步态识别-基于Pytorch实现的灵活+有效+快速的跨视角步态识别算法-附项目源码-优质项目实战.zip

# GaitSet #### 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).