training_details.md

Train TimeGen Framework

Use main.py for model training, inference.py for model inference and visualize.py for domain prompt visualization.

The detailed descriptions about command line arguments are as follows:

Parameter Name	Description
base (-b)	Paths to base configuration files.
train (-t)	Boolean flag to enable training. (default: true)
debug (-d)	Boolean flag to enter debug mode. (default: false)
seed (-s)	Seed for initializing random number generators. (default: 23)
logdir (-l)	Directory for logging data. (default: ./logs)
seq_len (-sl)	Sequence length for the model. (default: 24)
uncond (-uc)	Boolean flag for unconditional generation.
use_pam (-up)	Boolean flag to use the prototype assignment module.
batch_size (-bs)	Batch size for training. (default: 128)
num_latents (-nl)	Number of latent variables. (default: 16)
overwrite_learning_rate (-lr)	Learning rate to overwrite the config file. (default: None)
gpus	Comma-separated list of GPU ids to use for training.
ckpt_name	Checkpoint name to resume from for test or visualization. (default: last)
use_text	Use text as condition

Training and inference together

We provide end-to-end scripts that can be used for both training and inference.

python train_inference.py \
  --base config.yaml \
  --gpus 0, \
  --logdir ./logs/Your_Logidr  \
  -sl 168 \
  -up \
  -nl 16 \
  --batch_size 128 \
  -lr 0.0001  \

Training with Prototypes and Text

python main.py \
  --base config.yaml \
  --gpus 0, \
  --logdir ./logs/Your_Logidr  \
  -sl 168 \
  -up \
  -nl 16 \
  --batch_size 128 \
  -lr 0.0001  \
  --use_text \

Training with Prototypes

python main.py \
  --base config.yaml \
  --gpus 0,  \
  --logdir ./logs/Your_Logidr  \
  -sl 168  \
  -up  \
  -nl 16  \
  --batch_size 128 \
  -lr 0.0001  \

Training with neither Prototypes nor Text

python main.py \
  --base config.yaml \
  --gpus 0,  \
  --logdir ./logs/Your_Logidr  \
  -sl 168  \
  -nl 16  \
  --batch_size 128 \
  -lr 0.0001  \