File size: 3,774 Bytes
4409449 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 |
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}
|