Repository: microsoft/SGN
Branch: master
Commit: 79aa0dd89e33
Files: 22
Total size: 1.9 MB

Directory structure:
gitextract_teiivr1p/

├── .gitignore
├── CODE_OF_CONDUCT.md
├── LICENSE
├── README.md
├── SECURITY.md
├── data/
│   └── ntu/
│       ├── get_raw_denoised_data.py
│       ├── get_raw_skes_data.py
│       ├── seq_transformation.py
│       └── statistics/
│           ├── camera.txt
│           ├── label.txt
│           ├── performer.txt
│           ├── replication.txt
│           ├── samples_with_missing_skeletons.txt
│           ├── setup.txt
│           └── skes_available_name.txt
├── data.py
├── fit.py
├── main.py
├── model.py
├── results/
│   └── NTU/
│       └── SGN/
│           ├── 0_best.pth
│           └── 1_best.pth
└── util.py

================================================
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FILE: .gitignore
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================================================
FILE: CODE_OF_CONDUCT.md
================================================
# Microsoft Open Source Code of Conduct

This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).

Resources:

- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
- Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns


================================================
FILE: LICENSE
================================================
    MIT License

    Copyright (c) Microsoft Corporation.

    Permission is hereby granted, free of charge, to any person obtaining a copy
    of this software and associated documentation files (the "Software"), to deal
    in the Software without restriction, including without limitation the rights
    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
    copies of the Software, and to permit persons to whom the Software is
    furnished to do so, subject to the following conditions:

    The above copyright notice and this permission notice shall be included in all
    copies or substantial portions of the Software.

    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
    SOFTWARE


================================================
FILE: README.md
================================================
# Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition (SGN)

## Introduction

Skeleton-based human action recognition has attracted great interest thanks to the easy accessibility of the human skeleton data. Recently, there is a trend of using very deep feedforward neural networks to model the 3D coordinates of joints without considering the computational efficiency. In this work, we propose a simple yet effective semantics-guided neural network (SGN). We explicitly introduce the high level semantics of joints (joint type and frame index) into the network to enhance the feature representation capability. Intuitively, semantic information, i.e., the joint type and the frame index, together with dynamics (i.e., 3D coordinates) reveal the spatial and temporal configuration/structure of human body joints and are very important for action recognition.
In addition, we exploit the relationship of joints hierarchically through two modules, i.e., a joint-level module for modeling the correlations of joints in the same frame and a frame-level module for modeling the dependencies of frames by taking the joints in the same frame as a whole. A strong baseline is proposed to facilitate the study of this field. With an order of magnitude smaller model size than most previous works, SGN achieves the state-of-the-art performance.
 

<div align=center>
<img src="https://github.com/microsoft/SGN/blob/master/images/para.PNG" width = 50% height = 50% div align=center>
</div>

Figure 1: Comparisons of different methods on NTU60 (CS setting) in terms of accuracy and the number of parameters. Among these methods, the proposed SGN model achieves the best performance with an order of magnitude smaller model size.



## Framework
![image](https://github.com/microsoft/SGN/blob/master/images/framework.PNG)

Figure 2: Framework of the proposed end-to-end Semantics-Guided Neural Network (SGN). It consists of a joint-level module and a frame-level module. In DR, we learn the dynamics representation of a joint by fusing the position and velocity information of a joint. Two types of semantics, i.e., joint type and frame index, are incorporated into the joint-level module and the frame-level module, respectively. To model the dependencies of joints in the joint-level module, we use three GCN layers. To model the dependencies of frames, we use two CNN layers.

## Prerequisites
The code is built with the following libraries:
- Python 3.6
- [Anaconda](https://www.anaconda.com/)
- [PyTorch](https://pytorch.org/) 1.3

## Data Preparation

We use the dataset of NTU60 RGB+D as an example for description. We need to first dowload the [NTU-RGB+D](https://github.com/shahroudy/NTURGB-D) dataset.

- Extract the dataset to ./data/ntu/nturgb+d_skeletons/
- Process the data
```bash
 cd ./data/ntu
 # Get skeleton of each performer
 python get_raw_skes_data.py
 # Remove the bad skeleton 
 python get_raw_denoised_data.py
 # Transform the skeleton to the center of the first frame
 python seq_transformation.py
```


## Training

```bash
# For the CS setting
python  main.py --network SGN --train 1 --case 0
# For the CV setting
python  main.py --network SGN --train 1 --case 1
```

## Testing

- Test the pre-trained models (./results/NTU/SGN/)
```bash
# For the CS setting
python  main.py --network SGN --train 0 --case 0
# For the CV setting
python  main.py --network SGN --train 0 --case 1
```

## Reference

This repository holds the code for the following paper:

[Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition](https://arxiv.org/abs/1904.01189). CVPR, 2020.

If you find our paper and repo useful, please cite our paper. Thanks!

```
@inproceedings{zhang2020semantics,
  title={Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition},
  author={Zhang, Pengfei and Lan, Cuiling and Zeng, Wenjun and Xing, Junliang and Xue, Jianru and Zheng, Nanning},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2020},
}

```
## Contributing

This project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.



================================================
FILE: SECURITY.md
================================================
<!-- BEGIN MICROSOFT SECURITY.MD V0.0.5 BLOCK -->

## Security

Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/).

If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://docs.microsoft.com/en-us/previous-versions/tn-archive/cc751383(v=technet.10)), please report it to us as described below.

## Reporting Security Issues

**Please do not report security vulnerabilities through public GitHub issues.**

Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://msrc.microsoft.com/create-report).

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Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue:

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We prefer all communications to be in English.

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Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://www.microsoft.com/en-us/msrc/cvd).

<!-- END MICROSOFT SECURITY.MD BLOCK -->

================================================
FILE: data/ntu/get_raw_denoised_data.py
================================================
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import os
import os.path as osp
import numpy as np
import pickle
import logging

root_path = './'
raw_data_file = osp.join(root_path, 'raw_data', 'raw_skes_data.pkl')
save_path = osp.join(root_path, 'denoised_data')

if not osp.exists(save_path):
    os.mkdir(save_path)

rgb_ske_path = osp.join(save_path, 'rgb+ske')
if not osp.exists(rgb_ske_path):
    os.mkdir(rgb_ske_path)

actors_info_dir = osp.join(save_path, 'actors_info')
if not osp.exists(actors_info_dir):
    os.mkdir(actors_info_dir)

missing_count = 0
noise_len_thres = 11
noise_spr_thres1 = 0.8
noise_spr_thres2 = 0.69754
noise_mot_thres_lo = 0.089925
noise_mot_thres_hi = 2

noise_len_logger = logging.getLogger('noise_length')
noise_len_logger.setLevel(logging.INFO)
noise_len_logger.addHandler(logging.FileHandler(osp.join(save_path, 'noise_length.log')))
noise_len_logger.info('{:^20}\t{:^17}\t{:^8}\t{}'.format('Skeleton', 'bodyID', 'Motion', 'Length'))

noise_spr_logger = logging.getLogger('noise_spread')
noise_spr_logger.setLevel(logging.INFO)
noise_spr_logger.addHandler(logging.FileHandler(osp.join(save_path, 'noise_spread.log')))
noise_spr_logger.info('{:^20}\t{:^17}\t{:^8}\t{:^8}'.format('Skeleton', 'bodyID', 'Motion', 'Rate'))

noise_mot_logger = logging.getLogger('noise_motion')
noise_mot_logger.setLevel(logging.INFO)
noise_mot_logger.addHandler(logging.FileHandler(osp.join(save_path, 'noise_motion.log')))
noise_mot_logger.info('{:^20}\t{:^17}\t{:^8}'.format('Skeleton', 'bodyID', 'Motion'))

fail_logger_1 = logging.getLogger('noise_outliers_1')
fail_logger_1.setLevel(logging.INFO)
fail_logger_1.addHandler(logging.FileHandler(osp.join(save_path, 'denoised_failed_1.log')))

fail_logger_2 = logging.getLogger('noise_outliers_2')
fail_logger_2.setLevel(logging.INFO)
fail_logger_2.addHandler(logging.FileHandler(osp.join(save_path, 'denoised_failed_2.log')))

missing_skes_logger = logging.getLogger('missing_frames')
missing_skes_logger.setLevel(logging.INFO)
missing_skes_logger.addHandler(logging.FileHandler(osp.join(save_path, 'missing_skes.log')))
missing_skes_logger.info('{:^20}\t{}\t{}'.format('Skeleton', 'num_frames', 'num_missing'))

missing_skes_logger1 = logging.getLogger('missing_frames_1')
missing_skes_logger1.setLevel(logging.INFO)
missing_skes_logger1.addHandler(logging.FileHandler(osp.join(save_path, 'missing_skes_1.log')))
missing_skes_logger1.info('{:^20}\t{}\t{}\t{}\t{}\t{}'.format('Skeleton', 'num_frames', 'Actor1',
                                                              'Actor2', 'Start', 'End'))

missing_skes_logger2 = logging.getLogger('missing_frames_2')
missing_skes_logger2.setLevel(logging.INFO)
missing_skes_logger2.addHandler(logging.FileHandler(osp.join(save_path, 'missing_skes_2.log')))
missing_skes_logger2.info('{:^20}\t{}\t{}\t{}'.format('Skeleton', 'num_frames', 'Actor1', 'Actor2'))


def denoising_by_length(ske_name, bodies_data):
    """
    Denoising data based on the frame length for each bodyID.
    Filter out the bodyID which length is less or equal than the predefined threshold.

