modify the document

2021-11-20 23:10:21 +08:00 · 2021-11-20 23:05:15 +08:00 · 2021-11-20 22:49:38 +08:00 · 2021-11-20 22:47:20 +08:00 · 2021-11-20 22:43:27 +08:00 · 2021-11-20 22:41:09 +08:00
38 changed files with 134 additions and 1911 deletions
--- a/0
+++ b/0
--- a/192
+++ b/192
@ -1,192 +0,0 @@
-
-import os
-from scipy import misc as scisc
-import cv2
-import numpy as np
-from warnings import warn
-from time import sleep
-import argparse
-
-from multiprocessing import Pool
-from multiprocessing import TimeoutError as MP_TimeoutError
-
-START = "START"
-FINISH = "FINISH"
-WARNING = "WARNING"
-FAIL = "FAIL"
-
-
-def boolean_string(s):
-    if s.upper() not in {'FALSE', 'TRUE'}:
-        raise ValueError('Not a valid boolean string')
-    return s.upper() == 'TRUE'
-
-
-parser = argparse.ArgumentParser(description='Test')
-parser.add_argument('--input_path', default='', type=str,
-                    help='Root path of raw dataset.')
-parser.add_argument('--output_path', default='', type=str,
-                    help='Root path for output.')
-parser.add_argument('--log_file', default='./pretreatment.log', type=str,
-                    help='Log file path. Default: ./pretreatment.log')
-parser.add_argument('--log', default=False, type=boolean_string,
-                    help='If set as True, all logs will be saved. '
-                         'Otherwise, only warnings and errors will be saved.'
-                         'Default: False')
-parser.add_argument('--worker_num', default=1, type=int,
-                    help='How many subprocesses to use for data pretreatment. '
-                         'Default: 1')
-opt = parser.parse_args()
-
-INPUT_PATH = opt.input_path
-OUTPUT_PATH = opt.output_path
-IF_LOG = opt.log
-LOG_PATH = opt.log_file
-WORKERS = opt.worker_num
-
-T_H = 64
-T_W = 64
-
-
-def log2str(pid, comment, logs):
-    str_log = ''
-    if type(logs) is str:
-        logs = [logs]
-    for log in logs:
-        str_log += "# JOB %d : --%s-- %s\n" % (
-            pid, comment, log)
-    return str_log
-
-
-def log_print(pid, comment, logs):
-    str_log = log2str(pid, comment, logs)
-    if comment in [WARNING, FAIL]:
-        with open(LOG_PATH, 'a') as log_f:
-            log_f.write(str_log)
-    if comment in [START, FINISH]:
-        if pid % 500 != 0:
-            return
-    print(str_log, end='')
-
-
-def cut_img(img, seq_info, frame_name, pid):
-    # A silhouette contains too little white pixels
-    # might be not valid for identification.
-    if img.sum() <= 10000:
-        message = 'seq:%s, frame:%s, no data, %d.' % (
-            '-'.join(seq_info), frame_name, img.sum())
-        warn(message)
-        log_print(pid, WARNING, message)
-        return None
-    # Get the top and bottom point
-    y = img.sum(axis=1)
-    y_top = (y != 0).argmax(axis=0)
-    y_btm = (y != 0).cumsum(axis=0).argmax(axis=0)
-    img = img[y_top:y_btm + 1, :]
-    # As the height of a person is larger than the width,
-    # use the height to calculate resize ratio.
-    _r = img.shape[1] / img.shape[0]
-    _t_w = int(T_H * _r)
-    img = cv2.resize(img, (_t_w, T_H), interpolation=cv2.INTER_CUBIC)
-    # Get the median of x axis and regard it as the x center of the person.
-    sum_point = img.sum()
-    sum_column = img.sum(axis=0).cumsum()
-    x_center = -1
-    for i in range(sum_column.size):
-        if sum_column[i] > sum_point / 2:
-            x_center = i
-            break
-    if x_center < 0:
-        message = 'seq:%s, frame:%s, no center.' % (
-            '-'.join(seq_info), frame_name)
-        warn(message)
-        log_print(pid, WARNING, message)
-        return None
-    h_T_W = int(T_W / 2)
-    left = x_center - h_T_W
-    right = x_center + h_T_W
-    if left <= 0 or right >= img.shape[1]:
-        left += h_T_W
-        right += h_T_W
-        _ = np.zeros((img.shape[0], h_T_W))
-        img = np.concatenate([_, img, _], axis=1)
-    img = img[:, left:right]
-    return img.astype('uint8')
-
-
-def cut_pickle(seq_info, pid):
-    seq_name = '-'.join(seq_info)
-    log_print(pid, START, seq_name)
-    seq_path = os.path.join(INPUT_PATH, *seq_info)
-    out_dir = os.path.join(OUTPUT_PATH, *seq_info)
-    frame_list = os.listdir(seq_path)
-    frame_list.sort()
-    count_frame = 0
-    for _frame_name in frame_list:
-        frame_path = os.path.join(seq_path, _frame_name)
-        img = cv2.imread(frame_path)[:, :, 0]
-        img = cut_img(img, seq_info, _frame_name, pid)
-        if img is not None:
-            # Save the cut img
-            save_path = os.path.join(out_dir, _frame_name)
-            scisc.imsave(save_path, img)
-            count_frame += 1
-    # Warn if the sequence contains less than 5 frames
-    if count_frame < 5:
-        message = 'seq:%s, less than 5 valid data.' % (
-            '-'.join(seq_info))
-        warn(message)
-        log_print(pid, WARNING, message)
-
-    log_print(pid, FINISH,
-              'Contain %d valid frames. Saved to %s.'
-              % (count_frame, out_dir))
-
-
-pool = Pool(WORKERS)
-results = list()
-pid = 0
-
-print('Pretreatment Start.\n'
-      'Input path: %s\n'
-      'Output path: %s\n'
-      'Log file: %s\n'
-      'Worker num: %d' % (
-          INPUT_PATH, OUTPUT_PATH, LOG_PATH, WORKERS))
-
-id_list = os.listdir(INPUT_PATH)
-id_list.sort()
-# Walk the input path
-for _id in id_list:
-    seq_type = os.listdir(os.path.join(INPUT_PATH, _id))
-    seq_type.sort()
-    for _seq_type in seq_type:
-        view = os.listdir(os.path.join(INPUT_PATH, _id, _seq_type))
-        view.sort()
-        for _view in view:
-            seq_info = [_id, _seq_type, _view]
-            out_dir = os.path.join(OUTPUT_PATH, *seq_info)
-            os.makedirs(out_dir)
-            results.append(
-                pool.apply_async(
-                    cut_pickle,
-                    args=(seq_info, pid)))
-            sleep(0.02)
-            pid += 1
-
-pool.close()
-unfinish = 1
-while unfinish > 0:
-    unfinish = 0
-    for i, res in enumerate(results):
-        try:
-            res.get(timeout=0.1)
-        except Exception as e:
-            if type(e) == MP_TimeoutError:
-                unfinish += 1
-                continue
-            else:
-                print('\n\n\nERROR OCCUR: PID ##%d##, ERRORTYPE: %s\n\n\n',
-                      i, type(e))
-                raise e
-pool.join()
--- a/LSTM.png
+++ b/LSTM.png
--- a/ME.md
+++ b/ME.md
@ -1,157 +0,0 @@
-# GaitSet
-
-[![LICENSE](https://img.shields.io/badge/license-NPL%20(The%20996%20Prohibited%20License)-blue.svg)](https://github.com/996icu/996.ICU/blob/master/LICENSE)
-[![996.icu](https://img.shields.io/badge/link-996.icu-red.svg)](https://996.icu)
-
-GaitSet is a **flexible**, **effective** and **fast** network for cross-view gait recognition. The [paper](https://ieeexplore.ieee.org/document/9351667) has been published on IEEE TPAMI.
-
-#### Flexible 
-The input of GaitSet is a set of silhouettes. 
-
- There are **NOT ANY constrains** on an input,
-which means it can contain **any number** of **non-consecutive** silhouettes filmed under **different viewpoints**
-with **different walking conditions**.
-
- As the input is a set, the **permutation** of the elements in the input
-will **NOT change** the output at all.
-
-#### Effective
-It achieves **Rank@1=95.0%** on [CASIA-B](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp) 
-and  **Rank@1=87.1%** on [OU-MVLP](http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html),
-excluding  identical-view cases.
-
-#### Fast
-With 8 NVIDIA 1080TI GPUs, it only takes **7 minutes** to conduct an evaluation on
-[OU-MVLP](http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html) which contains 133,780 sequences
-and average 70 frames per sequence.
-
-## What's new
-The code and checkpoint for OUMVLP dataset have been released.
-See [OUMVLP](#oumvlp) for details.
-
-## Prerequisites
-
- Python 3.6
- PyTorch 0.4+
- GPU
-
-
-## Getting started
-### Installation
-
- (Not necessary) Install [Anaconda3](https://www.anaconda.com/download/)
- Install [CUDA 9.0](https://developer.nvidia.com/cuda-90-download-archive)
- install [cuDNN7.0](https://developer.nvidia.com/cudnn)
- Install [PyTorch](http://pytorch.org/)
-
-Noted that our code is tested based on [PyTorch 0.4](http://pytorch.org/)
-
-### Dataset & Preparation
-Download [CASIA-B Dataset](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp)
-
-**!!! ATTENTION !!! ATTENTION !!! ATTENTION !!!**
-
-Before training or test, please make sure you have prepared the dataset
-by this two steps:
- **Step1:** Organize the directory as: 
-`your_dataset_path/subject_ids/walking_conditions/views`.
-E.g. `CASIA-B/001/nm-01/000/`.
- **Step2:** Cut and align the raw silhouettes with `pretreatment.py`.
-(See [pretreatment](#pretreatment) for details.)
-Welcome to try different ways of pretreatment but note that
-the silhouettes after pretreatment **MUST have a size of 64x64**.
-
-Futhermore, you also can test our code on [OU-MVLP Dataset](http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html).
-The number of channels and the training batchsize is slightly different for this dataset.
-For more detail, please refer to [our paper](https://arxiv.org/abs/1811.06186).
-
-#### Pretreatment
-`pretreatment.py` uses the alignment method in
-[this paper](https://ipsjcva.springeropen.com/articles/10.1186/s41074-018-0039-6).
-Pretreatment your dataset by
-```
-python pretreatment.py --input_path='root_path_of_raw_dataset' --output_path='root_path_for_output'
-```
- `--input_path` **(NECESSARY)** Root path of raw dataset.
- `--output_path` **(NECESSARY)** Root path for output.
- `--log_file` Log file path. #Default: './pretreatment.log'
- `--log` If set as True, all logs will be saved. 
-Otherwise, only warnings and errors will be saved. #Default: False
- `--worker_num` How many subprocesses to use for data pretreatment. Default: 1
-
-### Configuration 
-
-In `config.py`, you might want to change the following settings:
- `dataset_path` **(NECESSARY)** root path of the dataset 
-(for the above example, it is "gaitdata")
- `WORK_PATH` path to save/load checkpoints
- `CUDA_VISIBLE_DEVICES` indices of GPUs
-
-### Train
-Train a model by
-```bash
-python train.py
-```
- `--cache` if set as TRUE all the training data will be loaded at once before the training start.
