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set -ex | |
# Training | |
GPU_ID=0 | |
DISPLAY_ID=$((GPU_ID*10+10)) | |
NAME='spaces_demo' | |
# Network configuration | |
BATCH_SIZE=1 | |
MLP_DIM='257 1024 512 256 128 1' | |
MLP_DIM_COLOR='513 1024 512 256 128 3' | |
# Reconstruction resolution | |
# NOTE: one can change here to reconstruct mesh in a different resolution. | |
# VOL_RES=256 | |
# CHECKPOINTS_NETG_PATH='./checkpoints/net_G' | |
# CHECKPOINTS_NETC_PATH='./checkpoints/net_C' | |
# TEST_FOLDER_PATH='./sample_images' | |
# command | |
CUDA_VISIBLE_DEVICES=${GPU_ID} python ./apps/eval_spaces.py \ | |
--name ${NAME} \ | |
--batch_size ${BATCH_SIZE} \ | |
--mlp_dim ${MLP_DIM} \ | |
--mlp_dim_color ${MLP_DIM_COLOR} \ | |
--num_stack 4 \ | |
--num_hourglass 2 \ | |
--resolution ${VOL_RES} \ | |
--hg_down 'ave_pool' \ | |
--norm 'group' \ | |
--norm_color 'group' \ | |
--load_netG_checkpoint_path ${CHECKPOINTS_NETG_PATH} \ | |
--load_netC_checkpoint_path ${CHECKPOINTS_NETC_PATH} \ | |
--results_path ${RESULTS_PATH} \ | |
--img_path ${INPUT_IMAGE_PATH} |