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YifYang993YifYang993
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‎command/get_rendered_images.sh

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cd CR-NeRF
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# cd CR-NeRF
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#Path of checkpoints
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ckpt_path1="ckpts/CR-NeRF-downscale=2.ckpt"
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# img_downscale=4
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#Path of in-the-wild dataset
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echo !!modify the following path!!
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root_dir1="/mnt/cephfs/dataset/NVS/nerfInWild/brandenburg_gate/"
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#Path to save the synthesized images
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save_dir1=/mnt/cephfs/dataset/NVS/nerfInWild/experimental_results

‎command/get_video_demo.sh

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# --ckpt_path ckpts/CR-NeRF-trevi_fountain.ckpt
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##generating videos of brandenburg_gate
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source /mnt/cephfs/home/yangyifan/miniconda/etc/profile.d/conda.sh
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conda activate crnerf
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cd /mnt/cephfs/home/yangyifan/yangyifan/code/learnToSyLf/CR-NeRF
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# source /mnt/cephfs/home/yangyifan/miniconda/etc/profile.d/conda.sh
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# conda activate crnerf
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# cd /mnt/cephfs/home/yangyifan/yangyifan/code/learnToSyLf/CR-NeRF
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echo !!modify the following path!!
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CUDA_VISIBLE_DEVICES=2 python appearance_modification_video.py \
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--save_dir /mnt/cephfs/dataset/NVS/nerfInWild/experimental_results \
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--chunk 4096 --encode_a --nerf_out_dim 64 --decoder_num_res_blocks 1\

‎command/test.sh

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# Description: test script for CR-Nerf
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cd /mnt/cephfs/home/yangyifan/yangyifan/code/learnToSyLf/CR-NeRF
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source /mnt/cephfs/home/yangyifan/miniconda/etc/profile.d/conda.sh
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conda activate crnerf
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exp_name1="test_only/test" #replace with your own experiment name
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# cd /mnt/cephfs/home/yangyifan/yangyifan/code/learnToSyLf/CR-NeRF
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# source /mnt/cephfs/home/yangyifan/miniconda/etc/profile.d/conda.sh
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# conda activate crnerf
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exp_name1="test_only/test1" #replace with your own experiment name
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root_dir1="/mnt/cephfs/dataset/NVS/nerfInWild/brandenburg_gate/" #repalce with your own dataset path
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save_dir1=/mnt/cephfs/dataset/NVS/nerfInWild/experimental_results #replace with your own save path
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model_mode1="1-1"
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decoder='linearStyle'
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ckpt_path1="ckpts/CR-NeRF-branden.ckpt"
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echo !!modify the following path!!
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ckpt_path1="/mnt/cephfs/dataset/NVS/nerfInWild/experimental_results/ckpts/debug/CR-NeRF-branden.ckpt"
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dataset_name1='phototourism'
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decoder_num_res_blocks=1
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img_downscale=4 #The provided model is trained with image resolution downscale ratio = 4.

‎command/train.sh

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@@ -4,13 +4,14 @@
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#set --encode_a to use the proposed cross ray appearance transfer module
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#set --use_mask to use the proposed transient handling module
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cd /mnt/cephfs/home/yangyifan/yangyifan/code/learnToSyLf/CR-NeRF
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source /mnt/cephfs/home/yangyifan/miniconda/etc/profile.d/conda.sh
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conda activate crnerf
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# cd /mnt/cephfs/home/yangyifan/yangyifan/code/learnToSyLf/CR-NeRF
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# source /mnt/cephfs/home/yangyifan/miniconda/etc/profile.d/conda.sh
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# conda activate crnerf
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nerf_out_dim1=64
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exp_name1="train/exp1"
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exp_name1="train/reproduce-20231111"
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model_mode1="1-1"
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decoder='linearStyle'
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echo !!modify the following path!!
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ckpt_path1="/mnt/cephfs/dataset/NVS/nerfInWild/experimental_results/ckpts/${exp_name1}/last.ckpt"
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root_dir1="/mnt/cephfs/dataset/NVS/nerfInWild/brandenburg_gate/"
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dataset_name1='phototourism'
@@ -21,7 +22,8 @@ img_downscale=4 ##Change this to 2 if you want to reproduce the results with im
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#train#
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CUDA_VISIBLE_DEVICES=3 python train_mask_grid_sample.py --root_dir $root_dir1 --dataset_name phototourism --save_dir /mnt/cephfs/dataset/NVS/nerfInWild/experimental_results --img_downscale $img_downscale --N_importance 64 --N_samples 64 --num_epochs 20 --batch_size 1024 --optimizer adam --lr 5e-4 --lr_scheduler cosine --N_emb_xyz 15 --N_vocab 1500 --maskrs_max 5e-2 --maskrs_min 6e-3 --maskrs_k 1e-3 --maskrd 0 --N_a 48 --weightKL 1e-5 --weightRecA 1e-3 --weightMS 1e-6 --chunk 1310720 --encode_a --encode_c --encode_random --model_mode 1-1 --decoder linearStyle --decoder_num_res_blocks $decoder_num_res_blocks --nerf_out_dim $nerf_out_dim1 --use_cache --proj_name style_gnerf --num_gpus 1 --use_mask --exp_name $exp_name1
25+
CUDA_VISIBLE_DEVICES=3,4,5,6 python train_mask_grid_sample.py --root_dir $root_dir1 --dataset_name phototourism --save_dir /mnt/cephfs/dataset/NVS/nerfInWild/experimental_results --img_downscale $img_downscale --N_importance 64 --N_samples 64 --num_epochs 20 --batch_size 1024 --optimizer adam --lr 5e-4 --lr_scheduler cosine --N_emb_xyz 15 --N_vocab 1500 --maskrs_max 5e-2 --maskrs_min 6e-3 --maskrs_k 1e-3 --maskrd 0 --N_a 48 --weightKL 1e-5 --weightRecA 1e-3 --weightMS 1e-6 --chunk 1310720 --encode_a --encode_c --encode_random --model_mode 1-1 --decoder linearStyle --decoder_num_res_blocks $decoder_num_res_blocks --nerf_out_dim $nerf_out_dim1 --use_cache --proj_name style_gnerf --num_gpus 0 --use_mask --exp_name $exp_name1
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@@ -39,6 +41,7 @@ CUDA_VISIBLE_DEVICES=3 python eval.py \
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--ckpt_path $ckpt_path1 \
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--chunk 2048 --img_wh 320 240 --encode_a --decoder $decoder --decoder_num_res_blocks $decoder_num_res_blocks --nerf_out_dim $nerf_out_dim1
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#calculate metrics#
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CUDA_VISIBLE_DEVICES=0 python eval_metric.py \
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--root_dir $root_dir1 \

