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general {
base_exp_dir = exp/CASE_NAME/wmask
tag = "train_retargeted_shadow_hand_seq_102_diffhand_model_"
recording = [
./,
./models
]
}
dataset {
data_dir = public_data/CASE_NAME/
render_cameras_name = cameras_sphere.npz
object_cameras_name = cameras_sphere.npz
obj_idx = 102
}
train {
learning_rate = 5e-4
# learning_rate = 5e-6
learning_rate_actions = 5e-6
learning_rate_alpha = 0.05
end_iter = 300000
# batch_size = 128 # 64
# batch_size = 4000
# batch_size = 3096 # 64
batch_size = 1024
validate_resolution_level = 4
warm_up_end = 5000
anneal_end = 0
use_white_bkgd = False
# save_freq = 10000
save_freq = 10000
val_freq = 20 # 2500
val_mesh_freq = 20 # 5000
report_freq = 10
### igr weight ###
igr_weight = 0.1
mask_weight = 0.1
}
model {
load_redmax_robot_actions = ""
penetrating_depth_penalty = 0.0
train_states = True
minn_dist_threshold = 0.000
obj_mass = 100.0
obj_mass = 30.0
# use_mano_hand_for_test = False
use_mano_hand_for_test = True
# train_residual_friction = False
train_residual_friction = True
# use_LBFGS = True
use_LBFGS = False
use_mano_hand_for_test = False
train_residual_friction = True
extract_delta_mesh = False
freeze_weights = True
# gt_act_xs_def = True
gt_act_xs_def = False
use_bending_network = True
use_delta_bending = True
use_passive_nets = True
use_split_network = True
n_timesteps = 60
# using_delta_glb_trans = True
using_delta_glb_trans = False
# train_multi_seqs = True
# optimize_with_intermediates = False
optimize_with_intermediates = True
loss_tangential_diff_coef = 1000
loss_tangential_diff_coef = 0
optimize_active_object = True
no_friction_constraint = False
optimize_glb_transformations = True
sim_model_path = "DiffHand/assets/hand_sphere_only_hand_testt.xml"
mano_sim_model_path = "rsc/mano/mano_mean_wcollision_scaled_scaled_0_9507_nroot.urdf"
mano_mult_const_after_cent = 1.0
sim_num_steps = 1000000
bending_net_type = "active_force_field_v18"
### try to train the residual friction ? ###
train_residual_friction = True
optimize_rules = True
load_optimized_init_actions = ""
use_optimizable_params = True
### grab train seq 224 ###
penetration_determining = "sdf_of_canon"
train_with_forces_to_active = False
loss_scale_coef = 1000.0
# penetration_proj_k_to_robot_friction = 40000000.0
# penetration_proj_k_to_robot_friction = 100000000.0 # as friction coefs here #
use_same_contact_spring_k = False
# sim_model_path = "/home/xueyi/diffsim/DiffHand/assets/hand_sphere_only_hand_testt.xml"
sim_model_path = "rsc/shadow_hand_description/shadowhand_new.urdf"
optimize_rules = False
penetration_determining = "sdf_of_canon"
optimize_rules = False
optim_sim_model_params_from_mano = False
optimize_rules = False
penetration_proj_k_to_robot_friction = 100000000.0 # as friction coefs here # ## confs ##
penetration_proj_k_to_robot = 40000000.0 #
penetrating_depth_penalty = 1
minn_dist_threshold_robot_to_obj = 0.0
minn_dist_threshold_robot_to_obj = 0.1
optim_sim_model_params_from_mano = False
optimize_rules = False
optim_sim_model_params_from_mano = True
optimize_rules = True
minn_dist_threshold_robot_to_obj = 0.0
optim_sim_model_params_from_mano = False
optimize_rules = False
minn_dist_threshold_robot_to_obj = 0.1
### kinematics confgs ###
obj_sdf_fn = "data/grab/102/102_obj.npy"
kinematic_mano_gt_sv_fn = "data/grab/102/102_sv_dict.npy"
scaled_obj_mesh_fn = "data/grab/102/102_obj.obj"
# ckpt_fn = ""
# load_optimized_init_transformations = ""
optim_sim_model_params_from_mano = True
optimize_rules = True
minn_dist_threshold_robot_to_obj = 0.0
optim_sim_model_params_from_mano = False
optimize_rules = False
optimize_rules = True
ckpt_fn = "ckpts/grab/102/retargeted_shadow.pth"
load_optimized_init_transformations = "ckpts/grab/102/retargeted_shadow.pth"
optimize_rules = False
optimize_rules = True
## opt roboto ##
opt_robo_glb_trans = True
opt_robo_glb_rot = False # opt rot # ## opt rot ##
opt_robo_states = True
use_multi_stages = False
minn_dist_threshold_robot_to_obj = 0.1
penetration_proj_k_to_robot = 40000
penetration_proj_k_to_robot_friction = 100000
drive_robot = "actions"
opt_robo_glb_trans = False
opt_robo_states = True
opt_robo_glb_rot = False
train_with_forces_to_active = True
load_redmax_robot_actions_fn = ""
optimize_rules = False
loss_scale_coef = 1.0
use_opt_rigid_translations=True
train_def = True
optimizable_rigid_translations=True
nerf {
D = 8,
d_in = 4,
d_in_view = 3,
W = 256,
multires = 10,
multires_view = 4,
output_ch = 4,
skips=[4],
use_viewdirs=True
}
sdf_network {
d_out = 257,
d_in = 3,
d_hidden = 256,
n_layers = 8,
skip_in = [4],
multires = 6,
bias = 0.5,
scale = 1.0,
geometric_init = True,
weight_norm = True,
}
variance_network {
init_val = 0.3
}
rendering_network {
d_feature = 256,
mode = idr,
d_in = 9,
d_out = 3,
d_hidden = 256,
n_layers = 4,
weight_norm = True,
multires_view = 4,
squeeze_out = True,
}
neus_renderer {
n_samples = 64,
n_importance = 64,
n_outside = 0,
up_sample_steps = 4 ,
perturb = 1.0,
}
bending_network {
multires = 6,
bending_latent_size = 32,
d_in = 3,
rigidity_hidden_dimensions = 64,
rigidity_network_depth = 5,
use_rigidity_network = False,
bending_n_timesteps = 10,
}
}
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