-This will accelerate the training.
-**Note that** if this arg is set as FALSE, samples will NOT be kept in the memory
-even they have been used in the former iterations. #Default: TRUE
-
-### Evaluation
-Evaluate the trained model by
-```bash
-python test.py
-```
- `--iter` iteration of the checkpoint to load. #Default: 80000
- `--batch_size` batch size of the parallel test. #Default: 1
- `--cache` if set as TRUE all the test data will be loaded at once before the transforming start.
-This might accelerate the testing. #Default: FALSE
-
-It will output Rank@1 of all three walking conditions. 
-Note that the test is **parallelizable**. 
-To conduct a faster evaluation, you could use `--batch_size` to change the batch size for test.
-
-#### OUMVLP
-Since the huge differences between OUMVLP and CASIA-B, the network setting on OUMVLP is slightly different.
- The alternated network's code can be found at `./work/OUMVLP_network`. Use them to replace the corresponding files in `./model/network`.
- The checkpoint can be found [here](https://1drv.ms/u/s!AurT2TsSKdxQuWN8drzIv_phTR5m?e=Gfbl3m).
- In `./config.py`, modify `'batch_size': (8, 16)` into `'batch_size': (32,16)`.
- Prepare your OUMVLP dataset according to the instructions in [Dataset & Preparation](#dataset--preparation).
-
-## To Do List
- Transformation: The script for transforming a set of silhouettes into a discriminative representation.
-
-## Authors & Contributors
-GaitSet is authored by
-[Hanqing Chao](https://www.linkedin.com/in/hanqing-chao-9aa42412b/), 
-[Yiwei He](https://www.linkedin.com/in/yiwei-he-4a6a6bbb/),
-[Junping Zhang](http://www.pami.fudan.edu.cn/~jpzhang/)
-and JianFeng Feng from Fudan Universiy.
-[Junping Zhang](http://www.pami.fudan.edu.cn/~jpzhang/)
-is the corresponding author.
-The code is developed by
-[Hanqing Chao](https://www.linkedin.com/in/hanqing-chao-9aa42412b/)
-and [Yiwei He](https://www.linkedin.com/in/yiwei-he-4a6a6bbb/).
-Currently, it is being maintained by
-[Hanqing Chao](https://www.linkedin.com/in/hanqing-chao-9aa42412b/)
-and Kun Wang.
-
-
-## Citation
-Please cite these papers in your publications if it helps your research:
-```
-@ARTICLE{chao2019gaitset,
-  author={Chao, Hanqing and Wang, Kun and He, Yiwei and Zhang, Junping and Feng, Jianfeng},
-  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
-  title={GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set}, 
-  year={2021},
-  pages={1-1},
-  doi={10.1109/TPAMI.2021.3057879}}
-```
-Link to paper:
- [GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set](https://ieeexplore.ieee.org/document/9351667)
-
-
-## License
-GaitSet is freely available for free non-commercial use, and may be redistributed under these conditions.
-For commercial queries, contact [Junping Zhang](http://www.pami.fudan.edu.cn/~jpzhang/).
--- a/README.md
+++ b/README.md
@ -1,16 +1,144 @@
-# 基于 OpenPifPaf 的多摄像头、多人实时跌倒检测模型
-利用 OpenPifPaf 对输入视频进行人体姿势估计,然后通过长短时记忆神经网络（LSTM）从前面得到的姿势信息中提取五个时间和空间特征以预测"跌倒"动作,支持多摄像头和多人实时检测。
-## 检测实例见 examples 文件夹
-## 安装
+# 基于 OpenPifPaf 的多摄像头多人实时跌倒等异常行为识别预警应用研究
+<p align="center">
+<img src="https://git.trustie.net/pkwhiuqat/HumanFallDetectionLSTM/raw/branch/master/documents/outfallingdown1.gif?raw=true" alt="outfallingdown"/>
+<p align="center">
+<img src="https://git.trustie.net/pkwhiuqat/HumanFallDetectionLSTM/raw/branch/master/documents/outfallingdown2.gif?raw=true" alt="outfallingdown" style="zoom:90%;"/>

+
+利用 OpenPifPaf 对输入视频进行人体姿势估计,然后通过长短时记忆神经网络（LSTM）从前面得到的姿势信息中提取五个时间和空间特征（作为当前的 *X*<sub>n</sub> 输入）以预测"跌倒"动作,支持多摄像头和多人实时检测。模型在 UP-Fall Detection 数据集上训练，基于 PyTorch 实现。
+
+<p align="center">
+<img src="https://git.trustie.net/pkwhiuqat/HumanFallDetectionLSTM/raw/branch/master/flowchart.png?raw=true" alt="LSTM" style="zoom:68%;" />
+<p align="center">
+<img src="https://git.trustie.net/pkwhiuqat/HumanFallDetectionLSTM/raw/branch/master/LSTM.png?raw=true" alt="LSTM" style="zoom:45%;" />
+
+## 安装
 ```shell script
 pip install -r requirements.txt
 ```

 ## 使用
 ```shell script
-python3 fall_detector.py --num_cams=1
+python fall_detector.py --num_cams=1
 ```

+
+## 完整运行代码
+
+    usage: fall_detector.py [-h] [--seed-threshold SEED_THRESHOLD]
+                            [--instance-threshold INSTANCE_THRESHOLD]
+                            [--keypoint-threshold KEYPOINT_THRESHOLD]
+                            [--decoder-workers DECODER_WORKERS]
+                            [--dense-connections]
+                            [--dense-coupling DENSE_COUPLING] [--caf-seeds]
+                            [--no-force-complete-pose]
+                            [--profile-decoder [PROFILE_DECODER]]
+                            [--cif-th CIF_TH] [--caf-th CAF_TH]
+                            [--connection-method {max,blend}] [--greedy]
+                            [--checkpoint CHECKPOINT] [--basenet BASENET]
+                            [--headnets HEADNETS [HEADNETS ...]] [--no-pretrain]
+                            [--two-scale] [--multi-scale] [--no-multi-scale-hflip]
+                            [--cross-talk CROSS_TALK] [--no-download-progress]
+                            [--head-dropout HEAD_DROPOUT] [--head-quad HEAD_QUAD]
+                            [--resolution RESOLUTION] [--resize RESIZE]
+                            [--num_cams NUM_CAMS] [--video VIDEO] [--debug]
+                            [--disable_cuda] [--plot_graph] [--joints]
+                            [--skeleton] [--coco_points] [--save_output]
+                            [--fps FPS] [--out-path OUT_PATH]
+                            [--input_direct INPUT_DIRECT]
+    
+    optional arguments:
+      -h, --help            show this help message and exit
+      --resolution RESOLUTION
+                            Resolution prescale factor from 640x480. Will be
+                            rounded to multiples of 16. (default: 0.4)
+      --resize RESIZE       Force input image resize. Example WIDTHxHEIGHT.
+                            (default: None)
+      --num_cams NUM_CAMS   Number of Cameras. (default: 1)
+      --video VIDEO         Path to the video file. For single video fall
+                            detection(--num_cams=1), save your videos as abc.xyz
+                            and set --video=abc.xyz For 2 video fall
+                            detection(--num_cams=2), save your videos as abc1.xyz
+                            & abc2.xyz and set --video=abc.xyz (default: None)
+      --debug               debug messages and autoreload (default: False)
+      --disable_cuda        disables cuda support and runs from gpu (default:
+                            False)
+    
+    decoder configuration:
+      --seed-threshold SEED_THRESHOLD
+                            minimum threshold for seeds (default: 0.5)
+      --instance-threshold INSTANCE_THRESHOLD
+                            filter instances by score (default: 0.2)
+      --keypoint-threshold KEYPOINT_THRESHOLD
+                            filter keypoints by score (default: None)
+      --decoder-workers DECODER_WORKERS
+                            number of workers for pose decoding (default: None)
+      --dense-connections   use dense connections (default: False)
+      --dense-coupling DENSE_COUPLING
+                            dense coupling (default: 0.01)
+      --caf-seeds           [experimental] (default: False)
+      --no-force-complete-pose
+      --profile-decoder [PROFILE_DECODER]
+                            specify out .prof file or nothing for default file
+                            name (default: None)
+    
+    CifCaf decoders:
+      --cif-th CIF_TH       cif threshold (default: 0.1)
+      --caf-th CAF_TH       caf threshold (default: 0.1)
+      --connection-method {max,blend}
+                            connection method to use, max is faster (default:
+                            blend)
+      --greedy              greedy decoding (default: False)
+    
+    network configuration:
+      --checkpoint CHECKPOINT
+                            Load a model from a checkpoint. Use "resnet50",
+                            "shufflenetv2k16w" or "shufflenetv2k30w" for
+                            pretrained OpenPifPaf models. (default: None)
+      --basenet BASENET     base network, e.g. resnet50 (default: None)
+      --headnets HEADNETS [HEADNETS ...]
+                            head networks (default: None)
+      --no-pretrain         create model without ImageNet pretraining (default: True)
+      --two-scale           [experimental] (default: False)
+      --multi-scale         [experimental] (default: False)
+      --no-multi-scale-hflip
+                            [experimental] (default: True)
+      --cross-talk CROSS_TALK
+                            [experimental] (default: 0.0)
+      --no-download-progress
+                            suppress model download progress bar (default: True)
+    
+    head:
+      --head-dropout HEAD_DROPOUT
+                            [experimental] zeroing probability of feature in head
+                            input (default: 0.0)
+      --head-quad HEAD_QUAD
+                            number of times to apply quad (subpixel conv) to heads
+                            (default: 1)
+    
+    Visualisation:
+      --plot_graph          Plot the graph of features extracted from keypoints of
+                            pose. (default: False)
+      --joints              Draw joints keypoints on the output video. (default: True)
+      --skeleton            Draw skeleton on the output video. (default: True)
+      --coco_points         Visualises the COCO points of the human pose. (default: False)
+      --save_output         Save the result in a video file. Output videos are
+                            saved in the same directory as input videos with "out"
+                            appended at the start of the title (default: False)
+      --fps FPS             FPS for the output video. (default: 18)
+      --out-path OUT_PATH   Save the output video at the path specified. .avi file
+                            format. (default: result.avi)
+      --input_direct INPUT_DIRECT
+                            Save the input link to images directory. (default: None)
+- 模型输入可以直接为摄像头作为视频源或者用下载好的视频作为视频源。
+- 如果在非服务器端可以通过设置在窗口进行实时画面的显示。
+
 ## 参考
-https://github.com/openpifpaf/openpifpaf
+- [OpenPifPaf](https://github.com/openpifpaf/openpifpaf)
+- [UP-fall detection Dataset](https://dx.doi.org/10.3390/s19091988)
+- [Multi-camera, multi-person, and real-time fall detection using long short term memory](https://doi.org/10.1117/12.2580700)
+- Lei Wang, Du Q. Huynh, Piotr Koniusz. A Comparative Review of Recent Kinect-based Action Recognition Algorithms[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2019.
+- Nusrat Tasnim , Mohammad Khairul Islam and Joong-Hwan Baek. Deep Learning Based Human Activity Recognition Using Spatio-Temporal Image Formation of Skeleton Joints[J].applied sciences.
