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SEED_VALUE: 1234 # Seed value
DEBUG: True # Debug mode
FULL_CONFIG: false
TRAIN:
SPLIT: 'train' # Training split name
NUM_WORKERS: 8 # Number of workers
BATCH_SIZE: 8 # Size of batches
END_EPOCH: 2000 # End epoch
RESUME: '' # Experiment path to be resumed training
PRETRAINED_VAE: '' # Pretrained vae/vqvae model path
PRETRAINED: '' # Pretrained model path
OPTIM:
target: AdamW
params:
lr: 2e-4
betas: [0.9, 0.99]
weight_decay: 0.0
LR_SCHEDULER:
target: CosineAnnealingLR
params:
T_max: ${eval:${LOGGER.VAL_EVERY_STEPS} * 100}
eta_min: 1e-6
EVAL:
SPLIT: 'val' # Validation split name
BATCH_SIZE: 16 # Validation Batch size
NUM_WORKERS: 8 # Validation Batch size
TEST:
CHECKPOINTS: '' # Pretrained model path
SPLIT: 'test' # Testing split name
BATCH_SIZE: 16 # Testing Batch size
NUM_WORKERS: 8 # Testing Batch size
SAVE_PREDICTIONS: False # Weather to save predictions
COUNT_TIME: False # Weather to count time during test
REPLICATION_TIMES: 20 # Number of times to replicate the test
REP_I: 0 # For counting replication times
model:
target: mGPT.models.mgpt.MotionGPT
params:
condition: 'text'
task: 't2m'
lm: ${lm.default}
motion_vae: ${vq.default}
# Related parameters
stage: ${TRAIN.STAGE}
debug: ${DEBUG}
codebook_size: ${model.params.motion_vae.params.code_num}
metrics_dict: ${METRIC.TYPE}
LOSS:
LAMBDA_REC: 1.0 # Lambda for reconstruction losses
LAMBDA_JOINT: 1.0 # Lambda for joint losses
LAMBDA_LATENT: 1e-5 # Lambda for latent losses
LAMBDA_KL: 1e-5 # Lambda for kl losses
LAMBDA_GEN: 1.0 # Lambda for text-motion generation losses
LAMBDA_CROSS: 1.0 # Lambda for cross-reconstruction losses
LAMBDA_CYCLE: 1.0 # Lambda for cycle losses
LAMBDA_PRIOR: 0.0 # Lambda for diffusion prior losses
LAMBDA_VELOCITY: 0.5 # Lambda for velocity losses
LAMBDA_COMMIT: 0.02 # Lambda for commitment losses
ABLATION:
RECONS_LOSS: 'l1_smooth'
METRIC:
TASK: 't2m'
FORCE_IN_METER: True
DIST_SYNC_ON_STEP: True
MM_NUM_SAMPLES: 100 # Number of samples for multimodal test
MM_NUM_REPEATS: 30 # Number of repeats for multimodal test
MM_NUM_TIMES: 10 # Number of times to repeat the multimodal test
DIVERSITY_TIMES: 300 # Number of times to repeat the diversity test
TM2T: ${evaluator.tm2t}
DATASET:
target: mGPT.data.HumanML3D.HumanML3DDataModule
CODE_PATH: 'VQVAE'
TASK_ROOT: ''
TASK_PATH: ''
NFEATS: 263
KIT:
MAX_MOTION_LEN: 196
MIN_MOTION_LEN: 24
MAX_TEXT_LEN: 20
PICK_ONE_TEXT: true
FRAME_RATE: 12.5
UNIT_LEN: 4
HUMANML3D:
MAX_MOTION_LEN: 196
MIN_MOTION_LEN: 40
MAX_TEXT_LEN: 20
PICK_ONE_TEXT: true
FRAME_RATE: 20.0
UNIT_LEN: 4
STD_TEXT: False
ABLATION:
# For MotionGPT
use_length: False
predict_ratio: 0.2
inbetween_ratio: 0.25
image_size: 256
# For Motion-latent-diffusion
VAE_TYPE: 'actor' # vae ablation: actor or mcross
VAE_ARCH: 'encoder_decoder' # mdiffusion vae architecture
PE_TYPE: 'actor' # mdiffusion mld or actor
DIFF_PE_TYPE: 'actor' # mdiffusion mld or actor
SKIP_CONNECT: False # skip connection for denoiser va
MLP_DIST: False # use linear to expand mean and std rather expand token nums
IS_DIST: False # Mcross distribution kl
PREDICT_EPSILON: True # noise or motion
LOGGER:
VAL_EVERY_STEPS: 10
LOGGERS: ['tensorboard', 'wandb']
TENSORBOARD:
target: pytorch_lightning.loggers.TensorBoardLogger
params:
save_dir: ${FOLDER_EXP}
name: 'tensorboard'
version: ''
WANDB:
target: pytorch_lightning.loggers.WandbLogger
params:
project: null
offline: False
id: null
version: ''
name: ${NAME}
save_dir: ${FOLDER_EXP}
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