    """
    noise_info = str()
    new_bodies_data = bodies_data.copy()
    for (bodyID, body_data) in new_bodies_data.items():
        length = len(body_data['interval'])
        if length <= noise_len_thres:
            noise_info += 'Filter out: %s, %d (length).\n' % (bodyID, length)
            noise_len_logger.info('{}\t{}\t{:.6f}\t{:^6d}'.format(ske_name, bodyID,
                                                                  body_data['motion'], length))
            del bodies_data[bodyID]
    if noise_info != '':
        noise_info += '\n'

    return bodies_data, noise_info


def get_valid_frames_by_spread(points):
    """
    Find the valid (or reasonable) frames (index) based on the spread of X and Y.

    :param points: joints or colors
    """
    num_frames = points.shape[0]
    valid_frames = []
    for i in range(num_frames):
        x = points[i, :, 0]
        y = points[i, :, 1]
        if (x.max() - x.min()) <= noise_spr_thres1 * (y.max() - y.min()):  # 0.8
            valid_frames.append(i)
    return valid_frames


def denoising_by_spread(ske_name, bodies_data):
    """
    Denoising data based on the spread of Y value and X value.
    Filter out the bodyID which the ratio of noisy frames is higher than the predefined
    threshold.

    bodies_data: contains at least 2 bodyIDs
    """
    noise_info = str()
    denoised_by_spr = False  # mark if this sequence has been processed by spread.

    new_bodies_data = bodies_data.copy()
    # for (bodyID, body_data) in bodies_data.items():
    for (bodyID, body_data) in new_bodies_data.items():
        if len(bodies_data) == 1:
            break
        valid_frames = get_valid_frames_by_spread(body_data['joints'].reshape(-1, 25, 3))
        num_frames = len(body_data['interval'])
        num_noise = num_frames - len(valid_frames)
        if num_noise == 0:
            continue

        ratio = num_noise / float(num_frames)
        motion = body_data['motion']
        if ratio >= noise_spr_thres2:  # 0.69754
            del bodies_data[bodyID]
            denoised_by_spr = True
            noise_info += 'Filter out: %s (spread rate >= %.2f).\n' % (bodyID, noise_spr_thres2)
            noise_spr_logger.info('%s\t%s\t%.6f\t%.6f' % (ske_name, bodyID, motion, ratio))
        else:  # Update motion
            joints = body_data['joints'].reshape(-1, 25, 3)[valid_frames]
            body_data['motion'] = min(motion, np.sum(np.var(joints.reshape(-1, 3), axis=0)))
            noise_info += '%s: motion %.6f -> %.6f\n' % (bodyID, motion, body_data['motion'])
            # TODO: Consider removing noisy frames for each bodyID

    if noise_info != '':
        noise_info += '\n'

    return bodies_data, noise_info, denoised_by_spr


def denoising_by_motion(ske_name, bodies_data, bodies_motion):
    """
    Filter out the bodyID which motion is out of the range of predefined interval

    """
    # Sort bodies based on the motion, return a list of tuples
    # bodies_motion = sorted(bodies_motion.items(), key=lambda x, y: cmp(x[1], y[1]), reverse=True)
    bodies_motion = sorted(bodies_motion.items(), key=lambda x: x[1], reverse=True)

    # Reserve the body data with the largest motion
    denoised_bodies_data = [(bodies_motion[0][0], bodies_data[bodies_motion[0][0]])]
    noise_info = str()

    for (bodyID, motion) in bodies_motion[1:]:
        if (motion < noise_mot_thres_lo) or (motion > noise_mot_thres_hi):
            noise_info += 'Filter out: %s, %.6f (motion).\n' % (bodyID, motion)
            noise_mot_logger.info('{}\t{}\t{:.6f}'.format(ske_name, bodyID, motion))
        else:
            denoised_bodies_data.append((bodyID, bodies_data[bodyID]))
    if noise_info != '':
        noise_info += '\n'

    return denoised_bodies_data, noise_info


def denoising_bodies_data(bodies_data):
    """
    Denoising data based on some heuristic methods, not necessarily correct for all samples.

    Return:
      denoised_bodies_data (list): tuple: (bodyID, body_data).
    """
    ske_name = bodies_data['name']
    bodies_data = bodies_data['data']

    # Step 1: Denoising based on frame length.
    bodies_data, noise_info_len = denoising_by_length(ske_name, bodies_data)

    if len(bodies_data) == 1:  # only has one bodyID left after step 1
        return bodies_data.items(), noise_info_len

    # Step 2: Denoising based on spread.
    bodies_data, noise_info_spr, denoised_by_spr = denoising_by_spread(ske_name, bodies_data)

    if len(bodies_data) == 1:
        return bodies_data.items(), noise_info_len + noise_info_spr

    bodies_motion = dict()  # get body motion
    for (bodyID, body_data) in bodies_data.items():
        bodies_motion[bodyID] = body_data['motion']
    # Sort bodies based on the motion
    # bodies_motion = sorted(bodies_motion.items(), key=lambda x, y: cmp(x[1], y[1]), reverse=True)
    bodies_motion = sorted(bodies_motion.items(), key=lambda x: x[1], reverse=True)
    denoised_bodies_data = list()
    for (bodyID, _) in bodies_motion:
        denoised_bodies_data.append((bodyID, bodies_data[bodyID]))

    return denoised_bodies_data, noise_info_len + noise_info_spr

    # TODO: Consider denoising further by integrating motion method

    # if denoised_by_spr:  # this sequence has been denoised by spread
    #     bodies_motion = sorted(bodies_motion.items(), lambda x, y: cmp(x[1], y[1]), reverse=True)
    #     denoised_bodies_data = list()
    #     for (bodyID, _) in bodies_motion:
    #         denoised_bodies_data.append((bodyID, bodies_data[bodyID]))
    #     return denoised_bodies_data, noise_info

    # Step 3: Denoising based on motion
    # bodies_data, noise_info = denoising_by_motion(ske_name, bodies_data, bodies_motion)

    # return bodies_data, noise_info


def get_one_actor_points(body_data, num_frames):
    """
    Get joints and colors for only one actor.
    For joints, each frame contains 75 X-Y-Z coordinates.
    For colors, each frame contains 25 x 2 (X, Y) coordinates.
    """
    joints = np.zeros((num_frames, 75), dtype=np.float32)
    colors = np.ones((num_frames, 1, 25, 2), dtype=np.float32) * np.nan
    start, end = body_data['interval'][0], body_data['interval'][-1]
    joints[start:end + 1] = body_data['joints'].reshape(-1, 75)
    colors[start:end + 1, 0] = body_data['colors']

    return joints, colors


def remove_missing_frames(ske_name, joints, colors):
    """
    Cut off missing frames which all joints positions are 0s

    For the sequence with 2 actors' data, also record the number of missing frames for
    actor1 and actor2, respectively (for debug).
    """
    num_frames = joints.shape[0]
    num_bodies = colors.shape[1]  # 1 or 2

    if num_bodies == 2:  # DEBUG
        missing_indices_1 = np.where(joints[:, :75].sum(axis=1) == 0)[0]
        missing_indices_2 = np.where(joints[:, 75:].sum(axis=1) == 0)[0]
        cnt1 = len(missing_indices_1)
        cnt2 = len(missing_indices_2)

        start = 1 if 0 in missing_indices_1 else 0
        end = 1 if num_frames - 1 in missing_indices_1 else 0
        if max(cnt1, cnt2) > 0:
            if cnt1 > cnt2:
                info = '{}\t{:^10d}\t{:^6d}\t{:^6d}\t{:^5d}\t{:^3d}'.format(ske_name, num_frames,
                                                                            cnt1, cnt2, start, end)
                missing_skes_logger1.info(info)
            else:
                info = '{}\t{:^10d}\t{:^6d}\t{:^6d}'.format(ske_name, num_frames, cnt1, cnt2)
                missing_skes_logger2.info(info)

    # Find valid frame indices that the data is not missing or lost
    # For two-subjects action, this means both data of actor1 and actor2 is missing.
    valid_indices = np.where(joints.sum(axis=1) != 0)[0]  # 0-based index
    missing_indices = np.where(joints.sum(axis=1) == 0)[0]
    num_missing = len(missing_indices)

    if num_missing > 0:  # Update joints and colors
        joints = joints[valid_indices]
        colors[missing_indices] = np.nan
        global missing_count
        missing_count += 1
        missing_skes_logger.info('{}\t{:^10d}\t{:^11d}'.format(ske_name, num_frames, num_missing))

    return joints, colors


def get_bodies_info(bodies_data):
    bodies_info = '{:^17}\t{}\t{:^8}\n'.format('bodyID', 'Interval', 'Motion')
    for (bodyID, body_data) in bodies_data.items():
        start, end = body_data['interval'][0], body_data['interval'][-1]
        bodies_info += '{}\t{:^8}\t{:f}\n'.format(bodyID, str([start, end]), body_data['motion'])

    return bodies_info + '\n'


def get_two_actors_points(bodies_data):
    """
    Get the first and second actor's joints positions and colors locations.

