From 1bc644a138fb144a18dc10c632c5b65d3a1db7ae Mon Sep 17 00:00:00 2001
From: Nianchen Deng <dengnianchen@sjtu.edu.cn>
Date: Wed, 28 Sep 2022 11:20:35 +0800
Subject: [PATCH] sync
---
.clang-format | 108 -
.vscode/c_cpp_properties.json | 10 +-
.vscode/launch.json | 61 +-
.vscode/settings.json | 14 -
LICENSE | 2 +-
README.md | 73 +-
a.drawio | 444 +++
args_test.py | 17 +
batch_collect_video.sh | 6 +-
batch_export_net.sh | 6 +-
batch_infer.sh | 4 +-
batch_test.sh | 19 +-
blender/gen_utils.py | 28 +-
clib/__init__.py | 173 +-
clib/_ext.pyi | 32 +
clib/include/cuda_utils.h | 59 +-
clib/include/encode.h | 12 +
clib/include/encode_debug.h | 7 +
clib/include/utils.h | 41 +-
clib/src/binding.cpp | 23 +-
clib/src/encode.cpp | 56 +
clib/src/encode_debug.cu | 132 +
clib/src/encode_gpu.cu | 190 +
components/fnr.py | 105 +-
components/foveation.py | 18 +-
components/refine.py | 2 +-
components/render.py | 55 +
configs/_todo/_hr_snerf_fast.json | 2 +-
configs/ablation_nerf+sph+cat.ini | 22 +
configs/ablation_nerf+sph.ini | 31 +
configs/ablation_nerf.ini | 29 +
configs/fovea.ini | 22 +
configs/fsnerf.ini | 24 +
configs/fsnerf2.ini | 24 +
configs/fsnerf_eval.ini | 24 +
configs/nerf.ini | 31 +
configs/nerf_default.json | 16 -
configs/nerf_iters.ini | 31 +
configs/nerf_llff.ini | 31 +
configs/{ => old}/_cnerf.json | 0
configs/{ => old}/_cnerf_ioc.json | 0
configs/{ => old}/_cnerfadv.json | 0
configs/{ => old}/_cnerfadv_ioc.json | 0
configs/{ => old}/_hr_snerf.json | 13 +-
configs/old/_hr_snerf_mhe.json | 23 +
configs/{ => old}/_hr_snerfadv.json | 0
configs/old/_hr_snerffast+ls.json | 9 +
configs/old/_hr_snerffast.json | 15 +
configs/old/_hr_snerffast_mhe+ls.json | 7 +
configs/old/_hr_snerffast_mhe.json | 14 +
configs/old/_hr_snerffast_mhe1.json | 11 +
configs/{ => old}/_lr_snerf.json | 0
configs/{ => old}/_lr_snerfadv.json | 0
configs/{ => old}/_mr_snerf.json | 0
configs/{ => old}/_mr_snerfadv.json | 0
configs/old/_mr_snerffast.json | 15 +
configs/old/nerf_bedroom.json | 28 +
configs/old/nerf_blender.json | 44 +
configs/old/nerf_default.json | 46 +
configs/old/nerf_mhe.json | 17 +
configs/old/nerf_mhe1.json | 11 +
configs/old/nerf_simple.json | 33 +
configs/{ => old}/nerf_voxels.json | 12 +-
configs/old/nerf_voxels_mhe.json | 13 +
configs/old/nerfadv_default.json | 24 +
configs/old/nerfadv_mhe.json | 24 +
configs/old/nerfadv_voxels.json | 15 +
configs/old/nerfadv_voxels_mhe.json | 31 +
configs/{ => old}/nsvf_default.json | 0
configs/{ => old}/nsvf_voxels.json | 0
configs/{ => old}/smnerf_voxels.json | 0
configs/{ => old}/smnerfadv_voxels.json | 0
configs/{ => old}/snerf4_voxels.json | 0
configs/old/snerf_voxels.json | 9 +
configs/{ => old}/snerfadv_voxels.json | 0
configs/{ => old}/snerfadvx4_voxels.json | 0
configs/old/snerffast.json | 26 +
configs/old/snerffast_periph+dr.json | 27 +
configs/old/snerffast_periph+dr2.json | 27 +
configs/old/snerffast_periph.json | 26 +
configs/{ => old}/snerfx4_voxels.json | 0
configs/{ => old}/svnerf_voxels.json | 0
configs/periph.ini | 22 +
configs/snerf_voxels.json | 25 -
cpp/.clang-format | 1 +
{cpp_old => cpp/__old}/Makefile.config | 0
{cpp_old => cpp/__old}/msl_infer/Encoder.cu | 0
{cpp_old => cpp/__old}/msl_infer/Encoder.h | 0
.../__old}/msl_infer/Enhancement.cu | 0
.../__old}/msl_infer/Enhancement.h | 0
{cpp_old => cpp/__old}/msl_infer/ImageGen.cpp | 0
{cpp_old => cpp/__old}/msl_infer/ImageGen.h | 0
.../__old}/msl_infer/InferPipeline.cpp | 0
.../__old}/msl_infer/InferPipeline.h | 0
cpp/{fnr_core => __old/msl_infer}/Msl.cpp | 0
cpp/{fnr_core => __old/msl_infer}/Msl.h | 0
{cpp_old => cpp/__old}/msl_infer/Net.cpp | 0
cpp/{fnr_core => __old/msl_infer}/Net.h | 0
cpp/{fnr_core => __old/msl_infer}/Nmsl2.cpp | 0
cpp/{fnr_core => __old/msl_infer}/Nmsl2.h | 0
cpp/{fnr_core => __old/msl_infer}/Renderer.cu | 0
cpp/{fnr_core => __old/msl_infer}/Renderer.h | 0
{cpp_old => cpp/__old}/msl_infer/Sampler.cu | 0
cpp/{fnr_core => __old/msl_infer}/Sampler.h | 0
.../__old}/msl_infer/SynthesisPipeline.cpp | 0
.../__old}/msl_infer/SynthesisPipeline.h | 0
{cpp_old => cpp/__old}/msl_infer/View.cu | 0
{cpp_old => cpp/__old}/msl_infer/View.h | 0
.../__old}/msl_infer_test/Makefile | 0
.../__old}/msl_infer_test/main.cpp | 0
.../__old}/nets/barbershop/fovea.trt | Bin
.../__old}/nets/barbershop/periph.trt | Bin
.../__old}/nets/classroom/fovea.trt | Bin
.../__old}/nets/classroom/periph.trt | Bin
cpp/{ => __old}/nets/fovea.mask | Bin
{cpp_old => cpp/__old}/nets/lobby/fovea.trt | Bin
{cpp_old => cpp/__old}/nets/lobby/periph.trt | Bin
cpp/{ => __old}/nets/mid.mask | Bin
cpp/{ => __old}/nets/old/fovea_mono/cat.trt | Bin
cpp/{ => __old}/nets/old/fovea_mono/fc1.trt | Bin
cpp/{ => __old}/nets/old/fovea_mono/fc2.trt | Bin
cpp/{ => __old}/nets/old/fovea_mono/msl.trt | Bin
cpp/{ => __old}/nets/old/fovea_stereo/cat.trt | Bin
cpp/{ => __old}/nets/old/fovea_stereo/fc1.trt | Bin
cpp/{ => __old}/nets/old/fovea_stereo/fc2.trt | Bin
cpp/{ => __old}/nets/old/periph/cat.trt | Bin
cpp/{ => __old}/nets/old/periph/fc1.trt | Bin
cpp/{ => __old}/nets/old/periph/fc2.trt | Bin
cpp/{ => __old}/nets/old/periph/msl.trt | Bin
{cpp_old => cpp/__old}/nets/stones/fovea.trt | Bin
{cpp_old => cpp/__old}/nets/stones/periph.trt | Bin
.../__old}/old/msl_infer/Encoder.cu | 0
.../__old}/old/msl_infer/Encoder.h | 0
.../__old}/old/msl_infer/Enhancement.cu | 0
.../__old}/old/msl_infer/Enhancement.h | 0
.../__old}/old/msl_infer/ImageGen.cpp | 0
.../__old}/old/msl_infer/ImageGen.h | 0
.../__old}/old/msl_infer/InferPipeline.cpp | 0
.../__old}/old/msl_infer/InferPipeline.h | 0
{cpp_old => cpp/__old/old}/msl_infer/Msl.cpp | 0
{cpp_old => cpp/__old/old}/msl_infer/Msl.h | 0
{cpp_old => cpp/__old}/old/msl_infer/Net.cpp | 3 +-
{cpp_old => cpp/__old/old}/msl_infer/Net.h | 0
.../__old/old}/msl_infer/Nmsl2.cpp | 0
{cpp_old => cpp/__old}/old/msl_infer/Nmsl2.h | 0
.../__old/old}/msl_infer/Renderer.cu | 0
.../__old/old}/msl_infer/Renderer.h | 0
.../__old}/old/msl_infer/Sampler.cu | 0
.../__old}/old/msl_infer/Sampler.h | 0
.../old/msl_infer/SynthesisPipeline.cpp | 0
.../__old}/old/msl_infer/SynthesisPipeline.h | 0
{cpp_old => cpp/__old}/old/msl_infer/View.cu | 0
{cpp_old => cpp/__old}/old/msl_infer/View.h | 0
.../__old}/old/msl_infer_test/Makefile | 0
.../__old}/old/msl_infer_test/main.cpp | 0
{cpp_old => cpp/__old}/old/utils/Formatter.h | 0
cpp/{ => __old/old}/utils/Logger.cpp | 0
{cpp_old => cpp/__old}/old/utils/Logger.h | 0
{cpp_old => cpp/__old/old}/utils/Resource.h | 92 +-
{cpp_old => cpp/__old}/old/utils/common.h | 0
{cpp_old => cpp/__old}/old/utils/cuda.h | 0
{cpp_old => cpp/__old}/old/utils/half.h | 0
.../__old}/old/utils/thread_index.h | 0
{cpp_old => cpp/__old}/utils/Formatter.h | 0
{cpp_old/old => cpp/__old}/utils/Logger.cpp | 0
{cpp_old => cpp/__old}/utils/Logger.h | 6 +-
{cpp_old/old => cpp/__old}/utils/Resource.h | 0
{cpp_old => cpp/__old}/utils/common.h | 0
{cpp_old => cpp/__old}/utils/cuda.h | 0
{cpp_old => cpp/__old}/utils/half.h | 0
cpp/{ => __old}/utils/thread_index.h | 0
cpp/fields/FsNeRF.cpp | 31 +
cpp/fields/FsNeRF.h | 22 +
cpp/{fnr_core => fields}/Net.cpp | 0
{cpp_old/old/msl_infer => cpp/fields}/Net.h | 0
cpp/fnr_core/Encoder.cu | 86 -
cpp/fnr_core/Encoder.h | 21 -
cpp/fnr_core/InferPipeline.cpp | 97 -
cpp/fnr_core/InferPipeline.h | 31 -
cpp/fnr_core/NeuralSynthesis.cpp | 193 +
cpp/fnr_core/NeuralSynthesis.h | 19 +
cpp/fnr_core/Sampler.cu | 46 -
cpp/models/FsNeRF.cpp | 107 +
cpp/models/FsNeRF.h | 48 +
cpp/models/Model.h | 11 +
cpp/modules/Encoder.cu | 60 +
cpp/modules/Encoder.h | 27 +
cpp/modules/Renderer.cu | 36 +
cpp/modules/Renderer.h | 23 +
cpp/modules/Sampler.cu | 118 +
cpp/modules/Sampler.h | 20 +
cpp/{fnr_core => modules}/Voxels.cu | 0
cpp/{fnr_core => modules}/Voxels.h | 0
.../bakes/barbershop_0/fovea.png | Bin
.../bakes/barbershop_0/fovea_l.png | Bin
.../bakes/barbershop_0/fovea_r.png | Bin
.../bakes/barbershop_0/mid.png | Bin
.../bakes/barbershop_0/mid_l.png | Bin
.../bakes/barbershop_0/mid_r.png | Bin
.../bakes/barbershop_0/periph.png | Bin
.../bakes/barbershop_0/periph_l.png | Bin
.../bakes/barbershop_0/periph_r.png | Bin
.../bakes/barbershop_0_HR/fovea.png | Bin
.../bakes/barbershop_0_HR/fovea_l.png | Bin
.../bakes/barbershop_0_HR/fovea_r.png | Bin
.../bakes/barbershop_0_HR/mid.png | Bin
.../bakes/barbershop_0_HR/mid_l.png | Bin
.../bakes/barbershop_0_HR/mid_r.png | Bin
.../bakes/barbershop_0_HR/periph.png | Bin
.../bakes/barbershop_0_HR/periph_l.png | Bin
.../bakes/barbershop_0_HR/periph_r.png | Bin
.../bakes/barbershop_0_noCE/fovea.png | Bin
.../bakes/barbershop_0_noCE/fovea_l.png | Bin
.../bakes/barbershop_0_noCE/fovea_r.png | Bin
.../bakes/barbershop_0_noCE/mid.png | Bin
.../bakes/barbershop_0_noCE/mid_l.png | Bin
.../bakes/barbershop_0_noCE/mid_r.png | Bin
.../bakes/barbershop_0_noCE/periph.png | Bin
.../bakes/barbershop_0_noCE/periph_l.png | Bin
.../bakes/barbershop_0_noCE/periph_r.png | Bin
.../nets/barbershop/fovea.onnx | Bin
cpp/{ => resources}/nets/barbershop/fovea.trt | Bin
.../nets/barbershop/periph.onnx | Bin
.../nets/barbershop/periph.trt | Bin
cpp/{ => resources}/nets/classroom/fovea.onnx | Bin
cpp/{ => resources}/nets/classroom/fovea.trt | Bin
.../nets/classroom/periph.onnx | Bin
cpp/{ => resources}/nets/classroom/periph.trt | Bin
{cpp_old => cpp/resources}/nets/fovea.mask | Bin
cpp/{ => resources}/nets/lobby/fovea.onnx | Bin
cpp/{ => resources}/nets/lobby/fovea.trt | Bin
cpp/{ => resources}/nets/lobby/periph.onnx | Bin
cpp/{ => resources}/nets/lobby/periph.trt | Bin
{cpp_old => cpp/resources}/nets/mid.mask | Bin
.../resources}/nets/old/fovea_mono/cat.trt | Bin
.../resources}/nets/old/fovea_mono/fc1.trt | Bin
.../resources}/nets/old/fovea_mono/fc2.trt | Bin
.../resources}/nets/old/fovea_mono/msl.trt | Bin
.../resources}/nets/old/fovea_stereo/cat.trt | Bin
.../resources}/nets/old/fovea_stereo/fc1.trt | Bin
.../resources}/nets/old/fovea_stereo/fc2.trt | Bin
.../resources}/nets/old/periph/cat.trt | Bin
.../resources}/nets/old/periph/fc1.trt | Bin
.../resources}/nets/old/periph/fc2.trt | Bin
.../resources}/nets/old/periph/msl.trt | Bin
cpp/{ => resources}/nets/stones/fovea.onnx | Bin
cpp/{ => resources}/nets/stones/fovea.trt | Bin
cpp/{ => resources}/nets/stones/periph.onnx | Bin
cpp/{ => resources}/nets/stones/periph.trt | Bin
cpp/utils/Eye.h | 3 -
cpp/utils/Logger.h | 93 -
cpp/utils/Resource.h | 144 -
cpp/utils/common.h | 91 +-
cpp/utils/common/fmt.h | 24 +
cpp/utils/common/logger.cpp | 68 +
cpp/utils/common/logger.h | 26 +
cpp/utils/cuda.h | 10 +-
cpp/utils/cuda/array.h | 23 +
cpp/utils/cuda/error.cpp | 12 +
cpp/utils/cuda/error.h | 16 +
cpp/utils/cuda/event.h | 19 +
cpp/utils/cuda/gl_texture.h | 33 +
cpp/utils/cuda/index.h | 34 +
cpp/utils/cuda/map_resources_scope.h | 25 +
cpp/utils/cuda/resource.h | 70 +
cpp/utils/cuda/resources.h | 18 +
cpp/utils/cuda/stream.h | 21 +
cpp/utils/debug.h | 9 +
cpp_old/msl_infer/Nmsl2.h | 25 -
cpp_old/msl_infer/Sampler.h | 15 -
cpp_old/old/msl_infer/Msl.cpp | 28 -
cpp_old/old/msl_infer/Msl.h | 15 -
cpp_old/old/msl_infer/Nmsl2.cpp | 65 -
cpp_old/old/msl_infer/Renderer.cu | 28 -
cpp_old/old/msl_infer/Renderer.h | 15 -
cpp_old/utils/Logger.cpp | 3 -
cpp_old/utils/thread_index.h | 15 -
data/__init__.py | 5 +-
data/dataset.py | 296 +-
data/dataset_factory.py | 23 -
data/loader.py | 201 +-
data/pano_dataset.py | 157 -
data/utils.py | 16 -
data/view_dataset.py | 156 -
fntest.py | 12 -
fovea_net.png | Bin 0 -> 75419 bytes
fovea_params_optim.py | 62 +
gt.png | Bin 0 -> 83250 bytes
key_test.py | 32 -
model/__common__.py | 11 +-
model/__init__.py | 30 +-
model/__old/__common__.py | 6 +
model/__old/__init__.py | 15 +
model/__old/{ => __old}/bg_net.py | 0
model/__old/{ => __old}/nerf_depth.py | 0
model/__old/{ => __old}/oracle.py | 0
model/{ => __old}/base.py | 20 +-
model/{ => __old}/cnerf.py | 23 +-
model/{ => __old}/mnerf.py | 2 +-
model/{ => __old}/mnerf_advance.py | 4 +-
model/__old/nerf.py | 212 +
model/{ => __old}/snerf_fast.py | 35 +-
model/{ => __old}/snerf_x.py | 9 +-
model/{ => __old}/utils.py | 5 +-
model/{ => __old}/vnerf.py | 2 +-
model/__todo/cnerf.py | 129 +
model/__todo/mnerf.py | 29 +
model/__todo/mnerf_advance.py | 36 +
model/__todo/snerf_x.py | 33 +
model/__todo/vnerf.py | 24 +
model/fs_nerf.py | 76 +
model/model.py | 48 +
model/nerf.py | 273 +-
modules/__common__.py | 5 +
modules/__init__.py | 10 +-
modules/{ => __old}/core.py | 53 +-
modules/__old/input_encoder.py | 417 ++
modules/__old/renderer.py | 364 ++
modules/__old/sampler.py | 112 +
modules/core/__init__.py | 4 +
modules/core/color_decoder.py | 41 +
modules/core/density_decoder.py | 16 +
modules/core/field.py | 19 +
modules/core/fs_nerf.py | 58 +
modules/core/nerf.py | 45 +
modules/generic/__init__.py | 1 -
modules/generic/linear.py | 104 -
modules/input_encoder.py | 242 +-
modules/renderer.py | 390 +-
modules/sampler.py | 466 +--
modules/space.py | 130 +-
notebook/__demo/layers/mc_0071(0).png | Bin 0 -> 134125 bytes
notebook/__demo/layers/mc_0071(1).png | Bin 0 -> 225734 bytes
notebook/__demo/layers/mc_0071(2).png | Bin 0 -> 192377 bytes
notebook/__demo/layers/mc_0071(3).png | Bin 0 -> 236006 bytes
notebook/dynamic_bar.ipynb | 393 ++
notebook/gen/gen_crop.ipynb | 83 +
notebook/{ => gen}/gen_demo_mono.ipynb | 193 +-
notebook/{ => gen}/gen_demo_stereo.ipynb | 164 +-
notebook/{ => gen}/gen_for_eval.ipynb | 0
notebook/gen/gen_layers.ipynb | 118 +
notebook/{ => gen}/gen_prebake.ipynb | 2 +-
notebook/{ => gen}/gen_teaser.ipynb | 0
notebook/{ => gen}/gen_test.ipynb | 10 +-
.../{ => gen}/gen_user_study_images.ipynb | 4 +-
notebook/{ => gen}/gen_video.ipynb | 2 +-
notebook/gen_crop.ipynb | 103 -
notebook/test/__general.ipynb | 237 ++
notebook/test/common.py | 10 +
.../constrast.ipynb} | 19 +-
notebook/test/fisheye_undistort.ipynb | 135 +
.../image_filter.ipynb} | 27 +-
.../load_dataset.ipynb} | 49 +-
notebook/test/sphere.ipynb | 142 +
notebook/{ => test}/test_eccv.ipynb | 2 +-
notebook/{ => test}/test_foveation.ipynb | 45 +-
notebook/{ => test}/test_mono_gen.ipynb | 9 +-
notebook/{ => test}/test_mono_view.ipynb | 4 +-
notebook/{ => test}/test_refinement.ipynb | 4 +-
.../{test_voxel.ipynb => test/voxel.ipynb} | 10 +-
notebook/test_lf_syn.ipynb | 134 -
notebook/test_sphere.ipynb | 154 -
notebook/test_ycbcr.ipynb | 60 -
periph_net.png | Bin 0 -> 82417 bytes
requirements.txt | 19 +
run_lf_syn.py | 143 -
run_spherical_view_syn.py | 764 ----
setup.py | 1 -
term_test.py | 15 -
test.ipynb | 127 -
test.py | 376 +-
test.txt | 10 -
test/utils.py | 1 +
test1.txt | 3455 -----------------
test_perf.py | 70 +
test_todo.sh | 24 +
tools/convert_data_desc.py | 2 +-
tools/convert_nerf_checkpoint.py | 106 +
tools/convert_old_snerffast_checkpoint.py | 198 +
{assets => tools/dash/assets}/dash_test.css | 0
dash_test.py => tools/dash/dash_test.py | 13 +-
tools/data/blender_gen/gen_fovea.py | 6 +-
tools/data/blender_gen/gen_periph.py | 6 +-
tools/data/colmap2dataset.py | 178 +
tools/data/extract.py | 40 +-
tools/data/extract360.py | 62 +
tools/data/gen_colmap.py | 73 -
tools/data/gen_seq.py | 52 +-
tools/data/gen_subset.py | 8 +-
tools/data/merge.py | 4 +-
tools/data/split.py | 96 +-
tools/data/split360.py | 73 +
tools/data/video2images.py | 35 +
.../debug}/voxel_sampler_export3d.py | 0
tools/dump_checkpoint.py | 24 +
tools/export_msl.py | 2 +-
tools/export_nmsl.py | 2 +-
tools/export_onnx.py | 67 +-
tools/export_script_model.py | 39 +
tools/export_snerf_fast.py | 4 +-
tools/gen_video.py | 246 +-
tools/image_scale.py | 40 +-
tools/process_nerf.py | 2 +-
train.py | 224 +-
train/__init__.py | 34 +-
train/train.py | 271 --
train/train_multi_scale.py | 16 +-
train/train_with_space.py | 32 +-
train/trainer.py | 294 ++
train_oracle.py | 374 --
update_cnerf.py | 29 -
upsampling/FSRCNN/README.md | 5 -
upsampling/FSRCNN/model.py | 61 -
upsampling/FSRCNN/solver.py | 104 -
upsampling/SRCNN/README.md | 10 -
upsampling/SRCNN/model.py | 30 -
upsampling/SRCNN/solver.py | 104 -
upsampling/SRGAN/README.md | 26 -
upsampling/SRGAN/model.py | 118 -
upsampling/SRGAN/solver.py | 163 -
upsampling/SubPixelCNN/model.py | 33 -
upsampling/SubPixelCNN/solver.py | 110 -
upsampling/run_upsampling.py | 112 -
upsampling/upsampling_dataset.py | 68 -
utils/args.py | 80 +
utils/colmap_read_model.py | 2 +-
utils/config.py | 30 +
utils/env.py | 12 +-
utils/export.py | 53 +
utils/geometry.py | 3 +-
utils/img.py | 49 +-
utils/interact.py | 2 +-
utils/logging.py | 25 +
utils/loss.py | 2 +-
{loss => utils/loss}/__init__.py | 5 +-
{loss => utils/loss}/cauchy.py | 10 +-
utils/loss/lpips.py | 22 +
{loss => utils/loss}/perc_loss.py | 0
{loss => utils/loss}/ssim.py | 0
utils/math.py | 15 +-
utils/mem_profiler.py | 12 +-
utils/misc.py | 183 +-
utils/netio.py | 32 +-
__init__.py => utils/nn.py | 0
utils/nn/__init__.py | 3 +
{modules/generic => utils/nn}/fn.py | 2 +
utils/nn/linear.py | 118 +
utils/{ => nn}/module.py | 64 +-
{modules/generic => utils/nn}/weight_init.py | 3 +-
utils/perf.py | 173 -
utils/profile.py | 325 ++
utils/samples.py | 89 -
utils/seqs.py | 2 +-
utils/sphere.py | 62 +-
utils/type.py | 28 -
utils/types/__common__.py | 6 +
utils/types/__init__.py | 24 +
utils/{ => types}/color.py | 75 +-
utils/types/data_pack.py | 178 +
utils/types/rays.py | 24 +
utils/types/samples.py | 51 +
utils/view.py | 257 +-
utils/voxels.py | 55 +-
463 files changed, 10425 insertions(+), 11421 deletions(-)
delete mode 100644 .clang-format
delete mode 100644 .vscode/settings.json
create mode 100644 a.drawio
create mode 100644 args_test.py
create mode 100644 clib/_ext.pyi
create mode 100644 clib/include/encode.h
create mode 100644 clib/include/encode_debug.h
create mode 100644 clib/src/encode.cpp
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rename {modules/generic => utils/nn}/fn.py (92%)
create mode 100644 utils/nn/linear.py
rename utils/{ => nn}/module.py (66%)
rename {modules/generic => utils/nn}/weight_init.py (99%)
delete mode 100644 utils/perf.py
create mode 100644 utils/profile.py
delete mode 100644 utils/samples.py
delete mode 100644 utils/type.py
create mode 100644 utils/types/__common__.py
create mode 100644 utils/types/__init__.py
rename utils/{ => types}/color.py (66%)
create mode 100644 utils/types/data_pack.py
create mode 100644 utils/types/rays.py
create mode 100644 utils/types/samples.py
diff --git a/.clang-format b/.clang-format
deleted file mode 100644
index edc803a..0000000
--- a/.clang-format
+++ /dev/null
@@ -1,108 +0,0 @@
----
-Language: Cpp
-# BasedOnStyle: LLVM
-AccessModifierOffset: -4
-AlignAfterOpenBracket: Align
-AlignConsecutiveAssignments: false
-AlignConsecutiveDeclarations: false
-AlignEscapedNewlines: Right
-AlignOperands: true
-AlignTrailingComments: true
-AllowAllParametersOfDeclarationOnNextLine: true
-AllowShortBlocksOnASingleLine: false
-AllowShortCaseLabelsOnASingleLine: false
-AllowShortFunctionsOnASingleLine: All
-AllowShortIfStatementsOnASingleLine: false
-AllowShortLoopsOnASingleLine: false
-AlwaysBreakAfterDefinitionReturnType: None
-AlwaysBreakAfterReturnType: None
-AlwaysBreakBeforeMultilineStrings: false
-AlwaysBreakTemplateDeclarations: false
-BinPackArguments: true
-BinPackParameters: true
-BraceWrapping:
- AfterClass: true
- AfterControlStatement: false
- AfterEnum: true
- AfterFunction: true
- AfterNamespace: true
- AfterObjCDeclaration: true
- AfterStruct: true
- AfterUnion: true
- BeforeCatch: false
- BeforeElse: false
- IndentBraces: false
- SplitEmptyFunction: true
- SplitEmptyRecord: true
- SplitEmptyNamespace: true
-BreakBeforeBinaryOperators: None
-BreakBeforeBraces: Attach
-BreakBeforeInheritanceComma: false
-BreakBeforeTernaryOperators: true
-BreakConstructorInitializersBeforeComma: false
-BreakConstructorInitializers: BeforeColon
-BreakAfterJavaFieldAnnotations: false
-BreakStringLiterals: true
-ColumnLimit: 100
-CommentPragmas: '^ IWYU pragma:'
-CompactNamespaces: false
-ConstructorInitializerAllOnOneLineOrOnePerLine: true
-ConstructorInitializerIndentWidth: 4
-ContinuationIndentWidth: 4
-Cpp11BracedListStyle: true
-DerivePointerAlignment: false
-DisableFormat: false
-ExperimentalAutoDetectBinPacking: false
-FixNamespaceComments: true
-ForEachMacros:
- - foreach
- - Q_FOREACH
- - BOOST_FOREACH
-IncludeCategories:
- - Regex: '^"(llvm|llvm-c|clang|clang-c)/'
- Priority: 2
- - Regex: '^(<|"(gtest|gmock|isl|json)/)'
- Priority: 3
- - Regex: '.*'
- Priority: 1
-IncludeIsMainRegex: '(Test)?$'
-IndentCaseLabels: false
-IndentWidth: 4
-IndentWrappedFunctionNames: false
-JavaScriptQuotes: Leave
-JavaScriptWrapImports: true
-KeepEmptyLinesAtTheStartOfBlocks: true
-MacroBlockBegin: ''
-MacroBlockEnd: ''
-MaxEmptyLinesToKeep: 1
-NamespaceIndentation: None
-ObjCBlockIndentWidth: 4
-ObjCSpaceAfterProperty: false
-ObjCSpaceBeforeProtocolList: true
-PenaltyBreakAssignment: 2
-PenaltyBreakBeforeFirstCallParameter: 19
-PenaltyBreakComment: 300
-PenaltyBreakFirstLessLess: 120
-PenaltyBreakString: 1000
-PenaltyExcessCharacter: 1000000
-PenaltyReturnTypeOnItsOwnLine: 60
-PointerAlignment: Right
-ReflowComments: true
-SortIncludes: false
-SortUsingDeclarations: true
-SpaceAfterCStyleCast: false
-SpaceAfterTemplateKeyword: true
-SpaceBeforeAssignmentOperators: true
-SpaceBeforeParens: ControlStatements
-SpaceInEmptyParentheses: false
-SpacesBeforeTrailingComments: 1
-SpacesInAngles: false
-SpacesInContainerLiterals: true
-SpacesInCStyleCastParentheses: false
-SpacesInParentheses: false
-SpacesInSquareBrackets: false
-Standard: Cpp11
-TabWidth: 4
-UseTab: Never
-...