‎command/train_ds2.sh

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#!Note: set N_vocab to 1500 for brandenburg_gate and Sacre coeur dataset, 3200 for trevi fountain datasets
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#use command --use_cache to use prepared dataset as described in docs/dataset.md for speeding up training
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#set --encode_a to use the proposed cross ray appearance transfer module
5+
#set --use_mask to use the proposed transient handling module
6+
7+
# cd /mnt/cephfs/home/yangyifan/yangyifan/code/learnToSyLf/CR-NeRF
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# source /mnt/cephfs/home/yangyifan/miniconda/etc/profile.d/conda.sh
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# conda activate crnerf
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nerf_out_dim1=64
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exp_name1="train/reproduce-ds2-20231111"
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model_mode1="1-1"
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decoder='linearStyle'
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echo !!modify the following path!!
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ckpt_path1="/mnt/cephfs/dataset/NVS/nerfInWild/experimental_results/ckpts/${exp_name1}/last.ckpt"
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root_dir1="/mnt/cephfs/dataset/NVS/nerfInWild/brandenburg_gate/"
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dataset_name1='phototourism'
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save_dir1=/mnt/cephfs/dataset/NVS/nerfInWild/experimental_results
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decoder_num_res_blocks=1
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img_downscale=2 ##Change this to 2 if you want to reproduce the results with image resolution downscale ratio = 2, note that downscale =2 requires more training and inference time due to the larger image size.
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#train#
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# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python train_mask_grid_sample.py --root_dir $root_dir1 --dataset_name phototourism --save_dir /mnt/cephfs/dataset/NVS/nerfInWild/experimental_results --img_downscale $img_downscale --N_importance 64 --N_samples 64 --num_epochs 20 --batch_size 1024 --optimizer adam --lr 5e-4 --lr_scheduler cosine --N_emb_xyz 15 --N_vocab 1500 --maskrs_max 5e-2 --maskrs_min 6e-3 --maskrs_k 1e-3 --maskrd 0 --N_a 48 --weightKL 1e-5 --weightRecA 1e-3 --weightMS 1e-6 --chunk 1310720 --encode_a --encode_c --encode_random --model_mode 1-1 --decoder linearStyle --decoder_num_res_blocks $decoder_num_res_blocks --nerf_out_dim $nerf_out_dim1 --use_cache --proj_name style_gnerf --num_gpus 8 --use_mask --exp_name $exp_name1
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#test#
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cd /mnt/cephfs/dataset/NVS/nerfInWild/experimental_results/logs/$exp_name1/codes
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#render image#
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CUDA_VISIBLE_DEVICES=3 python eval.py \
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--root_dir $root_dir1 \
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--save_dir $save_dir1 \
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--dataset_name $dataset_name1 --scene_name $exp_name1 \
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--split test_test --img_downscale $img_downscale \
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--N_samples 256 --N_importance 256 --N_emb_xyz 15 \
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--N_vocab 1500 \
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--ckpt_path $ckpt_path1 \
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--chunk 2048 --img_wh 320 240 --encode_a --decoder $decoder --decoder_num_res_blocks $decoder_num_res_blocks --nerf_out_dim $nerf_out_dim1
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#calculate metrics#
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CUDA_VISIBLE_DEVICES=0 python eval_metric.py \
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--root_dir $root_dir1 \
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--save_dir $save_dir1 \
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--dataset_name $dataset_name1 --scene_name $exp_name1 \
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--split test_test --img_downscale $img_downscale \
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--img_wh 320 240

