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# Installation
<!-- TOC -->
- [Requirements](#requirements)
- [Prepare environment](#prepare-environment)
- [Data Preparation](#data-preparation)
<!-- TOC -->
## Requirements
- Linux
- Python 3.7+
- PyTorch 1.6.0, 1.7.0, 1.7.1, 1.8.0, 1.8.1, 1.9.0 or 1.9.1.
- CUDA 9.2+
- GCC 5+
- [MMCV](https://github.com/open-mmlab/mmcv) (Please install mmcv-full>=1.3.17,<1.6.0 for GPU)
## Prepare environment
a. Create a conda virtual environment and activate it.
```shell
conda create -n motiondiffuse python=3.7 -y
conda activate motiondiffuse
```
b. Install PyTorch and torchvision following the [official instructions](https://pytorch.org/).
```shell
conda install pytorch={torch_version} torchvision cudatoolkit={cu_version} -c pytorch
```
E.g., install PyTorch 1.7.1 & CUDA 10.1.
```shell
conda install pytorch=1.7.1 torchvision cudatoolkit=10.1 -c pytorch
```
**Important:** Make sure that your compilation CUDA version and runtime CUDA version match.
c. Build mmcv-full
- mmcv-full
We recommend you to install the pre-build package as below.
For CPU:
```shell
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/{torch_version}/index.html
```
Please replace `{torch_version}` in the url to your desired one.
For GPU:
```shell
pip install "mmcv-full>=1.3.17,<=1.5.3" -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
```
Please replace `{cu_version}` and `{torch_version}` in the url to your desired one.
For example, to install mmcv-full with CUDA 10.1 and PyTorch 1.7.1, use the following command:
```shell
pip install "mmcv-full>=1.3.17,<=1.5.3" -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.7.1/index.html
```
See [here](https://mmcv.readthedocs.io/en/latest/get_started/installation.html) for different versions of MMCV compatible to different PyTorch and CUDA versions.
For more version download link, refer to [openmmlab-download](https://download.openmmlab.com/mmcv/dist/index.html).
d. Install other requirements
```shell
pip install -r requirements.txt
```
## Data Preparation
a. Download datasets
For both the HumanML3D dataset and the KIT-ML dataset, you could find the details as well as download link [[here]](https://github.com/EricGuo5513/HumanML3D).
b. Download pretrained weights for evaluation
We use the same evaluation protocol as [this repo](https://github.com/EricGuo5513/text-to-motion). You should download pretrained weights of the contrastive models in [t2m](https://drive.google.com/file/d/1DSaKqWX2HlwBtVH5l7DdW96jeYUIXsOP/view) and [kit](https://drive.google.com/file/d/1tX79xk0fflp07EZ660Xz1RAFE33iEyJR/view) for calculating FID and precisions. To dynamically estimate the length of the target motion, `length_est_bigru` and [Glove data](https://drive.google.com/drive/folders/1qxHtwffhfI4qMwptNW6KJEDuT6bduqO7?usp=sharing) are required.
c. Download pretrained weights for **MotionDiffuse**
The pretrained weights for our proposed MotionDiffuse can be downloaded from [here](https://drive.google.com/drive/folders/1qxHtwffhfI4qMwptNW6KJEDuT6bduqO7?usp=sharing)
Download the above resources and arrange them in the following file structure:
```text
MotionDiffuse
└── text2motion
├── checkpoints
│ ├── kit
│ │ └── kit_motiondiffuse
│ │ ├── meta
│ │ │ ├── mean.npy
│ │ │ └── std.npy
│ │ ├── model
│ │ │ └── latest.tar
│ │ └── opt.txt
│ └── t2m
│ └── t2m_motiondiffuse
│ ├── meta
│ │ ├── mean.npy
│ │ └── std.npy
│ ├── model
│ │ └── latest.tar
│ └── opt.txt
└── data
├── glove
│ ├── our_vab_data.npy
│ ├── our_vab_idx.pkl
│ └── out_vab_words.pkl
├── pretrained_models
│ ├── kit
│ │ └── text_mot_match
│ │ └── model
│ │ └── finest.tar
│ └── t2m
│ │ ├── text_mot_match
│ │ │ └── model
│ │ │ └── finest.tar
│ │ └── length_est_bigru
│ │ └── model
│ │ └── finest.tar
├── HumanML3D
│ ├── new_joint_vecs
│ │ └── ...
│ ├── new_joints
│ │ └── ...
│ ├── texts
│ │ └── ...
│ ├── Mean.npy
│ ├── Std.npy
│ ├── test.txt
│ ├── train_val.txt
│ ├── train.txt
│ └── val.txt
└── KIT-ML
├── new_joint_vecs
│ └── ...
├── new_joints
│ └── ...
├── texts
│ └── ...
├── Mean.npy
├── Std.npy
├── test.txt
├── train_val.txt
├── train.txt
└── val.txt
``` |