-
diff --git a/.vscode/c_cpp_properties.json b/.vscode/c_cpp_properties.json
index ea5ac16..f18fc86 100644
--- a/.vscode/c_cpp_properties.json
+++ b/.vscode/c_cpp_properties.json
@@ -4,13 +4,17 @@
"name": "Linux",
"includePath": [
"${workspaceFolder}/cpp/**",
- "/usr/local/cuda/include"
+ "${workspaceFolder}/clib/include",
+ "/usr/local/cuda/include",
+ "/home/dengnc/libtorch/include",
+ "/home/dengnc/libtorch/include/torch/csrc/api/include",
+ "/home/dengnc/miniconda3/include/**"
],
"defines": [],
"compilerPath": "/usr/bin/gcc",
"cStandard": "gnu17",
- "cppStandard": "gnu++14",
- "intelliSenseMode": "gcc-x64"
+ "cppStandard": "gnu++17",
+ "intelliSenseMode": "${default}"
}
],
"version": 4
diff --git a/.vscode/launch.json b/.vscode/launch.json
index 7ed687d..5fd3f99 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -4,8 +4,17 @@
// 欲了解更多信æ¯ï¼Œè¯·è®¿é—®: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
-
-
+ {
+ "name": "convert_nerf_checkpoint",
+ "type": "python",
+ "request": "launch",
+ "program": "tools/convert_nerf_checkpoint.py",
+ "args": [
+ "/home/dengnc/Work/ref_code/nerf-pytorch/logs/dvs_gas_nearrange/200000.tar"
+ ],
+ "console": "integratedTerminal",
+ "justMyCode": false
+ },
{
"name": "Debug/Voxel Sampler Export 3D",
"type": "python",
@@ -24,7 +33,7 @@
"program": "train.py",
"args": [
"-c",
- "_lr_snerf_voxels+ls",
+ "nerf_llff",
//"/home/dengnc/dvs/data/classroom/_nets/pano_t0.8/smnerf_voxels+ls+lbl/checkpoint_35.tar",
//"--prune",
//"1",
@@ -34,9 +43,9 @@
//"100",
//"--views",
//"5",
- "data/classroom/lr_view_t0.8_r360x80_train"
+ "/home/dengnc/Work/fov_nerf/data/__thesis/trex/train.json"
],
- "justMyCode": false,
+ "justMyCode": true,
"console": "integratedTerminal"
},
{
@@ -45,16 +54,48 @@
"request": "launch",
"program": "test.py",
"args": [
- "-m",
- "/home/dengnc/dvs/data/classroom/_nets/ms_train_t0.8/_cnerf/checkpoint_50.tar",
"-o",
"perf",
"color",
- "--output-type",
+ "--media",
"image",
- "/home/dengnc/dvs/data/classroom/lr_view_t0.8_r360x80_test.json",
"--views",
- "1"
+ "3",
+ //"-r",
+ //"100x200",
+ "/home/dengnc/Work/fov_nerf/data/__thesis/barbershop_old/_nets/train_t0.3/eval@snerffast4-rgb_e6_fc256x8_d1.20-6.00_s64_~p/checkpoint_50.tar",
+ "/home/dengnc/Work/fov_nerf/data/__thesis/barbershop_old/test_t0.3.json",
+ //"--batch",
+ //"8192"
+ ],
+ "justMyCode": false,
+ "console": "integratedTerminal"
+ },
+ {
+ "name": "Convert Colmap",
+ "type": "python",
+ "request": "launch",
+ "program": "tools/data/colmap2dataset.py",
+ "args": [
+ "data/__captured/sittingroom",
+ "--scale-down",
+ "4"
+ ],
+ "console": "integratedTerminal"
+ },
+ {
+ "name": "Generate Video",
+ "type": "python",
+ "request": "launch",
+ "program": "tools/gen_video.py",
+ "args": [
+ "data/__captured/sittingroom",
+ "data/__demo/realvideo/sittingroom_1.json",
+ "-f",
+ "40",
+ "-s",
+ "-d",
+ "0.6"
],
"console": "integratedTerminal"
}
diff --git a/.vscode/settings.json b/.vscode/settings.json
deleted file mode 100644
index 920979e..0000000
--- a/.vscode/settings.json
+++ /dev/null
@@ -1,14 +0,0 @@
-{
- "files.watcherExclude": {
- "**/data/**": true
- },
- "files.associations": {
- "string": "cpp",
- "__functional_03": "cpp",
- "functional": "cpp",
- "vector": "cpp",
- "__config": "cpp",
- "__nullptr": "cpp"
- },
- "python.pythonPath": "/home/dengnc/miniconda3/bin/python",
-}
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
index c283369..2b32f57 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
MIT License
-Copyright (c) 2020 Jiannan Ye
+Copyright (c) 2022 Nianchen Deng
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
diff --git a/README.md b/README.md
index 8d556ad..365f49b 100644
--- a/README.md
+++ b/README.md
@@ -1,51 +1,39 @@
# Configure environment
## 1. Install Conda packages:
-* Pytorch 1.8.1 with CUDA
-
-```
-$ conda install pytorch torchvision torchaudio cudatoolkit=<your cuda version> -c pytorch -c nvidia
-```
-
-Or ref to https://pytorch.org/get-started/locally/ for install guide
-
+* Pytorch with CUDA
+ ```bash
+ $ conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
+ ```
+ Or ref to https://pytorch.org/get-started/locally/ for install guide
* matplotlib
-
* tensorboard
-
-* plyfile
-
-```
-$ conda install -c conda-forge plyfile
-```
-
+* tqdm
+* configargparse
* (Optional) dash
-
-```
-$ conda install dash pandas
-```
+ ```bash
+ $ conda install dash pandas
+ ```
## 2. Install Pip packages:
* pyGlm
* tensorboardX
-* (optional) opencv-python
+* torch_tb_profiler
+* opencv-python
+* ipympl
+* lpips
* (optional) thop
* (optional) ConcurrentLogHandler
-# Useful commands
-## 1. Video generate:
-```
-$ ffmpeg -y -r 50 -i %04d.png -c:v libx264 -vframes 600 ../classroom_hmd_mono_hint.mp4
+## 4. Build extension "clib._ext"
+```bash
+$ python setup.py build_ext
```
+If build successed, a _ext.\*.so will be generated under build/lib.\*/clib directory. Move this file to clib/.
-## 2. Convert onnx to tensorRT
-```
-$ trtexec --onnx=in.onnx --fp16 --saveEngine=out.trt --workspace=4096
-```
+## 4. (Optional) Install FFMpeg with Extra Codecs:
-# Install FFMpeg with Extra Codecs:
-
-```
+```bash
sudo apt-get update -qq && sudo apt-get -y install \
autoconf \
automake \
@@ -109,4 +97,25 @@ PATH="$HOME/bin:$PATH" PKG_CONFIG_PATH="$HOME/ffmpeg_build/lib/pkgconfig" ./conf
PATH="$HOME/bin:$PATH" make && \
make install && \
hash -r
+```
+
+# Useful commands
+## 1. Video generate:
+```bash
+$ ffmpeg -y -r 50 -i %04d.png -c:v libx264 -vframes 600 ../classroom_hmd_mono_hint.mp4
+```
+
+## 2. Extract frames:
+```bash
+$ ffmpeg -i <video_path> -f image2 -q:v 2 -vf fps=<fps> <out_dir>/image%04d.png
+```
+
+## 3. Convert onnx to tensorRT
+```bash
+$ trtexec --onnx=in.onnx --fp16 --saveEngine=out.trt --workspace=4096
+```
+
+## 4. Generate dataset of specific path
+```bash
+$ python tools/data/gen_seq.py -s helix|look_around|scan_around -n <frames> --ref <train_dataset.json> <dataset_dir>
```
\ No newline at end of file
diff --git a/a.drawio b/a.drawio
new file mode 100644
index 0000000..5f9187a
--- /dev/null
+++ b/a.drawio
@@ -0,0 +1,444 @@
+<mxfile host="65bd71144e">
+ <diagram id="NXNcgxQNoOwNPU3BF2O1" name="第 1 页">
+ <mxGraphModel dx="729" dy="800" grid="1" gridSize="5" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="827" pageHeight="1169" math="1" shadow="0">
+ <root>
+ <mxCell id="0"/>
+ <mxCell id="1" parent="0"/>
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+ </mxCell>
+ <mxCell id="25" value="\(N_f\)" style="edgeLabel;html=1;align=center;verticalAlign=middle;resizable=0;points=[];fontFamily=Verdana;labelBackgroundColor=none;sketch=1;rounded=1;" parent="23" vertex="1" connectable="0">
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+ </mxCell>
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+ </mxCell>
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+ <mxCell id="136" style="edgeStyle=orthogonalEdgeStyle;rounded=1;sketch=1;html=1;exitX=1;exitY=0.5;exitDx=0;exitDy=0;entryX=0;entryY=0.5;entryDx=0;entryDy=0;fontFamily=Verdana;fontColor=#000000;endArrow=classic;endFill=1;" parent="169" source="138" target="142" edge="1">
+ <mxGeometry relative="1" as="geometry"/>
+ </mxCell>
+ <mxCell id="144" value="\(\tilde{d}^+\)" style="shape=parallelogram;perimeter=parallelogramPerimeter;whiteSpace=wrap;html=1;fixedSize=1;sketch=1;rounded=1;fillColor=#d5e8d4;strokeColor=#82b366;" parent="169" vertex="1">
+ <mxGeometry x="332.5" y="115" width="80" height="30" as="geometry"/>
+ </mxCell>
+ <mxCell id="137" style="edgeStyle=orthogonalEdgeStyle;rounded=1;sketch=1;html=1;exitX=1;exitY=0.5;exitDx=0;exitDy=0;entryX=0;entryY=0.5;entryDx=0;entryDy=0;fontFamily=Verdana;fontColor=#000000;endArrow=classic;endFill=1;" parent="169" source="138" target="144" edge="1">
+ <mxGeometry relative="1" as="geometry"/>
+ </mxCell>
+ <mxCell id="146" value="Field" style="shape=process;whiteSpace=wrap;html=1;backgroundOutline=1;fillColor=#f8cecc;strokeColor=#b85450;sketch=1;rounded=1;" parent="169" vertex="1">
+ <mxGeometry x="432.5" y="60" width="70" height="40" as="geometry"/>
+ </mxCell>
+ <mxCell id="141" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=1;sketch=1;html=1;fontFamily=Verdana;fontColor=#000000;endArrow=classic;endFill=1;" parent="169" source="142" target="146" edge="1">
+ <mxGeometry relative="1" as="geometry"/>
+ </mxCell>
+ <mxCell id="148" value="Density Decoder" style="shape=process;whiteSpace=wrap;html=1;backgroundOutline=1;fillColor=#dae8fc;strokeColor=#6c8ebf;sketch=1;rounded=1;" parent="169" vertex="1">
+ <mxGeometry x="532.5" y="60" width="70" height="40" as="geometry"/>
+ </mxCell>
+ <mxCell id="149" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=1;sketch=1;html=1;fontFamily=Verdana;fontColor=#000000;endArrow=classic;endFill=1;" parent="169" source="146" target="148" edge="1">
+ <mxGeometry relative="1" as="geometry"/>
+ </mxCell>
+ <mxCell id="150" value="Color Decoder" style="shape=process;whiteSpace=wrap;html=1;backgroundOutline=1;fillColor=#e1d5e7;strokeColor=#9673a6;sketch=1;rounded=1;" parent="169" vertex="1">
+ <mxGeometry x="532.5" y="110" width="70" height="40" as="geometry"/>
+ </mxCell>
+ <mxCell id="143" style="edgeStyle=orthogonalEdgeStyle;rounded=1;sketch=1;html=1;exitX=1;exitY=0.5;exitDx=0;exitDy=0;entryX=0;entryY=0.5;entryDx=0;entryDy=0;fontFamily=Verdana;fontColor=#000000;endArrow=classic;endFill=1;" parent="169" source="144" target="150" edge="1">
+ <mxGeometry relative="1" as="geometry"/>
+ </mxCell>
+ <mxCell id="145" style="edgeStyle=orthogonalEdgeStyle;rounded=1;sketch=1;html=1;exitX=1;exitY=0.5;exitDx=0;exitDy=0;entryX=0;entryY=0.5;entryDx=0;entryDy=0;fontFamily=Verdana;fontColor=#000000;endArrow=classic;endFill=1;" parent="169" source="146" target="150" edge="1">
+ <mxGeometry relative="1" as="geometry"/>
+ </mxCell>
+ <mxCell id="151" value="Volumn Renderer" style="whiteSpace=wrap;html=1;sketch=1;rounded=1;fillColor=#f5f5f5;fontColor=#333333;strokeColor=#666666;" parent="169" vertex="1">
+ <mxGeometry x="637.5" y="85" width="75" height="40" as="geometry"/>
+ </mxCell>
+ <mxCell id="152" style="edgeStyle=orthogonalEdgeStyle;rounded=1;sketch=1;html=1;exitX=1;exitY=0.5;exitDx=0;exitDy=0;fontFamily=Verdana;fontColor=#000000;endArrow=classic;endFill=1;entryX=0;entryY=0.5;entryDx=0;entryDy=0;" parent="169" source="150" target="151" edge="1">
+ <mxGeometry relative="1" as="geometry">
+ <mxPoint x="597.5" y="-495" as="targetPoint"/>
+ </mxGeometry>
+ </mxCell>
+ <mxCell id="153" value="Samples<sup>+</sup>" style="shape=parallelogram;perimeter=parallelogramPerimeter;whiteSpace=wrap;html=1;fixedSize=1;sketch=1;rounded=1;fillColor=#d5e8d4;strokeColor=#82b366;" parent="169" vertex="1">
+ <mxGeometry x="127.5" y="90" width="80" height="30" as="geometry"/>
+ </mxCell>
+ <mxCell id="154" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=1;sketch=1;html=1;fontFamily=Verdana;endArrow=classic;endFill=1;" parent="169" source="139" target="153" edge="1">
+ <mxGeometry relative="1" as="geometry">
+ <mxPoint x="102.5" y="105" as="sourcePoint"/>
+ <mxPoint x="227.5" y="105" as="targetPoint"/>
+ </mxGeometry>
+ </mxCell>
+ <mxCell id="147" style="edgeStyle=orthogonalEdgeStyle;rounded=1;sketch=1;html=1;exitX=1;exitY=0.5;exitDx=0;exitDy=0;entryX=0;entryY=0.5;entryDx=0;entryDy=0;fontFamily=Verdana;fontColor=#000000;endArrow=classic;endFill=1;" parent="169" source="148" target="151" edge="1">
+ <mxGeometry relative="1" as="geometry"/>
+ </mxCell>
+ <mxCell id="163" value="Pixel<br>Color" style="shape=parallelogram;perimeter=parallelogramPerimeter;whiteSpace=wrap;html=1;fixedSize=1;sketch=1;rounded=1;fillColor=#f5f5f5;strokeColor=#666666;fontColor=#333333;" parent="169" vertex="1">
+ <mxGeometry x="732.5" y="90" width="80" height="30" as="geometry"/>
+ </mxCell>
+ <mxCell id="161" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=1;sketch=1;html=1;fontFamily=Verdana;fontColor=#000000;endArrow=classic;endFill=1;entryX=0;entryY=0.5;entryDx=0;entryDy=0;" parent="169" source="151" target="163" edge="1">
+ <mxGeometry relative="1" as="geometry">
+ <mxPoint x="780" y="105" as="targetPoint"/>
+ </mxGeometry>
+ </mxCell>
+ </root>
+ </mxGraphModel>
+ </diagram>
+</mxfile>
\ No newline at end of file
diff --git a/args_test.py b/args_test.py
new file mode 100644
index 0000000..78c0caf
--- /dev/null
+++ b/args_test.py
@@ -0,0 +1,17 @@
+from configargparse import ArgumentParser
+from utils.args import BaseArgs
+
+
+class Args(BaseArgs):
+ a: int | None = 20
+ b: str = "hello"
+ c: list[int] = [4, 5]
+ flag: bool = False
+parser = ArgumentParser()
+args = Args()
+args.parse(debug=True)
+#args.setup_parser(parser, True)
+#parser.add_argument('--data', nargs='+', type=int,default=[10])
+#parser.parse_args("--flag --data 20 30", namespace=args)
+#setattr(args, "c", [4, 5])
+print(args)
\ No newline at end of file
diff --git a/batch_collect_video.sh b/batch_collect_video.sh
index 77773a5..dfc1dee 100755
--- a/batch_collect_video.sh
+++ b/batch_collect_video.sh
@@ -1,4 +1,4 @@
-#/usr/bin/bash
+#!/usr/bin/bash
curdir=$(pwd)
datadir="$curdir/data/__new/classroom_fovea_r360x80_t0.6"
videodir="$datadir/eval_video"
@@ -6,7 +6,7 @@ epochs=50
if [ ! -d "$videodir" ]; then
echo "make directory for Video"
- mkdir $videodir
+ mkdir "$videodir"
fi
# nets: 1, 2, 4, 8
@@ -22,7 +22,7 @@ for n_nets in 1 2 4 8; do
dst_path="$videodir/$exportname.mp4"
if [ -f "$videodir/$src_path" ]; then
if [ ! -f "$dst_path" ]; then
- ln -s $src_path $dst_path
+ ln -s $src_path "$dst_path"
fi
fi
done
diff --git a/batch_export_net.sh b/batch_export_net.sh
index 43db536..5de39b7 100755
--- a/batch_export_net.sh
+++ b/batch_export_net.sh
@@ -1,4 +1,4 @@
-#/usr/bin/bash
+#!/usr/bin/bash
datadir='data/__new/classroom_fovea_r360x80_t0.6'
onnxdir="$datadir/eval_onnx"
@@ -23,7 +23,7 @@ for n_nets in 1 2 4 8; do
for nf in 64 128 256 512 1024; do
for n_samples in 8 16 32 64 128; do
configid="eval@snerffast${n_nets}-rgb_e6_fc${nf}x${n_layers}_d1.00-7.00_s${n_samples}_~p"
- if (( $n_samples == 64 )); then
+ if ((n_samples == 64)); then
exportname="eval_${n_nets}x${nf}x${n_layers}"
else
exportname="eval_${n_nets}x${nf}x${n_layers}_${n_samples}"
@@ -45,4 +45,4 @@ for n_nets in 1 2 4 8; do
done
done
done
-done
\ No newline at end of file
+done
diff --git a/batch_infer.sh b/batch_infer.sh
index 5fffb3c..b5114b0 100755
--- a/batch_infer.sh
+++ b/batch_infer.sh
@@ -1,4 +1,4 @@
-#/usr/bin/bash
+#!/usr/bin/bash
testcase=$1
datadir='data/__new/classroom_fovea_r360x80_t0.6'
@@ -19,7 +19,7 @@ for nf in 64 128 256 512 1024; do
configid="eval@snerffast${n_nets}-rgb_e6_fc${nf}x${n_layers}_d1.00-7.00_s64_~p"
if [ ! -f "$datadir/$configid/model-epoch_$epochs.pth" ]; then
cont_epoch=0
- for ((i=$epochs-1;i>0;i--)) do
+ for ((i=epochs-1;i>0;i--)) do
if [ -f "$datadir/$configid/model-epoch_$i.pth" ]; then
cont_epoch=$i
break
diff --git a/batch_test.sh b/batch_test.sh
index 11587a9..1c697ae 100755
--- a/batch_test.sh
+++ b/batch_test.sh
@@ -3,11 +3,24 @@
test_dataset=$1
test_model_dir=$2
-for i in "$test_model_dir"*
+for misc_path in "$test_model_dir"/*/_misc/checkpoint_30.tar
do
- python test.py -m "$(pwd)/$i/checkpoint_50.tar" -o perf color --output-type image "$test_dataset"
+ [ -f "$misc_path" ] || continue
+ misc_dir=$(dirname "$misc_path")
+ echo mv "$misc_path" "${misc_dir%/*}"
+ mv "$misc_path" "${misc_dir%/*}"
+done
+
+
+for model_path in "$test_model_dir"/*/checkpoint_30.tar
+do
+ model_dir=$(dirname "$model_path")
+ #model_name=${model_dir#"$test_model_dir/"}
+ [ -d "$model_dir/output_30" ] || python test.py "$model_path" "$test_dataset" -o perf --media image
done
echo Test Finished
-ls $test_model_dir/*/output_50/perf* | awk -F"/" '{print $6, "\t", $8}'
\ No newline at end of file
+cd "$test_model_dir"
+ls */output_30/perf* | sed -r "s/()\/output_30\/perf.+_([0-9\.]+)ms_([0-9\.e\-]+)\.csv/\1\t\2\t\3/"
+
diff --git a/blender/gen_utils.py b/blender/gen_utils.py
index e46215e..ff72f69 100644
--- a/blender/gen_utils.py
+++ b/blender/gen_utils.py
@@ -9,9 +9,9 @@ from itertools import product
class Gen:
def __init__(self, root_dir: str, dataset_name: str, *,
- res: Tuple[int, int],
+ res: tuple[int, int],
fov: float,
- samples: List[int]) -> None:
+ samples: list[int]) -> None:
self.res = res
self.fov = fov
self.samples = samples
@@ -47,11 +47,11 @@ class Gen:
with open(self.data_desc_file, 'w') as fp:
json.dump(self.desc, fp, indent=4)
- def add_sample(self, i, x: List[float], render_only=False):
+ def add_sample(self, i, x: list[float], render_only=False):
self.cam_obj.location = x[:3]
if len(x) > 3:
self.cam_obj.rotation_euler = [math.radians(x[4]), math.radians(x[3]), 0]
- self.scene.render.filepath = self.data_dir + self.desc['view_file_pattern'] % i
+ self.scene.render.filepath = self.data_dir + self.desc['color_file'] % i
bpy.ops.render.render(write_still=True)
if not render_only:
self.desc['view_centers'].append(x[:3])
@@ -84,8 +84,8 @@ class Gen:
# Render missing views in data desc
for i in range(len(self.desc['view_centers'])):
- if not os.path.exists(self.data_dir + self.desc['view_file_pattern'] % i):
- x: List[float] = self.desc['view_centers'][i]
+ if not os.path.exists(self.data_dir + self.desc['color_file'] % i):
+ x: list[float] = self.desc['view_centers'][i]
if 'view_rots' in self.desc:
x += self.desc['view_rots'][i]
self.add_sample(i, x, render_only=True)
@@ -99,15 +99,15 @@ class Gen:
class GenView(Gen):
def __init__(self, root_dir: str, dataset_name: str, *,
- res: Tuple[int, int], fov: float, samples: List[int],
- tbox: Tuple[float, float, float], rbox: Tuple[float, float]) -> None:
+ res: tuple[int, int], fov: float, samples: list[int],
+ tbox: tuple[float, float, float], rbox: tuple[float, float]) -> None:
super().__init__(root_dir, dataset_name, res=res, fov=fov, samples=samples)
self.tbox = tbox
self.rbox = rbox
def init_desc(self):
return {
- 'view_file_pattern': 'view_%04d.png',
+ 'color_file': 'view_%04d.png',
"gl_coord": True,
'view_res': {
'x': self.res[0],
@@ -143,8 +143,8 @@ class GenView(Gen):
class GenPano(Gen):
def __init__(self, root_dir: str, dataset_name: str, *,
- samples: List[int], depth_range: Tuple[float, float],
- tbox: Tuple[float, float, float] = None) -> None:
+ samples: list[int], depth_range: tuple[float, float],
+ tbox: tuple[float, float, float] = None) -> None:
self.depth_range = depth_range
self.tbox = tbox
super().__init__(root_dir, dataset_name, res=[4096, 2048], fov=-1, samples=samples)
@@ -157,13 +157,15 @@ class GenPano(Gen):
}
} if self.tbox else {}
return {
- "type": "pano",
- 'view_file_pattern': 'view_%04d.png',
+ 'color_file': 'view_%04d.png',
"gl_coord": True,
'view_res': {
'x': self.res[0],
'y': self.res[1]
},
+ "cam_params": {
+ "type": "pano"
+ },
**range,
"depth_range": {
"min": self.depth_range[0],
diff --git a/clib/__init__.py b/clib/__init__.py
index da22125..9f945d9 100644
--- a/clib/__init__.py
+++ b/clib/__init__.py
@@ -2,6 +2,9 @@
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
+#
+# To install the _ext library, run the following command:
+# > python setup.py build_ext --inplace
''' Modified based on: https://github.com/erikwijmans/Pointnet2_PyTorch '''
from __future__ import (
@@ -11,32 +14,16 @@ from __future__ import (
print_function,
unicode_literals,
)
-import os
-import sys
-from typing import Tuple
import torch
import torch.nn.functional as F
-from torch.autograd import Function
-import torch.nn as nn
-import sys
import numpy as np
+from torch.autograd import Function
+from torch.autograd.function import FunctionCtx, once_differentiable
+
+import clib._ext as _ext
from utils.geometry import discretize_points
from utils import math
-try:
- import builtins
-except:
- import __builtin__ as builtins
-
-try:
- import clib._ext as _ext
-except ImportError:
- raise ImportError(
- "Could not import _ext module.\n"
- "Please see the setup instructions in the README"
- )
-
-
class BallRayIntersect(Function):
@staticmethod
@@ -59,47 +46,8 @@ class BallRayIntersect(Function):
ball_ray_intersect = BallRayIntersect.apply
-class AABBRayIntersect(Function):
- @staticmethod
- def forward(ctx, voxelsize, n_max, points, ray_start, ray_dir):
- # HACK: speed-up ray-voxel intersection by batching...
