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eval_gnfactor.sh
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eval_gnfactor.sh
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exp=3d_diffuser_actor_gnfactor
tasks=(
close_jar insert_onto_square_peg light_bulb_in meat_off_grill open_drawer place_shape_in_shape_sorter place_wine_at_rack_location push_buttons put_groceries_in_cupboard put_item_in_drawer put_money_in_safe reach_and_drag slide_block_to_color_target stack_blocks stack_cups sweep_to_dustpan_of_size turn_tap
)
data_dir=./data/peract/raw/test/
num_episodes=100
gripper_loc_bounds_file=tasks/18_peract_tasks_location_bounds.json
use_instruction=1
max_tries=2
verbose=1
interpolation_length=2
single_task_gripper_loc_bounds=0
embedding_dim=120
cameras="front"
fps_subsampling_factor=5
lang_enhanced=0
relative_action=0
seed=0
checkpoint=train_logs/diffuser_actor_gnfactor.pth
quaternion_format=wxyz # IMPORTANT: change this to be the same as the training script IF you're not using our checkpoint
num_ckpts=${#tasks[@]}
for ((i=0; i<$num_ckpts; i++)); do
CUDA_LAUNCH_BLOCKING=1 python online_evaluation_rlbench/evaluate_policy.py \
--tasks ${tasks[$i]} \
--checkpoint $checkpoint \
--diffusion_timesteps 100 \
--fps_subsampling_factor $fps_subsampling_factor \
--lang_enhanced $lang_enhanced \
--relative_action $relative_action \
--num_history 3 \
--test_model 3d_diffuser_actor \
--cameras $cameras \
--verbose $verbose \
--action_dim 8 \
--collision_checking 0 \
--predict_trajectory 1 \
--embedding_dim $embedding_dim \
--rotation_parametrization "6D" \
--single_task_gripper_loc_bounds $single_task_gripper_loc_bounds \
--data_dir $data_dir \
--num_episodes $num_episodes \
--output_file eval_logs/$exp/seed$seed/${tasks[$i]}.json \
--use_instruction $use_instruction \
--instructions instructions/peract/instructions.pkl \
--variations {0..60} \
--max_tries $max_tries \
--max_steps 20 \
--seed $seed \
--gripper_loc_bounds_file $gripper_loc_bounds_file \
--gripper_loc_bounds_buffer 0.08 \
--quaternion_format $quaternion_format \
--interpolation_length $interpolation_length \
--dense_interpolation 1
done