LSTC: Boosting Atomic Action Detection with Long-Short-Term Context

Last update: Oct 11, 2022

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Overview

LSTC: Boosting Atomic Action Detection with Long-Short-Term Context

This Repository contains the code on AVA of our ACM MM 2021 paper: LSTC: Boosting Atomic Action Detection with Long-Short-Term Context

Installation

See INSTALL.md for details on installing the codebase, including requirement and environment settings

Data

For data preparation and setup, our LSTC strictly follows the processing of PySlowFast, See DATASET.md for details on preparing the data.

Run the code

We take SlowFast-ResNet50 as an example

train the model

python3 tools/run_net.py --cfg config/AVA/SLOWFAST_32x12_R50_LFB.yaml \
    AVA.FEATURE_BANK_PATH 'path/to/feature/bank/folder' \
    TRAIN.CHECKPOINT_FILE_PATH 'path/to/pretrained/backbone' \
    OUTPUT_DIR 'path/to/output/folder'

test the model

python3 tools/run_net.py --cfg config/AVA/SLOWFAST_32x12_R50_LFB.yaml \
    AVA.FEATURE_BANK_PATH 'path/to/feature/bank/folder' \
    OUTPUT_DIR 'path/to/output/folder' \
    TRAIN.ENABLE False \ 
    TEST.ENABLE True

If you want to start the DDP training from command line with torch.distributed.launch, please set start_method='cmd' in tools/run_net.py

Resource

The codebase provide following resources for fast training and validation

Pretrained backbone on Kinetics

backbone	dataset	model type	link
ResNet50	Kinetics400	Caffe2	Google Drive/Baidu Disk (Code: y1wl)
ResNet101	Kinetics600	Caffe2	Google Drive/Baidu Disk (Code: slde)

Extracted long term feature bank

backbone	feature bank (LMDB)	dimension
ResNet50	Google Drive	1280
ResNet101	Google Drive	2304

Checkpoint file

backbone	checkpoint	model type
ResNet50	Google Drive/Baidu Disk (Code: fi0s)	pytorch
ResNet101	Google Drive/Baidu Disk (Code: g63o)	pytorch

Acknowledgement

This codebase is built upon PySlowFast.

Citation

If you find this repository helps your research, please refer following paper

@InProceedings{Yuxi_2021_ACM,
  author = {Li, Yuxi and Zhang, Boshen and Li, Jian and Wang, Yabiao and Wang, Chengjie and Li, Jilin and Huang, Feiyue and Lin, Weiyao},
  title = {LSTC: Boosting Atomic Action Detection with Long-Short-Term Context},
  booktitle = {ACM Conference on Multimedia},
  month = {October},
  year = {2021}
}

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Comments

extract the features of custom data sets

How to extract the features of custom data sets? Can you give me some advice? I ran the script and added yaml configuration file, and there was an error that the dimensions could not be spliced. How did you extract the features at that time? Do you need to resize the images? Can the resized features still be used as feature bank?