    # Arguments:
        bodies_data (dict): 3 key-value pairs: 'name', 'data', 'num_frames'.
        bodies_data['data'] is also a dict, while the key is bodyID, the value is
        the corresponding body_data which is also a dict with 4 keys:
          - joints: raw 3D joints positions. Shape: (num_frames x 25, 3)
          - colors: raw 2D color locations. Shape: (num_frames, 25, 2)
          - interval: a list which records the frame indices.
          - motion: motion amount

    # Return:
        joints, colors.
    """
    ske_name = bodies_data['name']
    label = int(ske_name[-2:])
    num_frames = bodies_data['num_frames']
    bodies_info = get_bodies_info(bodies_data['data'])

    bodies_data, noise_info = denoising_bodies_data(bodies_data)  # Denoising data
    bodies_info += noise_info

    bodies_data = list(bodies_data)
    if len(bodies_data) == 1:  # Only left one actor after denoising
        if label >= 50:  # DEBUG: Denoising failed for two-subjects action
            fail_logger_2.info(ske_name)

        bodyID, body_data = bodies_data[0]
        joints, colors = get_one_actor_points(body_data, num_frames)
        bodies_info += 'Main actor: %s' % bodyID
    else:
        if label < 50:  # DEBUG: Denoising failed for one-subject action
            fail_logger_1.info(ske_name)

        joints = np.zeros((num_frames, 150), dtype=np.float32)
        colors = np.ones((num_frames, 2, 25, 2), dtype=np.float32) * np.nan

        bodyID, actor1 = bodies_data[0]  # the 1st actor with largest motion
        start1, end1 = actor1['interval'][0], actor1['interval'][-1]
        joints[start1:end1 + 1, :75] = actor1['joints'].reshape(-1, 75)
        colors[start1:end1 + 1, 0] = actor1['colors']
        actor1_info = '{:^17}\t{}\t{:^8}\n'.format('Actor1', 'Interval', 'Motion') + \
                      '{}\t{:^8}\t{:f}\n'.format(bodyID, str([start1, end1]), actor1['motion'])
        del bodies_data[0]

        actor2_info = '{:^17}\t{}\t{:^8}\n'.format('Actor2', 'Interval', 'Motion')
        start2, end2 = [0, 0]  # initial interval for actor2 (virtual)

        while len(bodies_data) > 0:
            bodyID, actor = bodies_data[0]
            start, end = actor['interval'][0], actor['interval'][-1]
            if min(end1, end) - max(start1, start) <= 0:  # no overlap with actor1
                joints[start:end + 1, :75] = actor['joints'].reshape(-1, 75)
                colors[start:end + 1, 0] = actor['colors']
                actor1_info += '{}\t{:^8}\t{:f}\n'.format(bodyID, str([start, end]), actor['motion'])
                # Update the interval of actor1
                start1 = min(start, start1)
                end1 = max(end, end1)
            elif min(end2, end) - max(start2, start) <= 0:  # no overlap with actor2
                joints[start:end + 1, 75:] = actor['joints'].reshape(-1, 75)
                colors[start:end + 1, 1] = actor['colors']
                actor2_info += '{}\t{:^8}\t{:f}\n'.format(bodyID, str([start, end]), actor['motion'])
                # Update the interval of actor2
                start2 = min(start, start2)
                end2 = max(end, end2)
            del bodies_data[0]

        bodies_info += ('\n' + actor1_info + '\n' + actor2_info)

    with open(osp.join(actors_info_dir, ske_name + '.txt'), 'w') as fw:
        fw.write(bodies_info + '\n')

    return joints, colors


def get_raw_denoised_data():
    """
    Get denoised data (joints positions and color locations) from raw skeleton sequences.

    For each frame of a skeleton sequence, an actor's 3D positions of 25 joints represented
    by an 2D array (shape: 25 x 3) is reshaped into a 75-dim vector by concatenating each
    3-dim (x, y, z) coordinates along the row dimension in joint order. Each frame contains
    two actor's joints positions constituting a 150-dim vector. If there is only one actor,
    then the last 75 values are filled with zeros. Otherwise, select the main actor and the
    second actor based on the motion amount. Each 150-dim vector as a row vector is put into
    a 2D numpy array where the number of rows equals the number of valid frames. All such
    2D arrays are put into a list and finally the list is serialized into a cPickle file.

    For the skeleton sequence which contains two or more actors (mostly corresponds to the
    last 11 classes), the filename and actors' information are recorded into log files.
    For better understanding, also generate RGB+skeleton videos for visualization.
    """

    with open(raw_data_file, 'rb') as fr:  # load raw skeletons data
        raw_skes_data = pickle.load(fr)

    num_skes = len(raw_skes_data)
    print('Found %d available skeleton sequences.' % num_skes)

    raw_denoised_joints = []
    raw_denoised_colors = []
    frames_cnt = []

    for (idx, bodies_data) in enumerate(raw_skes_data):
        ske_name = bodies_data['name']
        print('Processing %s' % ske_name)
        num_bodies = len(bodies_data['data'])

        if num_bodies == 1:  # only 1 actor
            num_frames = bodies_data['num_frames']
            body_data = list(bodies_data['data'].values())[0]
            joints, colors = get_one_actor_points(body_data, num_frames)
        else:  # more than 1 actor, select two main actors
            joints, colors = get_two_actors_points(bodies_data)
            # Remove missing frames
            joints, colors = remove_missing_frames(ske_name, joints, colors)
            num_frames = joints.shape[0]  # Update
            # Visualize selected actors' skeletons on RGB videos.

        raw_denoised_joints.append(joints)
        raw_denoised_colors.append(colors)
        frames_cnt.append(num_frames)

        if (idx + 1) % 1000 == 0:
            print('Processed: %.2f%% (%d / %d), ' % \
                  (100.0 * (idx + 1) / num_skes, idx + 1, num_skes) + \
                  'Missing count: %d' % missing_count)

    raw_skes_joints_pkl = osp.join(save_path, 'raw_denoised_joints.pkl')
    with open(raw_skes_joints_pkl, 'wb') as f:
        pickle.dump(raw_denoised_joints, f, pickle.HIGHEST_PROTOCOL)

    raw_skes_colors_pkl = osp.join(save_path, 'raw_denoised_colors.pkl')
    with open(raw_skes_colors_pkl, 'wb') as f:
        pickle.dump(raw_denoised_colors, f, pickle.HIGHEST_PROTOCOL)

    frames_cnt = np.array(frames_cnt, dtype=np.int)
    np.savetxt(osp.join(save_path, 'frames_cnt.txt'), frames_cnt, fmt='%d')

    print('Saved raw denoised positions of {} frames into {}'.format(np.sum(frames_cnt),
                                                                     raw_skes_joints_pkl))
    print('Found %d files that have missing data' % missing_count)

if __name__ == '__main__':
    get_raw_denoised_data()


================================================
FILE: data/ntu/get_raw_skes_data.py
================================================
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import os.path as osp
import os
import numpy as np
import pickle
import logging


def get_raw_bodies_data(skes_path, ske_name, frames_drop_skes, frames_drop_logger):
    """
    Get raw bodies data from a skeleton sequence.

    Each body's data is a dict that contains the following keys:
      - joints: raw 3D joints positions. Shape: (num_frames x 25, 3)
      - colors: raw 2D color locations. Shape: (num_frames, 25, 2)
      - interval: a list which stores the frame indices of this body.
      - motion: motion amount (only for the sequence with 2 or more bodyIDs).