- G = min(2048, int(2 * 10 ** 9 / points.numel())) # HACK: avoid out-of-memory
- S, N = ray_start.shape[:2]
- K = int(np.ceil(N / G))
- G, K = 1, N # HACK
- H = K * G
- if H > N:
- ray_start = torch.cat([ray_start, ray_start[:, :H - N]], 1)
- ray_dir = torch.cat([ray_dir, ray_dir[:, :H - N]], 1)
- ray_start = ray_start.reshape(S * G, K, 3)
- ray_dir = ray_dir.reshape(S * G, K, 3)
- points = points[None].expand(S * G, *points.size()).contiguous()
-
- inds, min_depth, max_depth = _ext.aabb_intersect(
- ray_start.float(), ray_dir.float(), points.float(), voxelsize, n_max)
- min_depth = min_depth.type_as(ray_start)
- max_depth = max_depth.type_as(ray_start)
-
- inds = inds.reshape(S, H, -1)
- min_depth = min_depth.reshape(S, H, -1)
- max_depth = max_depth.reshape(S, H, -1)
- if H > N:
- inds = inds[:, :N]
- min_depth = min_depth[:, :N]
- max_depth = max_depth[:, :N]
-
- ctx.mark_non_differentiable(inds)
- ctx.mark_non_differentiable(min_depth)
- ctx.mark_non_differentiable(max_depth)
- return inds, min_depth, max_depth
-
- @staticmethod
- def backward(ctx, a, b, c):
- return None, None, None, None, None
-
-
def aabb_ray_intersect(voxelsize: float, n_max: int, points: torch.Tensor, ray_start: torch.Tensor,
- ray_dir: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ ray_dir: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
AABB-Ray intersect test
@@ -112,7 +60,33 @@ def aabb_ray_intersect(voxelsize: float, n_max: int, points: torch.Tensor, ray_s
:return `Tensor(S, N, n_max)`: min depths of every intersected voxels
:return `Tensor(S, N, n_max)`: max depths of every intersected voxels
"""
- return AABBRayIntersect.apply(voxelsize, n_max, points, ray_start, ray_dir)
+ # HACK: speed-up ray-voxel intersection by batching...
+ G = min(2048, int(2e9 / points.numel())) # HACK: avoid out-of-memory
+ S, N = ray_start.shape[:2]
+ K = math.ceil(N / G)
+ G, K = 1, N # HACK
+ H = K * G
+ if H > N:
+ ray_start = torch.cat([ray_start, ray_start[:, :H - N]], 1)
+ ray_dir = torch.cat([ray_dir, ray_dir[:, :H - N]], 1)
+ ray_start = ray_start.reshape(S * G, K, 3)
+ ray_dir = ray_dir.reshape(S * G, K, 3)
+ points = points[None].expand(S * G, *points.size()).contiguous()
+
+ inds, min_depth, max_depth = _ext.aabb_intersect(
+ ray_start.float(), ray_dir.float(), points.float(), voxelsize, n_max)
+ min_depth = min_depth.type_as(ray_start)
+ max_depth = max_depth.type_as(ray_start)
+
+ inds = inds.reshape(S, H, -1)
+ min_depth = min_depth.reshape(S, H, -1)
+ max_depth = max_depth.reshape(S, H, -1)
+ if H > N:
+ inds = inds[:, :N]
+ min_depth = min_depth[:, :N]
+ max_depth = max_depth[:, :N]
+
+ return inds, min_depth, max_depth
class SparseVoxelOctreeRayIntersect(Function):
@@ -122,7 +96,7 @@ class SparseVoxelOctreeRayIntersect(Function):
G = min(2048, int(2 * 10 ** 9 / (points.numel() + children.numel())))
S, N = ray_start.shape[:2]
K = int(np.ceil(N / G))
- G, K = 1, N # HACK
+ G, K = 1, N # HACK
H = K * G
if H > N:
ray_start = torch.cat([ray_start, ray_start[:, :H - N]], 1)
@@ -156,7 +130,7 @@ class SparseVoxelOctreeRayIntersect(Function):
def octree_ray_intersect(voxelsize: float, n_max: int, points: torch.Tensor, children: torch.Tensor,
- ray_start: torch.Tensor, ray_dir: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ ray_start: torch.Tensor, ray_dir: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Octree-Ray intersect test
@@ -267,7 +241,7 @@ class UniformRaySampling(Function):
def uniform_ray_sampling(pts_idx: torch.Tensor, min_depth: torch.Tensor, max_depth: torch.Tensor,
- step_size: float, max_ray_length: float, deterministic: bool = False) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ step_size: float, max_ray_length: float, deterministic: bool = False) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Sample along rays uniformly
@@ -357,7 +331,7 @@ class InverseCDFRaySampling(Function):
def inverse_cdf_sampling(pts_idx: torch.Tensor, min_depth: torch.Tensor, max_depth: torch.Tensor,
probs: torch.Tensor, steps: torch.Tensor, fixed_step_size: float = -1,
- deterministic: bool = False) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ deterministic: bool = False) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Sample along rays by inverse CDF
@@ -461,7 +435,7 @@ def parallel_ray_sampling(MARCH_SIZE, pts_idx, min_depth, max_depth, determinist
return sampled_idx, sampled_depth, sampled_dists
-def build_easy_octree(points: torch.Tensor, half_voxel: float) -> Tuple[torch.Tensor, torch.Tensor]:
+def build_easy_octree(points: torch.Tensor, half_voxel: float) -> tuple[torch.Tensor, torch.Tensor]:
"""
Build an octree.
@@ -476,4 +450,69 @@ def build_easy_octree(points: torch.Tensor, half_voxel: float) -> Tuple[torch.Te
center = (coords.max(0)[0] + coords.min(0)[0]) / 2
centers, children = _ext.build_octree(center, coords, int(depths))
centers = centers.float() * half_voxel + residual # transform back to float
- return centers, children
\ No newline at end of file
+ return centers, children
+
+
+class MultiresHashEncode(Function):
+ @staticmethod
+ def forward(ctx: FunctionCtx, levels: int, coarse_levels: int, res_list: torch.Tensor,
+ hash_table_offsets: torch.Tensor, x: torch.Tensor, hash_table: torch.Tensor,
+ grad_enabled: bool) -> torch.Tensor:
+ """
+ [summary]
+
+ :param ctx `FunctionCtx`: [description]
+ :param levels `int`: [description]
+ :param coarse_levels `int`: [description]
+ :param res_list `Tensor(L, D)`: [description]
+ :param hash_table_offsets `Tensor(L+1)`: [description]
+ :param x `Tensor(N, D)`: [description]
+ :param hash_table `Tensor(T, F)`: [description]
+ :return `Tensor(L, N, F)`: [description]
+ """
+
+ x = x.contiguous()
+ res_list = res_list.int().contiguous()
+ hash_table_offsets = hash_table_offsets.int().contiguous()
+ if grad_enabled and hash_table.requires_grad:
+ encoded, weights, indices = _ext.multires_hash_encode_with_grad(
+ levels, coarse_levels, x, res_list, hash_table, hash_table_offsets)
+ ctx.save_for_backward(weights, indices.long())
+ ctx.hash_table_shape = hash_table.shape
+ return encoded
+ print(hash_table)
+ return _ext.multires_hash_encode(levels, coarse_levels, x, res_list, hash_table,
+ hash_table_offsets)
+
+ @staticmethod
+ @once_differentiable
+ def backward(ctx: FunctionCtx, grad_output: torch.Tensor):
+ """
+ [summary]
+
+ :param ctx `FunctionCtx`: [description]
+ :param grad_output `Tensor(L, N, F)`: [description]
+ :return: [description]
+ """
+ weights, indices = ctx.saved_tensors # (L, N, C)
+ t = grad_output[..., None, :] * weights[..., None] # (L, N, C, F)
+ grad_hash_table = grad_output.new_zeros(*ctx.hash_table_shape)
+ grad_hash_table.index_put_([indices], t, accumulate=True)
+ return None, None, None, None, None, grad_hash_table, None
+
+
+def multires_hash_encode(levels: int, coarse_levels: int, res_list: torch.Tensor,
+ hash_table_offsets: torch.Tensor, x: torch.Tensor, hash_table: torch.Tensor) -> torch.Tensor:
+ """
+
+
+ :param levels `int`: [description]
+ :param coarse_levels `int`: [description]
+ :param res_list `Tensor(L, D)`: [description]
+ :param hash_table_offsets `Tensor(L+1)`: [description]
+ :param x `Tensor(N, D)`: [description]
+ :param hash_table `Tensor(T, F)`: [description]
+ :return `Tensor(L, N, F)`: [description]
+ """
+ return MultiresHashEncode.apply(levels, coarse_levels, res_list, hash_table_offsets, x,
+ hash_table, torch.is_grad_enabled())
diff --git a/clib/_ext.pyi b/clib/_ext.pyi
new file mode 100644
index 0000000..624f780
--- /dev/null
+++ b/clib/_ext.pyi
@@ -0,0 +1,32 @@
+import torch
+
+
+# intersect.h
+def ball_intersect(ray_start: torch.Tensor, ray_dir: torch.Tensor, points: torch.Tensor,
+ radius: float, n_max: int) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: ...
+
+
+def aabb_intersect(ray_start: torch.Tensor, ray_dir: torch.Tensor, points: torch.Tensor,
+ voxelsize: float, n_max: int) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: ...
+
+
+def svo_intersect(ray_start: torch.Tensor, ray_dir: torch.Tensor, points: torch.Tensor, children: torch.Tensor,
+ voxelsize: float, n_max: int) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: ...
+
+
+def triangle_intersect(ray_start: torch.Tensor, ray_dir: torch.Tensor, face_points: torch.Tensor,
+ cagesize: float, blur: float, n_max: int) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: ...
+
+
+# octree.h
+def build_octree(center: torch.Tensor, points: torch.Tensor,
+ depth: int) -> tuple[torch.Tensor, torch.Tensor]: ...
+
+
+# encode.h
+def multires_hash_encode(levels: int, coarse_levels: int, x: torch.Tensor, res_list: torch.Tensor,
+ hash_table: torch.Tensor, hash_table_offsets: torch.Tensor) -> torch.Tensor: ...
+
+
+def multires_hash_encode_with_grad(levels: int, coarse_levels: int, x: torch.Tensor, res_list: torch.Tensor,
+ hash_table: torch.Tensor, hash_table_offsets: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: ...
diff --git a/clib/include/cuda_utils.h b/clib/include/cuda_utils.h
index d4c4bb4..1ada784 100644
--- a/clib/include/cuda_utils.h
+++ b/clib/include/cuda_utils.h
@@ -1,5 +1,5 @@
// Copyright (c) Facebook, Inc. and its affiliates.
-//
+//
// This source code is licensed under the MIT license found in the
// LICENSE file in the root directory of this source tree.
@@ -9,38 +9,51 @@
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <cmath>
+#include <vector>
#include <cuda.h>
#include <cuda_runtime.h>
+#include <device_launch_parameters.h>
-#include <vector>
-
-#define TOTAL_THREADS 512
+#define LOG2_TOTAL_THREADS 10
+#define TOTAL_THREADS (2 << LOG2_TOTAL_THREADS)
-inline int opt_n_threads(int work_size) {
- const int pow_2 = std::log(static_cast<double>(work_size)) / std::log(2.0);
-
- return max(min(1 << pow_2, TOTAL_THREADS), 1);
+inline uint opt_n_threads(uint work_size) {
+ const uint pow_2 = std::log(work_size) / std::log(2.0);
+ return 1 << min(pow_2, LOG2_TOTAL_THREADS);
}
inline dim3 opt_block_config(int x, int y) {
- const int x_threads = opt_n_threads(x);
- const int y_threads =
- max(min(opt_n_threads(y), TOTAL_THREADS / x_threads), 1);
- dim3 block_config(x_threads, y_threads, 1);
+ const int x_threads = opt_n_threads(x);
+ const int y_threads = max(min(opt_n_threads(y), TOTAL_THREADS / x_threads), 1);
+ dim3 block_config(x_threads, y_threads, 1);
- return block_config;
+ return block_config;
}
-#define CUDA_CHECK_ERRORS() \
- do { \
- cudaError_t err = cudaGetLastError(); \
- if (cudaSuccess != err) { \
- fprintf(stderr, "CUDA kernel failed : %s\n%s at L:%d in %s\n", \
- cudaGetErrorString(err), __PRETTY_FUNCTION__, __LINE__, \
- __FILE__); \
- exit(-1); \
- } \
- } while (0)
+#define CUDA_CHECK_ERRORS() \
+ do { \
+ cudaError_t err = cudaGetLastError(); \
+ if (cudaSuccess != err) { \
+ fprintf(stderr, "CUDA kernel failed : %s\n%s at L:%d in %s\n", \
+ cudaGetErrorString(err), __PRETTY_FUNCTION__, __LINE__, __FILE__); \
+ exit(-1); \
+ } \
+ } while (0)
#endif
+
+template <typename T, uint N_ELEMS> struct vec {
+ __host__ __device__ T &operator[](uint idx) { return data[idx]; }
+
+ __host__ __device__ T operator[](uint idx) const { return data[idx]; }
+
+ T data[N_ELEMS];
+ static constexpr uint N = N_ELEMS;
+};
+
+template <uint N_FLOATS> using fvec = vec<float, N_FLOATS>;
+
+template <uint N_HALFS> using hvec = vec<__half, N_HALFS>;
+
+template <uint N_UINTS> using uvec = vec<uint, N_UINTS>;
diff --git a/clib/include/encode.h b/clib/include/encode.h
new file mode 100644
index 0000000..4189a23
--- /dev/null
+++ b/clib/include/encode.h
@@ -0,0 +1,12 @@
+
+#pragma once
+#include <torch/extension.h>
+
+at::Tensor multires_hash_encode(const int levels, const int coarse_levels, at::Tensor x,
+ at::Tensor res_list, at::Tensor hash_table,
+ at::Tensor hash_table_offsets);
+
+std::tuple<at::Tensor, at::Tensor, at::Tensor>
+multires_hash_encode_with_grad(const int levels, const int coarse_levels, at::Tensor x,
+ at::Tensor res_list, at::Tensor hash_table,
+ at::Tensor hash_table_offsets);
\ No newline at end of file
diff --git a/clib/include/encode_debug.h b/clib/include/encode_debug.h
new file mode 100644
index 0000000..199fcf6
--- /dev/null
+++ b/clib/include/encode_debug.h
@@ -0,0 +1,7 @@
+#pragma once
+#include <torch/extension.h>
+
+std::tuple<at::Tensor, at::Tensor, at::Tensor>
+multires_hash_encode_debug(const int levels, const int coarse_levels, at::Tensor x,
+ at::Tensor res_list, at::Tensor hash_table,
+ at::Tensor hash_table_offsets);
\ No newline at end of file
diff --git a/clib/include/utils.h b/clib/include/utils.h
index 925f769..ac708ab 100644
--- a/clib/include/utils.h
+++ b/clib/include/utils.h
@@ -1,5 +1,5 @@
// Copyright (c) Facebook, Inc. and its affiliates.
-//
+//
// This source code is licensed under the MIT license found in the
// LICENSE file in the root directory of this source tree.
@@ -7,24 +7,27 @@
#include <ATen/cuda/CUDAContext.h>
#include <torch/extension.h>
-#define CHECK_CUDA(x) \
- do { \
- TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor"); \
- } while (0)
+#define CHECK_CUDA(x) \
+ do { \
+ TORCH_CHECK(x.is_cuda(), #x " must be a CUDA tensor"); \
+ } while (0)
-#define CHECK_CONTIGUOUS(x) \
- do { \
- TORCH_CHECK(x.is_contiguous(), #x " must be a contiguous tensor"); \
- } while (0)
+#define CHECK_CONTIGUOUS(x) \
+ do { \
+ TORCH_CHECK(x.is_contiguous(), #x " must be a contiguous tensor"); \
+ } while (0)
-#define CHECK_IS_INT(x) \
- do { \
- TORCH_CHECK(x.scalar_type() == at::ScalarType::Int, \
- #x " must be an int tensor"); \
- } while (0)
+#define CHECK_IS_INT(x) \
+ do { \
+ TORCH_CHECK(x.scalar_type() == at::ScalarType::Int, #x " must be an int tensor"); \
+ } while (0)
-#define CHECK_IS_FLOAT(x) \
- do { \
- TORCH_CHECK(x.scalar_type() == at::ScalarType::Float, \
- #x " must be a float tensor"); \
- } while (0)
+#define CHECK_IS_FLOAT(x) \
+ do { \
+ TORCH_CHECK(x.scalar_type() == at::ScalarType::Float, #x " must be a float tensor"); \
+ } while (0)
+
+#define CHECK_CUDA_CONT_TENSOR(__TYPE__, __VAR__) \
+ CHECK_CONTIGUOUS(__VAR__); \
+ CHECK_IS_##__TYPE__(__VAR__); \
+ CHECK_CUDA(__VAR__);
diff --git a/clib/src/binding.cpp b/clib/src/binding.cpp
index a7274d0..520425b 100644
--- a/clib/src/binding.cpp
+++ b/clib/src/binding.cpp
@@ -1,21 +1,26 @@
// Copyright (c) Facebook, Inc. and its affiliates.
-//
+//
// This source code is licensed under the MIT license found in the
// LICENSE file in the root directory of this source tree.
#include "intersect.h"
#include "octree.h"
#include "sample.h"
-
+#include "encode.h"
+#include "encode_debug.h"
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def("ball_intersect", &ball_intersect);
- m.def("aabb_intersect", &aabb_intersect);
- m.def("svo_intersect", &svo_intersect);
- m.def("triangle_intersect", &triangle_intersect);
+ m.def("ball_intersect", &ball_intersect);
+ m.def("aabb_intersect", &aabb_intersect);
+ m.def("svo_intersect", &svo_intersect);
+ m.def("triangle_intersect", &triangle_intersect);
+
+ m.def("uniform_ray_sampling", &uniform_ray_sampling);
+ m.def("inverse_cdf_sampling", &inverse_cdf_sampling);
- m.def("uniform_ray_sampling", &uniform_ray_sampling);
- m.def("inverse_cdf_sampling", &inverse_cdf_sampling);
+ m.def("build_octree", &build_octree);
- m.def("build_octree", &build_octree);
+ m.def("multires_hash_encode", &multires_hash_encode);
+ m.def("multires_hash_encode_with_grad", &multires_hash_encode_with_grad);
+ m.def("multires_hash_encode_debug", &multires_hash_encode_debug);
}
\ No newline at end of file
diff --git a/clib/src/encode.cpp b/clib/src/encode.cpp
new file mode 100644
index 0000000..cf49bbc
--- /dev/null
+++ b/clib/src/encode.cpp
@@ -0,0 +1,56 @@
+#include "utils.h"
+
+void multires_hash_encode_kernel_wrapper_fullp(const uint n, const uint levels,
+ const uint coarse_levels, const uint dims,
+ const uint feature_dims, const float *x,
+ const uint *res_list, const void *hash_table,
+ const uint *hash_table_offsets, void *o_encoded,
+ const bool requires_grad, float *o_weights,
+ uint *o_indices);
+
+at::Tensor multires_hash_encode(const int levels, const int coarse_levels, at::Tensor x,
+ at::Tensor res_list, at::Tensor hash_table,
+ at::Tensor hash_table_offsets) {
+ CHECK_CUDA_CONT_TENSOR(FLOAT, x);
+ CHECK_CUDA_CONT_TENSOR(FLOAT, hash_table);
+ CHECK_CUDA_CONT_TENSOR(INT, res_list);
+ CHECK_CUDA_CONT_TENSOR(INT, hash_table_offsets);
+
+ const uint n = x.size(0);
+ const uint dims = x.size(1);
+ const uint feature_dims = hash_table.size(-1);
+
+ at::Tensor encoded =
+ torch::empty({levels, n, feature_dims}, at::device(x.device()).dtype(hash_table.dtype()));
+ multires_hash_encode_kernel_wrapper_fullp(
+ n, (uint)levels, (uint)coarse_levels, dims, feature_dims, x.data_ptr<float>(),
+ (uint *)res_list.data_ptr(), hash_table.data_ptr(), (uint *)hash_table_offsets.data_ptr(),
+ encoded.data_ptr(), false, nullptr, nullptr);
+ return encoded;
+}
+
+std::tuple<at::Tensor, at::Tensor, at::Tensor>
+multires_hash_encode_with_grad(const int levels, const int coarse_levels, at::Tensor x,
+ at::Tensor res_list, at::Tensor hash_table,
+ at::Tensor hash_table_offsets) {
+ CHECK_CUDA_CONT_TENSOR(FLOAT, x);
+ CHECK_CUDA_CONT_TENSOR(FLOAT, hash_table);
+ CHECK_CUDA_CONT_TENSOR(INT, res_list);
+ CHECK_CUDA_CONT_TENSOR(INT, hash_table_offsets);
+
+ const uint n = x.size(0);
+ const uint dims = x.size(1);
+ const uint feature_dims = hash_table.size(-1);
+
+ at::Tensor encoded =
+ torch::empty({levels, n, feature_dims}, at::device(x.device()).dtype(hash_table.dtype()));
+ at::Tensor weights =
+ torch::empty({levels, n, 1 << dims}, at::device(x.device()).dtype(at::kFloat));
+ at::Tensor indices =
+ torch::empty({levels, n, 1 << dims}, at::device(x.device()).dtype(at::kInt));
+ multires_hash_encode_kernel_wrapper_fullp(
+ n, (uint)levels, (uint)coarse_levels, dims, feature_dims, x.data_ptr<float>(),
+ (uint *)res_list.data_ptr(), hash_table.data_ptr(), (uint *)hash_table_offsets.data_ptr(),
+ encoded.data_ptr(), true, weights.data_ptr<float>(), (uint *)indices.data_ptr());
+ return std::make_tuple(encoded, weights, indices);
+}
\ No newline at end of file
diff --git a/clib/src/encode_debug.cu b/clib/src/encode_debug.cu
new file mode 100644
index 0000000..3f4b48d
--- /dev/null
+++ b/clib/src/encode_debug.cu
@@ -0,0 +1,132 @@
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "cuda_utils.h"
+#include "cutil_math.h" // required for float3 vector math
+#include "utils.h"
+
+namespace debug {
+template <uint DIMS> __device__ uint fast_hash(const uvec<DIMS> gpos, const uint hashmap_size) {
+ static_assert(DIMS <= 7, "fast_hash can only hash up to 7 dimensions.");
+
+ // While 1 is technically not a good prime for hashing (or a prime at all), it helps memory
+ // coherence and is sufficient for our use case of obtaining a uniformly colliding index from
+ // high-dimensional coordinates.