‎commandv1/train_ds2_weights.sh

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#!Note: set N_vocab to 1500 for brandenburg_gate and Sacre coeur dataset, 3200 for trevi fountain datasets
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#use command --use_cache to use prepared dataset as described in docs/dataset.md for speeding up training
4+
#set --encode_a to use the proposed cross ray appearance transfer module
5+
#set --use_mask to use the proposed transient handling module
6+
7+
cd /mnt/cephfs/home/yangyifan/yangyifan/code/learnToSyLf/CR-NeRF
8+
source /mnt/cephfs/home/yangyifan/miniconda/etc/profile.d/conda.sh
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conda activate crnerf
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nerf_out_dim1=64
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exp_name1="train/debug"
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model_mode1="1-1"
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decoder='linearStyle'
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ckpt_path1="/mnt/cephfs/dataset/NVS/nerfInWild/experimental_results/ckpts/${exp_name1}/last.ckpt"
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root_dir1="/mnt/cephfs/dataset/NVS/nerfInWild/brandenburg_gate/"
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dataset_name1='phototourism'
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save_dir1=/mnt/cephfs/dataset/NVS/nerfInWild/experimental_results
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decoder_num_res_blocks=1
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img_downscale=2 ##Change this to 2 if you want to reproduce the results with image resolution downscale ratio = 2, note that downscale =2 requires more training and inference time due to the larger image size.
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#train#
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CUDA_VISIBLE_DEVICES=3 python train_mask_grid_sample.py --root_dir $root_dir1 --dataset_name phototourism --save_dir /mnt/cephfs/dataset/NVS/nerfInWild/experimental_results --img_downscale $img_downscale --N_importance 64 --N_samples 64 --num_epochs 20 --batch_size 1024 --optimizer adam --lr 5e-4 --lr_scheduler cosine --N_emb_xyz 15 --N_vocab 1500 --maskrs_max 5e-2 --maskrs_min 6e-3 --maskrs_k 1e-3 --maskrd 0 --N_a 48 --weightKL 1e-5 --weightRecA 1e-3 --weightMS 1e-6 --chunk 1310720 --encode_a --encode_c --encode_random --model_mode 1-1 --decoder linearStyle --decoder_num_res_blocks $decoder_num_res_blocks --nerf_out_dim $nerf_out_dim1 --use_cache --proj_name style_gnerf --num_gpus 1 --use_mask --exp_name $exp_name1 --weightcontent 1e-7
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#test#
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cd /mnt/cephfs/dataset/NVS/nerfInWild/experimental_results/logs/$exp_name1/codes
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#render image#
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CUDA_VISIBLE_DEVICES=2 python eval.py \
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--root_dir $root_dir1 \
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--save_dir $save_dir1 \
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--dataset_name $dataset_name1 --scene_name $exp_name1 \
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--split test_test --img_downscale $img_downscale \
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--N_samples 256 --N_importance 256 --N_emb_xyz 15 \
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--N_vocab 1500 \
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--ckpt_path $ckpt_path1 \
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--chunk 2048 --img_wh 320 240 --encode_a --decoder $decoder --decoder_num_res_blocks $decoder_num_res_blocks --nerf_out_dim $nerf_out_dim1
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#calculate metrics#
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CUDA_VISIBLE_DEVICES=0 python eval_metric.py \
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--root_dir $root_dir1 \
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--save_dir $save_dir1 \
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--dataset_name $dataset_name1 --scene_name $exp_name1 \
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--split test_test --img_downscale $img_downscale \
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--img_wh 320 240

‎eval.py

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img_pred_ = (img_pred*255).astype(np.uint8)
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imgs += [img_pred_]
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imageio.imwrite(os.path.join(dir_name, f'{i:03d}.png'), img_pred_)
300+
print("image saving path",os.path.join(dir_name, f'{i:03d}.png'))")
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if args.dataset_name == 'blender' or \
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(args.dataset_name == 'phototourism' and args.split == 'test'):

‎eval_metric.py

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for i in tqdm(range(len(dataset))):
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sample = dataset[i]
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image_pre_path = os.path.join(dir_name, f_list[idx_list.index(f'{i:03d}')])
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print("image_pre_path",image_pre_path)
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img_pred = Image.open(image_pre_path).convert('RGB')
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img_pred = toTensor(img_pred) # (3, h, w)
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if args.dataset_name == 'blender':

‎train_mask_grid_sample.py

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if __name__ == '__main__':
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "6"
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# os.environ["CUDA_VISIBLE_DEVICES"] = "6"
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hparams_ = get_opts()
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print(hparams_.exp_name)
483483
if hparams_.testit:

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