+- Mickael Delamare, Cyril Laville, Adnane Cabani and Houcine Chafouk. Graph Convolutional Networks Skeleton-based Action Recognition for Continuous Data Stream: A Sliding Window Approach[J]. 16th International Conference on Computer Vision Theory and Applications.
+- Tasweer Ahmad, Lianwen Jin, Xin Zhang, Songxuan Lai, Guozhi Tang, and Luojun Lin. Graph Convolutional Neural Network for Human Action Recognition: A Comprehensive Survey[J].
+- Zehua Sun, Jun Liu, Qiuhong Ke, Hossein Rahmani, Mohammed Bennamoun, and Gang Wang. Human Action Recognition from Various Data Modalities: A Review[J].
--- a/algorithms.py
+++ b/algorithms.py
@ -35,7 +35,6 @@ def get_source(args):
    logging.debug('Image shape:', img.shape)
    return cam, tagged_df

-
 def resize(img, resize, resolution):
    # Resize the video
    if resize is None:
@ -124,10 +123,8 @@ def extract_keypoints_parallel(queue, args, self_counter, other_counter, consecu
    queue.put(None)
    return

-
 ###################################################### Post human estimation ###########################################################

-
 def show_tracked_img(img_dict, ip_set, num_matched, output_video, args):
    img = img_dict["img"]
    tagged_df = img_dict["tagged_df"]
--- a/config.py
+++ b/config.py
@ -1,27 +0,0 @@
-conf = {
-    "WORK_PATH": "./work",
-    "CUDA_VISIBLE_DEVICES": "0,1,2,3",
-    "data": {
-        'dataset_path': "your_dataset_path",
-        'resolution': '64',
-        'dataset': 'CASIA-B',
-        # In CASIA-B, data of subject #5 is incomplete.
-        # Thus, we ignore it in training.
-        # For more detail, please refer to
-        # function: utils.data_loader.load_data
-        'pid_num': 73,
-        'pid_shuffle': False,
-    },
-    "model": {
-        'hidden_dim': 256,
-        'lr': 1e-4,
-        'hard_or_full_trip': 'full',
-        'batch_size': (8, 16),
-        'restore_iter': 0,
-        'total_iter': 80000,
-        'margin': 0.2,
-        'num_workers': 3,
-        'frame_num': 30,
-        'model_name': 'GaitSet',
-    },
-}
--- a/documents/outfallingdown1.gif
+++ b/documents/outfallingdown1.gif
--- a/documents/outfallingdown2.gif
+++ b/documents/outfallingdown2.gif
--- a/documents/演示视频1.avi
+++ b/documents/演示视频1.avi
--- a/examples/outedlerly_fall.avi
+++ b/examples/outedlerly_fall.avi
--- a/documents/演示视频3.avi
+++ b/documents/演示视频3.avi
--- a/documents/行为识别开源技术报告.docx
+++ b/documents/行为识别开源技术报告.docx
--- a/documents/行为识别开源技术报告.pdf
+++ b/documents/行为识别开源技术报告.pdf
--- a/flowchart.png
+++ b/flowchart.png
--- a/gaitset/READ
+++ b/gaitset/READ
@ -1,157 +0,0 @@
-# GaitSet
-
-[![LICENSE](https://img.shields.io/badge/license-NPL%20(The%20996%20Prohibited%20License)-blue.svg)](https://github.com/996icu/996.ICU/blob/master/LICENSE)
-[![996.icu](https://img.shields.io/badge/link-996.icu-red.svg)](https://996.icu)
-
-GaitSet is a **flexible**, **effective** and **fast** network for cross-view gait recognition. The [paper](https://ieeexplore.ieee.org/document/9351667) has been published on IEEE TPAMI.
-
-#### Flexible 
-The input of GaitSet is a set of silhouettes. 
-
- There are **NOT ANY constrains** on an input,
-which means it can contain **any number** of **non-consecutive** silhouettes filmed under **different viewpoints**
-with **different walking conditions**.
-
- As the input is a set, the **permutation** of the elements in the input
-will **NOT change** the output at all.
-
-#### Effective
-It achieves **Rank@1=95.0%** on [CASIA-B](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp) 
-and  **Rank@1=87.1%** on [OU-MVLP](http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html),
-excluding  identical-view cases.
-
-#### Fast
-With 8 NVIDIA 1080TI GPUs, it only takes **7 minutes** to conduct an evaluation on
-[OU-MVLP](http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html) which contains 133,780 sequences
-and average 70 frames per sequence.
-
-## What's new
-The code and checkpoint for OUMVLP dataset have been released.
-See [OUMVLP](#oumvlp) for details.
-
-## Prerequisites
-
- Python 3.6
- PyTorch 0.4+
- GPU
-
-
-## Getting started
-### Installation
-
- (Not necessary) Install [Anaconda3](https://www.anaconda.com/download/)
- Install [CUDA 9.0](https://developer.nvidia.com/cuda-90-download-archive)
- install [cuDNN7.0](https://developer.nvidia.com/cudnn)
- Install [PyTorch](http://pytorch.org/)
-
-Noted that our code is tested based on [PyTorch 0.4](http://pytorch.org/)
-
-### Dataset & Preparation
-Download [CASIA-B Dataset](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp)
-
-**!!! ATTENTION !!! ATTENTION !!! ATTENTION !!!**
-
-Before training or test, please make sure you have prepared the dataset
-by this two steps:
- **Step1:** Organize the directory as: 
-`your_dataset_path/subject_ids/walking_conditions/views`.
-E.g. `CASIA-B/001/nm-01/000/`.
- **Step2:** Cut and align the raw silhouettes with `pretreatment.py`.
-(See [pretreatment](#pretreatment) for details.)
-Welcome to try different ways of pretreatment but note that
-the silhouettes after pretreatment **MUST have a size of 64x64**.
-
-Futhermore, you also can test our code on [OU-MVLP Dataset](http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html).
-The number of channels and the training batchsize is slightly different for this dataset.
-For more detail, please refer to [our paper](https://arxiv.org/abs/1811.06186).
-
-#### Pretreatment
-`pretreatment.py` uses the alignment method in
-[this paper](https://ipsjcva.springeropen.com/articles/10.1186/s41074-018-0039-6).
-Pretreatment your dataset by
-```
-python pretreatment.py --input_path='root_path_of_raw_dataset' --output_path='root_path_for_output'
-```
- `--input_path` **(NECESSARY)** Root path of raw dataset.
- `--output_path` **(NECESSARY)** Root path for output.
- `--log_file` Log file path. #Default: './pretreatment.log'
- `--log` If set as True, all logs will be saved. 
-Otherwise, only warnings and errors will be saved. #Default: False
- `--worker_num` How many subprocesses to use for data pretreatment. Default: 1
-
-### Configuration 
-
-In `config.py`, you might want to change the following settings:
- `dataset_path` **(NECESSARY)** root path of the dataset 
-(for the above example, it is "gaitdata")
- `WORK_PATH` path to save/load checkpoints
- `CUDA_VISIBLE_DEVICES` indices of GPUs
-
-### Train
-Train a model by
-```bash
-python train.py
-```
- `--cache` if set as TRUE all the training data will be loaded at once before the training start.
-This will accelerate the training.
-**Note that** if this arg is set as FALSE, samples will NOT be kept in the memory
-even they have been used in the former iterations. #Default: TRUE
-
-### Evaluation
-Evaluate the trained model by
-```bash
-python test.py
-```
- `--iter` iteration of the checkpoint to load. #Default: 80000
- `--batch_size` batch size of the parallel test. #Default: 1
- `--cache` if set as TRUE all the test data will be loaded at once before the transforming start.
-This might accelerate the testing. #Default: FALSE
-
-It will output Rank@1 of all three walking conditions. 
-Note that the test is **parallelizable**. 
-To conduct a faster evaluation, you could use `--batch_size` to change the batch size for test.
-
-#### OUMVLP
-Since the huge differences between OUMVLP and CASIA-B, the network setting on OUMVLP is slightly different.
- The alternated network's code can be found at `./work/OUMVLP_network`. Use them to replace the corresponding files in `./model/network`.
- The checkpoint can be found [here](https://1drv.ms/u/s!AurT2TsSKdxQuWN8drzIv_phTR5m?e=Gfbl3m).
- In `./config.py`, modify `'batch_size': (8, 16)` into `'batch_size': (32,16)`.
- Prepare your OUMVLP dataset according to the instructions in [Dataset & Preparation](#dataset--preparation).
-
-## To Do List
- Transformation: The script for transforming a set of silhouettes into a discriminative representation.
-
-## Authors & Contributors
-GaitSet is authored by
-[Hanqing Chao](https://www.linkedin.com/in/hanqing-chao-9aa42412b/), 
-[Yiwei He](https://www.linkedin.com/in/yiwei-he-4a6a6bbb/),
-[Junping Zhang](http://www.pami.fudan.edu.cn/~jpzhang/)
-and JianFeng Feng from Fudan Universiy.
-[Junping Zhang](http://www.pami.fudan.edu.cn/~jpzhang/)
-is the corresponding author.
-The code is developed by
-[Hanqing Chao](https://www.linkedin.com/in/hanqing-chao-9aa42412b/)
-and [Yiwei He](https://www.linkedin.com/in/yiwei-he-4a6a6bbb/).
-Currently, it is being maintained by
-[Hanqing Chao](https://www.linkedin.com/in/hanqing-chao-9aa42412b/)
-and Kun Wang.
-
-
-## Citation
-Please cite these papers in your publications if it helps your research:
-```
-@ARTICLE{chao2019gaitset,
-  author={Chao, Hanqing and Wang, Kun and He, Yiwei and Zhang, Junping and Feng, Jianfeng},
-  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
-  title={GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set}, 
-  year={2021},
-  pages={1-1},
-  doi={10.1109/TPAMI.2021.3057879}}
-```
-Link to paper:
- [GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set](https://ieeexplore.ieee.org/document/9351667)
-
-
-## License
-GaitSet is freely available for free non-commercial use, and may be redistributed under these conditions.
-For commercial queries, contact [Junping Zhang](http://www.pami.fudan.edu.cn/~jpzhang/).
--- a/gaitset/basic_blocks.py
+++ b/gaitset/basic_blocks.py
@ -1,49 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-class BasicConv2d(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, **kwargs):
-        super(BasicConv2d, self).__init__()
-        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, bias=False, **kwargs)
-
-    def forward(self, x):
-        x = self.conv(x)
-        return F.leaky_relu(x, inplace=True)
-
-
-class SetBlock(nn.Module):
-    def __init__(self, forward_block, pooling=False):
-        super(SetBlock, self).__init__()
-        self.forward_block = forward_block
-        self.pooling = pooling
-        if pooling:
-            self.pool2d = nn.MaxPool2d(2)
-    def forward(self, x):
-        n, s, c, h, w = x.size()
-        x = self.forward_block(x.view(-1,c,h,w))
-        if self.pooling:
-            x = self.pool2d(x)
-        _, c, h, w = x.size()
-        return x.view(n, s, c, h ,w)
-
-
-class HPM(nn.Module):
-    def __init__(self, in_dim, out_dim, bin_level_num=5):
-        super(HPM, self).__init__()
-        self.bin_num = [2**i for i in range(bin_level_num)]
-        self.fc_bin = nn.ParameterList([
-            nn.Parameter(
-                nn.init.xavier_uniform(
-                    torch.zeros(sum(self.bin_num), in_dim, out_dim)))])
-    def forward(self, x):
-        feature = list()
-        n, c, h, w = x.size()
-        for num_bin in self.bin_num:
-            z = x.view(n, c, num_bin, -1)
-            z = z.mean(3)+z.max(3)[0]
-            feature.append(z)
-        feature = torch.cat(feature, 2).permute(2, 0, 1).contiguous()
-        
-        feature = feature.matmul(self.fc_bin[0])
-        return feature.permute(1, 0, 2).contiguous()
--- a/gaitset/config.py
+++ b/gaitset/config.py
@ -1,27 +0,0 @@
-conf = {
-    "WORK_PATH": "./work",
-    "CUDA_VISIBLE_DEVICES": "0,1,2,3",
-    "data": {
-        'dataset_path': "your_dataset_path",
-        'resolution': '64',
-        'dataset': 'CASIA-B',
-        # In CASIA-B, data of subject #5 is incomplete.