    Return:
      a dict for a skeleton sequence with 3 key-value pairs:
        - name: the skeleton filename.
        - data: a dict which stores raw data of each body.
        - num_frames: the number of valid frames.
    """
    ske_file = osp.join(skes_path, ske_name + '.skeleton')
    assert osp.exists(ske_file), 'Error: Skeleton file %s not found' % ske_file
    # Read all data from .skeleton file into a list (in string format)
    print('Reading data from %s' % ske_file[-29:])
    with open(ske_file, 'r') as fr:
        str_data = fr.readlines()

    num_frames = int(str_data[0].strip('\r\n'))
    frames_drop = []
    bodies_data = dict()
    valid_frames = -1  # 0-based index
    current_line = 1

    for f in range(num_frames):
        num_bodies = int(str_data[current_line].strip('\r\n'))
        current_line += 1

        if num_bodies == 0:  # no data in this frame, drop it
            frames_drop.append(f)  # 0-based index
            continue

        valid_frames += 1
        joints = np.zeros((num_bodies, 25, 3), dtype=np.float32)
        colors = np.zeros((num_bodies, 25, 2), dtype=np.float32)

        for b in range(num_bodies):
            bodyID = str_data[current_line].strip('\r\n').split()[0]
            current_line += 1
            num_joints = int(str_data[current_line].strip('\r\n'))  # 25 joints
            current_line += 1

            for j in range(num_joints):
                temp_str = str_data[current_line].strip('\r\n').split()
                joints[b, j, :] = np.array(temp_str[:3], dtype=np.float32)
                colors[b, j, :] = np.array(temp_str[5:7], dtype=np.float32)
                current_line += 1

            if bodyID not in bodies_data:  # Add a new body's data
                body_data = dict()
                body_data['joints'] = joints[b]  # ndarray: (25, 3)
                body_data['colors'] = colors[b, np.newaxis]  # ndarray: (1, 25, 2)
                body_data['interval'] = [valid_frames]  # the index of the first frame
            else:  # Update an already existed body's data
                body_data = bodies_data[bodyID]
                # Stack each body's data of each frame along the frame order
                body_data['joints'] = np.vstack((body_data['joints'], joints[b]))
                body_data['colors'] = np.vstack((body_data['colors'], colors[b, np.newaxis]))
                pre_frame_idx = body_data['interval'][-1]
                body_data['interval'].append(pre_frame_idx + 1)  # add a new frame index

            bodies_data[bodyID] = body_data  # Update bodies_data

    num_frames_drop = len(frames_drop)
    assert num_frames_drop < num_frames, \
        'Error: All frames data (%d) of %s is missing or lost' % (num_frames, ske_name)
    if num_frames_drop > 0:
        frames_drop_skes[ske_name] = np.array(frames_drop, dtype=np.int)
        frames_drop_logger.info('{}: {} frames missed: {}\n'.format(ske_name, num_frames_drop,
                                                                    frames_drop))

    # Calculate motion (only for the sequence with 2 or more bodyIDs)
    if len(bodies_data) > 1:
        for body_data in bodies_data.values():
            body_data['motion'] = np.sum(np.var(body_data['joints'], axis=0))

    return {'name': ske_name, 'data': bodies_data, 'num_frames': num_frames - num_frames_drop}


def get_raw_skes_data():
    # # save_path = './data'
    # # skes_path = '/data/pengfei/NTU/nturgb+d_skeletons/'
    # stat_path = osp.join(save_path, 'statistics')
    #
    # skes_name_file = osp.join(stat_path, 'skes_available_name.txt')
    # save_data_pkl = osp.join(save_path, 'raw_skes_data.pkl')
    # frames_drop_pkl = osp.join(save_path, 'frames_drop_skes.pkl')
    #
    # frames_drop_logger = logging.getLogger('frames_drop')
    # frames_drop_logger.setLevel(logging.INFO)
    # frames_drop_logger.addHandler(logging.FileHandler(osp.join(save_path, 'frames_drop.log')))
    # frames_drop_skes = dict()

    skes_name = np.loadtxt(skes_name_file, dtype=str)

    num_files = skes_name.size
    print('Found %d available skeleton files.' % num_files)

    raw_skes_data = []
    frames_cnt = np.zeros(num_files, dtype=np.int)

    for (idx, ske_name) in enumerate(skes_name):
        bodies_data = get_raw_bodies_data(skes_path, ske_name, frames_drop_skes, frames_drop_logger)
        raw_skes_data.append(bodies_data)
        frames_cnt[idx] = bodies_data['num_frames']
        if (idx + 1) % 1000 == 0:
            print('Processed: %.2f%% (%d / %d)' % \
                  (100.0 * (idx + 1) / num_files, idx + 1, num_files))

    with open(save_data_pkl, 'wb') as fw:
        pickle.dump(raw_skes_data, fw, pickle.HIGHEST_PROTOCOL)
    np.savetxt(osp.join(save_path, 'raw_data', 'frames_cnt.txt'), frames_cnt, fmt='%d')

    print('Saved raw bodies data into %s' % save_data_pkl)
    print('Total frames: %d' % np.sum(frames_cnt))

    with open(frames_drop_pkl, 'wb') as fw:
        pickle.dump(frames_drop_skes, fw, pickle.HIGHEST_PROTOCOL)

if __name__ == '__main__':
    save_path = './'

    skes_path = './nturgb+d_skeletons/'
    stat_path = osp.join(save_path, 'statistics')
    if not osp.exists('./raw_data'):
        os.makedirs('./raw_data')

    skes_name_file = osp.join(stat_path, 'skes_available_name.txt')
    save_data_pkl = osp.join(save_path, 'raw_data', 'raw_skes_data.pkl')
    frames_drop_pkl = osp.join(save_path, 'raw_data', 'frames_drop_skes.pkl')

    frames_drop_logger = logging.getLogger('frames_drop')
    frames_drop_logger.setLevel(logging.INFO)
    frames_drop_logger.addHandler(logging.FileHandler(osp.join(save_path, 'raw_data', 'frames_drop.log')))
    frames_drop_skes = dict()

    get_raw_skes_data()

    with open(frames_drop_pkl, 'wb') as fw:
        pickle.dump(frames_drop_skes, fw, pickle.HIGHEST_PROTOCOL)
        


================================================
FILE: data/ntu/seq_transformation.py
================================================
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import os
import os.path as osp
import numpy as np
import pickle
import logging
import h5py
from sklearn.model_selection import train_test_split

root_path = './'
stat_path = osp.join(root_path, 'statistics')
setup_file = osp.join(stat_path, 'setup.txt')
camera_file = osp.join(stat_path, 'camera.txt')
performer_file = osp.join(stat_path, 'performer.txt')
replication_file = osp.join(stat_path, 'replication.txt')
label_file = osp.join(stat_path, 'label.txt')
skes_name_file = osp.join(stat_path, 'skes_available_name.txt')

denoised_path = osp.join(root_path, 'denoised_data')
raw_skes_joints_pkl = osp.join(denoised_path, 'raw_denoised_joints.pkl')
frames_file = osp.join(denoised_path, 'frames_cnt.txt')

save_path = './'


if not osp.exists(save_path):
    os.mkdir(save_path)


def remove_nan_frames(ske_name, ske_joints, nan_logger):
    num_frames = ske_joints.shape[0]
    valid_frames = []

    for f in range(num_frames):
        if not np.any(np.isnan(ske_joints[f])):
            valid_frames.append(f)
        else:
            nan_indices = np.where(np.isnan(ske_joints[f]))[0]
            nan_logger.info('{}\t{:^5}\t{}'.format(ske_name, f + 1, nan_indices))

    return ske_joints[valid_frames]

def seq_translation(skes_joints):
    for idx, ske_joints in enumerate(skes_joints):
        num_frames = ske_joints.shape[0]
        num_bodies = 1 if ske_joints.shape[1] == 75 else 2
        if num_bodies == 2:
            missing_frames_1 = np.where(ske_joints[:, :75].sum(axis=1) == 0)[0]
            missing_frames_2 = np.where(ske_joints[:, 75:].sum(axis=1) == 0)[0]
            cnt1 = len(missing_frames_1)
            cnt2 = len(missing_frames_2)

        i = 0  # get the "real" first frame of actor1
        while i < num_frames:
            if np.any(ske_joints[i, :75] != 0):
                break
            i += 1

        origin = np.copy(ske_joints[i, 3:6])  # new origin: joint-2

        for f in range(num_frames):
            if num_bodies == 1:
                ske_joints[f] -= np.tile(origin, 25)
            else:  # for 2 actors
                ske_joints[f] -= np.tile(origin, 50)

        if (num_bodies == 2) and (cnt1 > 0):
            ske_joints[missing_frames_1, :75] = np.zeros((cnt1, 75), dtype=np.float32)