+ constexpr uint primes[7] = {1, 2654435761, 805459861, 3674653429,
+ 2097192037, 1434869437, 2165219737};
+
+ uint result = gpos[0];
+#pragma unroll
+ for (uint dim = 1; dim < DIMS; ++dim)
+ result ^= gpos[dim] * primes[dim];
+
+ return result % hashmap_size;
+}
+
+template <uint DIMS> __device__ uint gidx(const uvec<DIMS> gpos, const uvec<DIMS> res) {
+ uint index = gpos[0] * res[1] + gpos[1];
+#pragma unroll
+ for (uint dim = 2; dim < DIMS; ++dim)
+ index = index * res[dim] + gpos[dim];
+ return index;
+}
+
+__global__ void multires_hash_encode_kernel(const uint n, const uint coarse_levels,
+ const uvec<3> *res_list, const fvec<2> *hash_table,
+ const uint *hash_table_offsets, const fvec<3> *x,
+ fvec<2> *o_encoded, fvec<3> *o_local_pos,
+ uint *o_idx) {
+ const uint i = blockDim.x * blockIdx.x + threadIdx.x;
+ if (i >= n)
+ return;
+
+ const uint level = blockIdx.y;
+ const uint hash_table_offset = hash_table_offsets[level];
+ const uint hash_table_size = hash_table_offsets[level + 1] - hash_table_offset;
+ const uvec<3> res = res_list[level];
+ hash_table += hash_table_offset;
+
+ fvec<3> pos = x[i];
+ uvec<3> gpos;
+#pragma unroll
+ for (uint dim = 0; dim < 3; ++dim) {
+ pos[dim] *= res[dim] - 1;
+ gpos[dim] = (uint)floor(pos[dim]);
+ pos[dim] -= gpos[dim];
+ }
+ // TODO: Debug codes
+ o_local_pos[n * level + i] = pos;
+
+ auto grid_idx = [&](const uvec<3> gpos) {
+ uint idx;
+ if (level >= coarse_levels)
+ idx = fast_hash(gpos, hash_table_size);
+ else
+ idx = gidx(gpos, res);
+ return idx;
+ };
+
+ // N-linear interpolation
+ fvec<2> result = {};
+
+#pragma unroll
+ for (uint corner_idx = 0; corner_idx < (1 << 3); ++corner_idx) {
+ float weight = 1;
+ uvec<3> corner_gpos;
+
+#pragma unroll
+ for (uint dim = 0; dim < 3; ++dim) {
+ if ((corner_idx & (1 << dim)) == 0) {
+ weight *= 1 - pos[dim];
+ corner_gpos[dim] = gpos[dim];
+ } else {
+ weight *= pos[dim];
+ corner_gpos[dim] = min(gpos[dim] + 1, res[dim] - 1);
+ }
+ }
+
+ auto idx = grid_idx(corner_gpos);
+ auto val = hash_table[idx];
+ o_idx[level * n * 8 + i * 8 + corner_idx] = idx;
+
+#pragma unroll
+ for (uint feature = 0; feature < 2; ++feature) {
+ result[feature] += weight * val[feature];
+ }
+ }
+
+ o_encoded[level * n + i] = result;
+}
+} // namespace debug
+
+std::tuple<at::Tensor, at::Tensor, at::Tensor>
+multires_hash_encode_debug(const int levels, const int coarse_levels, at::Tensor x,
+ at::Tensor res_list, at::Tensor hash_table,
+ at::Tensor hash_table_offsets) {
+ const uint n = x.size(0);
+ const uint dims = x.size(1);
+ const uint feature_dims = hash_table.size(-1);
+
+ res_list = res_list.to(at::kInt);
+ hash_table_offsets = hash_table_offsets.to(at::kInt);
+
+ at::Tensor encoded =
+ torch::empty({levels, n, feature_dims}, at::device(x.device()).dtype(hash_table.dtype()));
+ at::Tensor local_pos =
+ torch::empty({levels, n, dims}, at::device(x.device()).dtype(at::kFloat));
+ at::Tensor idxs = torch::empty({levels, n, 8}, at::device(x.device()).dtype(at::kInt));
+
+ cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+ const uint threads = opt_n_threads(n);
+ const dim3 blocks = {(uint)ceil((float)n / threads), (uint)levels, 1};
+
+ debug::multires_hash_encode_kernel<<<blocks, threads, 0, stream>>>(
+ n, (uint)coarse_levels, (uvec<3> *)res_list.data_ptr(), (fvec<2> *)hash_table.data_ptr(),
+ (uint *)hash_table_offsets.data_ptr(), (fvec<3> *)x.data_ptr(),
+ (fvec<2> *)encoded.data_ptr(), (fvec<3> *)local_pos.data_ptr(),
+ (uint *)idxs.data_ptr());
+ return std::make_tuple(encoded.transpose(0, 1).reshape({n, -1}),
+ local_pos.transpose(0, 1).unsqueeze(-2),
+ idxs.to(at::kLong).transpose(0, 1));
+}
\ No newline at end of file
diff --git a/clib/src/encode_gpu.cu b/clib/src/encode_gpu.cu
new file mode 100644
index 0000000..c0093ee
--- /dev/null
+++ b/clib/src/encode_gpu.cu
@@ -0,0 +1,190 @@
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "cuda_utils.h"
+#include "cutil_math.h" // required for float3 vector math
+
+template <uint DIMS> __device__ uint fast_hash(const uvec<DIMS> gpos, const uint hashmap_size) {
+ static_assert(DIMS <= 7, "fast_hash can only hash up to 7 dimensions.");
+
+ // While 1 is technically not a good prime for hashing (or a prime at all), it helps memory
+ // coherence and is sufficient for our use case of obtaining a uniformly colliding index from
+ // high-dimensional coordinates.
+ constexpr uint primes[7] = {1, 2654435761, 805459861, 3674653429,
+ 2097192037, 1434869437, 2165219737};
+
+ uint result = gpos[0];
+#pragma unroll
+ for (uint dim = 1; dim < DIMS; ++dim)
+ result ^= gpos[dim] * primes[dim];
+
+ return result % hashmap_size;
+}
+
+template <uint DIMS> __device__ uint gidx(const uvec<DIMS> gpos, const uvec<DIMS> res) {
+ uint index = gpos[0] * res[1] + gpos[1];
+#pragma unroll
+ for (uint dim = 2; dim < DIMS; ++dim)
+ index = index * res[dim] + gpos[dim];
+ return index;
+}
+
+template <typename T, uint DIMS, uint FEATURE_DIMS>
+__global__ void multires_hash_encode_kernel(const uint n, const uint coarse_levels,
+ const uvec<DIMS> *__restrict__ res_list,
+ const vec<T, FEATURE_DIMS> *__restrict__ hash_table,
+ const uint *__restrict__ hash_table_offsets,
+ const fvec<DIMS> *__restrict__ x,
+ vec<T, FEATURE_DIMS> *__restrict__ o_encoded,
+ const bool requires_grad, float *__restrict__ o_weights,
+ uint *__restrict__ o_indices) {
+ const uint i = blockDim.x * blockIdx.x + threadIdx.x;
+ if (i >= n)
+ return;
+
+ const uint level = blockIdx.y;
+ const uint hash_table_offset = hash_table_offsets[level];
+ const uint hash_table_size = hash_table_offsets[level + 1] - hash_table_offset;
+ const uvec<DIMS> res = res_list[level];
+ hash_table += hash_table_offset;
+
+ fvec<DIMS> pos = x[i];
+ uvec<DIMS> gpos;
+#pragma unroll
+ for (uint dim = 0; dim < DIMS; ++dim) {
+ pos[dim] *= res[dim] - 1;
+ gpos[dim] = (uint)floor(pos[dim]);
+ pos[dim] -= gpos[dim];
+ }
+
+ auto hash_idx = [&](const uvec<DIMS> gpos) {
+ uint idx;
+ if (level >= coarse_levels)
+ idx = fast_hash(gpos, hash_table_size);
+ else
+ idx = gidx(gpos, res);
+ return idx;
+ };
+
+ // N-linear interpolation
+ vec<T, FEATURE_DIMS> result = {};
+ auto n_corners = (1 << DIMS);
+
+#pragma unroll
+ for (uint corner_idx = 0; corner_idx < n_corners; ++corner_idx) {
+ float weight = 1;
+ uvec<DIMS> corner_gpos;
+
+#pragma unroll
+ for (uint dim = 0; dim < DIMS; ++dim) {
+ if ((corner_idx & (1 << dim)) == 0) {
+ weight *= 1 - pos[dim];
+ corner_gpos[dim] = gpos[dim];
+ } else {
+ weight *= pos[dim];
+ corner_gpos[dim] = gpos[dim] + 1;
+ }
+ }
+
+ auto idx = hash_idx(corner_gpos);
+ auto val = hash_table[idx];
+
+#pragma unroll
+ for (uint feature = 0; feature < FEATURE_DIMS; ++feature) {
+ result[feature] += (T)(weight * (float)val[feature]);
+ }
+
+ // For backward
+ if (requires_grad) {
+ auto j = (level * n + i) * n_corners + corner_idx;
+ o_indices[j] = idx + hash_table_offset;
+ o_weights[j] = weight;
+ }
+ }
+
+ o_encoded[level * n + i] = result;
+}
+
+template <typename T, uint FEATURE_DIMS>
+void multires_hash_encode_kernel_wrapper(const uint n, const uint levels, const uint coarse_levels,
+ const uint dims, const float *x, const uint *res_list,
+ const vec<T, FEATURE_DIMS> *hash_table,
+ const uint *hash_table_offsets,
+ vec<T, FEATURE_DIMS> *o_encoded, const bool requires_grad,
+ float *o_weights, uint *o_indices) {
+ cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+ const uint threads = opt_n_threads(n);
+ const dim3 blocks = {(uint)ceil((float)n / threads), levels, 1};
+
+#define DISPATCH_KERNEL_CASE(__DIMS__) \
+ case __DIMS__: \
+ multires_hash_encode_kernel<<<blocks, threads, 0, stream>>>( \
+ n, coarse_levels, (const uvec<__DIMS__> *)res_list, hash_table, hash_table_offsets, \
+ (const fvec<__DIMS__> *)x, o_encoded, requires_grad, o_weights, o_indices); \
+ break;
+
+ switch (dims) {
+ DISPATCH_KERNEL_CASE(2)
+ DISPATCH_KERNEL_CASE(3)
+ default:
+ throw std::invalid_argument("'dims' should be 2 or 3");
+ }
+ CUDA_CHECK_ERRORS();
+
+#undef DISPATCH_KERNEL_CASE
+}
+
+template <typename T>
+void multires_hash_encode_kernel_wrapper(const uint n, const uint levels, const uint coarse_levels,
+ const uint dims, const uint feature_dims, const float *x,
+ const uint *res_list, const void *hash_table,
+ const uint *hash_table_offsets, void *o_encoded,
+ const bool requires_grad, float *o_weights,
+ uint *o_indices) {
+#define KERNEL_WRAPPER_CASE(__FEATURE_DIMS__) \
+ case __FEATURE_DIMS__: \
+ multires_hash_encode_kernel_wrapper( \
+ n, levels, coarse_levels, dims, x, res_list, \
+ (const vec<T, __FEATURE_DIMS__> *)hash_table, hash_table_offsets, \
+ (vec<T, __FEATURE_DIMS__> *)o_encoded, requires_grad, o_weights, o_indices); \
+ break;
+
+ switch (feature_dims) {
+ KERNEL_WRAPPER_CASE(1)
+ KERNEL_WRAPPER_CASE(2)
+ KERNEL_WRAPPER_CASE(4)
+ KERNEL_WRAPPER_CASE(8)
+ KERNEL_WRAPPER_CASE(16)
+ default:
+ throw std::invalid_argument("'feature_dims' should be 1, 2, 4, 8, 16");
+ }
+
+#undef KERNEL_WRAPPER_CASE
+}
+
+#if !defined(__CUDA_NO_HALF_CONVERSIONS__)
+void multires_hash_encode_kernel_wrapper_halfp(const uint n, const uint levels,
+ const uint coarse_levels, const uint dims,
+ const uint feature_dims, const float *x,
+ const uint *res_list, const void *hash_table,
+ const uint *hash_table_offsets, void *o_encoded,
+ const bool requires_grad, float *o_weights,
+ uint *o_indices) {
+ multires_hash_encode_kernel_wrapper<__half>(n, levels, coarse_levels, dims, feature_dims, x,
+ res_list, hash_table, hash_table_offsets, o_encoded,
+ requires_grad, o_weights, o_indices);
+}
+#endif
+
+void multires_hash_encode_kernel_wrapper_fullp(const uint n, const uint levels,
+ const uint coarse_levels, const uint dims,
+ const uint feature_dims, const float *x,
+ const uint *res_list, const void *hash_table,
+ const uint *hash_table_offsets, void *o_encoded,
+ const bool requires_grad, float *o_weights,
+ uint *o_indices) {
+ multires_hash_encode_kernel_wrapper<float>(n, levels, coarse_levels, dims, feature_dims, x,
+ res_list, hash_table, hash_table_offsets, o_encoded,
+ requires_grad, o_weights, o_indices);
+}
\ No newline at end of file
diff --git a/components/fnr.py b/components/fnr.py
index fa56c6d..dfe0b55 100644
--- a/components/fnr.py
+++ b/components/fnr.py
@@ -1,6 +1,6 @@
import torch
import torch.nn.functional as nn_f
-from typing import Any, List, Mapping, Tuple
+from typing import Any
from torch import nn
from utils.view import *
from utils import math
@@ -10,28 +10,29 @@ from .foveation import Foveation
class FoveatedNeuralRenderer(object):
- def __init__(self, layers_fov: List[float],
- layers_res: List[Tuple[int, int]],
+ def __init__(self, layers_fov: list[float],
+ layers_res: list[tuple[int, int]],
layers_net: nn.ModuleList,
- output_res: Tuple[int, int], *,
+ output_res: tuple[int, int], *,
+ coord_sys: str = "gl",
device: torch.device = None):
super().__init__()
self.layers_net = layers_net.to(device=device)
self.layers_cam = [
- CameraParam({
+ Camera.create({
'fov': layers_fov[i],
'cx': 0.5,
'cy': 0.5,
'normalized': True
- }, layers_res[i], device=device)
+ }, layers_res[i], coord_sys=coord_sys, device=device)
for i in range(len(layers_fov))
]
- self.cam = CameraParam({
+ self.cam = Camera.create({
'fov': layers_fov[-1],
'cx': 0.5,
'cy': 0.5,
'normalized': True
- }, output_res, device=device)
+ }, output_res, coord_sys=coord_sys, device=device)
self.foveation = Foveation(layers_fov, layers_res, output_res, device=device)
self.device = device
@@ -44,14 +45,11 @@ class FoveatedNeuralRenderer(object):
self.device = device
return self
- def __call__(self, *args: Any, **kwds: Any) -> Any:
- return self.render(*args, **kwds)
-
- def render(self, view: Trans, gaze, right_gaze=None, *,
- stereo_disparity=0,
- using_mask=True,
- mono_periph_mode=0,
- ret_raw=False) -> Union[Mapping[str, torch.Tensor], Tuple[Mapping[str, torch.Tensor]]]:
+ def __call__(self, view: Trans, gaze, right_gaze=None, *,
+ stereo_disparity: float = 0,
+ using_mask: bool = True,
+ mono_periph_mode: int = 0,
+ ret_raw: bool = False) -> dict[str, torch.Tensor] | tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]:
if stereo_disparity > math.tiny:
left_view = Trans(
view.trans_point(torch.tensor([-stereo_disparity / 2, 0, 0], device=self.device)),
@@ -71,7 +69,7 @@ class FoveatedNeuralRenderer(object):
layer_mask=layers_mask[0])['color']
fovea_right = self._render(self.layers_net[0], self.layers_cam[0], right_view, right_gaze,
layer_mask=layers_mask[0])['color']
- if mono_periph_mode == 3:
+ if mono_periph_mode == 3 or mono_periph_mode == 4:
mid = self._render(self.layers_net[1], self.layers_cam[1], view,
((left_gaze[0] + right_gaze[0]) // 2, left_gaze[1]),
layer_mask=layers_mask[1])['color']
@@ -79,8 +77,8 @@ class FoveatedNeuralRenderer(object):
raw_left = [fovea_left, mid, periph]
raw_right = [fovea_right, mid, periph]
shift = int(left_gaze[0] - right_gaze[0]) // 2
- left_shifts = [0, 0, shift]
- right_shifts = [0, 0, -shift]
+ left_shifts = [0, 0, shift if mono_periph_mode == 3 else 0]
+ right_shifts = [0, 0, -shift if mono_periph_mode == 3 else 0]
else:
mid_left = self._render_mid(self.layers_net[1], self.layers_cam[1], left_view, left_gaze,
layer_mask=layers_mask[1], mono_view=view,
@@ -115,17 +113,22 @@ class FoveatedNeuralRenderer(object):
]
return self._gen_output(res_raw, gaze, ret_raw=ret_raw)
- def _render(self, net, cam: CameraParam, view: Trans, gaze=None, *,
- ret_depth=False,
- layer_mask=None) -> Mapping[str, torch.Tensor]:
+ def _render(self, net, cam: Camera, view: Trans, gaze=None, *,
+ ret_depth=False, layer_mask=None) -> dict[str, torch.Tensor]:
+ output_types = ["color"]
+ if ret_depth:
+ output_types.append("depth")
if gaze is not None:
cam = self._adjust_cam(cam, gaze)
- rays_o, rays_d = cam.get_global_rays(view, False) # (1, H, W, 3)
+ rays_d = view.trans_vector(cam.local_rays.reshape(*cam.res, -1)) # (1, H, W, 3)
+ rays_o = view.t.broadcast_to(rays_d.shape)
if layer_mask is not None:
infer_mask = layer_mask >= 0
- rays_o = rays_o[:, infer_mask]
- rays_d = rays_d[:, infer_mask]
- net_output = net(rays_o.view(-1, 3), rays_d.view(-1, 3), ret_depth=ret_depth)
+ net_input = Rays({
+ "rays_o": rays_o[:, infer_mask].reshape(-1, 3),
+ "rays_d": rays_d[:, infer_mask].reshape(-1, 3)
+ })
+ net_output = net(net_input, *output_types)
ret = {
'color': torch.zeros(1, cam.res[0], cam.res[1], 3, device=self.device)
}
@@ -136,17 +139,21 @@ class FoveatedNeuralRenderer(object):
ret['depth'][:, infer_mask] = net_output['depth']
return ret
else:
- net_output = net(rays_o.view(-1, 3), rays_d.view(-1, 3), ret_depth=ret_depth)
+ net_input = Rays({
+ "rays_o": rays_o.reshape(-1, 3),
+ "rays_d": rays_d.reshape(-1, 3)
+ })
+ net_output = net(net_input, *output_types)
return {
'color': net_output['color'].view(1, cam.res[0], cam.res[1], -1).permute(0, 3, 1, 2),
'depth': net_output['depth'].view(1, cam.res[0], cam.res[1]) if ret_depth else None
}
- def _render_mid(self, net, cam: CameraParam, view: Trans, gaze=None, *,
+ def _render_mid(self, net, cam: Camera, view: Trans, gaze=None, *,
layer_mask: torch.Tensor,
mono_view: Trans,
blend_view: bool,
- ret_depth=False) -> Mapping[str, torch.Tensor]:
+ ret_depth=False) -> dict[str, torch.Tensor]:
"""
[summary]
@@ -159,18 +166,23 @@ class FoveatedNeuralRenderer(object):
:param ret_depth: [description], defaults to False
:return: [description]
"""
+ output_types = ["color"]
+ if ret_depth:
+ output_types.append("depth")
if gaze is not None:
cam = self._adjust_cam(cam, gaze)
k = layer_mask[None, ..., None].clamp(1 if blend_view else 2, 2) - 1 # (1, H, W, 1)
rays_o = (1 - k) * view.t + k * mono_view.t # (1, H, W, 3)
- rays_d = view.trans_vector(cam.get_local_rays()) # (1, H, W, 3)
+ rays_d = view.trans_vector(cam.local_rays.reshape(*cam.res, -1)) # (1, H, W, 3)
if layer_mask is not None:
infer_mask = layer_mask >= 0
- rays_o = rays_o[:, infer_mask]
- rays_d = rays_d[:, infer_mask]
- net_output = net(rays_o.view(-1, 3), rays_d.view(-1, 3), ret_depth=ret_depth)
+ net_input = Rays({
+ "rays_o": rays_o[:, infer_mask].reshape(-1, 3),
+ "rays_d": rays_d[:, infer_mask].reshape(-1, 3)
+ })
+ net_output = net(net_input, *output_types)
ret = {
'color': torch.zeros(1, cam.res[0], cam.res[1], 3, device=self.device)
}
@@ -181,13 +193,18 @@ class FoveatedNeuralRenderer(object):
ret['depth'][:, infer_mask] = net_output['depth']
return ret
else:
- net_output = net(rays_o.view(-1, 3), rays_d.view(-1, 3), ret_depth=ret_depth)
+ net_input = {
+ "rays_o": rays_o.reshape(-1, 3),
+ "rays_d": rays_d.reshape(-1, 3)
+ }
+ net_output = net(net_input, *output_types)
return {
'color': net_output['color'].view(1, cam.res[0], cam.res[1], -1).permute(0, 3, 1, 2),
'depth': net_output['depth'].view(1, cam.res[0], cam.res[1]) if ret_depth else None
}
- def _gen_output(self, layers_img: List[torch.Tensor], gaze: Tuple[float, float], shifts=None, ret_raw=False) -> Mapping[str, torch.Tensor]:
+ def _gen_output(self, layers_img: list[torch.Tensor], gaze: tuple[float, float], shifts=None,
+ ret_raw=False) -> dict[str, torch.Tensor]:
refined = self._post_process(layers_img)
blended = self.foveation.synthesis(refined, gaze, shifts)
ret = {
@@ -196,10 +213,10 @@ class FoveatedNeuralRenderer(object):
}
if ret_raw:
ret['layers_raw'] = layers_img
- ret['blended_raw'] = self.foveation.synthesis(layers_img, gaze)
+ ret['blended_raw'] = self.foveation.synthesis(layers_img, gaze, shifts)
return ret
- def _post_process(self, layers_img: List[torch.Tensor]) -> List[torch.Tensor]:
+ def _post_process(self, layers_img: list[torch.Tensor]) -> list[torch.Tensor]:
return [
#grad_aware_median(constrast_enhance(layers_img[0], 3, 0.2), 3, 3, True),
constrast_enhance(layers_img[0], 3, 0.2),
@@ -207,20 +224,18 @@ class FoveatedNeuralRenderer(object):
constrast_enhance(layers_img[2], 5, 0.2)
]
- def _adjust_cam(self, layer_cam: CameraParam, gaze: Tuple[float, float]) -> CameraParam:
+ def _adjust_cam(self, layer_cam: Camera, gaze: tuple[float, float]) -> Camera:
fovea_offset = (
(gaze[0]) / self.cam.f[0].item() * layer_cam.f[0].item(),
(gaze[1]) / self.cam.f[1].item() * layer_cam.f[1].item()
)
- return CameraParam({
- 'fx': layer_cam.f[0].item(),
- 'fy': layer_cam.f[1].item(),
- 'cx': layer_cam.c[0].item() - fovea_offset[0],
- 'cy': layer_cam.c[1].item() - fovea_offset[1]
- }, layer_cam.res, device=self.device)
+ return Camera.create({
+ 'f': [layer_cam.f[0].item(), layer_cam.f[1].item()],
+ 'c': [layer_cam.c[0].item() - fovea_offset[0], layer_cam.c[1].item() - fovea_offset[1]]
+ }, layer_cam.res, coord_sys=layer_cam.coord_sys, device=self.device)
def _warp(self, trans: Trans, trans0: Trans,
- cam: CameraParam, z_list: torch.Tensor,
+ cam: Camera, z_list: torch.Tensor,
image: torch.Tensor, depthmap: torch.Tensor) -> torch.Tensor:
"""
[summary]
diff --git a/components/foveation.py b/components/foveation.py
index f7947b3..823ffd4 100644
--- a/components/foveation.py
+++ b/components/foveation.py
@@ -8,8 +8,8 @@ from utils import math
class Foveation(object):
- def __init__(self, layers_fov: List[float], layers_res: List[Tuple[float, float]],
- out_res: Tuple[int, int], *, blend: float = 0.6, device: torch.device = None):
+ def __init__(self, layers_fov: list[float], layers_res: list[tuple[float, float]],
+ out_res: tuple[int, int], *, blend: float = 0.6, device: torch.device = None):
self.layers_fov = layers_fov
self.layers_res = layers_res
self.out_res = out_res
@@ -20,15 +20,15 @@ class Foveation(object):
self._gen_layer_blendmap(i)
for i in range(self.n_layers - 1)
] # blend maps of fovea layers
- self.coords = misc.meshgrid(*out_res).to(device=device)
+ self.coords = misc.grid2d(*out_res, device=device)
def to(self, device: torch.device):
self.eye_fovea_blend = [x.to(device=device) for x in self.eye_fovea_blend]
self.coords = self.coords.to(device=device)
return self
- def synthesis(self, layers: List[torch.Tensor], fovea_center: Tuple[float, float],
- shifts: List[int] = None,
+ def synthesis(self, layers: list[torch.Tensor], fovea_center: tuple[float, float],
+ shifts: list[int] = None,
do_blend: bool = True,
crop_mode: bool = False) -> torch.Tensor:
"""
@@ -40,6 +40,7 @@ class Foveation(object):
"""
output: torch.Tensor = nn_f.interpolate(layers[-1], self.out_res,
mode='bilinear', align_corners=False)
+ #output.fill_(0) # TODO: debug
if shifts is not None:
output = img.horizontal_shift(output, shifts[-1])
c = torch.tensor([
@@ -99,11 +100,11 @@ class Foveation(object):
"""
size = self.get_layer_size_in_final_image(i)
R = size / 2
- p = misc.meshgrid(size, size).to(device=self.device) # (size, size, 2)
+ p = misc.grid2d(size, device=self.device) # (size, size, 2)
r = torch.norm(p - R, dim=2) # (size, size, 2)
return misc.smooth_step(R, R * self.blend, r)
- def get_layers_mask(self, gaze=None) -> List[torch.Tensor]:
+ def get_layers_mask(self, gaze=None) -> list[torch.Tensor]:
"""
Generate mask images for layers[:-1]
the meaning of values in mask images:
@@ -127,8 +128,7 @@ class Foveation(object):
else:
c = torch.tensor([0.5, 0.5], device=self.device)
layers_mask.append(torch.ones(*self.layers_res[i], device=self.device) * -1)
- coord = misc.meshgrid(
- *self.layers_res[i]).to(device=self.device) / self.layers_res[i][0]
+ coord = misc.grid2d(*self.layers_res[i], device=self.device) / self.layers_res[i][0]
r = 2 * torch.norm(coord - c, dim=-1)
inner_radius = self.get_source_layer_cover_size_in_target_layer(
self.layers_fov[i - 1], self.layers_fov[i], self.layers_res[i][0]) / self.layers_res[i][0] \
diff --git a/components/refine.py b/components/refine.py
index 2b29618..9a9f217 100644
--- a/components/refine.py
+++ b/components/refine.py
@@ -7,7 +7,7 @@ from utils import math
class GuideRefinement(object):
def __init__(self, guides_image, guides_view: view.Trans,
- guides_cam: view.CameraParam, net) -> None:
+ guides_cam: view.Camera, net) -> None:
rays_o, rays_d = guides_cam.get_global_rays(guides_view, flatten=True)
guides_inferred = torch.stack([
net(rays_o[i], rays_d[i]).view(
diff --git a/components/render.py b/components/render.py
new file mode 100644
index 0000000..030451a
--- /dev/null
+++ b/components/render.py
@@ -0,0 +1,55 @@
+from typing import SupportsFloat
+
+from model import Model
+from utils.view import *
+from utils.types import *
+
+
+def render(model: Model, cam: Camera, view: Trans, *output_types: str,
+ gaze: tuple[float, float] = (0, 0), extra_input: dict = None,
+ layer_mask: torch.Tensor = None, batch_size: int = None) -> ReturnData:
+ if len(output_types) == 0:
+ raise ValueError("'output_types' is empty")
+
+ local_rays = cam.local_rays + cam.local_rays.new_tensor([*gaze, 0]) # (H*W, 3)
+ rays_d = view.trans_vector(local_rays) # (B..., H*W, 3)
+ rays_o = view.t[..., None, :].expand_as(rays_d)
+ print(cam.local_rays)
+ exit()
+ input = Rays(rays_o=rays_o, rays_d=rays_d, **extra_input or {}) # (B..., H*W)
+
+ if layer_mask is not None:
+ selector = layer_mask.flatten().ge(0).nonzero()
+ input = input.transform(lambda value: value.index_select(len(input.shape), selector))
+ input = input.flatten() # (B..., X) -> (N)
+
+ output = ReturnData() # will be (N)
+ n = input.shape[0]
+ batch_size = batch_size or n
+ for offset in range(0, n, batch_size):
+ batch_slice = slice(offset, min(offset + batch_size, n))
+ batch_output = model(input.select(batch_slice), *output_types)
+ for key, value in batch_output.items():
+ if key == "rays_filter":
+ continue
+ match value:
+ case torch.Tensor():
+ if key not in output:
+ output[key] = value.new_full([n, *value.shape[1:]],
+ math.huge * (key == "depth"))
+ if 'rays_filter' in batch_output:
+ output[key][batch_slice][batch_output['rays_filter']] = batch_output[key]
+ else:
+ output[key][batch_slice] = batch_output[key]
+ case SupportsFloat():
+ output[key] = output.get(key, 0) + value
+ case _:
+ output[key] = output.get(key, []) + [value]
+
+ output = output.reshape(*view.shape, -1) # (N) -> (B..., X)
+ if layer_mask is not None:
+ output = output.transform(lambda value:
+ value.new_zeros(*view.shape, local_rays.shape[0],
+ *value.shape[len(view.shape) + 1:])
+ .index_copy(len(view.shape), selector, value))
+ return output.reshape(*view.shape, *cam.res) # (B..., H*W) -> (B..., H, W)
diff --git a/configs/_todo/_hr_snerf_fast.json b/configs/_todo/_hr_snerf_fast.json
index f000e8c..67e33d4 100644
--- a/configs/_todo/_hr_snerf_fast.json
+++ b/configs/_todo/_hr_snerf_fast.json
@@ -12,7 +12,7 @@
},
"sample_range": [1, 7],
"n_samples": 64,
- "multi_nets": 4
+ "multi_nets": 4,
"density_regularization_weight": 1e-4,
"density_regularization_scale": 1e4
},
diff --git a/configs/ablation_nerf+sph+cat.ini b/configs/ablation_nerf+sph+cat.ini
new file mode 100644
index 0000000..2fd3634
--- /dev/null
+++ b/configs/ablation_nerf+sph+cat.ini
@@ -0,0 +1,22 @@
+model=FsNeRF
+; n_samples=64
+; n_fields=1
+; depth=8
+; width=256
+; skips=[4]
+; act=relu
+; ln=false
+; xfreqs=6
+; raw_noise_std=0.