-        # Thus, we ignore it in training.
-        # For more detail, please refer to
-        # function: utils.data_loader.load_data
-        'pid_num': 73,
-        'pid_shuffle': False,
-    },
-    "model": {
-        'hidden_dim': 256,
-        'lr': 1e-4,
-        'hard_or_full_trip': 'full',
-        'batch_size': (8, 16),
-        'restore_iter': 0,
-        'total_iter': 80000,
-        'margin': 0.2,
-        'num_workers': 3,
-        'frame_num': 30,
-        'model_name': 'GaitSet',
-    },
-}
--- a/gaitset/model/_init_.py
+++ b/gaitset/model/_init_.py
@ -1,4 +0,0 @@
-# -*- coding: utf-8 -*-
-# @Author  : admin
-# @Time    : 2018/11/16
-
--- a/gaitset/model/initialization.py
+++ b/gaitset/model/initialization.py
@ -1,58 +0,0 @@
-# -*- coding: utf-8 -*-
-# @Author  : admin
-# @Time    : 2018/11/15
-import os
-from copy import deepcopy
-
-import numpy as np
-
-from .utils import load_data
-from .model import Model
-
-
-def initialize_data(config, train=False, test=False):
-    print("Initializing data source...")
-    train_source, test_source = load_data(**config['data'], cache=(train or test))
-    if train:
-        print("Loading training data...")
-        train_source.load_all_data()
-    if test:
-        print("Loading test data...")
-        test_source.load_all_data()
-    print("Data initialization complete.")
-    return train_source, test_source
-
-
-def initialize_model(config, train_source, test_source):
-    print("Initializing model...")
-    data_config = config['data']
-    model_config = config['model']
-    model_param = deepcopy(model_config)
-    model_param['train_source'] = train_source
-    model_param['test_source'] = test_source
-    model_param['train_pid_num'] = data_config['pid_num']
-    batch_size = int(np.prod(model_config['batch_size']))
-    model_param['save_name'] = '_'.join(map(str,[
-        model_config['model_name'],
-        data_config['dataset'],
-        data_config['pid_num'],
-        data_config['pid_shuffle'],
-        model_config['hidden_dim'],
-        model_config['margin'],
-        batch_size,
-        model_config['hard_or_full_trip'],
-        model_config['frame_num'],
-    ]))
-
-    m = Model(**model_param)
-    print("Model initialization complete.")
-    return m, model_param['save_name']
-
-
-def initialization(config, train=False, test=False):
-    print("Initialzing...")
-    WORK_PATH = config['WORK_PATH']
-    os.chdir(WORK_PATH)
-    os.environ["CUDA_VISIBLE_DEVICES"] = config["CUDA_VISIBLE_DEVICES"]
-    train_source, test_source = initialize_data(config, train, test)
-    return initialize_model(config, train_source, test_source)
--- a/gaitset/model/model.py
+++ b/gaitset/model/model.py
@ -1,272 +0,0 @@
-import math
-import os
-import os.path as osp
-import random
-import sys
-from datetime import datetime
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.autograd as autograd
-import torch.optim as optim
-import torch.utils.data as tordata
-
-from .network import TripletLoss, SetNet
-from .utils import TripletSampler
-
-
-class Model:
-    def __init__(self,
-                 hidden_dim,
-                 lr,
-                 hard_or_full_trip,
-                 margin,
-                 num_workers,
-                 batch_size,
-                 restore_iter,
-                 total_iter,
-                 save_name,
-                 train_pid_num,
-                 frame_num,
-                 model_name,
-                 train_source,
-                 test_source,
-                 img_size=64):
-
-        self.save_name = save_name
-        self.train_pid_num = train_pid_num
-        self.train_source = train_source
-        self.test_source = test_source
-
-        self.hidden_dim = hidden_dim
-        self.lr = lr
-        self.hard_or_full_trip = hard_or_full_trip
-        self.margin = margin
-        self.frame_num = frame_num
-        self.num_workers = num_workers
-        self.batch_size = batch_size
-        self.model_name = model_name
-        self.P, self.M = batch_size
-
-        self.restore_iter = restore_iter
-        self.total_iter = total_iter
-
-        self.img_size = img_size
-
-        self.encoder = SetNet(self.hidden_dim).float()
-        self.encoder = nn.DataParallel(self.encoder)
-        self.triplet_loss = TripletLoss(self.P * self.M, self.hard_or_full_trip, self.margin).float()
-        self.triplet_loss = nn.DataParallel(self.triplet_loss)
-        self.encoder.cuda()
-        self.triplet_loss.cuda()
-
-        self.optimizer = optim.Adam([
-            {'params': self.encoder.parameters()},
-        ], lr=self.lr)
-
-        self.hard_loss_metric = []
-        self.full_loss_metric = []
-        self.full_loss_num = []
-        self.dist_list = []
-        self.mean_dist = 0.01
-
-        self.sample_type = 'all'
-
-    def collate_fn(self, batch):
-        batch_size = len(batch)
-        feature_num = len(batch[0][0])
-        seqs = [batch[i][0] for i in range(batch_size)]
-        frame_sets = [batch[i][1] for i in range(batch_size)]
-        view = [batch[i][2] for i in range(batch_size)]
-        seq_type = [batch[i][3] for i in range(batch_size)]
-        label = [batch[i][4] for i in range(batch_size)]
-        batch = [seqs, view, seq_type, label, None]
-
-        def select_frame(index):
-            sample = seqs[index]
-            frame_set = frame_sets[index]
-            if self.sample_type == 'random':
-                frame_id_list = random.choices(frame_set, k=self.frame_num)
-                _ = [feature.loc[frame_id_list].values for feature in sample]
-            else:
-                _ = [feature.values for feature in sample]
-            return _
-
-        seqs = list(map(select_frame, range(len(seqs))))
-
-        if self.sample_type == 'random':
-            seqs = [np.asarray([seqs[i][j] for i in range(batch_size)]) for j in range(feature_num)]
-        else:
-            gpu_num = min(torch.cuda.device_count(), batch_size)
-            batch_per_gpu = math.ceil(batch_size / gpu_num)
-            batch_frames = [[
-                                len(frame_sets[i])
-                                for i in range(batch_per_gpu * _, batch_per_gpu * (_ + 1))
-                                if i < batch_size
-                                ] for _ in range(gpu_num)]
-            if len(batch_frames[-1]) != batch_per_gpu:
-                for _ in range(batch_per_gpu - len(batch_frames[-1])):
-                    batch_frames[-1].append(0)
-            max_sum_frame = np.max([np.sum(batch_frames[_]) for _ in range(gpu_num)])
-            seqs = [[
-                        np.concatenate([
-                                           seqs[i][j]
-                                           for i in range(batch_per_gpu * _, batch_per_gpu * (_ + 1))
-                                           if i < batch_size
-                                           ], 0) for _ in range(gpu_num)]
-                    for j in range(feature_num)]
-            seqs = [np.asarray([
-                                   np.pad(seqs[j][_],
-                                          ((0, max_sum_frame - seqs[j][_].shape[0]), (0, 0), (0, 0)),
-                                          'constant',
-                                          constant_values=0)
-                                   for _ in range(gpu_num)])
-                    for j in range(feature_num)]
-            batch[4] = np.asarray(batch_frames)
-
-        batch[0] = seqs
-        return batch
-
-    def fit(self):
-        if self.restore_iter != 0:
-            self.load(self.restore_iter)
-
-        self.encoder.train()
-        self.sample_type = 'random'
-        for param_group in self.optimizer.param_groups:
-            param_group['lr'] = self.lr
-        triplet_sampler = TripletSampler(self.train_source, self.batch_size)
-        train_loader = tordata.DataLoader(
-            dataset=self.train_source,
-            batch_sampler=triplet_sampler,
-            collate_fn=self.collate_fn,
-            num_workers=self.num_workers)
-
-        train_label_set = list(self.train_source.label_set)
-        train_label_set.sort()
-
-        _time1 = datetime.now()
-        for seq, view, seq_type, label, batch_frame in train_loader:
-            self.restore_iter += 1
-            self.optimizer.zero_grad()
-
-            for i in range(len(seq)):
-                seq[i] = self.np2var(seq[i]).float()
-            if batch_frame is not None:
-                batch_frame = self.np2var(batch_frame).int()
-
-            feature, label_prob = self.encoder(*seq, batch_frame)
-
-            target_label = [train_label_set.index(l) for l in label]
-            target_label = self.np2var(np.array(target_label)).long()
-
-            triplet_feature = feature.permute(1, 0, 2).contiguous()
-            triplet_label = target_label.unsqueeze(0).repeat(triplet_feature.size(0), 1)
-            (full_loss_metric, hard_loss_metric, mean_dist, full_loss_num
-             ) = self.triplet_loss(triplet_feature, triplet_label)
-            if self.hard_or_full_trip == 'hard':
-                loss = hard_loss_metric.mean()
-            elif self.hard_or_full_trip == 'full':
-                loss = full_loss_metric.mean()
-
-            self.hard_loss_metric.append(hard_loss_metric.mean().data.cpu().numpy())
-            self.full_loss_metric.append(full_loss_metric.mean().data.cpu().numpy())
-            self.full_loss_num.append(full_loss_num.mean().data.cpu().numpy())
-            self.dist_list.append(mean_dist.mean().data.cpu().numpy())
-
-            if loss > 1e-9:
-                loss.backward()
-                self.optimizer.step()
-
-            if self.restore_iter % 1000 == 0:
-                print(datetime.now() - _time1)
-                _time1 = datetime.now()
-
-            if self.restore_iter % 100 == 0:
-                self.save()
-                print('iter {}:'.format(self.restore_iter), end='')
-                print(', hard_loss_metric={0:.8f}'.format(np.mean(self.hard_loss_metric)), end='')
-                print(', full_loss_metric={0:.8f}'.format(np.mean(self.full_loss_metric)), end='')
-                print(', full_loss_num={0:.8f}'.format(np.mean(self.full_loss_num)), end='')
-                self.mean_dist = np.mean(self.dist_list)
-                print(', mean_dist={0:.8f}'.format(self.mean_dist), end='')
-                print(', lr=%f' % self.optimizer.param_groups[0]['lr'], end='')
-                print(', hard or full=%r' % self.hard_or_full_trip)
-                sys.stdout.flush()
-                self.hard_loss_metric = []
-                self.full_loss_metric = []
-                self.full_loss_num = []
-                self.dist_list = []
-
-            # Visualization using t-SNE
-            # if self.restore_iter % 500 == 0:
-            #     pca = TSNE(2)
-            #     pca_feature = pca.fit_transform(feature.view(feature.size(0), -1).data.cpu().numpy())
-            #     for i in range(self.P):
-            #         plt.scatter(pca_feature[self.M * i:self.M * (i + 1), 0],
-            #                     pca_feature[self.M * i:self.M * (i + 1), 1], label=label[self.M * i])
-            #
-            #     plt.show()
-