        if (num_bodies == 2) and (cnt2 > 0):
            ske_joints[missing_frames_2, 75:] = np.zeros((cnt2, 75), dtype=np.float32)

        skes_joints[idx] = ske_joints  # Update

    return skes_joints


def frame_translation(skes_joints, skes_name, frames_cnt):
    nan_logger = logging.getLogger('nan_skes')
    nan_logger.setLevel(logging.INFO)
    nan_logger.addHandler(logging.FileHandler("./nan_frames.log"))
    nan_logger.info('{}\t{}\t{}'.format('Skeleton', 'Frame', 'Joints'))

    for idx, ske_joints in enumerate(skes_joints):
        num_frames = ske_joints.shape[0]
        # Calculate the distance between spine base (joint-1) and spine (joint-21)
        j1 = ske_joints[:, 0:3]
        j21 = ske_joints[:, 60:63]
        dist = np.sqrt(((j1 - j21) ** 2).sum(axis=1))

        for f in range(num_frames):
            origin = ske_joints[f, 3:6]  # new origin: middle of the spine (joint-2)
            if (ske_joints[f, 75:] == 0).all():
                ske_joints[f, :75] = (ske_joints[f, :75] - np.tile(origin, 25)) / \
                                      dist[f] + np.tile(origin, 25)
            else:
                ske_joints[f] = (ske_joints[f] - np.tile(origin, 50)) / \
                                 dist[f] + np.tile(origin, 50)

        ske_name = skes_name[idx]
        ske_joints = remove_nan_frames(ske_name, ske_joints, nan_logger)
        frames_cnt[idx] = num_frames  # update valid number of frames
        skes_joints[idx] = ske_joints

    return skes_joints, frames_cnt


def align_frames(skes_joints, frames_cnt):
    """
    Align all sequences with the same frame length.

    """
    num_skes = len(skes_joints)
    max_num_frames = frames_cnt.max()  # 300
    aligned_skes_joints = np.zeros((num_skes, max_num_frames, 150), dtype=np.float32)

    for idx, ske_joints in enumerate(skes_joints):
        num_frames = ske_joints.shape[0]
        num_bodies = 1 if ske_joints.shape[1] == 75 else 2
        if num_bodies == 1:
            aligned_skes_joints[idx, :num_frames] = np.hstack((ske_joints,
                                                               np.zeros_like(ske_joints)))
        else:
            aligned_skes_joints[idx, :num_frames] = ske_joints

    return aligned_skes_joints


def one_hot_vector(labels):
    num_skes = len(labels)
    labels_vector = np.zeros((num_skes, 60))
    for idx, l in enumerate(labels):
        labels_vector[idx, l] = 1

    return labels_vector


def split_train_val(train_indices, method='sklearn', ratio=0.05):
    """
    Get validation set by splitting data randomly from training set with two methods.
    In fact, I thought these two methods are equal as they got the same performance.

    """
    if method == 'sklearn':
        return train_test_split(train_indices, test_size=ratio, random_state=10000)
    else:
        np.random.seed(10000)
        np.random.shuffle(train_indices)
        val_num_skes = int(np.ceil(0.05 * len(train_indices)))
        val_indices = train_indices[:val_num_skes]
        train_indices = train_indices[val_num_skes:]
        return train_indices, val_indices


def split_dataset(skes_joints, label, performer, camera, evaluation, save_path):
    train_indices, test_indices = get_indices(performer, camera, evaluation)
    m = 'sklearn'  # 'sklearn' or 'numpy'
    # Select validation set from training set
    train_indices, val_indices = split_train_val(train_indices, m)

    # Save labels and num_frames for each sequence of each data set
    train_labels = label[train_indices]
    val_labels = label[val_indices]
    test_labels = label[test_indices]

    # Save data into a .h5 file
    h5file = h5py.File(osp.join(save_path, 'NTU_%s.h5' % (evaluation)), 'w')
    # Training set
    h5file.create_dataset('x', data=skes_joints[train_indices])
    train_one_hot_labels = one_hot_vector(train_labels)
    h5file.create_dataset('y', data=train_one_hot_labels)
    # Validation set
    h5file.create_dataset('valid_x', data=skes_joints[val_indices])
    val_one_hot_labels = one_hot_vector(val_labels)
    h5file.create_dataset('valid_y', data=val_one_hot_labels)
    # Test set
    h5file.create_dataset('test_x', data=skes_joints[test_indices])
    test_one_hot_labels = one_hot_vector(test_labels)
    h5file.create_dataset('test_y', data=test_one_hot_labels)

    h5file.close()


def get_indices(performer, camera, evaluation='CS'):
    test_indices = np.empty(0)
    train_indices = np.empty(0)

    if evaluation == 'CS':  # Cross Subject (Subject IDs)
        train_ids = [1,  2,  4,  5,  8,  9,  13, 14, 15, 16,
                     17, 18, 19, 25, 27, 28, 31, 34, 35, 38]
        test_ids = [3,  6,  7,  10, 11, 12, 20, 21, 22, 23,
                    24, 26, 29, 30, 32, 33, 36, 37, 39, 40]

        # Get indices of test data
        for idx in test_ids:
            temp = np.where(performer == idx)[0]  # 0-based index
            test_indices = np.hstack((test_indices, temp)).astype(np.int)

        # Get indices of training data
        for train_id in train_ids:
            temp = np.where(performer == train_id)[0]  # 0-based index
            train_indices = np.hstack((train_indices, temp)).astype(np.int)
    else:  # Cross View (Camera IDs)
        train_ids = [2, 3]
        test_ids = 1
        # Get indices of test data
        temp = np.where(camera == test_ids)[0]  # 0-based index
        test_indices = np.hstack((test_indices, temp)).astype(np.int)

        # Get indices of training data
        for train_id in train_ids:
            temp = np.where(camera == train_id)[0]  # 0-based index
            train_indices = np.hstack((train_indices, temp)).astype(np.int)

    return train_indices, test_indices


if __name__ == '__main__':
    camera = np.loadtxt(camera_file, dtype=np.int)  # camera id: 1, 2, 3
    performer = np.loadtxt(performer_file, dtype=np.int)  # subject id: 1~40
    label = np.loadtxt(label_file, dtype=np.int) - 1  # action label: 0~59

    frames_cnt = np.loadtxt(frames_file, dtype=np.int)  # frames_cnt
    skes_name = np.loadtxt(skes_name_file, dtype=np.string_)

    with open(raw_skes_joints_pkl, 'rb') as fr:
        skes_joints = pickle.load(fr)  # a list

    skes_joints = seq_translation(skes_joints)

    skes_joints = align_frames(skes_joints, frames_cnt)  # aligned to the same frame length

    evaluations = ['CS', 'CV']
    for evaluation in evaluations:
        split_dataset(skes_joints, label, performer, camera, evaluation, save_path)


================================================
FILE: data/ntu/statistics/camera.txt
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================================================
FILE: data/ntu/statistics/performer.txt
================================================
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FILE: data/ntu/statistics/replication.txt
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================================================
FILE: data/ntu/statistics/samples_with_missing_skeletons.txt
================================================
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================================================
FILE: data/ntu/statistics/setup.txt
================================================
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================================================
FILE: data/ntu/statistics/skes_available_name.txt
================================================
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================================================
FILE: data.py
================================================
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
from torch.utils.data import Dataset, DataLoader
import os
import torch
import numpy as np
import h5py
import random
import os.path as osp
import sys
from six.moves import xrange
import math
import scipy.misc
if sys.version_info[0] == 2:
    import cPickle as pickle
else:
    import pickle
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt


class NTUDataset(Dataset):
    def __init__(self, x, y):
        self.x = x
        self.y = np.array(y, dtype='int')

    def __len__(self):
        return len(self.y)

    def __getitem__(self, index):
        return [self.x[index], int(self.y[index])]

class NTUDataLoaders(object):
    def __init__(self, dataset ='NTU', case = 0, aug = 1, seg = 30):
        self.dataset = dataset
        self.case = case
        self.aug = aug
        self.seg = seg
        self.create_datasets()
        self.train_set = NTUDataset(self.train_X, self.train_Y)
        self.val_set = NTUDataset(self.val_X, self.val_Y)
        self.test_set = NTUDataset(self.test_X, self.test_Y)

    def get_train_loader(self, batch_size, num_workers):
        if self.aug == 0:
            return DataLoader(self.train_set, batch_size=batch_size,
                              shuffle=True, num_workers=num_workers,
                              collate_fn=self.collate_fn_fix_val, pin_memory=False, drop_last=True)
        elif self.aug ==1:
            return DataLoader(self.train_set, batch_size=batch_size,
                              shuffle=True, num_workers=num_workers,
                              collate_fn=self.collate_fn_fix_train, pin_memory=True, drop_last=True)

