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#!/usr/bin/env bash
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} |