-            if self.restore_iter == self.total_iter:
-                break
-
-    def ts2var(self, x):
-        return autograd.Variable(x).cuda()
-
-    def np2var(self, x):
-        return self.ts2var(torch.from_numpy(x))
-
-    def transform(self, flag, batch_size=1):
-        self.encoder.eval()
-        source = self.test_source if flag == 'test' else self.train_source
-        self.sample_type = 'all'
-        data_loader = tordata.DataLoader(
-            dataset=source,
-            batch_size=batch_size,
-            sampler=tordata.sampler.SequentialSampler(source),
-            collate_fn=self.collate_fn,
-            num_workers=self.num_workers)
-
-        feature_list = list()
-        view_list = list()
-        seq_type_list = list()
-        label_list = list()
-
-        for i, x in enumerate(data_loader):
-            seq, view, seq_type, label, batch_frame = x
-            for j in range(len(seq)):
-                seq[j] = self.np2var(seq[j]).float()
-            if batch_frame is not None:
-                batch_frame = self.np2var(batch_frame).int()
-            # print(batch_frame, np.sum(batch_frame))
-
-            feature, _ = self.encoder(*seq, batch_frame)
-            n, num_bin, _ = feature.size()
-            feature_list.append(feature.view(n, -1).data.cpu().numpy())
-            view_list += view
-            seq_type_list += seq_type
-            label_list += label
-
-        return np.concatenate(feature_list, 0), view_list, seq_type_list, label_list
-
-    def save(self):
-        os.makedirs(osp.join('checkpoint', self.model_name), exist_ok=True)
-        torch.save(self.encoder.state_dict(),
-                   osp.join('checkpoint', self.model_name,
-                            '{}-{:0>5}-encoder.ptm'.format(
-                                self.save_name, self.restore_iter)))
-        torch.save(self.optimizer.state_dict(),
-                   osp.join('checkpoint', self.model_name,
-                            '{}-{:0>5}-optimizer.ptm'.format(
-                                self.save_name, self.restore_iter)))
-
-    # restore_iter: iteration index of the checkpoint to load
-    def load(self, restore_iter):
-        self.encoder.load_state_dict(torch.load(osp.join(
-            'checkpoint', self.model_name,
-            '{}-{:0>5}-encoder.ptm'.format(self.save_name, restore_iter))))
-        self.optimizer.load_state_dict(torch.load(osp.join(
-            'checkpoint', self.model_name,
-            '{}-{:0>5}-optimizer.ptm'.format(self.save_name, restore_iter))))
--- a/gaitset/model/network/_init_.py
+++ b/gaitset/model/network/_init_.py
@ -1,2 +0,0 @@
-from .triplet import TripletLoss
-from .gaitset import SetNet
--- a/gaitset/model/network/basic_blocks.py
+++ b/gaitset/model/network/basic_blocks.py
@ -1,28 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-class BasicConv2d(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, **kwargs):
-        super(BasicConv2d, self).__init__()
-        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, bias=False, **kwargs)
-
-    def forward(self, x):
-        x = self.conv(x)
-        return F.leaky_relu(x, inplace=True)
-
-
-class SetBlock(nn.Module):
-    def __init__(self, forward_block, pooling=False):
-        super(SetBlock, self).__init__()
-        self.forward_block = forward_block
-        self.pooling = pooling
-        if pooling:
-            self.pool2d = nn.MaxPool2d(2)
-    def forward(self, x):
-        n, s, c, h, w = x.size()
-        x = self.forward_block(x.view(-1,c,h,w))
-        if self.pooling:
-            x = self.pool2d(x)
-        _, c, h, w = x.size()
-        return x.view(n, s, c, h ,w)
--- a/gaitset/model/network/gaitset.py
+++ b/gaitset/model/network/gaitset.py
@ -1,120 +0,0 @@
-import torch
-import torch.nn as nn
-import numpy as np
-
-from .basic_blocks import SetBlock, BasicConv2d
-
-
-class SetNet(nn.Module):
-    def __init__(self, hidden_dim):
-        super(SetNet, self).__init__()
-        self.hidden_dim = hidden_dim
-        self.batch_frame = None
-
-        _set_in_channels = 1
-        _set_channels = [32, 64, 128]
-        self.set_layer1 = SetBlock(BasicConv2d(_set_in_channels, _set_channels[0], 5, padding=2))
-        self.set_layer2 = SetBlock(BasicConv2d(_set_channels[0], _set_channels[0], 3, padding=1), True)
-        self.set_layer3 = SetBlock(BasicConv2d(_set_channels[0], _set_channels[1], 3, padding=1))
-        self.set_layer4 = SetBlock(BasicConv2d(_set_channels[1], _set_channels[1], 3, padding=1), True)
-        self.set_layer5 = SetBlock(BasicConv2d(_set_channels[1], _set_channels[2], 3, padding=1))
-        self.set_layer6 = SetBlock(BasicConv2d(_set_channels[2], _set_channels[2], 3, padding=1))
-
-        _gl_in_channels = 32
-        _gl_channels = [64, 128]
-        self.gl_layer1 = BasicConv2d(_gl_in_channels, _gl_channels[0], 3, padding=1)
-        self.gl_layer2 = BasicConv2d(_gl_channels[0], _gl_channels[0], 3, padding=1)
-        self.gl_layer3 = BasicConv2d(_gl_channels[0], _gl_channels[1], 3, padding=1)
-        self.gl_layer4 = BasicConv2d(_gl_channels[1], _gl_channels[1], 3, padding=1)
-        self.gl_pooling = nn.MaxPool2d(2)
-
-        self.bin_num = [1, 2, 4, 8, 16]
-        self.fc_bin = nn.ParameterList([
-            nn.Parameter(
-                nn.init.xavier_uniform_(
-                    torch.zeros(sum(self.bin_num) * 2, 128, hidden_dim)))])
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.Conv1d)):
-                nn.init.xavier_uniform_(m.weight.data)
-            elif isinstance(m, nn.Linear):
-                nn.init.xavier_uniform_(m.weight.data)
-                nn.init.constant(m.bias.data, 0.0)
-            elif isinstance(m, (nn.BatchNorm2d, nn.BatchNorm1d)):
-                nn.init.normal(m.weight.data, 1.0, 0.02)
-                nn.init.constant(m.bias.data, 0.0)
-
-    def frame_max(self, x):
-        if self.batch_frame is None:
-            return torch.max(x, 1)
-        else:
-            _tmp = [
-                torch.max(x[:, self.batch_frame[i]:self.batch_frame[i + 1], :, :, :], 1)
-                for i in range(len(self.batch_frame) - 1)
-                ]
-            max_list = torch.cat([_tmp[i][0] for i in range(len(_tmp))], 0)
-            arg_max_list = torch.cat([_tmp[i][1] for i in range(len(_tmp))], 0)
-            return max_list, arg_max_list
-
-    def frame_median(self, x):
-        if self.batch_frame is None:
-            return torch.median(x, 1)
-        else:
-            _tmp = [
-                torch.median(x[:, self.batch_frame[i]:self.batch_frame[i + 1], :, :, :], 1)
-                for i in range(len(self.batch_frame) - 1)
-                ]
-            median_list = torch.cat([_tmp[i][0] for i in range(len(_tmp))], 0)
-            arg_median_list = torch.cat([_tmp[i][1] for i in range(len(_tmp))], 0)
-            return median_list, arg_median_list
-
-    def forward(self, silho, batch_frame=None):
-        # n: batch_size, s: frame_num, k: keypoints_num, c: channel
-        if batch_frame is not None:
-            batch_frame = batch_frame[0].data.cpu().numpy().tolist()
-            _ = len(batch_frame)
-            for i in range(len(batch_frame)):
-                if batch_frame[-(i + 1)] != 0:
-                    break
-                else:
-                    _ -= 1
-            batch_frame = batch_frame[:_]
-            frame_sum = np.sum(batch_frame)
-            if frame_sum < silho.size(1):
-                silho = silho[:, :frame_sum, :, :]
-            self.batch_frame = [0] + np.cumsum(batch_frame).tolist()
-        n = silho.size(0)
-        x = silho.unsqueeze(2)
-        del silho
-
-        x = self.set_layer1(x)
-        x = self.set_layer2(x)
-        gl = self.gl_layer1(self.frame_max(x)[0])
-        gl = self.gl_layer2(gl)
-        gl = self.gl_pooling(gl)
-
-        x = self.set_layer3(x)
-        x = self.set_layer4(x)
-        gl = self.gl_layer3(gl + self.frame_max(x)[0])
-        gl = self.gl_layer4(gl)
-
-        x = self.set_layer5(x)
-        x = self.set_layer6(x)
-        x = self.frame_max(x)[0]
-        gl = gl + x
-
-        feature = list()
-        n, c, h, w = gl.size()
-        for num_bin in self.bin_num:
-            z = x.view(n, c, num_bin, -1)
-            z = z.mean(3) + z.max(3)[0]
-            feature.append(z)
-            z = gl.view(n, c, num_bin, -1)
-            z = z.mean(3) + z.max(3)[0]
-            feature.append(z)
-        feature = torch.cat(feature, 2).permute(2, 0, 1).contiguous()
-
-        feature = feature.matmul(self.fc_bin[0])
-        feature = feature.permute(1, 0, 2).contiguous()
-
-        return feature, None
--- a/gaitset/model/network/triplet.py
+++ b/gaitset/model/network/triplet.py
@ -1,45 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class TripletLoss(nn.Module):
-    def __init__(self, batch_size, hard_or_full, margin):
-        super(TripletLoss, self).__init__()
-        self.batch_size = batch_size
-        self.margin = margin
-
-    def forward(self, feature, label):
-        # feature: [n, m, d], label: [n, m]
-        n, m, d = feature.size()
-        hp_mask = (label.unsqueeze(1) == label.unsqueeze(2)).byte().view(-1)
-        hn_mask = (label.unsqueeze(1) != label.unsqueeze(2)).byte().view(-1)
-
-        dist = self.batch_dist(feature)
-        mean_dist = dist.mean(1).mean(1)
-        dist = dist.view(-1)
-        # hard
-        hard_hp_dist = torch.max(torch.masked_select(dist, hp_mask).view(n, m, -1), 2)[0]
-        hard_hn_dist = torch.min(torch.masked_select(dist, hn_mask).view(n, m, -1), 2)[0]
-        hard_loss_metric = F.relu(self.margin + hard_hp_dist - hard_hn_dist).view(n, -1)
-
-        hard_loss_metric_mean = torch.mean(hard_loss_metric, 1)
-
-        # non-zero full
-        full_hp_dist = torch.masked_select(dist, hp_mask).view(n, m, -1, 1)
-        full_hn_dist = torch.masked_select(dist, hn_mask).view(n, m, 1, -1)
-        full_loss_metric = F.relu(self.margin + full_hp_dist - full_hn_dist).view(n, -1)
-
-        full_loss_metric_sum = full_loss_metric.sum(1)
-        full_loss_num = (full_loss_metric != 0).sum(1).float()
-
-        full_loss_metric_mean = full_loss_metric_sum / full_loss_num
-        full_loss_metric_mean[full_loss_num == 0] = 0
-
-        return full_loss_metric_mean, hard_loss_metric_mean, mean_dist, full_loss_num
-
-    def batch_dist(self, x):
-        x2 = torch.sum(x ** 2, 2)
-        dist = x2.unsqueeze(2) + x2.unsqueeze(2).transpose(1, 2) - 2 * torch.matmul(x, x.transpose(1, 2))
-        dist = torch.sqrt(F.relu(dist))
-        return dist
--- a/gaitset/model/utils/_init_.py
+++ b/gaitset/model/utils/_init_.py
@ -1,4 +0,0 @@
-from .data_loader import load_data
-from .data_set import DataSet
-from .evaluator import evaluation
-from .sampler import TripletSampler
--- a/gaitset/model/utils/data_loader.py
+++ b/gaitset/model/utils/data_loader.py
@ -1,60 +0,0 @@
-import os
-import os.path as osp
-
-import numpy as np
-
-from .data_set import DataSet
-
-
-def load_data(dataset_path, resolution, dataset, pid_num, pid_shuffle, cache=True):
-    seq_dir = list()
-    view = list()
-    seq_type = list()
-    label = list()
-
-    for _label in sorted(list(os.listdir(dataset_path))):
-        # In CASIA-B, data of subject #5 is incomplete.