    def get_val_loader(self, batch_size, num_workers):
        if self.dataset == 'NTU' or self.dataset == 'kinetics' or self.dataset == 'NTU120':
            return DataLoader(self.val_set, batch_size=batch_size,
                              shuffle=False, num_workers=num_workers,
                              collate_fn=self.collate_fn_fix_val, pin_memory=True, drop_last=True)
        else:
            return DataLoader(self.val_set, batch_size=batch_size,
                              shuffle=False, num_workers=num_workers,
                              collate_fn=self.collate_fn_fix_val, pin_memory=True, drop_last=True)


    def get_test_loader(self, batch_size, num_workers):
        return DataLoader(self.test_set, batch_size=batch_size,
                          shuffle=False, num_workers=num_workers,
                          collate_fn=self.collate_fn_fix_test, pin_memory=True, drop_last=True)

    def get_train_size(self):
        return len(self.train_Y)

    def get_val_size(self):
        return len(self.val_Y)

    def get_test_size(self):
        return len(self.test_Y)

    def create_datasets(self):
        if self.dataset == 'NTU':
            if self.case ==0:
                self.metric = 'CS'
            elif self.case == 1:
                self.metric = 'CV'
            path = osp.join('./data/ntu', 'NTU_' + self.metric + '.h5')

        f = h5py.File(path , 'r')
        self.train_X = f['x'][:]
        self.train_Y = np.argmax(f['y'][:],-1)
        self.val_X = f['valid_x'][:]
        self.val_Y = np.argmax(f['valid_y'][:], -1)
        self.test_X = f['test_x'][:]
        self.test_Y = np.argmax(f['test_y'][:], -1)
        f.close()

        ## Combine the training data and validation data togehter as ST-GCN
        self.train_X = np.concatenate([self.train_X, self.val_X], axis=0)
        self.train_Y = np.concatenate([self.train_Y, self.val_Y], axis=0)
        self.val_X = self.test_X
        self.val_Y = self.test_Y

    def collate_fn_fix_train(self, batch):
        """Puts each data field into a tensor with outer dimension batch size
        """
        x, y = zip(*batch)

        if self.dataset == 'kinetics' and self.machine == 'philly':
            x = np.array(x)
            x = x.reshape(x.shape[0], x.shape[1],-1)
            x = x.reshape(-1, x.shape[1] * x.shape[2], x.shape[3]*x.shape[4])
            x = list(x)

        x, y = self.Tolist_fix(x, y, train=1)
        lens = np.array([x_.shape[0] for x_ in x], dtype=np.int)
        idx = lens.argsort()[::-1]  # sort sequence by valid length in descending order
        y = np.array(y)[idx]
        x = torch.stack([torch.from_numpy(x[i]) for i in idx], 0)

        if self.dataset == 'NTU':
            if self.case == 0:
                theta = 0.3
            elif self.case == 1:
                theta = 0.5
        elif self.dataset == 'NTU120':
            theta = 0.3

        #### data augmentation
        x = _transform(x, theta)
        #### data augmentation
        y = torch.LongTensor(y)

        return [x, y]

    def collate_fn_fix_val(self, batch):
        """Puts each data field into a tensor with outer dimension batch size
        """
        x, y = zip(*batch)
        x, y = self.Tolist_fix(x, y, train=1)
        idx = range(len(x))
        y = np.array(y)

        x = torch.stack([torch.from_numpy(x[i]) for i in idx], 0)
        y = torch.LongTensor(y)

        return [x, y]

    def collate_fn_fix_test(self, batch):
        """Puts each data field into a tensor with outer dimension batch size
        """
        x, y = zip(*batch)
        x, labels = self.Tolist_fix(x, y ,train=2)
        idx = range(len(x))
        y = np.array(y)


        x = torch.stack([torch.from_numpy(x[i]) for i in idx], 0)
        y = torch.LongTensor(y)

        return [x, y]

    def Tolist_fix(self, joints, y, train = 1):
        seqs = []

        for idx, seq in enumerate(joints):
            zero_row = []
            for i in range(len(seq)):
                if (seq[i, :] == np.zeros((1, 150))).all():
                        zero_row.append(i)

            seq = np.delete(seq, zero_row, axis = 0)

            seq = turn_two_to_one(seq)
            seqs = self.sub_seq(seqs, seq, train=train)

        return seqs, y

    def sub_seq(self, seqs, seq , train = 1):
        group = self.seg

        if self.dataset == 'SYSU' or self.dataset == 'SYSU_same':
            seq = seq[::2, :]

        if seq.shape[0] < self.seg:
            pad = np.zeros((self.seg - seq.shape[0], seq.shape[1])).astype(np.float32)
            seq = np.concatenate([seq, pad], axis=0)

        ave_duration = seq.shape[0] // group

        if train == 1:
            offsets = np.multiply(list(range(group)), ave_duration) + np.random.randint(ave_duration, size=group)
            seq = seq[offsets]
            seqs.append(seq)

        elif train == 2:
            offsets1 = np.multiply(list(range(group)), ave_duration) + np.random.randint(ave_duration, size=group)
            offsets2 = np.multiply(list(range(group)), ave_duration) + np.random.randint(ave_duration, size=group)
            offsets3 = np.multiply(list(range(group)), ave_duration) + np.random.randint(ave_duration, size=group)
            offsets4 = np.multiply(list(range(group)), ave_duration) + np.random.randint(ave_duration, size=group)
            offsets5 = np.multiply(list(range(group)), ave_duration) + np.random.randint(ave_duration, size=group)

            seqs.append(seq[offsets1])
            seqs.append(seq[offsets2])
            seqs.append(seq[offsets3])
            seqs.append(seq[offsets4])
            seqs.append(seq[offsets5])

        return seqs

class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


def turn_two_to_one(seq):
    new_seq = list()
    for idx, ske in enumerate(seq):
        if (ske[0:75] == np.zeros((1, 75))).all():
            new_seq.append(ske[75:])
        elif (ske[75:] == np.zeros((1, 75))).all():
            new_seq.append(ske[0:75])
        else:
            new_seq.append(ske[0:75])
            new_seq.append(ske[75:])
    return np.array(new_seq)

def _rot(rot):
    cos_r, sin_r = rot.cos(), rot.sin()
    zeros = rot.new(rot.size()[:2] + (1,)).zero_()
    ones = rot.new(rot.size()[:2] + (1,)).fill_(1)

    r1 = torch.stack((ones, zeros, zeros),dim=-1)
    rx2 = torch.stack((zeros, cos_r[:,:,0:1], sin_r[:,:,0:1]), dim = -1)
    rx3 = torch.stack((zeros, -sin_r[:,:,0:1], cos_r[:,:,0:1]), dim = -1)
    rx = torch.cat((r1, rx2, rx3), dim = 2)

    ry1 = torch.stack((cos_r[:,:,1:2], zeros, -sin_r[:,:,1:2]), dim =-1)
    r2 = torch.stack((zeros, ones, zeros),dim=-1)
    ry3 = torch.stack((sin_r[:,:,1:2], zeros, cos_r[:,:,1:2]), dim =-1)
    ry = torch.cat((ry1, r2, ry3), dim = 2)

    rz1 = torch.stack((cos_r[:,:,2:3], sin_r[:,:,2:3], zeros), dim =-1)
    r3 = torch.stack((zeros, zeros, ones),dim=-1)
    rz2 = torch.stack((-sin_r[:,:,2:3], cos_r[:,:,2:3],zeros), dim =-1)
    rz = torch.cat((rz1, rz2, r3), dim = 2)

    rot = rz.matmul(ry).matmul(rx)
    return rot

def _transform(x, theta):
    x = x.contiguous().view(x.size()[:2] + (-1, 3))
    rot = x.new(x.size()[0],3).uniform_(-theta, theta)
    rot = rot.repeat(1, x.size()[1])
    rot = rot.contiguous().view((-1, x.size()[1], 3))
    rot = _rot(rot)
    x = torch.transpose(x, 2, 3)
    x = torch.matmul(rot, x)
    x = torch.transpose(x, 2, 3)

    x = x.contiguous().view(x.size()[:2] + (-1,))
    return x


================================================
FILE: fit.py
================================================
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
def add_fit_args(parser):
    """
    parser : argparse.ArgumentParser
    return a parser added with args required by fit
    """
    args = parser.add_argument_group('Training', 'model training')
    args.add_argument('--network', type=str,
                      help='the neural network to use')
    args.add_argument('--dataset', type=str, default='NTU',
                      help='select dataset to evlulate')
    args.add_argument('--start-epoch', default=0, type=int,
                      help='manual epoch number (useful on restarts)')
    args.add_argument('--max-epoches', type=int, default=150,
                      help='max number of epochs to run')
    args.add_argument('--lr', type=float, default=0.1,
                      help='initial learning rate')
    args.add_argument('--lr-factor', type=float, default=0.1,
                      help='the ratio to reduce lr on each step')
    args.add_argument('--weight-decay', '--wd', type=float, default=1e-4,
                      help='weight decay (default: 1e-4)')
    args.add_argument('--print-freq', '-p', type=int, default=10,
                      help='print frequency (default: 10)')
    args.add_argument('-b', '--batch-size', type=int, default=256,
                      help='mini-batch size (default: 256)')
    args.add_argument('--num-classes', type=int, default=60,
                      help='the number of classes')
    args.add_argument('--case', type=int, default=0,
                      help='select which case')
    args.add_argument('--train', type=int, default=1,
                      help='train or test')
    args.add_argument('--workers', type=int, default=2,
                      help='number of data loading workers (default: 2)')
    args.add_argument('--monitor', type=str, default='val_acc',
                      help='quantity to monitor (default: val_acc)')
    args.add_argument('--seg', type=int, default=20,
                      help='number of segmentation')
    return args