+; near: float # from dataset
+; far: float # from dataset
+; white_bg: bool # from dataset
+; trainer=Trainer
+; max_iters=200000
+max_epochs=50
+checkpoint_interval=10
+; batch_size=4096
+; loss=[Color_L2]
+; lr=5e-4
+lr_decay=0.9999954
+; profile_iters=0
\ No newline at end of file
diff --git a/configs/ablation_nerf+sph.ini b/configs/ablation_nerf+sph.ini
new file mode 100644
index 0000000..e1c66ba
--- /dev/null
+++ b/configs/ablation_nerf+sph.ini
@@ -0,0 +1,31 @@
+model=NeRF
+; color=rgb
+; n_samples=64
+sample_mode=spherical_radius
+perturb_sampling=true
+; depth=8
+; width=256
+; skips=[4]
+; act=relu
+; ln=false
+; color_decoder=NeRF
+n_importance=128
+; fine_depth=8
+; fine_width=256
+; fine_skips=[4]
+; xfreqs=10
+; dfreqs=4
+; raw_noise_std=0.
+; near: float # from dataset
+; far: float # from dataset
+; white_bg: bool # from dataset
+; coord: str # from dataset
+; trainer=Trainer
+; max_iters=200000
+max_epochs=20
+checkpoint_interval=5
+; batch_size=4096
+; loss=[Color_L2, CoarseColor_L2]
+; lr=5e-4
+lr_decay=0.9999954
+; profile_iters=0
\ No newline at end of file
diff --git a/configs/ablation_nerf.ini b/configs/ablation_nerf.ini
new file mode 100644
index 0000000..84261a6
--- /dev/null
+++ b/configs/ablation_nerf.ini
@@ -0,0 +1,29 @@
+model=NeRF
+n_samples=64
+; sample_mode=xyz
+perturb_sampling=true
+; depth=8
+; width=256
+; skips=[4]
+; act=relu
+; ln=false
+; color_decoder=NeRF
+; n_importance=128
+; fine_depth=8
+; fine_width=256
+; fine_skips=[4]
+; xfreqs=10
+; dfreqs=4
+; raw_noise_std=0.
+; near: float # from dataset
+; far: float # from dataset
+; white_bg: bool # from dataset
+; trainer=Trainer
+; max_iters=200000
+max_epochs=10
+checkpoint_interval=5
+; batch_size=4096
+; loss=[Color_L2]
+; lr=5e-4
+lr_decay=0.9999954
+; profile_iters=0
\ No newline at end of file
diff --git a/configs/fovea.ini b/configs/fovea.ini
new file mode 100644
index 0000000..3c4fe31
--- /dev/null
+++ b/configs/fovea.ini
@@ -0,0 +1,22 @@
+model=FsNeRF
+; n_samples=64
+n_fields=4
+; depth=8
+; width=256
+skips=[]
+; act=relu
+; ln=false
+; xfreqs=6
+; raw_noise_std=0.
+; near: float # from dataset
+; far: float # from dataset
+; white_bg: bool # from dataset
+; trainer=Trainer
+; max_iters=200000
+max_epochs=30
+; checkpoint_interval=10000(iters) or 10(epochs)
+; batch_size=4096
+; loss=[Color_L2]
+; lr=5e-4
+lr_decay=0.9999954
+; profile_iters=0
\ No newline at end of file
diff --git a/configs/fsnerf.ini b/configs/fsnerf.ini
new file mode 100644
index 0000000..2be69c8
--- /dev/null
+++ b/configs/fsnerf.ini
@@ -0,0 +1,24 @@
+model=FsNeRF
+; color=rgb
+; n_samples=64
+n_fields=4
+; depth=8
+; width=256
+; skips=[4]
+; act=relu
+; ln=false
+; xfreqs=6
+; raw_noise_std=0.
+; near: float # from dataset
+; far: float # from dataset
+; white_bg: bool # from dataset
+; coord: str # from dataset
+; trainer=Trainer
+; max_iters=200000
+max_epochs=30
+checkpoint_interval=10
+; batch_size=4096
+; loss=[Color_L2]
+; lr=5e-4
+lr_decay=0.9999954
+; profile_iters=0
\ No newline at end of file
diff --git a/configs/fsnerf2.ini b/configs/fsnerf2.ini
new file mode 100644
index 0000000..61ad373
--- /dev/null
+++ b/configs/fsnerf2.ini
@@ -0,0 +1,24 @@
+model=FsNeRF
+; color=rgb
+n_samples=256
+n_fields=2
+; depth=8
+; width=256
+; skips=[4]
+; act=relu
+; ln=false
+; xfreqs=6
+; raw_noise_std=0.
+; near: float # from dataset
+; far: float # from dataset
+; white_bg: bool # from dataset
+; coord: str # from dataset
+; trainer=Trainer
+; max_iters=200000
+max_epochs=30
+checkpoint_interval=10
+; batch_size=4096
+loss=[Color_L2]
+; lr=5e-4
+; lr_decay=0.9999954
+; profile_iters=0
\ No newline at end of file
diff --git a/configs/fsnerf_eval.ini b/configs/fsnerf_eval.ini
new file mode 100644
index 0000000..2ba322c
--- /dev/null
+++ b/configs/fsnerf_eval.ini
@@ -0,0 +1,24 @@
+model=FsNeRF
+; color=rgb
+; n_samples=64
+n_fields=4
+; depth=8
+; width=256
+; skips=[4]
+; act=relu
+; ln=false
+; xfreqs=6
+; raw_noise_std=0.
+; near: float # from dataset
+; far: float # from dataset
+; white_bg: bool # from dataset
+; coord: str # from dataset
+; trainer=Trainer
+; max_iters=200000
+max_epochs=30
+checkpoint_interval=50
+; batch_size=4096
+loss=[Color_L2]
+; lr=5e-4
+; lr_decay=0.9999954
+; profile_iters=0
\ No newline at end of file
diff --git a/configs/nerf.ini b/configs/nerf.ini
new file mode 100644
index 0000000..f265b24
--- /dev/null
+++ b/configs/nerf.ini
@@ -0,0 +1,31 @@
+model=NeRF
+; color=rgb
+; n_samples=64
+; sample_mode=xyz
+perturb_sampling=true
+; depth=8
+; width=256
+; skips=[4]
+; act=relu
+; ln=false
+; color_decoder=NeRF
+n_importance=128
+; fine_depth=8
+; fine_width=256
+; fine_skips=[4]
+; xfreqs=10
+; dfreqs=4
+; raw_noise_std=0.
+; near: float # from dataset
+; far: float # from dataset
+; white_bg: bool # from dataset
+; coord: str # from dataset
+; trainer=Trainer
+; max_iters=200000
+max_epochs=20
+checkpoint_interval=5
+; batch_size=4096
+; loss=[Color_L2, CoarseColor_L2]
+; lr=5e-4
+lr_decay=0.9999954
+; profile_iters=0
\ No newline at end of file
diff --git a/configs/nerf_default.json b/configs/nerf_default.json
deleted file mode 100644
index dd4dd75..0000000
--- a/configs/nerf_default.json
+++ /dev/null
@@ -1,16 +0,0 @@
-{
- "model": "NeRF",
- "args": {
- "color": "rgb",
- "encode_x": 10,
- "encode_d": 4,
- "core_params": {
- "nf": 256,
- "n_layers": 8,
- "skips": [ 4 ]
- },
- "n_featdim": 0,
- "sample_range": [0, 10],
- "n_samples": 256
- }
-}
\ No newline at end of file
diff --git a/configs/nerf_iters.ini b/configs/nerf_iters.ini
new file mode 100644
index 0000000..270efa5
--- /dev/null
+++ b/configs/nerf_iters.ini
@@ -0,0 +1,31 @@
+model=NeRF
+; color=rgb
+; n_samples=64
+; sample_mode=xyz
+perturb_sampling=true
+; depth=8
+; width=256
+; skips=[4]
+; act=relu
+; ln=false
+; color_decoder=NeRF
+n_importance=128
+; fine_depth=8
+; fine_width=256
+; fine_skips=[4]
+; xfreqs=10
+; dfreqs=4
+; raw_noise_std=0.
+; near: float # from dataset
+; far: float # from dataset
+; white_bg: bool # from dataset
+; coord: str # from dataset
+; trainer=Trainer
+max_iters=200000
+; max_epochs=20
+; checkpoint_interval=5
+; batch_size=4096
+; loss=[Color_L2, CoarseColor_L2]
+; lr=5e-4
+lr_decay=0.9999954
+; profile_iters=0
\ No newline at end of file
diff --git a/configs/nerf_llff.ini b/configs/nerf_llff.ini
new file mode 100644
index 0000000..9a20ac3
--- /dev/null
+++ b/configs/nerf_llff.ini
@@ -0,0 +1,31 @@
+model=NeRF
+; color=rgb
+; n_samples=64
+sample_mode=xyz_disp
+perturb_sampling=true
+; depth=8
+; width=256
+; skips=[4]
+; act=relu
+; ln=false
+; color_decoder=NeRF
+n_importance=128
+; fine_depth=8
+; fine_width=256
+; fine_skips=[4]
+; xfreqs=10
+; dfreqs=4
+; raw_noise_std=1e0
+; near: float # from dataset
+; far: float # from dataset
+; white_bg: bool # from dataset
+; coord: str # from dataset
+; trainer=Trainer
+; max_iters=200000
+max_epochs=50
+checkpoint_interval=10
+; batch_size=4096
+; loss=[Color_L2, CoarseColor_L2]
+; lr=5e-4
+lr_decay=0.9999908
+; profile_iters=0
\ No newline at end of file
diff --git a/configs/_cnerf.json b/configs/old/_cnerf.json
similarity index 100%
rename from configs/_cnerf.json
rename to configs/old/_cnerf.json
diff --git a/configs/_cnerf_ioc.json b/configs/old/_cnerf_ioc.json
similarity index 100%
rename from configs/_cnerf_ioc.json
rename to configs/old/_cnerf_ioc.json
diff --git a/configs/_cnerfadv.json b/configs/old/_cnerfadv.json
similarity index 100%
rename from configs/_cnerfadv.json
rename to configs/old/_cnerfadv.json
diff --git a/configs/_cnerfadv_ioc.json b/configs/old/_cnerfadv_ioc.json
similarity index 100%
rename from configs/_cnerfadv_ioc.json
rename to configs/old/_cnerfadv_ioc.json
diff --git a/configs/_hr_snerf.json b/configs/old/_hr_snerf.json
similarity index 50%
rename from configs/_hr_snerf.json
rename to configs/old/_hr_snerf.json
index b2d8d11..45265a3 100644
--- a/configs/_hr_snerf.json
+++ b/configs/old/_hr_snerf.json
@@ -1,14 +1,11 @@
{
- "model": "NeRF",
+ "parent": "snerf_voxels",
"args": {
- "spherical": true,
- "color": "rgb",
- "encode_x": 10,
- "encode_d": 4,
"core_params": {
"nf": 256,
"n_layers": 8,
- "skips": [4]
+ "skips": [4],
+ "act": "leakyrelu"
},
"space": "voxels",
"steps": [16, 64, 32],
@@ -17,8 +14,6 @@
"train": {
"max_epochs": 50,
"prune_epochs": [10],
- "split_epochs": [10, 30],
- "density_regularization_weight": 1e-4,
- "density_regularization_scale": 1e4
+ "split_epochs": [10, 30]
}
}
\ No newline at end of file
diff --git a/configs/old/_hr_snerf_mhe.json b/configs/old/_hr_snerf_mhe.json
new file mode 100644
index 0000000..c36da22
--- /dev/null
+++ b/configs/old/_hr_snerf_mhe.json
@@ -0,0 +1,23 @@
+{
+ "parent": "nerf_default",
+ "args": {
+ "spherical": true,
+ "encode_x": ["MultiresHash", {
+ "layers": 16,
+ "log2_hashsize": 19,
+ "features": 4,
+ "res0": [8, 8, 4]
+ }],
+ "core_params": {
+ "nf": 64,
+ "n_layers": 4,
+ "act": "leakyrelu"
+ },
+ "space": "voxels",
+ "steps": [64, 256, 128],
+ "n_samples": 256
+ },
+ "train": {
+ "prune_epochs": [5]
+ }
+}
\ No newline at end of file
diff --git a/configs/_hr_snerfadv.json b/configs/old/_hr_snerfadv.json
similarity index 100%
rename from configs/_hr_snerfadv.json
rename to configs/old/_hr_snerfadv.json
diff --git a/configs/old/_hr_snerffast+ls.json b/configs/old/_hr_snerffast+ls.json
new file mode 100644
index 0000000..5c4b305
--- /dev/null
+++ b/configs/old/_hr_snerffast+ls.json
@@ -0,0 +1,9 @@
+{
+ "parent": "_hr_snerffast",
+ "args": {
+ },
+ "train": {
+ "density_regularization_weight": 1e-4,
+ "density_regularization_scale": 1e4
+ }
+}
\ No newline at end of file
diff --git a/configs/old/_hr_snerffast.json b/configs/old/_hr_snerffast.json
new file mode 100644
index 0000000..5af2d17
--- /dev/null
+++ b/configs/old/_hr_snerffast.json
@@ -0,0 +1,15 @@
+{
+ "parent": "snerffast",
+ "args": {
+ "core_params": {
+ "nf": 512,
+ "n_layers": 4,
+ "act": "relu"
+ },
+ "multi_nets": 4,
+ "n_samples": 64
+ },
+ "train": {
+ "max_epochs": 50
+ }
+}
\ No newline at end of file
diff --git a/configs/old/_hr_snerffast_mhe+ls.json b/configs/old/_hr_snerffast_mhe+ls.json
new file mode 100644
index 0000000..d236bde
--- /dev/null
+++ b/configs/old/_hr_snerffast_mhe+ls.json
@@ -0,0 +1,7 @@
+{
+ "parent": "_hr_snerffast_mhe",
+ "train": {
+ "density_regularization_weight": 1e-4,
+ "density_regularization_scale": 1e4
+ }
+}
\ No newline at end of file
diff --git a/configs/old/_hr_snerffast_mhe.json b/configs/old/_hr_snerffast_mhe.json
new file mode 100644
index 0000000..a532c2a
--- /dev/null
+++ b/configs/old/_hr_snerffast_mhe.json
@@ -0,0 +1,14 @@
+{
+ "parent": "snerffast",
+ "args": {
+ "encode_x": ["LayeredMultiresHash", {
+ "layers": 16,
+ "log2_hashsize": 19,
+ "features": 4,
+ "res0": [16, 8]
+ }]
+ },
+ "train": {
+ "max_epochs": 50
+ }
+}
\ No newline at end of file
diff --git a/configs/old/_hr_snerffast_mhe1.json b/configs/old/_hr_snerffast_mhe1.json
new file mode 100644
index 0000000..0525a01
--- /dev/null
+++ b/configs/old/_hr_snerffast_mhe1.json
@@ -0,0 +1,11 @@
+{
+ "parent": "_hr_snerffast_mhe",
+ "args": {
+ "encode_x": ["LayeredMultiresHash", {
+ "layers": 8,
+ "log2_hashsize": 19,
+ "features": 8,
+ "res0": [8, 4]
+ }]
+ }
+}
\ No newline at end of file
diff --git a/configs/_lr_snerf.json b/configs/old/_lr_snerf.json
similarity index 100%
rename from configs/_lr_snerf.json
rename to configs/old/_lr_snerf.json
diff --git a/configs/_lr_snerfadv.json b/configs/old/_lr_snerfadv.json
similarity index 100%
rename from configs/_lr_snerfadv.json
rename to configs/old/_lr_snerfadv.json
diff --git a/configs/_mr_snerf.json b/configs/old/_mr_snerf.json
similarity index 100%
rename from configs/_mr_snerf.json
rename to configs/old/_mr_snerf.json
diff --git a/configs/_mr_snerfadv.json b/configs/old/_mr_snerfadv.json
similarity index 100%
rename from configs/_mr_snerfadv.json
rename to configs/old/_mr_snerfadv.json
diff --git a/configs/old/_mr_snerffast.json b/configs/old/_mr_snerffast.json
new file mode 100644
index 0000000..d26a351
--- /dev/null
+++ b/configs/old/_mr_snerffast.json
@@ -0,0 +1,15 @@
+{
+ "parent": "snerffast",
+ "args": {
+ "core_params": {
+ "nf": 256,
+ "n_layers": 4,
+ "act": "relu"
+ },
+ "multi_nets": 2,
+ "n_samples": 32
+ },
+ "train": {
+ "max_epochs": 50
+ }
+}
\ No newline at end of file
diff --git a/configs/old/nerf_bedroom.json b/configs/old/nerf_bedroom.json
new file mode 100644
index 0000000..0c6fce8
--- /dev/null
+++ b/configs/old/nerf_bedroom.json
@@ -0,0 +1,28 @@
+{
+ "model": "NeRF",
+ "args": {
+ "color": "rgb",
+ "encode_x": ["Freq", {
+ "freqs": 10,
+ "cat_input": true
+ }],
+ "encode_d": ["Freq", {
+ "freqs": 4,
+ "angular": true
+ }],
+ "core_params": {
+ "nf": 256,
+ "n_layers": 8,
+ "skips": [ 4 ],
+ "act": "relu"
+ },
+ "sample_range": [10, 60],
+ "n_samples": 256
+ },
+ "train": {
+ "max_epochs": 5,
+ "opti": ["Adam", {
+ "lr": 5e-4
+ }]
+ }
+}
\ No newline at end of file
diff --git a/configs/old/nerf_blender.json b/configs/old/nerf_blender.json
new file mode 100644
index 0000000..e157d1d
--- /dev/null
+++ b/configs/old/nerf_blender.json
@@ -0,0 +1,44 @@
+{
+ "model": ["NeRF", {
+ "color": "rgb",
+ "sampler": {
+ "range": [2, 6],
+ "n_samples": 128
+ },
+ "encoder_x": ["Freq", {
+ "freqs": 10,
+ "cat_input": true
+ }],
+ "encoder_d": ["Freq", {
+ "freqs": 4,
+ "angular": true
+ }],
+ "field": {
+ "shape": [8, 256],
+ "skips": [4]
+ },
+ "color_decoder": "NeRF",
+ "cascade": true,
+ "pdf_sampler": {
+ "n_importance": 64
+ },
+ "fine_field": {
+ "shape": [8, 256],
+ "skips": [4]
+ }
+ }],
+ "train": ["Trainer", {
+ "max_iters": 200000,
+ "checkpoint_interval": 10000,
+ "opti": ["Adam", {
+ "lr": 5e-4
+ }],
+ "scheduler": ["ExponentialLR", {
+ "gamma": 0.9999954
+ }],
+ "loss": {
+ "Color_L2": "mse_loss(color, gt_color)"
+ //"Density_Reg": ["cauchy_loss(energies)", {"weight": 1e-4, "s": 1e4}]
+ }
+ }]
+}
\ No newline at end of file
diff --git a/configs/old/nerf_default.json b/configs/old/nerf_default.json
new file mode 100644
index 0000000..fd436c2
--- /dev/null
+++ b/configs/old/nerf_default.json
@@ -0,0 +1,46 @@
+{
+ "model": ["NeRF", {
+ "color": "rgb",
+ "sampler": {
+ "range": [0, 10],
+ "n_samples": 128
+ },
+ "encoder_x": ["Freq", {
+ "freqs": 10,
+ "cat_input": true
+ }],
+ "encoder_d": ["Freq", {
+ "freqs": 4,
+ "cat_input": true
+ }],
+ "field": {
+ "shape": [8, 256],
+ "skips": [4]
+ },
+ "color_decoder": "NeRF",
+ "cascade": true,
+ "pdf_sampler": {
+ "n_importance": 64
+ },
+ "fine_field": {
+ "shape": [8, 256],
+ "skips": [4]
+ }
+ }],
+ "train": ["Trainer", {
+ "batch_size": 1024,
+ "max_iters": 200000,
+ "checkpoint_interval": 10000,
+ "opti": ["Adam", {
+ "lr": 5e-4
+ }],
+ "scheduler": ["ExponentialLR", {
+ "gamma": 0.9999954
+ }],
+ "loss": {
+ "Color_L2": "mse_loss(color, gt_color)",
+ "Coarse_Color_L2": "mse_loss(coarse_color, gt_color)",
+ //"Density_Reg": ["cauchy_loss(energies)", {"weight": 1e-4, "s": 1e4}]
+ }
+ }]
+}
\ No newline at end of file
diff --git a/configs/old/nerf_mhe.json b/configs/old/nerf_mhe.json
new file mode 100644
index 0000000..f2e56ba
--- /dev/null
+++ b/configs/old/nerf_mhe.json
@@ -0,0 +1,17 @@
+{
+ "parent": "nerf_default",
+ "args": {
+ "encode_x": ["MultiresHash", {
+ "layers": 10,
+ "log2_hashsize": 19,
+ "features": 2,
+ "res0": 16,
+ "scale_up": 1.5
+ }],
+ "core_params": {
+ "nf": 64,
+ "n_layers": 3,
+ "act": "leakyrelu"
+ }
+ }
+}
\ No newline at end of file
diff --git a/configs/old/nerf_mhe1.json b/configs/old/nerf_mhe1.json
new file mode 100644
index 0000000..098e913
--- /dev/null
+++ b/configs/old/nerf_mhe1.json
@@ -0,0 +1,11 @@
+{
+ "parent": "nerf_mhe",
+ "args": {
+ "encode_x": ["MultiresHash", {
+ "layers": 4,
+ "log2_hashsize": 19,
+ "features": 4,
+ "res0": 16
+ }]
+ }
+}
\ No newline at end of file
diff --git a/configs/old/nerf_simple.json b/configs/old/nerf_simple.json
new file mode 100644
index 0000000..79d3bf4
--- /dev/null
+++ b/configs/old/nerf_simple.json
@@ -0,0 +1,33 @@
+{
+ "model": ["NeRF", {
+ "color": "rgb",
+ "sampler": {
+ "range": [0, 10],
+ "n_samples": 256
+ },
+ "encoder_x": ["Freq", {
+ "freqs": 10,
+ "cat_input": true
+ }],
+ "encoder_d": ["Freq", {
+ "freqs": 4,
+ "angular": true
+ }],
+ "field": {
+ "shape": [8, 256],
+ "skips": [4]
+ },
+ "color_decoder": "NeRF",
+ "cascade": false
+ }],
+ "train": ["Trainer", {
+ "max_epochs": 20,
+ "opti": ["Adam", {
+ "lr": 5e-4
+ }],
+ "loss": {
+ "Color_L2": "mse_loss(color, gt_color)"
+ //"Density_Reg": ["cauchy_loss(energies)", {"weight": 1e-4, "s": 1e4}]
+ }
+ }]
+}
\ No newline at end of file
diff --git a/configs/nerf_voxels.json b/configs/old/nerf_voxels.json
similarity index 53%
rename from configs/nerf_voxels.json
rename to configs/old/nerf_voxels.json
index d7f0297..33494dc 100644
--- a/configs/nerf_voxels.json
+++ b/configs/old/nerf_voxels.json
@@ -1,18 +1,8 @@
{
- "model": "NeRF",
+ "parent": "nerf_default",
"args": {
- "color": "rgb",
- "encode_x": 10,
- "encode_d": 4,
- "core_params": {
- "nf": 256,
- "n_layers": 8,
- "skips": [ 4 ]
- },
- "n_featdim": 0,
"space": "voxels",
"voxel_size": 0.5,
- "sample_range": [0, 10],
"n_samples": 50
},
"train": {
diff --git a/configs/old/nerf_voxels_mhe.json b/configs/old/nerf_voxels_mhe.json
new file mode 100644
index 0000000..a81dce4
--- /dev/null
+++ b/configs/old/nerf_voxels_mhe.json
@@ -0,0 +1,13 @@
+{
+ "parent": "nerf_mhe",
+ "args": {
+ "space": "voxels",
+ "voxel_size": 0.05
+ },
+ "train": {
+ "max_epochs": 10,
+ "prune_epochs": [2],
+ "density_regularization_weight": 1e-4,
+ "density_regularization_scale": 1e4
+ }
+}
\ No newline at end of file
diff --git a/configs/old/nerfadv_default.json b/configs/old/nerfadv_default.json
new file mode 100644
index 0000000..434378e
--- /dev/null
+++ b/configs/old/nerfadv_default.json
@@ -0,0 +1,24 @@
+{
+ "parent": "nerf_default",
+ "args": {
+ "core": "nerfadv",
+ "core_params": {
+ "density_net": {
+ "nf": 256,
+ "n_layers": 4,
+ "act": "leakyrelu"
+ },
+ "color_net": {
+ "nf": 256,
+ "n_layers": 3,
+ "act": "leakyrelu"
+ },
+ "specular_net": {
+ "nf": 128,
+ "n_layers": 1,
+ "act": "leakyrelu"
+ },
+ "appearance": "combined"
+ }
+ }
+}
\ No newline at end of file
diff --git a/configs/old/nerfadv_mhe.json b/configs/old/nerfadv_mhe.json
new file mode 100644
index 0000000..9dcedba
--- /dev/null
+++ b/configs/old/nerfadv_mhe.json
@@ -0,0 +1,24 @@
+{
+ "parent": "nerfadv_default",
+ "args": {
+ "encode_x": ["MultiresHash", {
+ "layers": 16,
+ "log2_hashsize": 19,
+ "features": 2,
+ "res0": 16
+ }],
+ "core_params": {
+ "density_net": {
+ "nf": 64,
+ "n_layers": 1,
+ "act": "leakyrelu"
+ },
+ "color_net": {
+ "nf": 64,
+ "n_layers": 2,
+ "act": "leakyrelu"
+ },
+ "appearance": "combined"
+ }
+ }
+}
\ No newline at end of file
diff --git a/configs/old/nerfadv_voxels.json b/configs/old/nerfadv_voxels.json
new file mode 100644
index 0000000..f64be92
--- /dev/null
+++ b/configs/old/nerfadv_voxels.json
@@ -0,0 +1,15 @@
+{
+ "parent": "nerfadv_default",
+ "args": {
+ "space": "voxels",
+ "voxel_size": 0.5,
+ "n_samples": 32
+ },
+ "train": {
+ "max_epochs": 50,
+ "prune_epochs": [5],
+ "split_epochs": [10],
+ "density_regularization_weight": 1e-4,
+ "density_regularization_scale": 1e4
+ }
+}
\ No newline at end of file
diff --git a/configs/old/nerfadv_voxels_mhe.json b/configs/old/nerfadv_voxels_mhe.json
new file mode 100644
index 0000000..199ecd9
--- /dev/null
+++ b/configs/old/nerfadv_voxels_mhe.json
@@ -0,0 +1,31 @@
+{
+ "parent": "nerfadv_voxels",
+ "args": {
+ "encode_x": ["MultiresHash", {
+ "layers": 8,
+ "log2_hashsize": 19,
+ "features": 2,
+ "res0": 16
+ }],
+ "core_params": {
+ "density_net": {
+ "nf": 64,
+ "n_layers": 1,
+ "act": "leakyrelu"
+ },
+ "color_net": {
+ "nf": 64,
+ "n_layers": 2,
+ "act": "leakyrelu"
+ },
+ "appearance": "combined"
+ },
+ "steps": [16, 16, 16],
+ "n_samples": 64
+ },
+ "train": {
+ "max_epochs": 30,
+ "prune_epochs": [5],
+ "split_epochs": [10]
+ }
+}
\ No newline at end of file
diff --git a/configs/nsvf_default.json b/configs/old/nsvf_default.json
similarity index 100%
rename from configs/nsvf_default.json
rename to configs/old/nsvf_default.json
diff --git a/configs/nsvf_voxels.json b/configs/old/nsvf_voxels.json
similarity index 100%
rename from configs/nsvf_voxels.json
rename to configs/old/nsvf_voxels.json
diff --git a/configs/smnerf_voxels.json b/configs/old/smnerf_voxels.json
similarity index 100%
rename from configs/smnerf_voxels.json
rename to configs/old/smnerf_voxels.json
diff --git a/configs/smnerfadv_voxels.json b/configs/old/smnerfadv_voxels.json
similarity index 100%
rename from configs/smnerfadv_voxels.json
rename to configs/old/smnerfadv_voxels.json
diff --git a/configs/snerf4_voxels.json b/configs/old/snerf4_voxels.json
similarity index 100%
rename from configs/snerf4_voxels.json
rename to configs/old/snerf4_voxels.json
diff --git a/configs/old/snerf_voxels.json b/configs/old/snerf_voxels.json
new file mode 100644
index 0000000..29d6ee1
--- /dev/null
+++ b/configs/old/snerf_voxels.json
@@ -0,0 +1,9 @@
+{
+ "parent": "nerf_voxels",
+ "args": {
+ "spherical": true,
+ "space": "voxels",
+ "steps": [4, 16, 8],
+ "n_samples": 16
+ }
+}
\ No newline at end of file
diff --git a/configs/snerfadv_voxels.json b/configs/old/snerfadv_voxels.json
similarity index 100%
rename from configs/snerfadv_voxels.json
rename to configs/old/snerfadv_voxels.json
diff --git a/configs/snerfadvx4_voxels.json b/configs/old/snerfadvx4_voxels.json
similarity index 100%
rename from configs/snerfadvx4_voxels.json
rename to configs/old/snerfadvx4_voxels.json
diff --git a/configs/old/snerffast.json b/configs/old/snerffast.json
new file mode 100644
index 0000000..69b46d6
--- /dev/null
+++ b/configs/old/snerffast.json
@@ -0,0 +1,26 @@
+{
+ "model": ["SnerfFast", {
+ "color": "rgb",
+ "sampler": {
+ "n_samples": 64
