File size: 2,557 Bytes
aceb411 | 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 | common:
# The number of historical images
img_history_size: 1
# The number of historical point clouds
pcd_history_size: 1
# The number of future actions to predict
action_chunk_size: 16
# The number of cameras to be used in the model
num_cameras: 3
# Dimension for state
state_dim: 14
# Dimension for action
action_dim: 14
# The number of patches in the image
num_patches: 196
dataset:
# We will extract the data from raw dataset
# and store them in the disk buffer by producer
# When training, we will read the data
# randomly from the buffer by consumer
# The producer will replace the data which has been
# read by the consumer with new data
# The path to the buffer (at least 400GB)
buf_path: /ssd/lingxuan/data/buffer
# The number of chunks in the buffer
buf_num_chunks: 512
# The number of samples (step rather than episode) in each chunk
buf_chunk_size: 512
# We will filter the episodes with length less than `epsd_len_thresh_low`
epsd_len_thresh_low: 32
# For those more than `epsd_len_thresh_high`,
# we will randomly sample `epsd_len_thresh_high` steps each time we load the episode
# to better balance the training datasets
epsd_len_thresh_high: 2048
# How to fit the image size
image_aspect_ratio: pad
# Maximum number of language tokens
tokenizer_max_length: 1024
model:
# Config for condition adpators
act_adaptor: mlp3x_silu # Will be reinitialized in finetune mode
st_adaptor: mlp3x_silu # Will be reinitialized in finetune mode
img_adapter: mlp2x_silu # Shared between pretrain and finetune
lang_adapter: mlp2x_silu # Shared between pretrain and finetune
# Config for H-RDT structure (backbone - shared between pretrain and finetune)
hrdt:
hidden_size: 2176
depth: 16
num_heads: 16
norm_eps: 0.00001
# make SwiGLU hidden layer size multiple of large power of 2
multiple_of: 256
ffn_dim_multiplier: null
# Grouped Query Attention
num_kv_heads: 8
# output_size: ${...common.action_dim} # i.e., action dimension (TODO)
output_size: 14
use_flash_attn: true
# For noise scheduler (flow matching)
noise_scheduler:
num_inference_timesteps: 5
timestep_max: 0.999
sampler_type: uniform
time_noise:
a: 5
beta_m: 100
# For EMA (params averaging)
# We do not use EMA currently
ema:
update_after_step: 0
inv_gamma: 1.0
power: 0.75
min_value: 0.0
max_value: 0.9999
# Encoder configurations
vision:
feature_dim: 2176
text:
feature_dim: 4096 |