================================================
FILE: main.py
================================================
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import argparse
import time
import shutil
import os
os.environ["CUDA_VISIBLE_DEVICES"] = '1'
import os.path as osp
import csv
import numpy as np

np.random.seed(1337)

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import ReduceLROnPlateau, MultiStepLR
from model import SGN
from data import NTUDataLoaders, AverageMeter
import fit
from util import make_dir, get_num_classes

parser = argparse.ArgumentParser(description='Skeleton-Based Action Recgnition')
fit.add_fit_args(parser)
parser.set_defaults(
    network='SGN',
    dataset = 'NTU',
    case = 0,
    batch_size=64,
    max_epochs=120,
    monitor='val_acc',
    lr=0.001,
    weight_decay=0.0001,
    lr_factor=0.1,
    workers=16,
    print_freq = 20,
    train = 0,
    seg = 20,
    )
args = parser.parse_args()

def main():

    args.num_classes = get_num_classes(args.dataset)
    model = SGN(args.num_classes, args.dataset, args.seg, args)

    total = get_n_params(model)
    print(model)
    print('The number of parameters: ', total)
    print('The modes is:', args.network)

    if torch.cuda.is_available():
        print('It is using GPU!')
        model = model.cuda()

    criterion = LabelSmoothingLoss(args.num_classes, smoothing=0.1).cuda()
    optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)

    if args.monitor == 'val_acc':
        mode = 'max'
        monitor_op = np.greater
        best = -np.Inf
        str_op = 'improve'
    elif args.monitor == 'val_loss':
        mode = 'min'
        monitor_op = np.less
        best = np.Inf
        str_op = 'reduce'

    scheduler = MultiStepLR(optimizer, milestones=[60, 90, 110], gamma=0.1)
    # Data loading
    ntu_loaders = NTUDataLoaders(args.dataset, args.case, seg=args.seg)
    train_loader = ntu_loaders.get_train_loader(args.batch_size, args.workers)
    val_loader = ntu_loaders.get_val_loader(args.batch_size, args.workers)
    train_size = ntu_loaders.get_train_size()
    val_size = ntu_loaders.get_val_size()


    test_loader = ntu_loaders.get_test_loader(32, args.workers)

    print('Train on %d samples, validate on %d samples' % (train_size, val_size))

    best_epoch = 0
    output_dir = make_dir(args.dataset)

    save_path = os.path.join(output_dir, args.network)
    if not os.path.exists(save_path):
        os.makedirs(save_path)

    checkpoint = osp.join(save_path, '%s_best.pth' % args.case)
    earlystop_cnt = 0
    csv_file = osp.join(save_path, '%s_log.csv' % args.case)
    log_res = list()

    lable_path = osp.join(save_path, '%s_lable.txt'% args.case)
    pred_path = osp.join(save_path, '%s_pred.txt' % args.case)

    # Training
    if args.train ==1:
        for epoch in range(args.start_epoch, args.max_epochs):

            print(epoch, optimizer.param_groups[0]['lr'])

            t_start = time.time()
            train_loss, train_acc = train(train_loader, model, criterion, optimizer, epoch)
            val_loss, val_acc = validate(val_loader, model, criterion)
            log_res += [[train_loss, train_acc.cpu().numpy(),\
                         val_loss, val_acc.cpu().numpy()]]

            print('Epoch-{:<3d} {:.1f}s\t'
                  'Train: loss {:.4f}\taccu {:.4f}\tValid: loss {:.4f}\taccu {:.4f}'
                  .format(epoch + 1, time.time() - t_start, train_loss, train_acc, val_loss, val_acc))

            current = val_loss if mode == 'min' else val_acc

            ####### store tensor in cpu
            current = current.cpu()

            if monitor_op(current, best):
                print('Epoch %d: %s %sd from %.4f to %.4f, '
                      'saving model to %s'
                      % (epoch + 1, args.monitor, str_op, best, current, checkpoint))
                best = current
                best_epoch = epoch + 1
                save_checkpoint({
                    'epoch': epoch + 1,
                    'state_dict': model.state_dict(),
                    'best': best,
                    'monitor': args.monitor,
                    'optimizer': optimizer.state_dict(),
                }, checkpoint)
                earlystop_cnt = 0
            else:
                print('Epoch %d: %s did not %s' % (epoch + 1, args.monitor, str_op))
                earlystop_cnt += 1

            scheduler.step()

        print('Best %s: %.4f from epoch-%d' % (args.monitor, best, best_epoch))
        with open(csv_file, 'w') as fw:
            cw = csv.writer(fw)
            cw.writerow(['loss', 'acc', 'val_loss', 'val_acc'])
            cw.writerows(log_res)
        print('Save train and validation log into into %s' % csv_file)

    ### Test
    args.train = 0
    model = SGN(args.num_classes, args.dataset, args.seg, args)
    model = model.cuda()
    test(test_loader, model, checkpoint, lable_path, pred_path)


def train(train_loader, model, criterion, optimizer, epoch):
    losses = AverageMeter()
    acces = AverageMeter()
    model.train()

    for i, (inputs, target) in enumerate(train_loader):

        output = model(inputs.cuda())
        target = target.cuda(async = True)
        loss = criterion(output, target)

        # measure accuracy and record loss
        acc = accuracy(output.data, target)
        losses.update(loss.item(), inputs.size(0))
        acces.update(acc[0], inputs.size(0))

        # backward
        optimizer.zero_grad()  # clear gradients out before each mini-batch
        loss.backward()
        optimizer.step()

        if (i + 1) % args.print_freq == 0:
            print('Epoch-{:<3d} {:3d} batches\t'
                  'loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'accu {acc.val:.3f} ({acc.avg:.3f})'.format(
                   epoch + 1, i + 1, loss=losses, acc=acces))

    return losses.avg, acces.avg


def validate(val_loader, model, criterion):
    losses = AverageMeter()
    acces = AverageMeter()
    model.eval()

    for i, (inputs, target) in enumerate(val_loader):
        with torch.no_grad():
            output = model(inputs.cuda())
        target = target.cuda(async=True)
        with torch.no_grad():
            loss = criterion(output, target)

        # measure accuracy and record loss
        acc = accuracy(output.data, target)
        losses.update(loss.item(), inputs.size(0))
        acces.update(acc[0], inputs.size(0))

    return losses.avg, acces.avg


def test(test_loader, model, checkpoint, lable_path, pred_path):
    acces = AverageMeter()
    # load learnt model that obtained best performance on validation set
    model.load_state_dict(torch.load(checkpoint)['state_dict'])
    model.eval()

    label_output = list()
    pred_output = list()

    t_start = time.time()
    for i, (inputs, target) in enumerate(test_loader):
        with torch.no_grad():
            output = model(inputs.cuda())
            output = output.view((-1, inputs.size(0)//target.size(0), output.size(1)))
            output = output.mean(1)

        label_output.append(target.cpu().numpy())
        pred_output.append(output.cpu().numpy())

        acc = accuracy(output.data, target.cuda(async=True))
        acces.update(acc[0], inputs.size(0))


    label_output = np.concatenate(label_output, axis=0)
    np.savetxt(lable_path, label_output, fmt='%d')
    pred_output = np.concatenate(pred_output, axis=0)
    np.savetxt(pred_path, pred_output, fmt='%f')

    print('Test: accuracy {:.3f}, time: {:.2f}s'
          .format(acces.avg, time.time() - t_start))


def accuracy(output, target):
    batch_size = target.size(0)
    _, pred = output.topk(1, 1, True, True)
    pred = pred.t()
    correct = pred.eq(target.view(1, -1).expand_as(pred))
    correct = correct.view(-1).float().sum(0, keepdim=True)

    return correct.mul_(100.0 / batch_size)

def save_checkpoint(state, filename='checkpoint.pth.tar', is_best=False):
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, 'model_best.pth.tar')

def get_n_params(model):
    pp=0
    for p in list(model.parameters()):
        nn=1
        for s in list(p.size()):
            nn = nn*s
        pp += nn
    return pp

class LabelSmoothingLoss(nn.Module):
    def __init__(self, classes, smoothing=0.0, dim=-1):
        super(LabelSmoothingLoss, self).__init__()
        self.confidence = 1.0 - smoothing
        self.smoothing = smoothing
        self.cls = classes
        self.dim = dim

    def forward(self, pred, target):
        pred = pred.log_softmax(dim=self.dim)
        with torch.no_grad():
            true_dist = torch.zeros_like(pred)
            true_dist.fill_(self.smoothing / (self.cls - 1))
            true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence)
        return torch.mean(torch.sum(-true_dist * pred, dim=self.dim))

if __name__ == '__main__':
    main()
    


