A pytorch reproduction of { Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation }.

huguyuehuhu, updated 🕥 2022-11-22 02:35:11

A PyTorch Reproduction of HCN

Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation. Chao Li, Qiaoyong Zhong, Di Xie, Shiliang Pu, IJCAI 2018.

Arxiv Preprint

Features

1. Dataset

  • [x] NTU RGB+D: Cross View (CV), Cross Subject (CS)
  • [ ] SBU Kinect Interaction
  • [ ] PKU-MMD

2. Tasks

  • [x] Action recognition
  • [ ] Action detection

3. Visualization

  • Visdom supported.

Prerequisites

Our code is based on Python3.5. There are a few dependencies to run the code in the following: - Python >= 3.5 - PyTorch == 0.4.0 - torchnet - Visdom - Other version info about some Python packages can be found in requirements.txt

Usage

Data preparation

NTU RGB+D

To transform raw NTU RGB+D data into numpy array (memmap format ) by this command: commandline python ./feeder/ntu_gendata.py --data_path <path for raw skeleton dataset> --out_folder <path for new dataset>

Other Datasets

Not supported now.

Training

Before you start the training, you have to launch visdom server. commandline python -m visdom

To train the model, you should note that: - --dataset_dir is the parents path for all the datasets, - --num the number of experiments trials (type: list). commandline python main.py --dataset_dir <parents path for all the datasets> --mode train --model_name HCN --dataset_name NTU-RGB-D-CV --num 01 To run a new trial with different parameters, you need to: - Firstly, run the above training command with a new trial number, e.g, --num 03, thus you will got an error. - Secondly, copy a parameters file from the ./HCN/experiments/NTU-RGB-D-CV/HCN01/params.json to the path of your new trial "./HCN/experiments/NTU-RGB-D-CV/HCN03/params.json" and modify it as you want. - At last, run the above training command again, it will works.

Testing

commandline python main.py --dataset_dir <parents path for all the datasets> --mode test --load True --model_name HCN --dataset_name NTU-RGB-D-CV --num 01

Load and Training

You also can load a half trained model, and start training it from a specific checkpoint by the following command: commandline python main.py --dataset_dir <parents path for all the datasets> --mode load_train --load True --model_name HCN --dataset_name NTU-RGB-D-CV --num 01 --load_model <path for trained model>

Results

Table

The expected Top-1 accuracy of the model for NTU-RGD+D are shown here (There is an accuracy gap. I am not the author of original HCN paper, the repo was reproduced according to the paper text and have not been tuned carefully):

| Model | Normalized
Sequence
Length | FC
Neuron
Numbers | NTU RGB+D
Cross Subject (%) |NTU RGB+D
Cross View (%) | | :------: | :------: | :------:| :------:| :------: | | HCN[1]| 32 | 256 | 86.5 | 91.1 | | HCN | 32 | 256 | 84.2 | 89.2 | | HCN | 64 | 512 | 84.9 | 90.9 |

[1] http://arxiv.org/pdf/1804.06055.pdf

Figures

  • Loss & accuracy[CV]

Confusion matrix

  • Loss & accuracy[CS]

Reference

[1] Chao Li, Qiaoyong Zhong, Di Xie, Shiliang Pu. Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation. IJCAI 2018.

[2] yysijie/st-gcn: referred for some code of dataset processing.

Issues

Bump pillow from 5.1.0 to 9.3.0

opened on 2022-11-22 02:35:06 by dependabot[bot]

Bumps pillow from 5.1.0 to 9.3.0.

Release notes

Sourced from pillow's releases.

9.3.0

https://pillow.readthedocs.io/en/stable/releasenotes/9.3.0.html

Changes

... (truncated)

Changelog

Sourced from pillow's changelog.