opened by yan-ctrl 0
extract the feature bank of the data set

Hello, when I want to extract the feature bank of the data set, I encountered a new problem. My yaml configuration file and the error I encountered are like this. Is the pre-training weight wrong? I hope you can give me an answer. SLOWFAST_32x2_BANK1.yaml: TRAIN: ENABLE: False DATASET: ava BATCH_SIZE: 64 EVAL_PERIOD: 20 CHECKPOINT_PERIOD: 1 AUTO_RESUME: True CHECKPOINT_FILE_PATH: "/home/tuxiangone/bang/Behavior_Model/LSTC/pretrained/lstc-resnet50.pyth" DATA: NUM_FRAMES: 32 SAMPLING_RATE: 2 TRAIN_JITTER_SCALES: [256, 320] TRAIN_CROP_SIZE: 224 TEST_CROP_SIZE: 224 INPUT_CHANNEL_NUM: [3, 3] DETECTION: ENABLE: True ALIGNED: True AVA: FEATURE_EXTRACTION: True FRAME_DIR: '/home/tuxiangone/bang/Behavior_Model/SlowFast/data/ava/frames' FRAME_LIST_DIR: '/home/tuxiangone/bang/Behavior_Model/SlowFast/data/ava/frame_lists' ANNOTATION_DIR: '/home/tuxiangone/bang/Behavior_Model/SlowFast/data/ava/annotations' DETECTION_SCORE_THRESH: 0.8 TRAIN_PREDICT_BOX_LISTS: [ "ava_train_v2.2.csv", "ava_train_predicted_boxes.csv", ] TEST_PREDICT_BOX_LISTS: ["ava_val_predicted_boxes.csv"] TEST_GT_BOX_LISTS: ["ava_val_v2.2.csv"] FEATURE_BANK_PATH: "output/feature_bank" SLIDING_WINDOW_SIZE: 15 GATHER_BANK: False SLOWFAST: ALPHA: 4 BETA_INV: 8 FUSION_CONV_CHANNEL_RATIO: 2 FUSION_KERNEL_SZ: 7 RESNET: ZERO_INIT_FINAL_BN: True WIDTH_PER_GROUP: 64 NUM_GROUPS: 1 DEPTH: 50 TRANS_FUNC: bottleneck_transform STRIDE_1X1: False NUM_BLOCK_TEMP_KERNEL: [[3, 3], [4, 4], [6, 6], [3, 3]] SPATIAL_DILATIONS: [[1, 1], [1, 1], [1, 1], [2, 2]] SPATIAL_STRIDES: [[1, 1], [2, 2], [2, 2], [1, 1]] NONLOCAL: LOCATION: [[[], []], [[], []], [[], []], [[], []]] GROUP: [[1, 1], [1, 1], [1, 1], [1, 1]] INSTANTIATION: dot_product POOL: [[[1, 2, 2], [1, 2, 2]], [[1, 2, 2], [1, 2, 2]], [[1, 2, 2], [1, 2, 2]], [[1, 2, 2], [1, 2, 2]]] BN: USE_PRECISE_STATS: False FREEZE: False NUM_BATCHES_PRECISE: 200 SOLVER: BASE_LR: 0.1 LR_POLICY: steps_with_relative_lrs STEPS: [0, 10, 15, 20] LRS: [1, 0.1, 0.01, 0.001] MAX_EPOCH: 20 MOMENTUM: 0.9 WEIGHT_DECAY: 1e-7 WARMUP_EPOCHS: 5.0 WARMUP_START_LR: 0.000125 OPTIMIZING_METHOD: sgd MODEL: NUM_CLASSES: 80 ARCH: slowfast MODEL_NAME: BankContext LOSS_FUNC: bce DROPOUT_RATE: 0.5 HEAD_ACT: sigmoid TEST: ENABLE: True DATASET: ava BATCH_SIZE: 8 CHECKPOINT_FILE_PATH: "/home/tuxiangone/bang/Behavior_Model/LSTC/pretrained/SLOWFAST_8x8_R50_KINETICS.pkl" DATA_LOADER: NUM_WORKERS: 2 PIN_MEMORY: True NUM_GPUS: 1 NUM_SHARDS: 1 RNG_SEED: 0 OUTPUT_DIR: "output/raw_bank"

CACHE:

ENABLE: True

LOG_MODEL_INFO: False My error: [INFO: checkpoint.py: 401]: loading checkpoint from /home/tuxiangone/bang/Behavior_Model/LSTC/pretrained/SLOWFAST_8x8_R50_KINETICS.pkl [09/01 16:48:34][INFO] slowfast.utils.checkpoint: 401: loading checkpoint from /home/tuxiangone/bang/Behavior_Model/LSTC/pretrained/SLOWFAST_8x8_R50_KINETICS.pkl Traceback (most recent call last): File "tools/extract_feature.py", line 175, in launch_job( File "/home/tuxiangone/bang/Behavior_Model/LSTC/build/lib/slowfast/utils/misc.py", line 307, in launch_job func(cfg=cfg) File "tools/extract_feature.py", line 144, in extract_feature cu.load_test_checkpoint(cfg, model) File "/home/tuxiangone/bang/Behavior_Model/LSTC/build/lib/slowfast/utils/checkpoint.py", line 405, in load_test_checkpoint load_checkpoint( File "/home/tuxiangone/bang/Behavior_Model/LSTC/build/lib/slowfast/utils/checkpoint.py", line 268, in load_checkpoint checkpoint = torch.load(f, map_location="cpu") File "/home/tuxiangone/anaconda3/envs/pytorch/lib/python3.8/site-packages/torch/serialization.py", line 608, in load return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args) File "/home/tuxiangone/anaconda3/envs/pytorch/lib/python3.8/site-packages/torch/serialization.py", line 777, in _legacy_load magic_number = pickle_module.load(f, **pickle_load_args) UnicodeDecodeError: 'utf-8' codec can't decode byte 0xd4 in position 1: invalid continuation byte

opened by yan-ctrl 3
module name"lmdb "

Hello, I reported an error during training, no module name"lmdb ".Could you please send me this script? I carefully read setup.py, which is basically the same as that of SlowFast, and there is no information about the generation of lmdb package, so it can't be trained normally. Can you update this library?

opened by yan-ctrl 0
feature bank (LMDB)

I like your paper very much. Can you tell me how to get the feature bank (LMDB)? I want to apply your work to my own data set. Do you run the script extract_feature.py directly or extract it like LFB?

opened by yan-ctrl 9

Owner

Tencent YouTu Research

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LSTC: Boosting Atomic Action Detection with Long-Short-Term Context

Related tags

Overview

LSTC: Boosting Atomic Action Detection with Long-Short-Term Context

Installation

Data

Run the code

Resource

Pretrained backbone on Kinetics

Extracted long term feature bank

Checkpoint file

Acknowledgement

Citation

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Comments

extract the features of custom data sets

extract the feature bank of the data set

CACHE:

ENABLE: True

module name"lmdb "

feature bank (LMDB)

Owner

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