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general { | |
# base_exp_dir = exp/CASE_NAME/wmask | |
base_exp_dir = /data2/datasets/xueyi/neus/exp/CASE_NAME/wmask | |
tag = "train_retargeted_shadow_hand_seq_102_diffhand_model_curriculum_" | |
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_actions = 5e-6 | |
# learning_rate = 5e-6 | |
# learning_rate = 5e-5 | |
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 { | |
penetration_proj_k_to_robot = 40 | |
penetrating_depth_penalty = 1.0 | |
penetrating_depth_penalty = 0.0 | |
train_states = True | |
penetration_proj_k_to_robot = 4000000000.0 | |
minn_dist_threshold = 0.000 | |
# minn_dist_threshold = 0.01 | |
obj_mass = 100.0 | |
obj_mass = 30.0 | |
optimize_rules = True | |
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 | |
### for ts = 3 ### | |
# use_delta_bending = False | |
### for ts = 3 ### | |
use_delta_bending = True | |
use_passive_nets = True | |
# use_passive_nets = False # sv mesh root # | |
use_split_network = True | |
penetration_determining = "plane_primitives" | |
n_timesteps = 3 # | |
# n_timesteps = 5 # | |
n_timesteps = 7 | |
n_timesteps = 60 | |
using_delta_glb_trans = True | |
using_delta_glb_trans = False | |
optimize_with_intermediates = False | |
optimize_with_intermediates = True | |
loss_tangential_diff_coef = 1000 | |
loss_tangential_diff_coef = 0 | |
optimize_active_object = False | |
optimize_active_object = True | |
# optimize_expanded_pts = False | |
# optimize_expanded_pts = 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 | |
### cube ### | |
load_optimized_init_actions = "" | |
optimize_rules = False | |
## optimize rules ## penetration proj k to robot ## | |
optimize_rules = True | |
penetration_proj_k_to_robot = 4000000.0 | |
use_optimizable_params = True | |
penetration_determining = "ball_primitives" # uing ball primitives | |
optimize_rules = True # | |
penetration_proj_k_to_robot = 4000000.0 # | |
use_optimizable_params = True | |
train_with_forces_to_active = False | |
# penetration_determining = "ball_primitives" | |
### obj sdf and normals for colllision eteftion and responses ## | |
### grab train seq 54; cylinder ### | |
penetration_determining = "sdf_of_canon" | |
optimize_rules = True | |
train_with_forces_to_active = False | |
### grab train seq 1 ### | |
penetration_determining = "sdf_of_canon" | |
train_with_forces_to_active = False | |
### 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 | |
use_same_contact_spring_k = False | |
sim_model_path = "DiffHand/assets/hand_sphere_only_hand_testt.xml" | |
sim_model_path = "rsc/shadow_hand_description/shadowhand_new.urdf" | |
penetration_determining = "sdf_of_canon" | |
optimize_rules = True | |
# optimize_rules = True | |
optimize_rules = False | |
optimize_rules = True | |
optimize_rules = False | |
optim_sim_model_params_from_mano = True | |
optimize_rules = True | |
optim_sim_model_params_from_mano = False | |
optimize_rules = False | |
penetration_proj_k_to_robot_friction = 100000000.0 | |
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 = True | |
optimize_rules = True | |
optim_sim_model_params_from_mano = False | |
optimize_rules = False | |
optim_sim_model_params_from_mano = False | |
optimize_rules = False | |
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 | |
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 = True | |
ckpt_fn = "ckpts/grab/102/retargeted_shadow.pth" | |
ckpt_fn = "/data2/datasets/xueyi/neus/exp/hand_test_routine_2_light_color_wtime_active_passive/wmask_reverse_value_totviews_tag_train_retargeted_shadow_hand_states_optrobot__seq_102_optactswreacts_redmaxacts_rules_/checkpoints/ckpt_035459.pth" | |
load_optimized_init_transformations = "ckpts/grab/102/retargeted_shadow.pth" | |
optimize_rules = True | |
## opt roboto ## | |
opt_robo_glb_trans = True | |
opt_robo_glb_rot = False # opt rot # ## opt rot ## | |
opt_robo_states = True | |
load_redmax_robot_actions_fn = "ckpts/grab/102/diffhand_act.npy" | |
ckpt_fn = "" | |
use_multi_stages = True | |
train_with_forces_to_active = True | |
# optimize_rules = False | |
loss_scale_coef = 1.0 ## loss scale coef ## loss scale coef #### | |
use_opt_rigid_translations=True | |
train_def = True | |
# optimizable_rigid_translations = False # | |
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, | |
} | |
} | |