+ },
+ "encoder_x": ["Freq", {
+ "freqs": 6,
+ "cat_input": true
+ }],
+ "num_fields": 4,
+ "field": {
+ "shape": [8, 256],
+ "skips": []
+ }
+ }],
+ "train": ["Trainer", {
+ "max_epochs": 20,
+ "opti": ["Adam", {
+ "lr": 5e-4
+ }],
+ "loss": {
+ "Color_L2": "mse_loss(color, gt_color)"
+ }
+ }]
+}
\ No newline at end of file
diff --git a/configs/old/snerffast_periph+dr.json b/configs/old/snerffast_periph+dr.json
new file mode 100644
index 0000000..657618e
--- /dev/null
+++ b/configs/old/snerffast_periph+dr.json
@@ -0,0 +1,27 @@
+{
+ "model": ["SnerfFast", {
+ "color": "rgb",
+ "sampler": {
+ "n_samples": 32
+ },
+ "encoder_x": ["Freq", {
+ "freqs": 6,
+ "cat_input": true
+ }],
+ "num_fields": 2,
+ "field": {
+ "shape": [4, 256],
+ "skips": []
+ }
+ }],
+ "train": ["Trainer", {
+ "max_epochs": 20,
+ "opti": ["Adam", {
+ "lr": 5e-4
+ }],
+ "loss": {
+ "Color_L2": "mse_loss(color, gt_color)",
+ "Density_Reg": ["cauchy_loss(energies)", {"weight": 1e-4, "s": 1e4}]
+ }
+ }]
+}
\ No newline at end of file
diff --git a/configs/old/snerffast_periph+dr2.json b/configs/old/snerffast_periph+dr2.json
new file mode 100644
index 0000000..d9d0380
--- /dev/null
+++ b/configs/old/snerffast_periph+dr2.json
@@ -0,0 +1,27 @@
+{
+ "model": ["SnerfFast", {
+ "color": "rgb",
+ "sampler": {
+ "n_samples": 32
+ },
+ "encoder_x": ["Freq", {
+ "freqs": 6,
+ "cat_input": true
+ }],
+ "num_fields": 2,
+ "field": {
+ "shape": [4, 256],
+ "skips": []
+ }
+ }],
+ "train": ["Trainer", {
+ "max_epochs": 20,
+ "opti": ["Adam", {
+ "lr": 5e-4
+ }],
+ "loss": {
+ "Color_L2": "mse_loss(color, gt_color)",
+ "Density_Reg": ["cauchy_loss(densities)", {"s": 4}]
+ }
+ }]
+}
\ No newline at end of file
diff --git a/configs/old/snerffast_periph.json b/configs/old/snerffast_periph.json
new file mode 100644
index 0000000..7fdd357
--- /dev/null
+++ b/configs/old/snerffast_periph.json
@@ -0,0 +1,26 @@
+{
+ "model": ["SnerfFast", {
+ "color": "rgb",
+ "sampler": {
+ "n_samples": 32
+ },
+ "encoder_x": ["Freq", {
+ "freqs": 6,
+ "cat_input": true
+ }],
+ "num_fields": 2,
+ "field": {
+ "shape": [4, 256],
+ "skips": []
+ }
+ }],
+ "train": ["Trainer", {
+ "max_epochs": 20,
+ "opti": ["Adam", {
+ "lr": 5e-4
+ }],
+ "loss": {
+ "Color_L2": "mse_loss(color, gt_color)"
+ }
+ }]
+}
\ No newline at end of file
diff --git a/configs/snerfx4_voxels.json b/configs/old/snerfx4_voxels.json
similarity index 100%
rename from configs/snerfx4_voxels.json
rename to configs/old/snerfx4_voxels.json
diff --git a/configs/svnerf_voxels.json b/configs/old/svnerf_voxels.json
similarity index 100%
rename from configs/svnerf_voxels.json
rename to configs/old/svnerf_voxels.json
diff --git a/configs/periph.ini b/configs/periph.ini
new file mode 100644
index 0000000..095c9a1
--- /dev/null
+++ b/configs/periph.ini
@@ -0,0 +1,22 @@
+model=FsNeRF
+; n_samples=64
+n_fields=2
+depth=4
+; width=256
+skips=[]
+; act=relu
+; ln=false
+; xfreqs=6
+; raw_noise_std=0.
+; near: float # from dataset
+; far: float # from dataset
+; white_bg: bool # from dataset
+; trainer=Trainer
+; max_iters=200000
+max_epochs=50
+; checkpoint_interval=10000(iters) or 10(epochs)
+; batch_size=4096
+; loss=[Color_L2]
+; lr=5e-4
+lr_decay=0.9999954
+; profile_iters=0
\ No newline at end of file
diff --git a/configs/snerf_voxels.json b/configs/snerf_voxels.json
deleted file mode 100644
index 96e2c3f..0000000
--- a/configs/snerf_voxels.json
+++ /dev/null
@@ -1,25 +0,0 @@
-{
- "model": "NeRF",
- "args": {
- "spherical": true,
- "color": "rgb",
- "encode_x": 10,
- "encode_d": 4,
- "core_params": {
- "nf": 256,
- "n_layers": 8,
- "skips": [ 4 ]
- },
- "n_featdim": 0,
- "space": "voxels",
- "steps": [4, 16, 8],
- "n_samples": 16
- },
- "train": {
- "max_epochs": 50,
- "prune_epochs": [5],
- "split_epochs": [10],
- "density_regularization_weight": 1e-4,
- "density_regularization_scale": 1e4
- }
-}
\ No newline at end of file
diff --git a/cpp/.clang-format b/cpp/.clang-format
new file mode 100644
index 0000000..0571700
--- /dev/null
+++ b/cpp/.clang-format
@@ -0,0 +1 @@
+{ BasedOnStyle: LLVM, UseTab: Never, IndentWidth: 4, TabWidth: 4, BreakBeforeBraces: Attach, AllowShortIfStatementsOnASingleLine: false, IndentCaseLabels: false, ColumnLimit: 0, AccessModifierOffset: -4, NamespaceIndentation: All, FixNamespaceComments: false, SortIncludes: Never, ColumnLimit: 100 }
\ No newline at end of file
diff --git a/cpp_old/Makefile.config b/cpp/__old/Makefile.config
similarity index 100%
rename from cpp_old/Makefile.config
rename to cpp/__old/Makefile.config
diff --git a/cpp_old/msl_infer/Encoder.cu b/cpp/__old/msl_infer/Encoder.cu
similarity index 100%
rename from cpp_old/msl_infer/Encoder.cu
rename to cpp/__old/msl_infer/Encoder.cu
diff --git a/cpp_old/msl_infer/Encoder.h b/cpp/__old/msl_infer/Encoder.h
similarity index 100%
rename from cpp_old/msl_infer/Encoder.h
rename to cpp/__old/msl_infer/Encoder.h
diff --git a/cpp_old/msl_infer/Enhancement.cu b/cpp/__old/msl_infer/Enhancement.cu
similarity index 100%
rename from cpp_old/msl_infer/Enhancement.cu
rename to cpp/__old/msl_infer/Enhancement.cu
diff --git a/cpp_old/msl_infer/Enhancement.h b/cpp/__old/msl_infer/Enhancement.h
similarity index 100%
rename from cpp_old/msl_infer/Enhancement.h
rename to cpp/__old/msl_infer/Enhancement.h
diff --git a/cpp_old/msl_infer/ImageGen.cpp b/cpp/__old/msl_infer/ImageGen.cpp
similarity index 100%
rename from cpp_old/msl_infer/ImageGen.cpp
rename to cpp/__old/msl_infer/ImageGen.cpp
diff --git a/cpp_old/msl_infer/ImageGen.h b/cpp/__old/msl_infer/ImageGen.h
similarity index 100%
rename from cpp_old/msl_infer/ImageGen.h
rename to cpp/__old/msl_infer/ImageGen.h
diff --git a/cpp_old/msl_infer/InferPipeline.cpp b/cpp/__old/msl_infer/InferPipeline.cpp
similarity index 100%
rename from cpp_old/msl_infer/InferPipeline.cpp
rename to cpp/__old/msl_infer/InferPipeline.cpp
diff --git a/cpp_old/msl_infer/InferPipeline.h b/cpp/__old/msl_infer/InferPipeline.h
similarity index 100%
rename from cpp_old/msl_infer/InferPipeline.h
rename to cpp/__old/msl_infer/InferPipeline.h
diff --git a/cpp/fnr_core/Msl.cpp b/cpp/__old/msl_infer/Msl.cpp
old mode 100644
new mode 100755
similarity index 100%
rename from cpp/fnr_core/Msl.cpp
rename to cpp/__old/msl_infer/Msl.cpp
diff --git a/cpp/fnr_core/Msl.h b/cpp/__old/msl_infer/Msl.h
old mode 100644
new mode 100755
similarity index 100%
rename from cpp/fnr_core/Msl.h
rename to cpp/__old/msl_infer/Msl.h
diff --git a/cpp_old/msl_infer/Net.cpp b/cpp/__old/msl_infer/Net.cpp
similarity index 100%
rename from cpp_old/msl_infer/Net.cpp
rename to cpp/__old/msl_infer/Net.cpp
diff --git a/cpp/fnr_core/Net.h b/cpp/__old/msl_infer/Net.h
old mode 100644
new mode 100755
similarity index 100%
rename from cpp/fnr_core/Net.h
rename to cpp/__old/msl_infer/Net.h
diff --git a/cpp/fnr_core/Nmsl2.cpp b/cpp/__old/msl_infer/Nmsl2.cpp
old mode 100644
new mode 100755
similarity index 100%
rename from cpp/fnr_core/Nmsl2.cpp
rename to cpp/__old/msl_infer/Nmsl2.cpp
diff --git a/cpp/fnr_core/Nmsl2.h b/cpp/__old/msl_infer/Nmsl2.h
old mode 100644
new mode 100755
similarity index 100%
rename from cpp/fnr_core/Nmsl2.h
rename to cpp/__old/msl_infer/Nmsl2.h
diff --git a/cpp/fnr_core/Renderer.cu b/cpp/__old/msl_infer/Renderer.cu
old mode 100644
new mode 100755
similarity index 100%
rename from cpp/fnr_core/Renderer.cu
rename to cpp/__old/msl_infer/Renderer.cu
diff --git a/cpp/fnr_core/Renderer.h b/cpp/__old/msl_infer/Renderer.h
old mode 100644
new mode 100755
similarity index 100%
rename from cpp/fnr_core/Renderer.h
rename to cpp/__old/msl_infer/Renderer.h
diff --git a/cpp_old/msl_infer/Sampler.cu b/cpp/__old/msl_infer/Sampler.cu
similarity index 100%
rename from cpp_old/msl_infer/Sampler.cu
rename to cpp/__old/msl_infer/Sampler.cu
diff --git a/cpp/fnr_core/Sampler.h b/cpp/__old/msl_infer/Sampler.h
old mode 100644
new mode 100755
similarity index 100%
rename from cpp/fnr_core/Sampler.h
rename to cpp/__old/msl_infer/Sampler.h
diff --git a/cpp_old/msl_infer/SynthesisPipeline.cpp b/cpp/__old/msl_infer/SynthesisPipeline.cpp
similarity index 100%
rename from cpp_old/msl_infer/SynthesisPipeline.cpp
rename to cpp/__old/msl_infer/SynthesisPipeline.cpp
diff --git a/cpp_old/msl_infer/SynthesisPipeline.h b/cpp/__old/msl_infer/SynthesisPipeline.h
similarity index 100%
rename from cpp_old/msl_infer/SynthesisPipeline.h
rename to cpp/__old/msl_infer/SynthesisPipeline.h
diff --git a/cpp_old/msl_infer/View.cu b/cpp/__old/msl_infer/View.cu
similarity index 100%
rename from cpp_old/msl_infer/View.cu
rename to cpp/__old/msl_infer/View.cu
diff --git a/cpp_old/msl_infer/View.h b/cpp/__old/msl_infer/View.h
similarity index 100%
rename from cpp_old/msl_infer/View.h
rename to cpp/__old/msl_infer/View.h
diff --git a/cpp_old/msl_infer_test/Makefile b/cpp/__old/msl_infer_test/Makefile
similarity index 100%
rename from cpp_old/msl_infer_test/Makefile
rename to cpp/__old/msl_infer_test/Makefile
diff --git a/cpp_old/msl_infer_test/main.cpp b/cpp/__old/msl_infer_test/main.cpp
similarity index 100%
rename from cpp_old/msl_infer_test/main.cpp
rename to cpp/__old/msl_infer_test/main.cpp
diff --git a/cpp_old/nets/barbershop/fovea.trt b/cpp/__old/nets/barbershop/fovea.trt
similarity index 100%
rename from cpp_old/nets/barbershop/fovea.trt
rename to cpp/__old/nets/barbershop/fovea.trt
diff --git a/cpp_old/nets/barbershop/periph.trt b/cpp/__old/nets/barbershop/periph.trt
similarity index 100%
rename from cpp_old/nets/barbershop/periph.trt
rename to cpp/__old/nets/barbershop/periph.trt
diff --git a/cpp_old/nets/classroom/fovea.trt b/cpp/__old/nets/classroom/fovea.trt
similarity index 100%
rename from cpp_old/nets/classroom/fovea.trt
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diff --git a/cpp_old/nets/classroom/periph.trt b/cpp/__old/nets/classroom/periph.trt
similarity index 100%
rename from cpp_old/nets/classroom/periph.trt
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diff --git a/cpp/nets/fovea.mask b/cpp/__old/nets/fovea.mask
similarity index 100%
rename from cpp/nets/fovea.mask
rename to cpp/__old/nets/fovea.mask
diff --git a/cpp_old/nets/lobby/fovea.trt b/cpp/__old/nets/lobby/fovea.trt
similarity index 100%
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diff --git a/cpp_old/nets/lobby/periph.trt b/cpp/__old/nets/lobby/periph.trt
similarity index 100%
rename from cpp_old/nets/lobby/periph.trt
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diff --git a/cpp/nets/mid.mask b/cpp/__old/nets/mid.mask
similarity index 100%
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diff --git a/cpp/nets/old/fovea_mono/cat.trt b/cpp/__old/nets/old/fovea_mono/cat.trt
similarity index 100%
rename from cpp/nets/old/fovea_mono/cat.trt
rename to cpp/__old/nets/old/fovea_mono/cat.trt
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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diff --git a/cpp/nets/old/periph/cat.trt b/cpp/__old/nets/old/periph/cat.trt
similarity index 100%
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similarity index 100%
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similarity index 100%
rename from cpp/nets/old/periph/fc2.trt
rename to cpp/__old/nets/old/periph/fc2.trt
diff --git a/cpp/nets/old/periph/msl.trt b/cpp/__old/nets/old/periph/msl.trt
similarity index 100%
rename from cpp/nets/old/periph/msl.trt
rename to cpp/__old/nets/old/periph/msl.trt
diff --git a/cpp_old/nets/stones/fovea.trt b/cpp/__old/nets/stones/fovea.trt
similarity index 100%
rename from cpp_old/nets/stones/fovea.trt
rename to cpp/__old/nets/stones/fovea.trt
diff --git a/cpp_old/nets/stones/periph.trt b/cpp/__old/nets/stones/periph.trt
similarity index 100%
rename from cpp_old/nets/stones/periph.trt
rename to cpp/__old/nets/stones/periph.trt
diff --git a/cpp_old/old/msl_infer/Encoder.cu b/cpp/__old/old/msl_infer/Encoder.cu
similarity index 100%
rename from cpp_old/old/msl_infer/Encoder.cu
rename to cpp/__old/old/msl_infer/Encoder.cu
diff --git a/cpp_old/old/msl_infer/Encoder.h b/cpp/__old/old/msl_infer/Encoder.h
similarity index 100%
rename from cpp_old/old/msl_infer/Encoder.h
rename to cpp/__old/old/msl_infer/Encoder.h
diff --git a/cpp_old/old/msl_infer/Enhancement.cu b/cpp/__old/old/msl_infer/Enhancement.cu
similarity index 100%
rename from cpp_old/old/msl_infer/Enhancement.cu
rename to cpp/__old/old/msl_infer/Enhancement.cu
diff --git a/cpp_old/old/msl_infer/Enhancement.h b/cpp/__old/old/msl_infer/Enhancement.h
similarity index 100%
rename from cpp_old/old/msl_infer/Enhancement.h
rename to cpp/__old/old/msl_infer/Enhancement.h
diff --git a/cpp_old/old/msl_infer/ImageGen.cpp b/cpp/__old/old/msl_infer/ImageGen.cpp
similarity index 100%
rename from cpp_old/old/msl_infer/ImageGen.cpp
rename to cpp/__old/old/msl_infer/ImageGen.cpp
diff --git a/cpp_old/old/msl_infer/ImageGen.h b/cpp/__old/old/msl_infer/ImageGen.h
similarity index 100%
rename from cpp_old/old/msl_infer/ImageGen.h
rename to cpp/__old/old/msl_infer/ImageGen.h
diff --git a/cpp_old/old/msl_infer/InferPipeline.cpp b/cpp/__old/old/msl_infer/InferPipeline.cpp
similarity index 100%
rename from cpp_old/old/msl_infer/InferPipeline.cpp
rename to cpp/__old/old/msl_infer/InferPipeline.cpp
diff --git a/cpp_old/old/msl_infer/InferPipeline.h b/cpp/__old/old/msl_infer/InferPipeline.h
similarity index 100%
rename from cpp_old/old/msl_infer/InferPipeline.h
rename to cpp/__old/old/msl_infer/InferPipeline.h
diff --git a/cpp_old/msl_infer/Msl.cpp b/cpp/__old/old/msl_infer/Msl.cpp
old mode 100755
new mode 100644
similarity index 100%
rename from cpp_old/msl_infer/Msl.cpp
rename to cpp/__old/old/msl_infer/Msl.cpp
diff --git a/cpp_old/msl_infer/Msl.h b/cpp/__old/old/msl_infer/Msl.h
old mode 100755
new mode 100644
similarity index 100%
rename from cpp_old/msl_infer/Msl.h
rename to cpp/__old/old/msl_infer/Msl.h
diff --git a/cpp_old/old/msl_infer/Net.cpp b/cpp/__old/old/msl_infer/Net.cpp
similarity index 94%
rename from cpp_old/old/msl_infer/Net.cpp
rename to cpp/__old/old/msl_infer/Net.cpp
index a0f64d9..c226a29 100644
--- a/cpp_old/old/msl_infer/Net.cpp
+++ b/cpp/__old/old/msl_infer/Net.cpp
@@ -117,8 +117,7 @@ void Net::_deserialize(const std::string &path)
std::vector<void *> Net::_getBindings()
{
std::vector<void *> bindings(mEngine->getNbBindings());
- for (auto it = mResources.resources.begin();
- it != mResources.resources.end(); ++it)
+ for (auto it = mResources.resources.begin(); it != mResources.resources.end(); ++it)
{
auto idx = mEngine->getBindingIndex(it->first.c_str());
if (idx < 0)
diff --git a/cpp_old/msl_infer/Net.h b/cpp/__old/old/msl_infer/Net.h
old mode 100755
new mode 100644
similarity index 100%
rename from cpp_old/msl_infer/Net.h
rename to cpp/__old/old/msl_infer/Net.h
diff --git a/cpp_old/msl_infer/Nmsl2.cpp b/cpp/__old/old/msl_infer/Nmsl2.cpp
old mode 100755
new mode 100644
similarity index 100%
rename from cpp_old/msl_infer/Nmsl2.cpp
rename to cpp/__old/old/msl_infer/Nmsl2.cpp
diff --git a/cpp_old/old/msl_infer/Nmsl2.h b/cpp/__old/old/msl_infer/Nmsl2.h
similarity index 100%
rename from cpp_old/old/msl_infer/Nmsl2.h
rename to cpp/__old/old/msl_infer/Nmsl2.h
diff --git a/cpp_old/msl_infer/Renderer.cu b/cpp/__old/old/msl_infer/Renderer.cu
old mode 100755
new mode 100644
similarity index 100%
rename from cpp_old/msl_infer/Renderer.cu
rename to cpp/__old/old/msl_infer/Renderer.cu
diff --git a/cpp_old/msl_infer/Renderer.h b/cpp/__old/old/msl_infer/Renderer.h
old mode 100755
new mode 100644
similarity index 100%
rename from cpp_old/msl_infer/Renderer.h
rename to cpp/__old/old/msl_infer/Renderer.h
diff --git a/cpp_old/old/msl_infer/Sampler.cu b/cpp/__old/old/msl_infer/Sampler.cu
similarity index 100%
rename from cpp_old/old/msl_infer/Sampler.cu
rename to cpp/__old/old/msl_infer/Sampler.cu
diff --git a/cpp_old/old/msl_infer/Sampler.h b/cpp/__old/old/msl_infer/Sampler.h
similarity index 100%
rename from cpp_old/old/msl_infer/Sampler.h
rename to cpp/__old/old/msl_infer/Sampler.h
diff --git a/cpp_old/old/msl_infer/SynthesisPipeline.cpp b/cpp/__old/old/msl_infer/SynthesisPipeline.cpp
similarity index 100%
rename from cpp_old/old/msl_infer/SynthesisPipeline.cpp
rename to cpp/__old/old/msl_infer/SynthesisPipeline.cpp
diff --git a/cpp_old/old/msl_infer/SynthesisPipeline.h b/cpp/__old/old/msl_infer/SynthesisPipeline.h
similarity index 100%
rename from cpp_old/old/msl_infer/SynthesisPipeline.h
rename to cpp/__old/old/msl_infer/SynthesisPipeline.h
diff --git a/cpp_old/old/msl_infer/View.cu b/cpp/__old/old/msl_infer/View.cu
similarity index 100%
rename from cpp_old/old/msl_infer/View.cu
rename to cpp/__old/old/msl_infer/View.cu
diff --git a/cpp_old/old/msl_infer/View.h b/cpp/__old/old/msl_infer/View.h
similarity index 100%
rename from cpp_old/old/msl_infer/View.h
rename to cpp/__old/old/msl_infer/View.h
diff --git a/cpp_old/old/msl_infer_test/Makefile b/cpp/__old/old/msl_infer_test/Makefile
similarity index 100%
rename from cpp_old/old/msl_infer_test/Makefile
rename to cpp/__old/old/msl_infer_test/Makefile
diff --git a/cpp_old/old/msl_infer_test/main.cpp b/cpp/__old/old/msl_infer_test/main.cpp
similarity index 100%
rename from cpp_old/old/msl_infer_test/main.cpp
rename to cpp/__old/old/msl_infer_test/main.cpp
diff --git a/cpp_old/old/utils/Formatter.h b/cpp/__old/old/utils/Formatter.h
similarity index 100%
rename from cpp_old/old/utils/Formatter.h
rename to cpp/__old/old/utils/Formatter.h
diff --git a/cpp/utils/Logger.cpp b/cpp/__old/old/utils/Logger.cpp
similarity index 100%
rename from cpp/utils/Logger.cpp
rename to cpp/__old/old/utils/Logger.cpp
diff --git a/cpp_old/old/utils/Logger.h b/cpp/__old/old/utils/Logger.h
similarity index 100%
rename from cpp_old/old/utils/Logger.h
rename to cpp/__old/old/utils/Logger.h
diff --git a/cpp_old/utils/Resource.h b/cpp/__old/old/utils/Resource.h
old mode 100755
new mode 100644
similarity index 65%
rename from cpp_old/utils/Resource.h
rename to cpp/__old/old/utils/Resource.h
index 041ac86..274481c
--- a/cpp_old/utils/Resource.h
+++ b/cpp/__old/old/utils/Resource.h
@@ -1,77 +1,100 @@
#pragma once
#include <map>
#include <vector>
+#include <cuda_gl_interop.h>
+#include "Logger.h"
-class Resource {
+class Resource
+{
public:
virtual ~Resource() {}
- virtual void *getBuffer() const = 0;
+ virtual void *data() const = 0;
virtual size_t size() const = 0;
};
-class CudaBuffer : public Resource {
+class CudaBuffer : public Resource
+{
public:
CudaBuffer(void *buffer = nullptr, size_t size = 0)
: _buffer(buffer), _ownBuffer(false), _size(size) {}
- CudaBuffer(size_t size) : _buffer(nullptr), _ownBuffer(true), _size(size) {
+ CudaBuffer(size_t size) : _buffer(nullptr), _ownBuffer(true), _size(size)
+ {
CHECK_EX(cudaMalloc(&_buffer, size));
}
CudaBuffer(const CudaBuffer &rhs) = delete;
- virtual ~CudaBuffer() {
+ virtual ~CudaBuffer()
+ {
if (!_ownBuffer || _buffer == nullptr)
return;
- try {
+ try
+ {
CHECK_EX(cudaFree(_buffer));
- } catch (std::exception &ex) {
+ }
+ catch (std::exception &ex)
+ {
Logger::instance.warning(std::string("Exception raised in destructor: ") + ex.what());
}
_buffer = nullptr;
_ownBuffer = false;
}
- virtual void *getBuffer() const { return _buffer; }
- template <class T> T *getBuffer() const { return (T *)getBuffer(); }
+ virtual void *data() const { return _buffer; }
virtual size_t size() const { return _size; }
+ template <class T1>
+ T1 *data() const { return (T1 *)data(); }
+
+ template <class T1>
+ operator T1 *() const { return (T1 *)data(); }
+
private:
void *_buffer;
bool _ownBuffer;
size_t _size;
};
-template <typename T> class CudaArray : public CudaBuffer {
+template <typename T>
+class CudaArray : public CudaBuffer
+{
public:
CudaArray(size_t n) : CudaBuffer(n * sizeof(T)) {}
CudaArray(T *buffer, size_t n) : CudaBuffer(buffer, n * sizeof(T)) {}
- CudaArray(const std::vector<T> &hostArray) : CudaBuffer(hostArray.size() * sizeof(T)) {
- cudaMemcpy(getBuffer(), hostArray.data(), size(), cudaMemcpyHostToDevice);
+ CudaArray(const std::vector<T> &hostArray) : CudaBuffer(hostArray.size() * sizeof(T))
+ {
+ cudaMemcpy(data(), hostArray.data(), size(), cudaMemcpyHostToDevice);
}
CudaArray(const CudaArray<T> &rhs) = delete;
size_t n() const { return size() / sizeof(T); }
- operator T *() { return (T *)getBuffer(); }
- CudaArray<T> *subArray(size_t offset, size_t n = -1) {
+ operator T *() { return (T *)data(); }
+ CudaArray<T> *subArray(size_t offset, size_t n = -1)
+ {
if (n == -1)
n = this->n() - offset;
return new CudaArray<T>(*this + offset, n);
}
};
-class GraphicsResource : public Resource {
+class GraphicsResource : public Resource
+{
public:
cudaGraphicsResource_t getHandler() { return _res; }
- virtual ~GraphicsResource() {
+ virtual ~GraphicsResource()
+ {
if (_res == nullptr)
return;
- try {
+ try
+ {
CHECK_EX(cudaGraphicsUnregisterResource(_res));
- } catch (std::exception &ex) {
+ }
+ catch (std::exception &ex)
+ {
Logger::instance.warning(std::string("Exception raised in destructor: ") + ex.what());
}
_res = nullptr;
@@ -86,9 +109,12 @@ protected:
GraphicsResource() : _res(nullptr), _size(0) {}
};
-template <typename T> class GlTextureResource : public GraphicsResource {
+template <typename T>
+class GlTextureResource : public GraphicsResource
+{
public:
- GlTextureResource(GLuint textureID, glm::uvec2 textureSize) {
+ GlTextureResource(GLuint textureID, glm::uvec2 textureSize)
+ {
CHECK_EX(cudaGraphicsGLRegisterImage(&_res, textureID, GL_TEXTURE_2D,
cudaGraphicsRegisterFlagsWriteDiscard));
_size = textureSize.x * textureSize.y * sizeof(T);
@@ -97,17 +123,21 @@ public:
virtual ~GlTextureResource() { cudaGraphicsUnmapResources(1, &_res, 0); }
- virtual void *getBuffer() const {
+ virtual void *data() const
+ {
cudaArray_t buffer;
- try {
+ try
+ {
CHECK_EX(cudaGraphicsSubResourceGetMappedArray(&buffer, _res, 0, 0));
- } catch (...) {
+ }
+ catch (...)