-        # Thus, we ignore it in training.
-        if dataset == 'CASIA-B' and _label == '005':
-            continue
-        label_path = osp.join(dataset_path, _label)
-        for _seq_type in sorted(list(os.listdir(label_path))):
-            seq_type_path = osp.join(label_path, _seq_type)
-            for _view in sorted(list(os.listdir(seq_type_path))):
-                _seq_dir = osp.join(seq_type_path, _view)
-                seqs = os.listdir(_seq_dir)
-                if len(seqs) > 0:
-                    seq_dir.append([_seq_dir])
-                    label.append(_label)
-                    seq_type.append(_seq_type)
-                    view.append(_view)
-
-    pid_fname = osp.join('partition', '{}_{}_{}.npy'.format(
-        dataset, pid_num, pid_shuffle))
-    if not osp.exists(pid_fname):
-        pid_list = sorted(list(set(label)))
-        if pid_shuffle:
-            np.random.shuffle(pid_list)
-        pid_list = [pid_list[0:pid_num], pid_list[pid_num:]]
-        os.makedirs('partition', exist_ok=True)
-        np.save(pid_fname, pid_list)
-
-    pid_list = np.load(pid_fname)
-    train_list = pid_list[0]
-    test_list = pid_list[1]
-    train_source = DataSet(
-        [seq_dir[i] for i, l in enumerate(label) if l in train_list],
-        [label[i] for i, l in enumerate(label) if l in train_list],
-        [seq_type[i] for i, l in enumerate(label) if l in train_list],
-        [view[i] for i, l in enumerate(label)
-         if l in train_list],
-        cache, resolution)
-    test_source = DataSet(
-        [seq_dir[i] for i, l in enumerate(label) if l in test_list],
-        [label[i] for i, l in enumerate(label) if l in test_list],
-        [seq_type[i] for i, l in enumerate(label) if l in test_list],
-        [view[i] for i, l in enumerate(label)
-         if l in test_list],
-        cache, resolution)
-
-    return train_source, test_source
--- a/gaitset/model/utils/data_set.py
+++ b/gaitset/model/utils/data_set.py
@ -1,90 +0,0 @@
-import torch.utils.data as tordata
-import numpy as np
-import os.path as osp
-import os
-import pickle
-import cv2
-import xarray as xr
-
-
-class DataSet(tordata.Dataset):
-    def __init__(self, seq_dir, label, seq_type, view, cache, resolution):
-        self.seq_dir = seq_dir
-        self.view = view
-        self.seq_type = seq_type
-        self.label = label
-        self.cache = cache
-        self.resolution = int(resolution)
-        self.cut_padding = int(float(resolution)/64*10)
-        self.data_size = len(self.label)
-        self.data = [None] * self.data_size
-        self.frame_set = [None] * self.data_size
-
-        self.label_set = set(self.label)
-        self.seq_type_set = set(self.seq_type)
-        self.view_set = set(self.view)
-        _ = np.zeros((len(self.label_set),
-                      len(self.seq_type_set),
-                      len(self.view_set))).astype('int')
-        _ -= 1
-        self.index_dict = xr.DataArray(
-            _,
-            coords={'label': sorted(list(self.label_set)),
-                    'seq_type': sorted(list(self.seq_type_set)),
-                    'view': sorted(list(self.view_set))},
-            dims=['label', 'seq_type', 'view'])
-
-        for i in range(self.data_size):
-            _label = self.label[i]
-            _seq_type = self.seq_type[i]
-            _view = self.view[i]
-            self.index_dict.loc[_label, _seq_type, _view] = i
-
-    def load_all_data(self):
-        for i in range(self.data_size):
-            self.load_data(i)
-
-    def load_data(self, index):
-        return self.__getitem__(index)
-
-    def __loader__(self, path):
-        return self.img2xarray(
-            path)[:, :, self.cut_padding:-self.cut_padding].astype(
-            'float32') / 255.0
-
-    def __getitem__(self, index):
-        # pose sequence sampling
-        if not self.cache:
-            data = [self.__loader__(_path) for _path in self.seq_dir[index]]
-            frame_set = [set(feature.coords['frame'].values.tolist()) for feature in data]
-            frame_set = list(set.intersection(*frame_set))
-        elif self.data[index] is None:
-            data = [self.__loader__(_path) for _path in self.seq_dir[index]]
-            frame_set = [set(feature.coords['frame'].values.tolist()) for feature in data]
-            frame_set = list(set.intersection(*frame_set))
-            self.data[index] = data
-            self.frame_set[index] = frame_set
-        else:
-            data = self.data[index]
-            frame_set = self.frame_set[index]
-
-        return data, frame_set, self.view[
-            index], self.seq_type[index], self.label[index],
-
-    def img2xarray(self, flie_path):
-        imgs = sorted(list(os.listdir(flie_path)))
-        frame_list = [np.reshape(
-            cv2.imread(osp.join(flie_path, _img_path)),
-            [self.resolution, self.resolution, -1])[:, :, 0]
-                      for _img_path in imgs
-                      if osp.isfile(osp.join(flie_path, _img_path))]
-        num_list = list(range(len(frame_list)))
-        data_dict = xr.DataArray(
-            frame_list,
-            coords={'frame': num_list},
-            dims=['frame', 'img_y', 'img_x'],
-        )
-        return data_dict
-
-    def __len__(self):
-        return len(self.label)
--- a/gaitset/model/utils/evaluator.py
+++ b/gaitset/model/utils/evaluator.py
@ -1,49 +0,0 @@
-import torch
-import torch.nn.functional as F
-import numpy as np
-
-
-def cuda_dist(x, y):
-    x = torch.from_numpy(x).cuda()
-    y = torch.from_numpy(y).cuda()
-    dist = torch.sum(x ** 2, 1).unsqueeze(1) + torch.sum(y ** 2, 1).unsqueeze(
-        1).transpose(0, 1) - 2 * torch.matmul(x, y.transpose(0, 1))
-    dist = torch.sqrt(F.relu(dist))
-    return dist
-
-
-def evaluation(data, config):
-    dataset = config['dataset'].split('-')[0]
-    feature, view, seq_type, label = data
-    label = np.array(label)
-    view_list = list(set(view))
-    view_list.sort()
-    view_num = len(view_list)
-    sample_num = len(feature)
-
-    probe_seq_dict = {'CASIA': [['nm-05', 'nm-06'], ['bg-01', 'bg-02'], ['cl-01', 'cl-02']],
-                      'OUMVLP': [['00']]}
-    gallery_seq_dict = {'CASIA': [['nm-01', 'nm-02', 'nm-03', 'nm-04']],
-                        'OUMVLP': [['01']]}
-
-    num_rank = 5
-    acc = np.zeros([len(probe_seq_dict[dataset]), view_num, view_num, num_rank])
-    for (p, probe_seq) in enumerate(probe_seq_dict[dataset]):
-        for gallery_seq in gallery_seq_dict[dataset]:
-            for (v1, probe_view) in enumerate(view_list):
-                for (v2, gallery_view) in enumerate(view_list):
-                    gseq_mask = np.isin(seq_type, gallery_seq) & np.isin(view, [gallery_view])
-                    gallery_x = feature[gseq_mask, :]
-                    gallery_y = label[gseq_mask]
-
-                    pseq_mask = np.isin(seq_type, probe_seq) & np.isin(view, [probe_view])
-                    probe_x = feature[pseq_mask, :]
-                    probe_y = label[pseq_mask]
-
-                    dist = cuda_dist(probe_x, gallery_x)
-                    idx = dist.sort(1)[1].cpu().numpy()
-                    acc[p, v1, v2, :] = np.round(
-                        np.sum(np.cumsum(np.reshape(probe_y, [-1, 1]) == gallery_y[idx[:, 0:num_rank]], 1) > 0,
-                               0) * 100 / dist.shape[0], 2)
-
-    return acc
--- a/gaitset/model/utils/sampler.py
+++ b/gaitset/model/utils/sampler.py
@ -1,26 +0,0 @@
-import torch.utils.data as tordata
-import random
-
-
-class TripletSampler(tordata.sampler.Sampler):
-    def __init__(self, dataset, batch_size):
-        self.dataset = dataset
-        self.batch_size = batch_size
-
-    def __iter__(self):
-        while (True):
-            sample_indices = list()
-            pid_list = random.sample(
-                list(self.dataset.label_set),
-                self.batch_size[0])
-            for pid in pid_list:
-                _index = self.dataset.index_dict.loc[pid, :, :].values
-                _index = _index[_index > 0].flatten().tolist()
-                _index = random.choices(
-                    _index,
-                    k=self.batch_size[1])
-                sample_indices += _index
-            yield sample_indices
-
-    def __len__(self):
-        return self.dataset.data_size
--- a/gaitset/pretreatment.py
+++ b/gaitset/pretreatment.py
@ -1,192 +0,0 @@
-
-import os
-from scipy import misc as scisc
-import cv2
-import numpy as np
-from warnings import warn
-from time import sleep
-import argparse
-
-from multiprocessing import Pool
-from multiprocessing import TimeoutError as MP_TimeoutError
-
-START = "START"
-FINISH = "FINISH"
-WARNING = "WARNING"
-FAIL = "FAIL"
-
-
-def boolean_string(s):
-    if s.upper() not in {'FALSE', 'TRUE'}:
-        raise ValueError('Not a valid boolean string')
-    return s.upper() == 'TRUE'
-
-
-parser = argparse.ArgumentParser(description='Test')
-parser.add_argument('--input_path', default='', type=str,
-                    help='Root path of raw dataset.')
