configs

Configuration

• Configuration
  • Experiment Name Explanation
  • Configuration Explanation
    • Asset Path Configuration
    • Network Structure Configuration
    • Experiment Configuration
    • Default Configuration

Experiment Name Explanation

Taking 1222_PELearn_Diff_Latent1_MEncDec49_MdiffEnc49_bs64_clip_uncond75_01 as an example:

• 1222: Eperiment date, for managing experiments
• PELearn: Ablation study abbreviations, here PELearn means we use learnable positional embedding for ablation study
• Diff: Stage flag, indicating whether it is the vae phase or the diffusion phase, VAE stands for the former and Diff stands for the latter
• Latent1: Latent size, here Latent1 indicates the latent vector shape is (1,256)
• MEncDec49: Numbers of head and layer of transformer-based Motion encoder & decoder
• MdiffEnc49: Numbers of head and layer of transformer-based Diffusion denoiser
• bs64: Batch size
• clip: Text encoder type, clip indicates here we use pretrained clip model as our text encoder
• uncond75_01: Classifier-free guidence parameter, here uncond75_01 indicates the classifier-free guidence scale is 7.5 and probability is 0.1

Configuration Explanation

We use yaml files for configuration. For training & evaluation, our whole configurations are combined of 4 parts.

Asset Path Configuration

Network Structure Configuration

Experiment Configuration

Default Configuration

Asset Path Configuration

The assest configuration defines the file path of resources like datasets, dependence and so on.

By default, the program will use the configs/asssets.yaml as asset configuration. You can either directly replace the file path in configs/asssets.yaml with yours or create a new yaml file refer to the annotations in configs/asssets.yaml and then in the cli line command add --cfg_asset to specify your own yaml file.

Network Structure Configuration

The network structure configuration defines the network structure settings. Our model ara mainly combined of four parts: Motion VAE, Text Encoder, Diffusion Denoiser, Diffusion Scheduler.

In addition, we use the evaluators from previous work for fair comparision with other motion generation work, so we also need evluators network for evaluation.

In conclusion, our network mainly combines the five components below.

1. Motion VAE
2. Text Encoder
3. Diffusion Denoiser
4. Diffusion Scheduler
5. Evaluators

By default, the program will use the yaml files in configs/modules folder as the configuration for each part of network. If you want to change the configurations of some part of network, you have two methods:

1. Directly modify the traget to use different structure and params to modify the parameters.
2. Create a new set of module configurations, you can create 5 new yaml files by refering the annotations in configs/modules files in subfoler of configs. Then specify the your modules folder name model.target in Experiment Configuration. Take config_novae_humanml3d.yaml as an example, we specify the model.target=modules_novae which means the experiment model will use the configuration files in configs/modules_novae.

Experiment Configuration

The experiment configuration defines the settings apart from network structure like dataset settings, training settings, evaluation settings and so on.

For more details of the configuration, you can refer to the annotations in config_mld_humanml3d.yaml

Default Configuration

The default configuration defines the default settings and will be overwritten by the configurations above.

By default, the program will use the yaml file configs/base.yaml folder as the basic configuration.