50	ImageNet	71.89	90.73	x	x	x	10398	ckpt	log	json
tsm_r50_1x1x16_100e_kinetics400_rgb	short-side 320	8	ResNet50	ImageNet	72.80	90.75	x	x	x	10398	ckpt	log	json
tsm_nl_embedded_gaussian_r50_1x1x8_50e_kinetics400_rgb	short-side 320	8x4	ResNet50	ImageNet	72.03	90.25	71.81	90.36	x	8931	ckpt	log	json
tsm_nl_gaussian_r50_1x1x8_50e_kinetics400_rgb	short-side 320	8x4	ResNet50	ImageNet	70.70	89.90	x	x	x	10125	ckpt	log	json
tsm_nl_dot_product_r50_1x1x8_50e_kinetics400_rgb	short-side 320	8x4	ResNet50	ImageNet	71.60	90.34	x	x	x	8358	ckpt	log	json
tsm_mobilenetv2_dense_1x1x8_100e_kinetics400_rgb	short-side 320	8	MobileNetV2	ImageNet	68.46	88.64	x	x	x	3385	ckpt	log	json

Diving48

config	gpus	backbone	pretrain	top1 acc	top5 acc	gpu_mem(M)	ckpt	log	json
tsm_r50_video_1x1x8_50e_diving48_rgb	8	ResNet50	ImageNet	75.99	97.16	7070	ckpt	log	json
tsm_r50_video_1x1x16_50e_diving48_rgb	8	ResNet50	ImageNet	81.62	97.66	7070	ckpt	log	json

Something-Something V1

config	resolution	gpus	backbone	pretrain	top1 acc (efficient/accurate)	top5 acc (efficient/accurate)	reference top1 acc (efficient/accurate)	reference top5 acc (efficient/accurate)	gpu_mem(M)	ckpt	log	json
tsm_r50_1x1x8_50e_sthv1_rgb	height 100	8	ResNet50	ImageNet	45.58 / 47.70	75.02 / 76.12	45.50 / 47.33	74.34 / 76.60	7077	ckpt	log	json
tsm_r50_flip_1x1x8_50e_sthv1_rgb	height 100	8	ResNet50	ImageNet	47.10 / 48.51	76.02 / 77.56	45.50 / 47.33	74.34 / 76.60	7077	ckpt	log	json
tsm_r50_randaugment_1x1x8_50e_sthv1_rgb	height 100	8	ResNet50	ImageNet	47.16 / 48.90	76.07 / 77.92	45.50 / 47.33	74.34 / 76.60	7077	ckpt	log	json
tsm_r50_ptv_randaugment_1x1x8_50e_sthv1_rgb	height 100	8	ResNet50	ImageNet	47.65 / 48.66	76.67 / 77.41	45.50 / 47.33	74.34 / 76.60	7077	ckpt	log	json
tsm_r50_ptv_augmix_1x1x8_50e_sthv1_rgb	height 100	8	ResNet50	ImageNet	46.26 / 47.68	75.92 / 76.49	45.50 / 47.33	74.34 / 76.60	7077	ckpt	log	json
tsm_r50_flip_randaugment_1x1x8_50e_sthv1_rgb	height 100	8	ResNet50	ImageNet	47.85 / 50.31	76.78 / 78.18	45.50 / 47.33	74.34 / 76.60	7077	ckpt	log	json
tsm_r50_1x1x16_50e_sthv1_rgb	height 100	8	ResNet50	ImageNet	47.62 / 49.28	76.63 / 77.82	47.05 / 48.61	76.40 / 77.96	10390	ckpt	log	json
tsm_r101_1x1x8_50e_sthv1_rgb	height 100	8	ResNet50	ImageNet	45.72 / 48.43	74.67 / 76.72	46.64 / 48.13	75.40 / 77.31	9800	ckpt	log	json

Something-Something V2

config	resolution	gpus	backbone	pretrain	top1 acc (efficient/accurate)	top5 acc (efficient/accurate)	reference top1 acc (efficient/accurate)	reference top5 acc (efficient/accurate)	gpu_mem(M)	ckpt	log	json
tsm_r50_1x1x8_50e_sthv2_rgb	height 256	8	ResNet50	ImageNet	59.11 / 61.82	85.39 / 86.80	xx / 61.2	xx / xx	7069	ckpt	log	json
tsm_r50_1x1x16_50e_sthv2_rgb	height 256	8	ResNet50	ImageNet	61.06 / 63.19	86.66 / 87.93	xx / 63.1	xx / xx	10400	ckpt	log	json
tsm_r101_1x1x8_50e_sthv2_rgb	height 256	8	ResNet101	ImageNet	60.88 / 63.84	86.56 / 88.30	xx / 63.3	xx / xx	9727	ckpt	log	json