-parser.add_argument('--output_path', default='', type=str,
-                    help='Root path for output.')
-parser.add_argument('--log_file', default='./pretreatment.log', type=str,
-                    help='Log file path. Default: ./pretreatment.log')
-parser.add_argument('--log', default=False, type=boolean_string,
-                    help='If set as True, all logs will be saved. '
-                         'Otherwise, only warnings and errors will be saved.'
-                         'Default: False')
-parser.add_argument('--worker_num', default=1, type=int,
-                    help='How many subprocesses to use for data pretreatment. '
-                         'Default: 1')
-opt = parser.parse_args()
-
-INPUT_PATH = opt.input_path
-OUTPUT_PATH = opt.output_path
-IF_LOG = opt.log
-LOG_PATH = opt.log_file
-WORKERS = opt.worker_num
-
-T_H = 64
-T_W = 64
-
-
-def log2str(pid, comment, logs):
-    str_log = ''
-    if type(logs) is str:
-        logs = [logs]
-    for log in logs:
-        str_log += "# JOB %d : --%s-- %s\n" % (
-            pid, comment, log)
-    return str_log
-
-
-def log_print(pid, comment, logs):
-    str_log = log2str(pid, comment, logs)
-    if comment in [WARNING, FAIL]:
-        with open(LOG_PATH, 'a') as log_f:
-            log_f.write(str_log)
-    if comment in [START, FINISH]:
-        if pid % 500 != 0:
-            return
-    print(str_log, end='')
-
-
-def cut_img(img, seq_info, frame_name, pid):
-    # A silhouette contains too little white pixels
-    # might be not valid for identification.
-    if img.sum() <= 10000:
-        message = 'seq:%s, frame:%s, no data, %d.' % (
-            '-'.join(seq_info), frame_name, img.sum())
-        warn(message)
-        log_print(pid, WARNING, message)
-        return None
-    # Get the top and bottom point
-    y = img.sum(axis=1)
-    y_top = (y != 0).argmax(axis=0)
-    y_btm = (y != 0).cumsum(axis=0).argmax(axis=0)
-    img = img[y_top:y_btm + 1, :]
-    # As the height of a person is larger than the width,
-    # use the height to calculate resize ratio.
-    _r = img.shape[1] / img.shape[0]
-    _t_w = int(T_H * _r)
-    img = cv2.resize(img, (_t_w, T_H), interpolation=cv2.INTER_CUBIC)
-    # Get the median of x axis and regard it as the x center of the person.
-    sum_point = img.sum()
-    sum_column = img.sum(axis=0).cumsum()
-    x_center = -1
-    for i in range(sum_column.size):
-        if sum_column[i] > sum_point / 2:
-            x_center = i
-            break
-    if x_center < 0:
-        message = 'seq:%s, frame:%s, no center.' % (
-            '-'.join(seq_info), frame_name)
-        warn(message)
-        log_print(pid, WARNING, message)
-        return None
-    h_T_W = int(T_W / 2)
-    left = x_center - h_T_W
-    right = x_center + h_T_W
-    if left <= 0 or right >= img.shape[1]:
-        left += h_T_W
-        right += h_T_W
-        _ = np.zeros((img.shape[0], h_T_W))
-        img = np.concatenate([_, img, _], axis=1)
-    img = img[:, left:right]
-    return img.astype('uint8')
-
-
-def cut_pickle(seq_info, pid):
-    seq_name = '-'.join(seq_info)
-    log_print(pid, START, seq_name)
-    seq_path = os.path.join(INPUT_PATH, *seq_info)
-    out_dir = os.path.join(OUTPUT_PATH, *seq_info)
-    frame_list = os.listdir(seq_path)
-    frame_list.sort()
-    count_frame = 0
-    for _frame_name in frame_list:
-        frame_path = os.path.join(seq_path, _frame_name)
-        img = cv2.imread(frame_path)[:, :, 0]
-        img = cut_img(img, seq_info, _frame_name, pid)
-        if img is not None:
-            # Save the cut img
-            save_path = os.path.join(out_dir, _frame_name)
-            scisc.imsave(save_path, img)
-            count_frame += 1
-    # Warn if the sequence contains less than 5 frames
-    if count_frame < 5:
-        message = 'seq:%s, less than 5 valid data.' % (
-            '-'.join(seq_info))
-        warn(message)
-        log_print(pid, WARNING, message)
-
-    log_print(pid, FINISH,
-              'Contain %d valid frames. Saved to %s.'
-              % (count_frame, out_dir))
-
-
-pool = Pool(WORKERS)
-results = list()
-pid = 0
-
-print('Pretreatment Start.\n'
-      'Input path: %s\n'
-      'Output path: %s\n'
-      'Log file: %s\n'
-      'Worker num: %d' % (
-          INPUT_PATH, OUTPUT_PATH, LOG_PATH, WORKERS))
-
-id_list = os.listdir(INPUT_PATH)
-id_list.sort()
-# Walk the input path
-for _id in id_list:
-    seq_type = os.listdir(os.path.join(INPUT_PATH, _id))
-    seq_type.sort()
-    for _seq_type in seq_type:
-        view = os.listdir(os.path.join(INPUT_PATH, _id, _seq_type))
-        view.sort()
-        for _view in view:
-            seq_info = [_id, _seq_type, _view]
-            out_dir = os.path.join(OUTPUT_PATH, *seq_info)
-            os.makedirs(out_dir)
-            results.append(
-                pool.apply_async(
-                    cut_pickle,
-                    args=(seq_info, pid)))
-            sleep(0.02)
-            pid += 1
-
-pool.close()
-unfinish = 1
-while unfinish > 0:
-    unfinish = 0
-    for i, res in enumerate(results):
-        try:
-            res.get(timeout=0.1)
-        except Exception as e:
-            if type(e) == MP_TimeoutError:
-                unfinish += 1
-                continue
-            else:
-                print('\n\n\nERROR OCCUR: PID ##%d##, ERRORTYPE: %s\n\n\n',
-                      i, type(e))
-                raise e
-pool.join()
--- a/gaitset/test.py
+++ b/gaitset/test.py
@ -1,80 +0,0 @@
-from datetime import datetime
-import numpy as np
-import argparse
-
-from model.initialization import initialization
-from model.utils import evaluation
-from config import conf
-
-
-def boolean_string(s):
-    if s.upper() not in {'FALSE', 'TRUE'}:
-        raise ValueError('Not a valid boolean string')
-    return s.upper() == 'TRUE'
-
-
-parser = argparse.ArgumentParser(description='Test')
-parser.add_argument('--iter', default='80000', type=int,
-                    help='iter: iteration of the checkpoint to load. Default: 80000')
-parser.add_argument('--batch_size', default='1', type=int,
-                    help='batch_size: batch size for parallel test. Default: 1')
-parser.add_argument('--cache', default=False, type=boolean_string,
-                    help='cache: if set as TRUE all the test data will be loaded at once'
-                         ' before the transforming start. Default: FALSE')
-opt = parser.parse_args()
-
-
-# Exclude identical-view cases
-def de_diag(acc, each_angle=False):
-    result = np.sum(acc - np.diag(np.diag(acc)), 1) / 10.0
-    if not each_angle:
-        result = np.mean(result)
-    return result
-
-
-m = initialization(conf, test=opt.cache)[0]
-
-# load model checkpoint of iteration opt.iter
-print('Loading the model of iteration %d...' % opt.iter)
-m.load(opt.iter)
-print('Transforming...')
-time = datetime.now()
-test = m.transform('test', opt.batch_size)
-print('Evaluating...')
-acc = evaluation(test, conf['data'])
-print('Evaluation complete. Cost:', datetime.now() - time)
-
-# Print rank-1 accuracy of the best model
-# e.g.
-# ===Rank-1 (Include identical-view cases)===
-# NM: 95.405,     BG: 88.284,     CL: 72.041
-for i in range(1):
-    print('===Rank-%d (Include identical-view cases)===' % (i + 1))
-    print('NM: %.3f,\tBG: %.3f,\tCL: %.3f' % (
-        np.mean(acc[0, :, :, i]),
-        np.mean(acc[1, :, :, i]),
-        np.mean(acc[2, :, :, i])))
-
-# Print rank-1 accuracy of the best model，excluding identical-view cases
-# e.g.
-# ===Rank-1 (Exclude identical-view cases)===
-# NM: 94.964,     BG: 87.239,     CL: 70.355
-for i in range(1):
-    print('===Rank-%d (Exclude identical-view cases)===' % (i + 1))
-    print('NM: %.3f,\tBG: %.3f,\tCL: %.3f' % (
-        de_diag(acc[0, :, :, i]),
-        de_diag(acc[1, :, :, i]),
-        de_diag(acc[2, :, :, i])))
-
-# Print rank-1 accuracy of the best model (Each Angle)
-# e.g.