================================================
FILE: model.py
================================================
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
from torch import nn
import torch
import math

class SGN(nn.Module):
    def __init__(self, num_classes, dataset, seg, args, bias = True):
        super(SGN, self).__init__()

        self.dim1 = 256
        self.dataset = dataset
        self.seg = seg
        num_joint = 25
        bs = args.batch_size
        if args.train:
            self.spa = self.one_hot(bs, num_joint, self.seg)
            self.spa = self.spa.permute(0, 3, 2, 1).cuda()
            self.tem = self.one_hot(bs, self.seg, num_joint)
            self.tem = self.tem.permute(0, 3, 1, 2).cuda()
        else:
            self.spa = self.one_hot(32 * 5, num_joint, self.seg)
            self.spa = self.spa.permute(0, 3, 2, 1).cuda()
            self.tem = self.one_hot(32 * 5, self.seg, num_joint)
            self.tem = self.tem.permute(0, 3, 1, 2).cuda()

        self.tem_embed = embed(self.seg, 64*4, norm=False, bias=bias)
        self.spa_embed = embed(num_joint, 64, norm=False, bias=bias)
        self.joint_embed = embed(3, 64, norm=True, bias=bias)
        self.dif_embed = embed(3, 64, norm=True, bias=bias)
        self.maxpool = nn.AdaptiveMaxPool2d((1, 1))
        self.cnn = local(self.dim1, self.dim1 * 2, bias=bias)
        self.compute_g1 = compute_g_spa(self.dim1 // 2, self.dim1, bias=bias)
        self.gcn1 = gcn_spa(self.dim1 // 2, self.dim1 // 2, bias=bias)
        self.gcn2 = gcn_spa(self.dim1 // 2, self.dim1, bias=bias)
        self.gcn3 = gcn_spa(self.dim1, self.dim1, bias=bias)
        self.fc = nn.Linear(self.dim1 * 2, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))

        nn.init.constant_(self.gcn1.w.cnn.weight, 0)
        nn.init.constant_(self.gcn2.w.cnn.weight, 0)
        nn.init.constant_(self.gcn3.w.cnn.weight, 0)


    def forward(self, input):
        
        # Dynamic Representation
        bs, step, dim = input.size()
        num_joints = dim //3
        input = input.view((bs, step, num_joints, 3))
        input = input.permute(0, 3, 2, 1).contiguous()
        dif = input[:, :, :, 1:] - input[:, :, :, 0:-1]
        dif = torch.cat([dif.new(bs, dif.size(1), num_joints, 1).zero_(), dif], dim=-1)
        pos = self.joint_embed(input)
        tem1 = self.tem_embed(self.tem)
        spa1 = self.spa_embed(self.spa)
        dif = self.dif_embed(dif)
        dy = pos + dif
        # Joint-level Module
        input= torch.cat([dy, spa1], 1)
        g = self.compute_g1(input)
        input = self.gcn1(input, g)
        input = self.gcn2(input, g)
        input = self.gcn3(input, g)
        # Frame-level Module
        input = input + tem1
        input = self.cnn(input)
        # Classification
        output = self.maxpool(input)
        output = torch.flatten(output, 1)
        output = self.fc(output)

        return output

    def one_hot(self, bs, spa, tem):

        y = torch.arange(spa).unsqueeze(-1)
        y_onehot = torch.FloatTensor(spa, spa)

        y_onehot.zero_()
        y_onehot.scatter_(1, y, 1)

        y_onehot = y_onehot.unsqueeze(0).unsqueeze(0)
        y_onehot = y_onehot.repeat(bs, tem, 1, 1)

        return y_onehot

class norm_data(nn.Module):
    def __init__(self, dim= 64):
        super(norm_data, self).__init__()

        self.bn = nn.BatchNorm1d(dim* 25)

    def forward(self, x):
        bs, c, num_joints, step = x.size()
        x = x.view(bs, -1, step)
        x = self.bn(x)
        x = x.view(bs, -1, num_joints, step).contiguous()
        return x

class embed(nn.Module):
    def __init__(self, dim = 3, dim1 = 128, norm = True, bias = False):
        super(embed, self).__init__()

        if norm:
            self.cnn = nn.Sequential(
                norm_data(dim),
                cnn1x1(dim, 64, bias=bias),
                nn.ReLU(),
                cnn1x1(64, dim1, bias=bias),
                nn.ReLU(),
            )
        else:
            self.cnn = nn.Sequential(
                cnn1x1(dim, 64, bias=bias),
                nn.ReLU(),
                cnn1x1(64, dim1, bias=bias),
                nn.ReLU(),
            )

    def forward(self, x):
        x = self.cnn(x)
        return x

class cnn1x1(nn.Module):
    def __init__(self, dim1 = 3, dim2 =3, bias = True):
        super(cnn1x1, self).__init__()
        self.cnn = nn.Conv2d(dim1, dim2, kernel_size=1, bias=bias)

    def forward(self, x):
        x = self.cnn(x)
        return x

class local(nn.Module):
    def __init__(self, dim1 = 3, dim2 = 3, bias = False):
        super(local, self).__init__()
        self.maxpool = nn.AdaptiveMaxPool2d((1, 20))
        self.cnn1 = nn.Conv2d(dim1, dim1, kernel_size=(1, 3), padding=(0, 1), bias=bias)
        self.bn1 = nn.BatchNorm2d(dim1)
        self.relu = nn.ReLU()
        self.cnn2 = nn.Conv2d(dim1, dim2, kernel_size=1, bias=bias)
        self.bn2 = nn.BatchNorm2d(dim2)
        self.dropout = nn.Dropout2d(0.2)

    def forward(self, x1):
        x1 = self.maxpool(x1)
        x = self.cnn1(x1)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.dropout(x)
        x = self.cnn2(x)
        x = self.bn2(x)
        x = self.relu(x)

        return x

class gcn_spa(nn.Module):
    def __init__(self, in_feature, out_feature, bias = False):
        super(gcn_spa, self).__init__()
        self.bn = nn.BatchNorm2d(out_feature)
        self.relu = nn.ReLU()
        self.w = cnn1x1(in_feature, out_feature, bias=False)
        self.w1 = cnn1x1(in_feature, out_feature, bias=bias)


    def forward(self, x1, g):
        x = x1.permute(0, 3, 2, 1).contiguous()
        x = g.matmul(x)
        x = x.permute(0, 3, 2, 1).contiguous()
        x = self.w(x) + self.w1(x1)
        x = self.relu(self.bn(x))
        return x

class compute_g_spa(nn.Module):
    def __init__(self, dim1 = 64 *3, dim2 = 64*3, bias = False):
        super(compute_g_spa, self).__init__()
        self.dim1 = dim1
        self.dim2 = dim2
        self.g1 = cnn1x1(self.dim1, self.dim2, bias=bias)
        self.g2 = cnn1x1(self.dim1, self.dim2, bias=bias)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x1):

        g1 = self.g1(x1).permute(0, 3, 2, 1).contiguous()
        g2 = self.g2(x1).permute(0, 3, 1, 2).contiguous()
        g3 = g1.matmul(g2)
        g = self.softmax(g3)
        return g
    


================================================
FILE: util.py
================================================
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import os
import csv
import numpy as np
import matplotlib.pyplot as plt
import torch.nn as nn
import torch
import os.path as osp

def make_dir(dataset):
    if dataset == 'NTU':
        output_dir = os.path.join('./results/NTU/')
    elif dataset == 'NTU120':
        output_dir = os.path.join('./results/NTU120/')

    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    return output_dir

def get_num_classes(dataset):
    if dataset == 'NTU':
        return 60
    elif dataset == 'NTU120':
        return 120