MixUp & CutMix on Something-Something V1

config	resolution	gpus	backbone	pretrain	top1 acc (efficient/accurate)	top5 acc (efficient/accurate)	delta top1 acc (efficient/accurate)	delta top5 acc (efficient/accurate)	ckpt	log	json
tsm_r50_mixup_1x1x8_50e_sthv1_rgb	height 100	8	ResNet50	ImageNet	46.35 / 48.49	75.07 / 76.88	+0.77 / +0.79	+0.05 / +0.70	ckpt	log	json
tsm_r50_cutmix_1x1x8_50e_sthv1_rgb	height 100	8	ResNet50	ImageNet	45.92 / 47.46	75.23 / 76.71	+0.34 / -0.24	+0.21 / +0.59	ckpt	log	json

Jester

config	resolution	gpus	backbone	pretrain	top1 acc (efficient/accurate)	ckpt	log	json
tsm_r50_1x1x8_50e_jester_rgb	height 100	8	ResNet50	ImageNet	96.5 / 97.2	ckpt	log	json

HMDB51

config	gpus	backbone	pretrain	top1 acc	top5 acc	gpu_mem(M)	ckpt	log	json
tsm_k400_pretrained_r50_1x1x8_25e_hmdb51_rgb	8	ResNet50	Kinetics400	72.68	92.03	10388	ckpt	log	json
tsm_k400_pretrained_r50_1x1x16_25e_hmdb51_rgb	8	ResNet50	Kinetics400	74.77	93.86	10388	ckpt	log	json

UCF101

config	gpus	backbone	pretrain	top1 acc	top5 acc	gpu_mem(M)	ckpt	log	json
tsm_k400_pretrained_r50_1x1x8_25e_ucf101_rgb	8	ResNet50	Kinetics400	94.50	99.58	10389	ckpt	log	json
tsm_k400_pretrained_r50_1x1x16_25e_ucf101_rgb	8	ResNet50	Kinetics400	94.58	99.37	10389	ckpt	log	json

Notes:

1. The gpus indicates the number of gpu we used to get the checkpoint. It is noteworthy that the configs we provide are used for 8 gpus as default. According to the Linear Scaling Rule, you may 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.
2. The inference_time is got by this benchmark script, where we use the sampling frames strategy of the test setting and only care about the model inference time, not including the IO time and pre-processing time. For each setting, we use 1 gpu and set batch size (videos per gpu) to 1 to calculate the inference time.
3. The values in columns named after "reference" are the results got by training on the original repo, using the same model settings. The checkpoints for reference repo can be downloaded here.
4. There are two kinds of test settings for Something-Something dataset, efficient setting (center crop x 1 clip) and accurate setting (Three crop x 2 clip), which is referred from the original repo. We use efficient setting as default provided in config files, and it can be changed to accurate setting by

...
test_pipeline = [
    dict(
        type='SampleFrames',
        clip_len=1,
        frame_interval=1,
        num_clips=16,   # `num_clips = 8` when using 8 segments
        twice_sample=True,    # set `twice_sample=True` for twice sample in accurate setting
        test_mode=True),
    dict(type='RawFrameDecode'),
    dict(type='Resize', scale=(-1, 256)),
    # dict(type='CenterCrop', crop_size=224), it is used for efficient setting
    dict(type='ThreeCrop', crop_size=256),  # it is used for accurate setting
    dict(type='Normalize', **img_norm_cfg),
    dict(type='FormatShape', input_format='NCHW'),
    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
    dict(type='ToTensor', keys=['imgs'])
]

5. When applying Mixup and CutMix, we use the hyper parameter alpha=0.2.
6. The validation set of Kinetics400 we used consists of 19796 videos. These videos are available at Kinetics400-Validation. The corresponding data list (each line is of the format 'video_id, num_frames, label_index') and the label map are also available.

For more details on data preparation, you can refer to corresponding parts in Data Preparation.

Train

You can use the following command to train a model.

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

Example: train TSM model on Kinetics-400 dataset in a deterministic option with periodic validation.

python tools/train.py configs/recognition/tsm/tsm_r50_1x1x8_50e_kinetics400_rgb.py \
    --work-dir work_dirs/tsm_r50_1x1x8_100e_kinetics400_rgb \
    --validate --seed 0 --deterministic

For more details, you can refer to Training setting part in getting_started.

Test

You can use the following command to test a model.

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

Example: test TSM model on Kinetics-400 dataset and dump the result to a json file.

python tools/test.py configs/recognition/tsm/tsm_r50_1x1x8_50e_kinetics400_rgb.py \
    checkpoints/SOME_CHECKPOINT.pth --eval top_k_accuracy mean_class_accuracy \
    --out result.json

For more details, you can refer to Test a dataset part in getting_started.