+ {
return nullptr;
}
return buffer;
}
- operator T *() { return (T *)getBuffer(); }
+ operator T *() { return (T *)data(); }
glm::uvec2 textureSize() { return _textureSize; }
@@ -115,19 +145,22 @@ private:
glm::uvec2 _textureSize;
};
-class Resources {
+class Resources
+{
public:
std::map<std::string, Resource *> resources;
std::vector<cudaGraphicsResource_t> graphicsResources;
- void addResource(const std::string &name, Resource *res) {
+ void addResource(const std::string &name, Resource *res)
+ {
auto gres = dynamic_cast<GraphicsResource *>(res);
if (gres != nullptr)
graphicsResources.push_back(gres->getHandler());
resources[name] = res;
}
- void clear() {
+ void clear()
+ {
resources.clear();
graphicsResources.clear();
}
@@ -135,10 +168,11 @@ public:
template <typename T, typename T2 = T>
void dumpArray(std::ostream &so, CudaArray<T> &arr, size_t maxDumpRows = 0,
- size_t elemsPerRow = 1) {
+ size_t elemsPerRow = 1)
+{
int chns = sizeof(T) / sizeof(T2);
T2 *hostArr = new T2[arr.n() * chns];
- cudaMemcpy(hostArr, arr.getBuffer(), arr.n() * sizeof(T), cudaMemcpyDeviceToHost);
+ cudaMemcpy(hostArr, arr.data(), arr.n() * sizeof(T), cudaMemcpyDeviceToHost);
dumpHostBuffer<T2>(so, hostArr, arr.n() * sizeof(T), chns * elemsPerRow, maxDumpRows);
delete[] hostArr;
}
\ No newline at end of file
diff --git a/cpp_old/old/utils/common.h b/cpp/__old/old/utils/common.h
similarity index 100%
rename from cpp_old/old/utils/common.h
rename to cpp/__old/old/utils/common.h
diff --git a/cpp_old/old/utils/cuda.h b/cpp/__old/old/utils/cuda.h
similarity index 100%
rename from cpp_old/old/utils/cuda.h
rename to cpp/__old/old/utils/cuda.h
diff --git a/cpp_old/old/utils/half.h b/cpp/__old/old/utils/half.h
similarity index 100%
rename from cpp_old/old/utils/half.h
rename to cpp/__old/old/utils/half.h
diff --git a/cpp_old/old/utils/thread_index.h b/cpp/__old/old/utils/thread_index.h
similarity index 100%
rename from cpp_old/old/utils/thread_index.h
rename to cpp/__old/old/utils/thread_index.h
diff --git a/cpp_old/utils/Formatter.h b/cpp/__old/utils/Formatter.h
similarity index 100%
rename from cpp_old/utils/Formatter.h
rename to cpp/__old/utils/Formatter.h
diff --git a/cpp_old/old/utils/Logger.cpp b/cpp/__old/utils/Logger.cpp
old mode 100644
new mode 100755
similarity index 100%
rename from cpp_old/old/utils/Logger.cpp
rename to cpp/__old/utils/Logger.cpp
diff --git a/cpp_old/utils/Logger.h b/cpp/__old/utils/Logger.h
similarity index 83%
rename from cpp_old/utils/Logger.h
rename to cpp/__old/utils/Logger.h
index d89723f..66ef84d 100755
--- a/cpp_old/utils/Logger.h
+++ b/cpp/__old/utils/Logger.h
@@ -64,8 +64,4 @@ public:
}
externalLogFunc((int)severity, msg);
}
-};
-
-
-#define CHECK(__ERR_CODE__) do { if (!Logger::instance.checkErr((__ERR_CODE__), __FILE__, __LINE__)) return false; } while (0)
-#define CHECK_EX(__ERR_CODE__) do { if (!Logger::instance.checkErr((__ERR_CODE__), __FILE__, __LINE__)) throw std::exception(); } while (0)
+};
\ No newline at end of file
diff --git a/cpp_old/old/utils/Resource.h b/cpp/__old/utils/Resource.h
old mode 100644
new mode 100755
similarity index 100%
rename from cpp_old/old/utils/Resource.h
rename to cpp/__old/utils/Resource.h
diff --git a/cpp_old/utils/common.h b/cpp/__old/utils/common.h
similarity index 100%
rename from cpp_old/utils/common.h
rename to cpp/__old/utils/common.h
diff --git a/cpp_old/utils/cuda.h b/cpp/__old/utils/cuda.h
similarity index 100%
rename from cpp_old/utils/cuda.h
rename to cpp/__old/utils/cuda.h
diff --git a/cpp_old/utils/half.h b/cpp/__old/utils/half.h
similarity index 100%
rename from cpp_old/utils/half.h
rename to cpp/__old/utils/half.h
diff --git a/cpp/utils/thread_index.h b/cpp/__old/utils/thread_index.h
old mode 100644
new mode 100755
similarity index 100%
rename from cpp/utils/thread_index.h
rename to cpp/__old/utils/thread_index.h
diff --git a/cpp/fields/FsNeRF.cpp b/cpp/fields/FsNeRF.cpp
new file mode 100644
index 0000000..5aad8bc
--- /dev/null
+++ b/cpp/fields/FsNeRF.cpp
@@ -0,0 +1,31 @@
+#include "FsNeRF.h"
+
+namespace fields
+{
+ FsNeRF::FsNeRF(const std::string &netPath) : _net(nullptr) {
+ _net = new Net();
+ if (!_net->load(netPath)) {
+ dispose();
+ throw std::runtime_error("Failed to load net: " + netPath);
+ }
+ }
+
+ void FsNeRF::bindResources(Resource *resEncoded, Resource *resDepths, Resource *resColors)
+ {
+ _net->bindResource("Encoded", resEncoded);
+ _net->bindResource("Depths", resDepths);
+ _net->bindResource("Colors", resColors);
+ }
+
+ bool FsNeRF::infer() { return _net->infer(); }
+
+ void FsNeRF::dispose()
+ {
+ if (_net != nullptr)
+ {
+ _net->dispose();
+ delete _net;
+ _net = nullptr;
+ }
+ }
+}
\ No newline at end of file
diff --git a/cpp/fields/FsNeRF.h b/cpp/fields/FsNeRF.h
new file mode 100644
index 0000000..61ec2f1
--- /dev/null
+++ b/cpp/fields/FsNeRF.h
@@ -0,0 +1,22 @@
+#pragma once
+#include "../utils/common.h"
+#include "Net.h"
+
+namespace fields
+{
+ class FsNeRF
+ {
+ public:
+
+ FsNeRF(const std::string &netPath);
+
+ virtual void bindResources(Resource *resEncoded, Resource *resDepths, Resource *resColors);
+
+ virtual bool infer();
+
+ virtual void dispose();
+
+ private:
+ Net *_net;
+ };
+}
\ No newline at end of file
diff --git a/cpp/fnr_core/Net.cpp b/cpp/fields/Net.cpp
similarity index 100%
rename from cpp/fnr_core/Net.cpp
rename to cpp/fields/Net.cpp
diff --git a/cpp_old/old/msl_infer/Net.h b/cpp/fields/Net.h
similarity index 100%
rename from cpp_old/old/msl_infer/Net.h
rename to cpp/fields/Net.h
diff --git a/cpp/fnr_core/Encoder.cu b/cpp/fnr_core/Encoder.cu
deleted file mode 100644
index bc43161..0000000
--- a/cpp/fnr_core/Encoder.cu
+++ /dev/null
@@ -1,86 +0,0 @@
-#include "Encoder.h"
-#include "../utils/cuda.h"
-
-/// idx3.z = 0: x, y, z, sin(x), sin(y), sin(z), cos(x), cos(y), cos(z)
-/// idx3.z = 1: sin(2x), sin(2y), sin(2z), cos(2x), cos(2y), cos(2z)
-/// ...
-/// idx3.z = n_freq-1: sin(2^(n_freq-1)x), sin(2^(n_freq-1)y), sin(2^(n_freq-1)z),
-/// cos(2^(n_freq-1)x), cos(2^(n_freq-1)y), cos(2^(n_freq-1)z)
-/// Dispatch (n, in_chns, n_freqs)
-__global__ void cu_encode0(float *output, float *input, uint n, uint nFreqs) {
- glm::uvec3 idx3 = IDX3;
- if (idx3.x >= n)
- return;
- uint inChns = blockDim.y;
- uint outChns = inChns * (nFreqs * 2 + 1);
- uint i = idx3.x, chn = idx3.y;
- output[i * outChns + chn] = input[i * inChns + chn];
-}
-
-__global__ void cu_encode(float *output, float *input, float *freqs, uint n, bool catInput) {
- glm::uvec3 idx3 = IDX3;
- if (idx3.x >= n)
- return;
- uint offset = (uint)catInput;
- uint inChns = blockDim.y, nFreqs = blockDim.z;
- uint i = idx3.x, chn = idx3.y, freq = idx3.z;
- uint elem = i * inChns + chn;
- uint outChns = inChns * (nFreqs * 2 + offset);
- uint base = i * outChns + chn;
- if (freq == 0 && catInput)
- output[base] = input[elem];
- float x = freqs[freq] * input[elem];
- float s, c;
- __sincosf(x, &s, &c);
- output[base + inChns * (freq * 2 + offset)] = s;
- output[base + inChns * (freq * 2 + offset + 1)] = c;
-}
-
-__global__ void cu_encode2(glm::vec2 *output, glm::vec2 *input, float *freqs, uint n) {
- glm::uvec3 idx3 = IDX3;
- if (idx3.x >= n)
- return;
- uint nFreqs = blockDim.y;
- uint i = idx3.x, freq = idx3.y;
- uint outChns = nFreqs * 2 + 1;
- uint base = i * outChns;
- if (freq == 0)
- output[base] = input[i];
- glm::vec2 x = freqs[freq] * input[i];
- glm::vec2 s, c;
- __sincosf(x.x, &s.x, &c.x);
- __sincosf(x.y, &s.y, &c.y);
- output[base + (freq * 2 + 1)] = s;
- output[base + (freq * 2 + 2)] = c;
-}
-
-/**
- * @brief
- *
- * @param output encoded data, n x out_chns
- * @param input coord data, n x in_chns
- */
-void Encoder::encode(sptr<CudaArray<float>> output, sptr<CudaArray<float>> input) {
- std::ostringstream sout;
- sout << "Encoder => input size: (" << input->n() / _chns << ", " << _chns << "), output size: ("
- << output->n() / outDim() << ", " << outDim() << ")";
- //Logger::instance.info(sout.str());
- uint n = input->n() / _chns;
- dim3 blkSize(1024 / _chns / _multires, _chns, _multires);
- dim3 grdSize(ceilDiv(n, blkSize.x), 1, 1);
- CU_INVOKE(cu_encode)(*output, *input, *_freqs, n, _catInput);
- // blkSize = dim3(1024 / _chns, _chns);
- // grdSize = dim3(ceilDiv(n, blkSize.x), 1, 1);
- // CU_INVOKE(cu_encode0)(*output, *input, n, _multires);
- CHECK_EX(cudaGetLastError());
-}
-
-void Encoder::_genFreqArray() {
- float *arr = new float[_multires];
- arr[0] = 1.0f;
- for (auto i = 1; i < _multires; ++i)
- arr[i] = arr[i - 1] * 2.0f;
- _freqs = sptr<CudaArray<float>>(new CudaArray<float>(_multires));
- cudaMemcpy(_freqs->getBuffer(), arr, _multires * sizeof(float), cudaMemcpyHostToDevice);
- delete[] arr;
-}
diff --git a/cpp/fnr_core/Encoder.h b/cpp/fnr_core/Encoder.h
deleted file mode 100644
index c84fce5..0000000
--- a/cpp/fnr_core/Encoder.h
+++ /dev/null
@@ -1,21 +0,0 @@
-#pragma once
-#include "../utils/common.h"
-
-class Encoder {
-public:
- Encoder(unsigned int multires, unsigned int chns, bool catInput)
- : _multires(multires), _chns(chns), _catInput(catInput) {
- _genFreqArray();
- }
-
- unsigned int outDim() const { return _chns * ((int)_catInput + _multires * 2); }
- void encode(sptr<CudaArray<float>> output, sptr<CudaArray<float>> input);
-
-private:
- unsigned int _multires;
- unsigned int _chns;
- bool _catInput;
- sptr<CudaArray<float>> _freqs;
-
- void _genFreqArray();
-};
\ No newline at end of file
diff --git a/cpp/fnr_core/InferPipeline.cpp b/cpp/fnr_core/InferPipeline.cpp
deleted file mode 100644
index e36112b..0000000
--- a/cpp/fnr_core/InferPipeline.cpp
+++ /dev/null
@@ -1,97 +0,0 @@
-#include "InferPipeline.h"
-#include "Nmsl2.h"
-
-InferPipeline::InferPipeline(sptr<Msl> net, uint nRays, uint nSamplesPerRay, glm::vec2 depthRange,
- uint encodeDim, uint coordChns)
- : _nRays(nRays),
- _nSamplesPerRay(nSamplesPerRay),
- _coordChns(coordChns),
- _net(net),
- _sampler(new Sampler(depthRange, nSamplesPerRay, coordChns == 3)),
- _encoder(new Encoder(encodeDim, coordChns)),
- _renderer(new Renderer()) {
- auto nSamples = _nRays * _nSamplesPerRay;
- _coords = sptr<CudaArray<float>>(new CudaArray<float>(nSamples * coordChns));
- _depths = sptr<CudaArray<float>>(new CudaArray<float>(nSamples));
- _encoded = sptr<CudaArray<float>>(new CudaArray<float>(nSamples * _encoder->outDim()));
- _layeredColors = sptr<CudaArray<glm::vec4>>(new CudaArray<glm::vec4>(nSamples));
- _net->bindResources(_encoded.get(), _depths.get(), _layeredColors.get());
-}
-
-void InferPipeline::run(sptr<CudaArray<glm::vec4>> o_colors, sptr<CudaArray<glm::vec3>> rays,
- glm::vec3 origin, bool showPerf) {
- rays = sptr<CudaArray<glm::vec3>>(rays->subArray(0, _nRays));
- o_colors = sptr<CudaArray<glm::vec4>>(o_colors->subArray(0, _nRays));
- CudaEvent eStart, eSampled, eEncoded, eInferred, eRendered;
-
- cudaEventRecord(eStart);
-
- _sampler->sampleOnRays(_coords, _depths, rays, origin);
- CHECK_EX(cudaDeviceSynchronize());
-
- cudaEventRecord(eSampled);
-
- _encoder->encode(_encoded, _coords);
- CHECK_EX(cudaDeviceSynchronize());
-
- cudaEventRecord(eEncoded);
-
- _net->infer();
- CHECK_EX(cudaDeviceSynchronize());
-
- cudaEventRecord(eInferred);
-
- _renderer->render(o_colors, _layeredColors);
-
- cudaEventRecord(eRendered);
-
- if (showPerf) {
- CHECK_EX(cudaDeviceSynchronize());
-
- float timeTotal, timeSample, timeEncode, timeInfer, timeRender;
- cudaEventElapsedTime(&timeTotal, eStart, eRendered);
- cudaEventElapsedTime(&timeSample, eStart, eSampled);
- cudaEventElapsedTime(&timeEncode, eSampled, eEncoded);
- cudaEventElapsedTime(&timeInfer, eEncoded, eInferred);
- cudaEventElapsedTime(&timeRender, eInferred, eRendered);
-
- std::ostringstream sout;
- sout << "Infer pipeline: " << timeTotal << "ms (Sample: " << timeSample
- << "ms, Encode: " << timeEncode << "ms, Infer: " << timeInfer
- << "ms, Render: " << timeRender << "ms)";
- Logger::instance.info(sout.str().c_str());
- }
-
- /*
- {
- std::ostringstream sout;
- sout << "Rays:" << std::endl;
- dumpArray<glm::vec3, float>(sout, *rays, 10);
- Logger::instance.info(sout.str());
- }
- {
- std::ostringstream sout;
- sout << "Spherical coords:" << std::endl;
- dumpArray(sout, *_coords, 10, _coordChns * _nSamplesPerRay);
- Logger::instance.info(sout.str());
- }
- {
- std::ostringstream sout;
- sout << "Depths:" << std::endl;
- dumpArray(sout, *_depths, 10, _nSamplesPerRay);
- Logger::instance.info(sout.str());
- }
- {
- std::ostringstream sout;
- sout << "Encoded:" << std::endl;
- dumpArray(sout, *_encoded, 10, _encoder->outDim() * _nSamplesPerRay);
- Logger::instance.info(sout.str());
- }
- {
- std::ostringstream sout;
- sout << "Color:" << std::endl;
- dumpArray<glm::vec4, float>(sout, *o_colors, 10);
- Logger::instance.info(sout.str());
- }
- */
-}
\ No newline at end of file
diff --git a/cpp/fnr_core/InferPipeline.h b/cpp/fnr_core/InferPipeline.h
deleted file mode 100644
index d384d1b..0000000
--- a/cpp/fnr_core/InferPipeline.h
+++ /dev/null
@@ -1,31 +0,0 @@
-#pragma once
-#include "../utils/common.h"
-#include "Sampler.h"
-#include "Encoder.h"
-#include "Renderer.h"
-#include "Msl.h"
-
-class InferPipeline {
-public:
- InferPipeline(sptr<Msl> net, uint nRays, uint nSamplesPerRay,
- glm::vec2 depthRange, uint encodeDim, uint coordChns);
-
- void run(sptr<CudaArray<glm::vec4>> o_colors, sptr<CudaArray<glm::vec3>> rays, glm::vec3 origin,
- bool showPerf = false);
-
- uint nRays() const { return _nRays; }
-
-private:
- uint _nRays;
- uint _nSamplesPerRay;
- uint _coordChns;
- sptr<Msl> _net;
- sptr<Sampler> _sampler;
- sptr<Encoder> _encoder;
- sptr<Renderer> _renderer;
- sptr<CudaArray<float>> _coords;
- sptr<CudaArray<float>> _depths;
- sptr<CudaArray<float>> _encoded;
- sptr<CudaArray<glm::vec4>> _layeredColors;
-
-};
\ No newline at end of file
diff --git a/cpp/fnr_core/NeuralSynthesis.cpp b/cpp/fnr_core/NeuralSynthesis.cpp
new file mode 100644
index 0000000..c19dfc8
--- /dev/null
+++ b/cpp/fnr_core/NeuralSynthesis.cpp
@@ -0,0 +1,193 @@
+#include "NeuralSynthesis.h"
+#include "InferPipeline.h"
+#include "Enhancement.h"
+#include "ImageGen.h"
+
+constexpr auto NUM_LAYERS = 3u;
+constexpr auto STEREO_FOVEA_R = NUM_LAYERS;
+constexpr auto NUM_NETS = 2u;
+
+class NeuralSynthesis_Impl {
+public:
+ NeuralSynthesis_Impl(models::Model& model, Camera& cam);
+
+ void run(View& view);
+
+ GLuint getGlResultTexture(uint index);
+
+private:
+ models::Model& model;
+ Camera& _cam;
+ uint _nRays;
+ sptr<InferPipeline> _infers[NUM_NETS];
+ sptr<Enhancement> _enhancements[NUM_LAYERS];
+ sptr<ImageGen> _imageGens[NUM_LAYERS + 1];
+ sptr<CudaArray<glm::vec3>> _rays;
+ sptr<CudaArray<glm::vec4>> _clrs;
+ sptr<CudaArray<glm::vec4>> _imageData[NUM_LAYERS + 1];
+
+};
+
+NeuralSynthesis_Impl::NeuralSynthesis_Impl(const std::string& dataDir, glm::vec2 depthRange,
+ uint nSamples[], uint encodeDim, uint coordChns, sptr<Camera> cam,
+ const std::vector<sptr<Camera>>& layerCams, bool stereo) :
+ _fullCam(cam), _stereo(stereo) {
+ // Load nets
+ for (uint i = 0; i < NUM_NETS; ++i)
+ _nets[i].reset(new Msl());
+ _nets[0]->load(dataDir + "/fovea.trt");
+ _nets[1]->load(dataDir + "/periph.trt");
+
+ // Init cams
+ for (uint i = 0; i < NUM_LAYERS; ++i)
+ _cams[i] = layerCams[i];
+
+ uint nRays[NUM_LAYERS];
+ uint nTotRays = 0;
+ for (uint i = 0; i < NUM_LAYERS; ++i)
+ nTotRays += nRays[i] = _cams[i]->nRays();
+ if (_stereo)
+ nTotRays += nRays[0];
+
+ // Init infers
+ _infers[0].reset(new InferPipeline(_nets[0], nRays[0], nSamples[0],
+ depthRange, encodeDim, coordChns));
+ _infers[1].reset(new InferPipeline(_nets[1], nRays[1] + nRays[2], nSamples[1],
+ depthRange, encodeDim, coordChns));
+
+ // Init image gens
+ for (uint i = 0; i < NUM_LAYERS; ++i)
+ _imageGens[i].reset(new ImageGen(_cams[i]->res()));
+ if (_stereo)
+ _imageGens[STEREO_FOVEA_R].reset(new ImageGen(_cams[0]->res()));
+
+ // Init enhancements
+ glm::vec2 enhancementParams[] = {
+ {3.0f, 0.2f}, {5.0f, 0.2f}, {5.0f, 0.2f}
+ };
+ for (uint i = 0; i < NUM_LAYERS; ++i)
+ _enhancements[i].reset(new Enhancement(_cams[i]->res(), enhancementParams[i]));
+
+ // Create buffers
+ _rays.reset(new CudaArray<glm::vec3>(nTotRays));
+ _clrs.reset(new CudaArray<glm::vec4>(nTotRays));
+ for (uint i = 0; i < NUM_LAYERS; ++i)
+ _imageData[i].reset(new CudaArray<glm::vec4>(_cams[i]->nPixels()));
+ if (_stereo)
+ _imageData[STEREO_FOVEA_R].reset(new CudaArray<glm::vec4>(_cams[0]->nPixels()));
+}
+
+
+void NeuralSynthesis_Impl::run(View& view, glm::vec2 foveaPos, bool showPerf, glm::vec2 foveaPosR) {
+ CudaEvent eStart, eGenRays, eInferred, eGenImage, eEnhance;
+ uint offset;
+
+ cudaEventRecord(eStart);
+
+ glm::vec2 foveaOffset(foveaPos - (glm::vec2)_fullCam->res() / 2.0f);
+ foveaOffset /= _fullCam->f();
+ glm::vec3 foveaOffset3(foveaOffset.x, foveaOffset.y, 0.0f);
+
+ glm::vec2 foveaOffsetR(foveaPosR - (glm::vec2)_fullCam->res() / 2.0f);
+ foveaOffsetR /= _fullCam->f();
+ glm::vec3 foveaOffset3R(foveaOffsetR.x, foveaOffsetR.y, 0.0f);
+
+ auto viewL = view.getStereoEye(0.06f, Eye_Left);
+ auto viewR = view.getStereoEye(0.06f, Eye_Right);
+
+ if (_stereo) {
+ offset = 0;
+ _cams[0]->getRays(sptr<CudaArray<glm::vec3>>(_rays->subArray(offset)), viewL, foveaOffset3);
+ offset += _cams[0]->nRays();