-# ===Rank-1 of each angle (Exclude identical-view cases)===
-# NM: [90.80 97.90 99.40 96.90 93.60 91.70 95.00 97.80 98.90 96.80 85.80]
-# BG: [83.80 91.20 91.80 88.79 83.30 81.00 84.10 90.00 92.20 94.45 79.00]
-# CL: [61.40 75.40 80.70 77.30 72.10 70.10 71.50 73.50 73.50 68.40 50.00]
-np.set_printoptions(precision=2, floatmode='fixed')
-for i in range(1):
-    print('===Rank-%d of each angle (Exclude identical-view cases)===' % (i + 1))
-    print('NM:', de_diag(acc[0, :, :, i], True))
-    print('BG:', de_diag(acc[1, :, :, i], True))
-    print('CL:', de_diag(acc[2, :, :, i], True))
--- a/gaitset/train.py
+++ b/gaitset/train.py
@ -1,22 +0,0 @@
-from model.initialization import initialization
-from config import conf
-import argparse
-
-
-def boolean_string(s):
-    if s.upper() not in {'FALSE', 'TRUE'}:
-        raise ValueError('Not a valid boolean string')
-    return s.upper() == 'TRUE'
-
-
-parser = argparse.ArgumentParser(description='Train')
-parser.add_argument('--cache', default=True, type=boolean_string,
-                    help='cache: if set as TRUE all the training data will be loaded at once'
-                         ' before the training start. Default: TRUE')
-opt = parser.parse_args()
-
-m = initialization(conf, train=opt.cache)[0]
-
-print("Training START")
-m.fit()
-print("Training COMPLETE")
--- a/gaitset/work/basic_blocks.py
+++ b/gaitset/work/basic_blocks.py
@ -1,49 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-class BasicConv2d(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, **kwargs):
-        super(BasicConv2d, self).__init__()
-        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, bias=False, **kwargs)
-
-    def forward(self, x):
-        x = self.conv(x)
-        return F.leaky_relu(x, inplace=True)
-
-
-class SetBlock(nn.Module):
-    def __init__(self, forward_block, pooling=False):
-        super(SetBlock, self).__init__()
-        self.forward_block = forward_block
-        self.pooling = pooling
-        if pooling:
-            self.pool2d = nn.MaxPool2d(2)
-    def forward(self, x):
-        n, s, c, h, w = x.size()
-        x = self.forward_block(x.view(-1,c,h,w))
-        if self.pooling:
-            x = self.pool2d(x)
-        _, c, h, w = x.size()
-        return x.view(n, s, c, h ,w)
-
-
-class HPM(nn.Module):
-    def __init__(self, in_dim, out_dim, bin_level_num=5):
-        super(HPM, self).__init__()
-        self.bin_num = [2**i for i in range(bin_level_num)]
-        self.fc_bin = nn.ParameterList([
-            nn.Parameter(
-                nn.init.xavier_uniform(
-                    torch.zeros(sum(self.bin_num), in_dim, out_dim)))])
-    def forward(self, x):
-        feature = list()
-        n, c, h, w = x.size()
-        for num_bin in self.bin_num:
-            z = x.view(n, c, num_bin, -1)
-            z = z.mean(3)+z.max(3)[0]
-            feature.append(z)
-        feature = torch.cat(feature, 2).permute(2, 0, 1).contiguous()
-        
-        feature = feature.matmul(self.fc_bin[0])
-        return feature.permute(1, 0, 2).contiguous()
--- a/gaitset/work/gaitset.py
+++ b/gaitset/work/gaitset.py
@ -1,88 +0,0 @@
-class SetNet(nn.Module):
-    def __init__(self, hidden_dim):
-        super(SetNet, self).__init__()
-        self.hidden_dim = hidden_dim
-        self.batch_frame = None
-        
-        _in_channels = 1
-        _channels = [64,128,256]
-        self.set_layer1 = SetBlock(BasicConv2d(_in_channels, _channels[0], 5, padding=2))
-        self.set_layer2 = SetBlock(BasicConv2d(_channels[0], _channels[0], 3, padding=1), True)
-        self.set_layer3 = SetBlock(BasicConv2d(_channels[0], _channels[1], 3, padding=1))
-        self.set_layer4 = SetBlock(BasicConv2d(_channels[1], _channels[1], 3, padding=1), True)
-        self.set_layer5 = SetBlock(BasicConv2d(_channels[1], _channels[2], 3, padding=1))
-        self.set_layer6 = SetBlock(BasicConv2d(_channels[2], _channels[2], 3, padding=1))
-        
-        self.gl_layer1 = BasicConv2d(_channels[0], _channels[1], 3, padding=1)
-        self.gl_layer2 = BasicConv2d(_channels[1], _channels[1], 3, padding=1)
-        self.gl_layer3 = BasicConv2d(_channels[1], _channels[2], 3, padding=1)
-        self.gl_layer4 = BasicConv2d(_channels[2], _channels[2], 3, padding=1)
-        self.gl_pooling = nn.MaxPool2d(2)
-        
-        self.gl_hpm = HPM(_channels[-1], hidden_dim)
-        self.x_hpm = HPM(_channels[-1], hidden_dim)
-        
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.Conv1d)):
-                nn.init.xavier_uniform(m.weight.data)
-            elif isinstance(m, nn.Linear):
-                nn.init.xavier_uniform(m.weight.data)
-                nn.init.constant(m.bias.data, 0.0)
-            elif isinstance(m, (nn.BatchNorm2d, nn.BatchNorm1d)):
-                nn.init.normal(m.weight.data, 1.0, 0.02)
-                nn.init.constant(m.bias.data, 0.0)
-                    
-
-    def frame_max(self, x):
-        if self.batch_frame is None:
-            return torch.max(x, 1)
-        else:
-            _tmp = [
-                torch.max(x[:, self.batch_frame[i]:self.batch_frame[i+1], :, :, :], 1)
-                for i in range(len(self.batch_frame)-1)
-            ]
-            max_list = torch.cat([_tmp[i][0] for i in range(len(_tmp))], 0)
-            arg_max_list = torch.cat([_tmp[i][1] for i in range(len(_tmp))], 0)
-            return max_list, arg_max_list
-
-                
-    def forward(self, silho, batch_frame=None):
-        silho = silho/255
-        # n: batch_size, s: frame_num, k: keypoints_num, c: channel
-        if batch_frame is not None:
-            batch_frame = batch_frame[0].data.cpu().numpy().tolist()
-            _ = len(batch_frame)
-            for i in range(len(batch_frame)):
-                if batch_frame[-(i+1)] != 0:
-                    break
-                else:
-                    _ -= 1
-            batch_frame = batch_frame[:_]
-            frame_sum = np.sum(batch_frame)
-            if frame_sum < silho.size(1):
-                silho = silho[:, :frame_sum,:,:]
-            self.batch_frame = [0]+np.cumsum(batch_frame).tolist()
-        n = silho.size(0)
-        x = silho.unsqueeze(2)
-        del silho
-        
-        x = self.set_layer1(x)
-        x = self.set_layer2(x)
-        gl = self.gl_layer1(self.frame_max(x)[0])
-        gl = self.gl_layer2(gl)
-        gl = self.gl_pooling(gl)
-        
-        x = self.set_layer3(x)
-        x = self.set_layer4(x)
-        gl = self.gl_layer3(gl+self.frame_max(x)[0])
-        gl = self.gl_layer4(gl)
-        
-        x = self.set_layer5(x)
-        x = self.set_layer6(x)
-        x = self.frame_max(x)[0]
-        gl = gl+x
-        
-        gl_f = self.gl_hpm(gl)
-        x_f = self.x_hpm(x)
-        
-        return torch.cat([gl_f, x_f], 1), None
--- a/helpers.py
+++ b/helpers.py
@ -10,7 +10,6 @@ def pop_and_add(l, val, max_length):
        l.pop(0)
    l.append(val)

-
 def last_ip(ips):
    for i, ip in enumerate(reversed(ips)):
        if ip is not None:
@ -71,5 +70,4 @@ def get_hist(img, bbox, nbins=3):
    hist = cv2.calcHist([img], [0, 1], mask, [nbins, 2*nbins], [0, 180, 0, 256])
    cv2.normalize(hist, hist, alpha=1, norm_type=cv2.NORM_L1)

-
    return hist
--- a/test.py
+++ b/test.py
@ -1,80 +0,0 @@
-from datetime import datetime
-import numpy as np
-import argparse
-
-from model.initialization import initialization
-from model.utils import evaluation
-from config import conf
-
-
-def boolean_string(s):
-    if s.upper() not in {'FALSE', 'TRUE'}:
-        raise ValueError('Not a valid boolean string')
-    return s.upper() == 'TRUE'
-
-
-parser = argparse.ArgumentParser(description='Test')
-parser.add_argument('--iter', default='80000', type=int,
-                    help='iter: iteration of the checkpoint to load. Default: 80000')
-parser.add_argument('--batch_size', default='1', type=int,
-                    help='batch_size: batch size for parallel test. Default: 1')
-parser.add_argument('--cache', default=False, type=boolean_string,
-                    help='cache: if set as TRUE all the test data will be loaded at once'
-                         ' before the transforming start. Default: FALSE')
-opt = parser.parse_args()
-
-
-# Exclude identical-view cases
-def de_diag(acc, each_angle=False):
-    result = np.sum(acc - np.diag(np.diag(acc)), 1) / 10.0
-    if not each_angle:
-        result = np.mean(result)
-    return result
-
-
-m = initialization(conf, test=opt.cache)[0]
-
-# load model checkpoint of iteration opt.iter
-print('Loading the model of iteration %d...' % opt.iter)
-m.load(opt.iter)
-print('Transforming...')
-time = datetime.now()
-test = m.transform('test', opt.batch_size)
-print('Evaluating...')
-acc = evaluation(test, conf['data'])
-print('Evaluation complete. Cost:', datetime.now() - time)
-
-# Print rank-1 accuracy of the best model
-# e.g.
-# ===Rank-1 (Include identical-view cases)===
-# NM: 95.405,     BG: 88.284,     CL: 72.041
-for i in range(1):
-    print('===Rank-%d (Include identical-view cases)===' % (i + 1))
-    print('NM: %.3f,\tBG: %.3f,\tCL: %.3f' % (
-        np.mean(acc[0, :, :, i]),
-        np.mean(acc[1, :, :, i]),
-        np.mean(acc[2, :, :, i])))
-
-# Print rank-1 accuracy of the best model，excluding identical-view cases
-# e.g.
-# ===Rank-1 (Exclude identical-view cases)===
-# NM: 94.964,     BG: 87.239,     CL: 70.355
-for i in range(1):
-    print('===Rank-%d (Exclude identical-view cases)===' % (i + 1))
-    print('NM: %.3f,\tBG: %.3f,\tCL: %.3f' % (
-        de_diag(acc[0, :, :, i]),
-        de_diag(acc[1, :, :, i]),
-        de_diag(acc[2, :, :, i])))
-
-# Print rank-1 accuracy of the best model (Each Angle)
-# e.g.
-# ===Rank-1 of each angle (Exclude identical-view cases)===
-# NM: [90.80 97.90 99.40 96.90 93.60 91.70 95.00 97.80 98.90 96.80 85.80]
-# BG: [83.80 91.20 91.80 88.79 83.30 81.00 84.10 90.00 92.20 94.45 79.00]
-# CL: [61.40 75.40 80.70 77.30 72.10 70.10 71.50 73.50 73.50 68.40 50.00]
-np.set_printoptions(precision=2, floatmode='fixed')
-for i in range(1):
-    print('===Rank-%d of each angle (Exclude identical-view cases)===' % (i + 1))
-    print('NM:', de_diag(acc[0, :, :, i], True))
-    print('BG:', de_diag(acc[1, :, :, i], True))
-    print('CL:', de_diag(acc[2, :, :, i], True))
--- a/train.py
+++ b/train.py
@ -1,22 +0,0 @@
-from model.initialization import initialization
-from config import conf
-import argparse
-
-
-def boolean_string(s):
-    if s.upper() not in {'FALSE', 'TRUE'}:
-        raise ValueError('Not a valid boolean string')
-    return s.upper() == 'TRUE'
-
-
-parser = argparse.ArgumentParser(description='Train')
-parser.add_argument('--cache', default=True, type=boolean_string,
-                    help='cache: if set as TRUE all the training data will be loaded at once'
-                         ' before the training start. Default: TRUE')
-opt = parser.parse_args()
-
-m = initialization(conf, train=opt.cache)[0]
-
-print("Training START")
-m.fit()
-print("Training COMPLETE")
Author	SHA1	Message	Date
王洋	4c6839c77c	modify the document	2021-11-20 23:10:21 +08:00
王洋	7588e4157a	modify the document	2021-11-20 23:05:15 +08:00
王洋	21f282f3d5	modify the document	2021-11-20 22:49:38 +08:00
王洋	b238c81513	modify the document	2021-11-20 22:47:20 +08:00
王洋	e4ad2a71f1	modify the document	2021-11-20 22:43:27 +08:00
王洋	9910d1b831	modify the document	2021-11-20 22:41:09 +08:00
pkwhiuqat	b59db8c572	Update algorithms.py	2021-11-20 21:42:40 +08:00
pkwhiuqat	b5de475d96	Update helpers.py	2021-11-20 21:40:45 +08:00
王洋	4f61205050	add documents	2021-11-20 20:23:39 +08:00
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