change work_dirs

configs/recognition/st_gcn_aaai18/kinetics-skeleton/train.yaml

@@ -5,6 +5,7 @@ argparse_cfg:
   work_dir:
     bind_to: processor_cfg.work_dir
     help: the dir to save logs and models
+    default: ./work_dir/recognition/st_gcn_aaai18/kinetics-skeleton
   batch_size:
     bind_to: processor_cfg.batch_size
   resume_from:
@@ -56,7 +57,7 @@ processor_cfg:
 
   # runtime setting
   workflow: [['train', 5], ['val', 1]]
-  work_dir: ./work_dir/recognition/st_gcn/kinetics-skeleton
+  work_dir: 
   log_level: 0
   total_epochs: 50
   training_hooks:

configs/recognition/st_gcn_aaai18/ntu-rgbd-xsub/train.yaml

@@ -5,6 +5,7 @@ argparse_cfg:
   work_dir:
     bind_to: processor_cfg.work_dir
     help: the dir to save logs and models
+    default: ./work_dir/recognition/st_gcn_aaai18/ntu-rgbd-xsub
   batch_size:
     bind_to: processor_cfg.batch_size
   resume_from:
@@ -54,7 +55,7 @@ processor_cfg:
 
   # runtime setting
   workflow: [['train', 5], ['val', 1]]
-  work_dir: ./work_dir/recognition/st_gcn/ntu-rgbd-xsub
+  work_dir: 
   log_level: 0
   total_epochs: 80
   training_hooks:

configs/recognition/st_gcn_aaai18/ntu-rgbd-xview/train.yaml

@@ -5,6 +5,7 @@ argparse_cfg:
   work_dir:
     bind_to: processor_cfg.work_dir
     help: the dir to save logs and models
+    default: ./work_dir/recognition/st_gcn_aaai18/ntu-rgbd-xview
   batch_size:
     bind_to: processor_cfg.batch_size
   resume_from:
@@ -54,7 +55,7 @@ processor_cfg:
 
   # runtime setting
   workflow: [['train', 5], ['val', 1]]
-  work_dir: ./work_dir/recognition/st_gcn/ntu-rgbd-xview
+  work_dir: 
   log_level: 0
   total_epochs: 80
   training_hooks:

doc/MMSKL_EXTENSIONS.md

@@ -0,0 +1,62 @@
+## Start Action Recognition Using ST-GCN
+
+This repository holds the codebase for the paper:
+
+**Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition** Sijie Yan, Yuanjun Xiong and Dahua Lin, AAAI 2018. [[Arxiv Preprint]](https://arxiv.org/abs/1801.07455)
+
+<div align="center">
+    <img src="../demo/recognition/pipeline.png">
+</div>
+
+
+### Data Preparation
+
+We experimented on two skeleton-based action recognition datasts: **Kinetics-skeleton** and **NTU RGB+D**.
+Before training and testing, for the convenience of fast data loading,
+the datasets should be converted to the proper format.
+Please download the pre-processed data from
+[GoogleDrive](https://drive.google.com/open?id=103NOL9YYZSW1hLoWmYnv5Fs8mK-Ij7qb)
+and extract files with
+```
+cd st-gcn
+unzip <path to st-gcn-processed-data.zip>
+```
+
+If you want to process data by yourself, please refer to [SKELETON_DATA.md](./SKELETON_DATA.md) for more details.
+
+### Evaluate Pretrained Models
+
+The evaluation of pre-trained models on three datasets can be achieved by:
+
+``` shell
+mmskl configs/recognition/st_gcn_aaai18/$DATASET/test.yaml
+```
+where the `$DATASET` must be `ntu-rgbd-xsub`, `ntu-rgbd-xview` or `kinetics-skeleton`.
+Models will be downloaded automatically before testing.
+The expected accuracies are shown here:
+
+| Dataset                 | Top-1 Accuracy (%) | Top-5 Accuracy (%) |                                                      Download                                                      |
+|:------------------------|:------------------:|:------------------:|:------------------------------------------------------------------------------------------------------------------:|
+| Kinetics-skeleton       |       31.60        |       53.68        | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmskeleton/models/st-gcn/st_gcn.kinetics-6fa43f73.pth)  |
+| NTU RGB+D Cross View    |       88.76        |       98.83        | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmskeleton/models/st-gcn/st_gcn.ntu-xview-9ba67746.pth) |
+| NTU RGB+D Cross Subject |       81.57        |       96.85        | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmskeleton/models/st-gcn/st_gcn.ntu-xsub-300b57d4.pth)  |
+
+
+### Training
+
+To train a ST-GCN model, run
+
+``` shell
+mmskl configs/recognition/st_gcn_aaai18/$DATASET/train.yaml [optional arguments]
+```
+
+The usage of optional arguments can be checked via adding `--help` argument.
+All outputs (log files and ) will be saved to the default working directory.
+That can be changed by modifying the configuration file
+or adding a optional argument `--work_dir $WORKING_DIRECTORY` in the command line.
+
+After that, evaluate your models by:
+
+``` shell
+mmskl configs/recognition/st_gcn_aaai18/$DATASET/test.yaml --checkpoint $CHECKPOINT_FILE
+```