9.3.0 (2022-10-29)

  • Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [wiredfool]

  • Initialize libtiff buffer when saving #6699 [radarhere]

  • Inline fname2char to fix memory leak #6329 [nulano]

  • Fix memory leaks related to text features #6330 [nulano]

  • Use double quotes for version check on old CPython on Windows #6695 [hugovk]

  • Remove backup implementation of Round for Windows platforms #6693 [cgohlke]

  • Fixed set_variation_by_name offset #6445 [radarhere]

  • Fix malloc in _imagingft.c:font_setvaraxes #6690 [cgohlke]

  • Release Python GIL when converting images using matrix operations #6418 [hmaarrfk]

  • Added ExifTags enums #6630 [radarhere]

  • Do not modify previous frame when calculating delta in PNG #6683 [radarhere]

  • Added support for reading BMP images with RLE4 compression #6674 [npjg, radarhere]

  • Decode JPEG compressed BLP1 data in original mode #6678 [radarhere]

  • Added GPS TIFF tag info #6661 [radarhere]

  • Added conversion between RGB/RGBA/RGBX and LAB #6647 [radarhere]

  • Do not attempt normalization if mode is already normal #6644 [radarhere]

... (truncated)

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Bump nbconvert from 5.3.1 to 6.5.1

opened on 2022-08-23 17:23:07 by dependabot[bot]

Bumps nbconvert from 5.3.1 to 6.5.1.

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6.5.0

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Bump mistune from 0.8.3 to 2.0.3

opened on 2022-07-29 22:37:47 by dependabot[bot]

Bumps mistune from 0.8.3 to 2.0.3.

Release notes

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Version 2.0.2

Fix escape_url via lepture/mistune#295

Version 2.0.1

Fix XSS for image link syntax.

Version 2.0.0

First release of Mistune v2.

Version 2.0.0 RC1

In this release, we have a Security Fix for harmful links.

Version 2.0.0 Alpha 1

This is the first release of v2. An alpha version for users to have a preview of the new mistune.

Version 0.8.4

  • Support an escaped pipe char in a table cell. #150
  • Fix ordered and unordered list. #152
  • Fix spaces between = in HTML tags
  • Add max_recursive_depth for list and blockquote.
  • Fix fences code block.
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Version 2.0.4


Released on Jul 15, 2022

    

  • Fix url plugin in &lt;a&gt; tag
    • 

  • Fix * formatting
    • 



Version 2.0.3

Released on Jun 27, 2022

  • Fix table plugin
  • Security fix for CVE-2022-34749

Version 2.0.2


Released on Jan 14, 2022

Fix escape_url


Version 2.0.1

Released on Dec 30, 2021

XSS fix for image link syntax.

Version 2.0.0


Released on Dec 5, 2021

This is the first non-alpha release of mistune v2.


Version 2.0.0rc1

Released on Feb 16, 2021

Version 2.0.0a6


</tr></table>

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Commits
  • 3f422f1 Version bump 2.0.3
  • a6d4321 Fix asteris emphasis regex CVE-2022-34749
  • 5638e46 Merge pull request #307 from jieter/patch-1
  • 0eba471 Fix typo in guide.rst
  • 61e9337 Fix table plugin
  • 76dec68 Add documentation for renderer heading when TOC enabled
  • 799cd11 Version bump 2.0.2
  • babb0cf Merge pull request #295 from dairiki/bug.escape_url
  • fc2cd53 Make mistune.util.escape_url less aggressive
  • 3e8d352 Version bump 2.0.1
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Bump numpy from 1.14.3 to 1.22.0

opened on 2022-06-21 21:24:52 by dependabot[bot]

Bumps numpy from 1.14.3 to 1.22.0.

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v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

  • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
  • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
  • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
  • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
  • A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

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Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

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Bump notebook from 5.5.0 to 6.4.12

opened on 2022-06-16 23:15:16 by dependabot[bot]

Bumps notebook from 5.5.0 to 6.4.12.

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Official implementation of HCN

opened on 2022-06-09 04:18:36 by xiaoyong

Hi All,

Thank @huguyuehuhu and other contributors for the excellent implementation of HCN. Here is good news! We finally released the official souce code of HCN at hikvision-research/skelact, along with some new works from our lab. The HCN model was originally implemented in TensorFlow and reimplemented in PyTorch. The performance of HCN matches with the paper (with slightly different implementation details). Please check the SkelAct repo, and we welcome contributions from the community.

Guyue Hu
GitHub Repository

pytorch-implmention hcn skeleton-based-action-recognition nturgb-d