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_base_ = ['../_base_/datasets/human_ml3d_bs128.py']
# checkpoint saving
checkpoint_config = dict(interval=1)
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
# optimizer
optimizer = dict(type='Adam', lr=1e-4)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(policy='step', step=[])
runner = dict(type='EpochBasedRunner', max_epochs=50)
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
input_feats = 263
max_seq_len = 196
latent_dim = 512
time_embed_dim = 2048
text_latent_dim = 256
ff_size = 1024
num_layers = 8
num_heads = 4
dropout = 0.1
cond_mask_prob = 0.1
# model settings
model = dict(
type='MotionDiffusion',
model=dict(
type='MDMTransformer',
input_feats=input_feats,
latent_dim=latent_dim,
ff_size=ff_size,
num_layers=num_layers,
num_heads=num_heads,
dropout=dropout,
time_embed_dim=time_embed_dim,
cond_mask_prob=cond_mask_prob,
guide_scale=2.5,
clip_version='ViT-B/32',
use_official_ckpt=True
),
loss_recon=dict(type='MSELoss', loss_weight=1, reduction='none'),
diffusion_train=dict(
beta_scheduler='cosine',
diffusion_steps=1000,
model_mean_type='start_x',
model_var_type='fixed_small',
),
diffusion_test=dict(
beta_scheduler='cosine',
diffusion_steps=1000,
model_mean_type='start_x',
model_var_type='fixed_small',
),
inference_type='ddpm'
) |