+ _cams[1]->getRays(sptr<CudaArray<glm::vec3>>(_rays->subArray(offset)), view, (foveaOffset3 + foveaOffset3R) / 2.0f);
+ offset += _cams[1]->nRays();
+ _cams[2]->getRays(sptr<CudaArray<glm::vec3>>(_rays->subArray(offset)), view, {});
+ offset += _cams[2]->nRays();
+ _cams[0]->getRays(sptr<CudaArray<glm::vec3>>(_rays->subArray(offset)), viewR, foveaOffset3R);
+ } else {
+ offset = 0;
+ for (uint i = 0; i < NUM_LAYERS; ++i) {
+ _cams[i]->getRays(sptr<CudaArray<glm::vec3>>(_rays->subArray(offset)),
+ view, i == NUM_LAYERS - 1 ? glm::vec3() : foveaOffset3);
+ offset += _cams[i]->nRays();
+ }
+ }
+
+ cudaEventRecord(eGenRays);
+
+ if (_stereo) {
+ offset = 0;
+ _infers[0]->run(sptr<CudaArray<glm::vec4>>(_clrs->subArray(offset)),
+ sptr<CudaArray<glm::vec3>>(_rays->subArray(offset)), viewL.t(), showPerf);
+ offset += _infers[0]->nRays();
+ _infers[1]->run(sptr<CudaArray<glm::vec4>>(_clrs->subArray(offset)),
+ sptr<CudaArray<glm::vec3>>(_rays->subArray(offset)), view.t(), showPerf);
+ offset += _infers[1]->nRays();
+ _infers[0]->run(sptr<CudaArray<glm::vec4>>(_clrs->subArray(offset)),
+ sptr<CudaArray<glm::vec3>>(_rays->subArray(offset)), viewR.t(), showPerf);
+ } else {
+ offset = 0;
+ for (uint i = 0; i < NUM_NETS; ++i) {
+ _infers[i]->run(sptr<CudaArray<glm::vec4>>(_clrs->subArray(offset)),
+ sptr<CudaArray<glm::vec3>>(_rays->subArray(offset)), view.t(), showPerf);
+ offset += _infers[i]->nRays();
+ }
+ }
+
+ cudaEventRecord(eInferred);
+
+ offset = 0;
+ for (uint i = 0; i < NUM_LAYERS; ++i) {
+ _cams[i]->restoreImage(_imageData[i], sptr<CudaArray<glm::vec4>>(_clrs->subArray(offset)));
+ offset += _cams[i]->nRays();
+ }
+ if (_stereo)
+ _cams[0]->restoreImage(_imageData[STEREO_FOVEA_R], sptr<CudaArray<glm::vec4>>(_clrs->subArray(offset)));
+
+ cudaEventRecord(eGenImage);
+
+ for (uint i = 0; i < NUM_LAYERS; ++i)
+ _enhancements[i]->run(_imageData[i]);
+ if (_stereo)
+ _enhancements[0]->run(_imageData[STEREO_FOVEA_R]);
+
+ cudaEventRecord(eEnhance);
+ CHECK_EX(cudaDeviceSynchronize());
+
+ for (uint i = 0; i < NUM_LAYERS; ++i)
+ _imageGens[i]->run(_imageData[i]);
+ if (_stereo)
+ _imageGens[STEREO_FOVEA_R]->run(_imageData[STEREO_FOVEA_R]);
+
+ float timeTotal, timeGenRays, timeInfer, timeGenImage, timeEnhance;
+ cudaEventElapsedTime(&timeTotal, eStart, eGenImage);
+ cudaEventElapsedTime(&timeGenRays, eStart, eGenRays);
+ cudaEventElapsedTime(&timeInfer, eGenRays, eInferred);
+ cudaEventElapsedTime(&timeGenImage, eInferred, eGenImage);
+ cudaEventElapsedTime(&timeEnhance, eGenImage, eEnhance);
+ if (showPerf) {
+ std::ostringstream sout;
+ sout << "Synthesis => Total: " << timeTotal << "ms (Gen rays: " << timeGenRays
+ << "ms, Infer: " << timeInfer << "ms, Gen image: " << timeGenImage
+ << "ms, Enhance: " << timeEnhance << "ms)";
+ Logger::instance.info(sout.str().c_str());
+ }
+}
+
+GLuint NeuralSynthesis_Impl::getGlResultTexture(uint index) {
+ return _imageGens[index]->getGlResultTexture();
+}
+
+NeuralSynthesis::NeuralSynthesis(const std::string& dataDir, glm::vec2 depthRange,
+ uint nSamples[], uint encodeDim, uint coordChns, sptr<Camera> cam,
+ const std::vector<sptr<Camera>>& layerCams, bool stereo) :
+ _impl(new NeuralSynthesis_Impl(dataDir, depthRange, nSamples, encodeDim, coordChns, cam, layerCams, stereo)) {
+}
+
+void NeuralSynthesis::run(View& view, glm::vec2 foveaPos, bool showPerf, glm::vec2 foveaPosR) {
+ _impl->run(view, foveaPos, showPerf, foveaPosR);
+}
+
+GLuint NeuralSynthesis::getGlResultTexture(uint index) {
+ return _impl->getGlResultTexture(index);
+}
diff --git a/cpp/fnr_core/NeuralSynthesis.h b/cpp/fnr_core/NeuralSynthesis.h
new file mode 100644
index 0000000..de1b55c
--- /dev/null
+++ b/cpp/fnr_core/NeuralSynthesis.h
@@ -0,0 +1,19 @@
+#pragma once
+#include "../utils/common.h"
+#include "View.h"
+#include "../models/Model.h"
+
+class NeuralSynthesis_Impl;
+
+class NeuralSynthesis {
+public:
+ NeuralSynthesis(models::Model& model, Camera& cam);
+
+ void operator()(View& view);
+
+ GLuint getGlResultTexture(uint index);
+
+private:
+ sptr<NeuralSynthesis_Impl> _impl;
+
+};
\ No newline at end of file
diff --git a/cpp/fnr_core/Sampler.cu b/cpp/fnr_core/Sampler.cu
deleted file mode 100644
index 348703d..0000000
--- a/cpp/fnr_core/Sampler.cu
+++ /dev/null
@@ -1,46 +0,0 @@
-#include "Sampler.h"
-#define _USE_MATH_DEFINES
-#include <math.h>
-#include "../utils/cuda.h"
-
-__device__ glm::vec3 _raySphereIntersect(glm::vec3 p, glm::vec3 v, float r, float &o_depth) {
- float pp = glm::dot(p, p);
- float vv = glm::dot(v, v);
- float pv = glm::dot(p, v);
- o_depth = (sqrtf(pv * pv - vv * (pp - r * r)) - pv) / vv;
- return p + o_depth * v;
-}
-
-__device__ float _getAngle(float x, float y) {
- return -atan(x / y) + (y < 0) * (float)M_PI + 0.5f * (float)M_PI;
-}
-
-/**
- * Dispatch with block_size=(n_samples, *), grid_size=(1, nRays/*)
- * Index with (sample_idx, ray_idx)
- */
-__global__ void cu_sampleOnRays(float *o_coords, float *o_depths, glm::vec3 *rays, uint nRays,
- glm::vec3 origin, Range range, bool outputRadius) {
- glm::uvec3 idx3 = IDX3;
- uint idx = flattenIdx(idx3);
- uint sampleIdx = idx3.x;
- uint rayIdx = idx3.y;
- if (rayIdx >= nRays)
- return;
- float r_reciprocal = range.get(sampleIdx);
- glm::vec3 p = _raySphereIntersect(origin, rays[rayIdx], 1.0f / r_reciprocal, o_depths[idx]);
- glm::vec3 sp(r_reciprocal, _getAngle(p.x, p.z), acos(p.y * r_reciprocal));
- if (outputRadius)
- ((glm::vec3 *)o_coords)[idx] = sp;
- else
- ((glm::vec2 *)o_coords)[idx] = {sp.y, sp.z};
-}
-
-void Sampler::sampleOnRays(sptr<CudaArray<float>> o_coords, sptr<CudaArray<float>> o_depths,
- sptr<CudaArray<glm::vec3>> rays, glm::vec3 rayCenter) {
- dim3 blkSize(_dispRange.steps(), 1024 / _dispRange.steps());
- dim3 grdSize(1, (uint)ceil(rays->n() / (float)blkSize.y));
- CU_INVOKE(cu_sampleOnRays)
- (*o_coords, *o_depths, *rays, rays->n(), rayCenter, _dispRange, _outputRadius);
- CHECK_EX(cudaGetLastError());
-}
\ No newline at end of file
diff --git a/cpp/models/FsNeRF.cpp b/cpp/models/FsNeRF.cpp
new file mode 100644
index 0000000..c4b0bcc
--- /dev/null
+++ b/cpp/models/FsNeRF.cpp
@@ -0,0 +1,107 @@
+#include "FsNeRF.h"
+
+namespace models
+{
+ FsNeRF::FsNeRF(const Args &args, int nRays)
+ : _nRays(nRays),
+ _nSamples(args.nSamples),
+ _xChns(3 - !args.withRadius),
+ _field(new fields::FsNeRF(args.modelPath)),
+ _sampler(new modules::Sampler({args.near, args.far}, "spherical_radius", args.nSamples, args.withRadius)),
+ _encoder(new modules::Encoder(args.xfreqs, 3 - !args.withRadius, false)),
+ _renderer(new modules::Renderer(args.whiteBg))
+ {
+ auto n = _nRays * _nSamples;
+ _x = darray<float>(new CudaArray<float>(n * _xChns));
+ _depths = darray<float>(new CudaArray<float>(n));
+ _encoded = darray<float>(new CudaArray<float>(n * _encoder->outChns()));
+ _rgbd = darray<glm::vec4>(new CudaArray<glm::vec4>(n));
+ _field->bindResources(_encoded.get(), _depths.get(), _rgbd.get());
+ }
+
+ FsNeRF::~FsNeRF()
+ {
+ delete _sampler;
+ delete _encoder;
+ delete _renderer;
+ }
+
+ void FsNeRF::operator()(darray<glm::vec4> o_colors, const darray<glm::vec3> dirs,
+ glm::vec3 origin, bool showPerf = false)
+ {
+ CudaEvent eStart, eSampled, eEncoded, eInferred, eRendered;
+
+ cudaEventRecord(eStart);
+
+ (*_sampler)(_x, _depths, origin, darray<glm::vec3>(dirs->subArray(0, _nRays)));
+ CHECK_EX(cudaDeviceSynchronize());
+
+ cudaEventRecord(eSampled);
+
+ (*_encoder)(_encoded, _x);
+ CHECK_EX(cudaDeviceSynchronize());
+
+ cudaEventRecord(eEncoded);
+
+ _field->infer();
+ CHECK_EX(cudaDeviceSynchronize());
+
+ cudaEventRecord(eInferred);
+
+ (*_renderer)(darray<glm::vec4>(o_colors->subArray(0, _nRays)), _depths, _rgbd);
+ CHECK_EX(cudaDeviceSynchronize());
+
+ cudaEventRecord(eRendered);
+
+ if (showPerf)
+ {
+ CHECK_EX(cudaDeviceSynchronize());
+
+ float timeTotal, timeSample, timeEncode, timeInfer, timeRender;
+ cudaEventElapsedTime(&timeTotal, eStart, eRendered);
+ cudaEventElapsedTime(&timeSample, eStart, eSampled);
+ cudaEventElapsedTime(&timeEncode, eSampled, eEncoded);
+ cudaEventElapsedTime(&timeInfer, eEncoded, eInferred);
+ cudaEventElapsedTime(&timeRender, eInferred, eRendered);
+
+ std::ostringstream sout;
+ sout << "Infer pipeline: " << timeTotal << "ms (Sample: " << timeSample
+ << "ms, Encode: " << timeEncode << "ms, Infer: " << timeInfer
+ << "ms, Render: " << timeRender << "ms)";
+ Logger::instance.info(sout.str().c_str());
+ }
+
+ /*
+ {
+ std::ostringstream sout;
+ sout << "Rays:" << std::endl;
+ dumpArray<glm::vec3, float>(sout, *rays, 10);
+ Logger::instance.info(sout.str());
+ }
+ {
+ std::ostringstream sout;
+ sout << "Spherical coords:" << std::endl;
+ dumpArray(sout, *_coords, 10, _xChns * _nSamples);
+ Logger::instance.info(sout.str());
+ }
+ {
+ std::ostringstream sout;
+ sout << "Depths:" << std::endl;
+ dumpArray(sout, *_depths, 10, _nSamples);
+ Logger::instance.info(sout.str());
+ }
+ {
+ std::ostringstream sout;
+ sout << "Encoded:" << std::endl;
+ dumpArray(sout, *_encoded, 10, _encoder->outDim() * _nSamples);
+ Logger::instance.info(sout.str());
+ }
+ {
+ std::ostringstream sout;
+ sout << "Color:" << std::endl;
+ dumpArray<glm::vec4, float>(sout, *o_colors, 10);
+ Logger::instance.info(sout.str());
+ }
+ */
+ }
+}
\ No newline at end of file
diff --git a/cpp/models/FsNeRF.h b/cpp/models/FsNeRF.h
new file mode 100644
index 0000000..9ff9635
--- /dev/null
+++ b/cpp/models/FsNeRF.h
@@ -0,0 +1,48 @@
+#pragma once
+#include "../utils/common.h"
+#include "../modules/Sampler.h"
+#include "../modules/Encoder.h"
+#include "../modules/Renderer.h"
+#include "../fields/FsNeRF.h"
+#include "Model.h"
+
+namespace models
+{
+ class FsNeRF : public Model
+ {
+ public:
+ class Args
+ {
+ public:
+ std::string modelPath;
+ uint nSamples;
+ uint withRadius;
+ uint xfreqs;
+ float near;
+ float far;
+ bool whiteBg;
+ };
+
+ FsNeRF(const Args &args, int nRays);
+
+ ~FsNeRF();
+
+ virtual void operator()(darray<glm::vec4> o_colors, const darray<glm::vec3> dirs,
+ glm::vec3 origin, bool showPerf = false);
+
+ uint nRays() const { return _nRays; }
+
+ private:
+ uint _nRays;
+ uint _nSamples;
+ uint _xChns;
+ fields::FsNeRF *_field;
+ modules::Sampler *_sampler;
+ modules::Encoder *_encoder;
+ modules::Renderer *_renderer;
+ darray<float> _x;
+ darray<float> _depths;
+ darray<float> _encoded;
+ darray<glm::vec4> _rgbd;
+ };
+}
\ No newline at end of file
diff --git a/cpp/models/Model.h b/cpp/models/Model.h
new file mode 100644
index 0000000..c9e02ff
--- /dev/null
+++ b/cpp/models/Model.h
@@ -0,0 +1,11 @@
+#pragma once
+#include "../utils/common.h"
+
+namespace models
+{
+ class Model
+ {
+ virtual void operator()(darray<glm::vec4> o_colors, const darray<glm::vec3> dirs,
+ glm::vec3 origin, bool showPerf = false) = 0;
+ };
+}
\ No newline at end of file
diff --git a/cpp/modules/Encoder.cu b/cpp/modules/Encoder.cu
new file mode 100644
index 0000000..0066fd9
--- /dev/null
+++ b/cpp/modules/Encoder.cu
@@ -0,0 +1,60 @@
+#include "Encoder.h"
+#include "../utils/cuda.h"
+
+/// idx3.z = 0: x, y, z, sin(x), sin(y), sin(z), cos(x), cos(y), cos(z)
+/// idx3.z = 1: sin(2x), sin(2y), sin(2z), cos(2x), cos(2y), cos(2z)
+/// ...
+/// idx3.z = n_freq-1: sin(2^(n_freq-1)x), sin(2^(n_freq-1)y), sin(2^(n_freq-1)z),
+/// cos(2^(n_freq-1)x), cos(2^(n_freq-1)y), cos(2^(n_freq-1)z)
+/// Dispatch (n, in_chns, n_freqs)
+
+__global__ void cu_encode(float *output, const float *input, const float *freqs, uint n,
+ bool includeInput)
+{
+ DEFINE_IDX3(i, chn, freq);
+ if (i >= n)
+ return;
+ uint offset = (uint)includeInput;
+ uint inChns = blockDim.y, nFreqs = blockDim.z;
+ uint elem = i * inChns + chn;
+ uint outChns = inChns * (nFreqs * 2 + offset);
+ uint base = i * outChns + chn;
+ if (freq == 0 && includeInput)
+ output[base] = input[elem];
+ float x = freqs[freq] * input[elem];
+ float s, c;
+ __sincosf(x, &s, &c);
+ output[base + inChns * (freq * 2 + offset)] = s;
+ output[base + inChns * (freq * 2 + offset + 1)] = c;
+}
+
+namespace modules
+{
+ /**
+ * @brief
+ *
+ * @param output (n, out_chns) encoded positions
+ * @param input (n, in_chns) positions
+ */
+ void Encoder::operator()(darray<float> output, const darray<float> input)
+ {
+ uint n = input->n() / _chns;
+ std::ostringstream sout;
+ sout << "Encoder => input size: (" << n << ", " << _chns << "), "
+ << "output size: (" << n << ", " << outChns() << ")";
+ // Logger::instance.info(sout.str());
+ dim3 blkSize(1024 / _chns / _multires, _chns, _multires);
+ dim3 grdSize(ceilDiv(n, blkSize.x), 1, 1);
+ CU_INVOKE(cu_encode)(*output, *input, *_freqs, n, _includeInput);
+ CHECK_EX(cudaGetLastError());
+ }
+
+ void Encoder::_genFreqArray()
+ {
+ std::vector<float> freqsHost(_multires);
+ freqsHost[0] = 1.0f;
+ for (auto i = 1; i < _multires; ++i)
+ freqsHost[i] = freqsHost[i - 1] * 2.0f;
+ _freqs = darray<float>(new CudaArray<float>(freqsHost));
+ }
+}
\ No newline at end of file
diff --git a/cpp/modules/Encoder.h b/cpp/modules/Encoder.h
new file mode 100644
index 0000000..ad4b8c8
--- /dev/null
+++ b/cpp/modules/Encoder.h
@@ -0,0 +1,27 @@
+#pragma once
+#include "../utils/common.h"
+
+namespace modules
+{
+ class Encoder
+ {
+ public:
+ Encoder(unsigned int multires, unsigned int chns, bool includeInput)
+ : _multires(multires), _chns(chns), _includeInput(includeInput)
+ {
+ _genFreqArray();
+ }
+
+ unsigned int outChns() const { return _chns * (_includeInput + _multires * 2); }
+
+ void operator()(darray<float> output, const darray<float> input);
+
+ private:
+ unsigned int _multires;
+ unsigned int _chns;
+ bool _includeInput;
+ darray<float> _freqs;
+
+ void _genFreqArray();
+ };
+}
\ No newline at end of file
diff --git a/cpp/modules/Renderer.cu b/cpp/modules/Renderer.cu
new file mode 100644
index 0000000..2480606
--- /dev/null
+++ b/cpp/modules/Renderer.cu
@@ -0,0 +1,36 @@
+#include "Renderer.h"
+#include "../utils/cuda.h"
+
+/// Dispatch (nRays)
+__global__ void cu_render(glm::vec4 *o_colors, const float *depths, const glm::vec4 *rgbd,
+ uint nSamples, uint nRays)
+{
+ DEFINE_IDX(rayIdx);
+ if (rayIdx >= nRays)
+ return;
+ glm::vec4 outColor;
+ auto depth = 1e10f;
+ for (int si = nSamples - 1, i = (rayIdx + 1) * nSamples - 1; si >= 0; --si, --i)
+ {
+ auto depth1 = depth;
+ auto c = rgbd[i];
+ depth = depths[i];
+ c.a = 1.0f - exp(-max(c.a, 0.f) * (depth1 - depth));
+ outColor = outColor * (1 - c.a) + c * c.a;
+ }
+ outColor.a = 1.0f;
+ o_colors[rayIdx] = outColor;
+}
+
+namespace modules
+{
+ void Renderer::operator()(darray<glm::vec4> o_colors, const darray<float> depths,
+ const darray<glm::vec4> rgbd)
+ {
+ uint n = o_colors->n();
+ dim3 blkSize(1024);
+ dim3 grdSize(ceilDiv(n, blkSize.x));
+ CU_INVOKE(cu_render)(*o_colors, *depths, *rgbd, rgbd->n() / n, n);
+ CHECK_EX(cudaGetLastError());
+ }
+}
\ No newline at end of file
diff --git a/cpp/modules/Renderer.h b/cpp/modules/Renderer.h
new file mode 100644
index 0000000..22cacb0
--- /dev/null
+++ b/cpp/modules/Renderer.h
@@ -0,0 +1,23 @@
+#pragma once
+#include "../utils/common.h"
+
+namespace modules
+{
+ class Renderer
+ {
+ public:
+ Renderer(bool whiteBg) : _whiteBg(whiteBg) {}
+
+ /**
+ * @brief
+ *
+ * @param o_colors
+ * @param layeredColors
+ */
+ void operator()(darray<glm::vec4> o_colors, const darray<float> depths,
+ const darray<glm::vec4> rgbd);
+
+ private:
+ bool _whiteBg;
+ };
+}
\ No newline at end of file
diff --git a/cpp/modules/Sampler.cu b/cpp/modules/Sampler.cu
new file mode 100644
index 0000000..687f9ba
--- /dev/null
+++ b/cpp/modules/Sampler.cu
@@ -0,0 +1,118 @@
+#include "Sampler.h"
+#define _USE_MATH_DEFINES
+#include <math.h>
+#include "../utils/cuda.h"
+
+__device__ glm::vec3 _raySphereIntersect(glm::vec3 p, glm::vec3 v, float r, float &o_depth)
+{
+ float pp = glm::dot(p, p);
+ float vv = glm::dot(v, v);
+ float pv = glm::dot(p, v);
+ o_depth = (sqrtf(pv * pv - vv * (pp - r * r)) - pv) / vv;
+ return p + o_depth * v;
+}
+
+__device__ float _getAngle(float x, float y)
+{
+ return -atan(x / y) - (y < 0) * (float)M_PI + 0.5f * (float)M_PI;
+}
+
+/**
+ * Dispatch with block_size=(*, n_samples), grid_size=(nRays/*, 1)
+ * Index with (ray_idx, sample_idx)
+ */
+__global__ void cu_sampleXyz(glm::vec3 *o_coords, float *o_depths, glm::vec3 origin,
+ const glm::vec3 *dirs, uint nRays, uint nSamples, glm::vec2 range)
+{
+ DEFINE_IDX2(rayIdx, sampleIdx);
+ if (rayIdx >= nRays)
+ return;
+ uint idx = rayIdx * nSamples + sampleIdx;
+ float z = (range.y - range.x) / (nSamples - 1) * sampleIdx + range.x;
+ o_coords[idx] = origin + dirs[rayIdx] * z;
+ o_depths[idx] = z;
+}
+
+/**
+ * Dispatch with block_size=(*, n_samples), grid_size=(nRays/*, 1)
+ * Index with (ray_idx, sample_idx)
+ */
+__global__ void cu_sampleXyzDisp(glm::vec3 *o_coords, float *o_depths, glm::vec3 origin,
+ const glm::vec3 *dirs, uint nRays, uint nSamples, glm::vec2 range)
+{
+ DEFINE_IDX2(rayIdx, sampleIdx);
+ if (rayIdx >= nRays)
+ return;
+ uint idx = rayIdx * nSamples + sampleIdx;
+ float z = 1.f / ((range.y - range.x) / (nSamples - 1) * sampleIdx + range.x);
+ o_coords[idx] = origin + dirs[rayIdx] * z;
+ o_depths[idx] = z;
+}
+
+/**
+ * Dispatch with block_size=(*, n_samples), grid_size=(nRays/*, 1)
+ * Index with (ray_idx, sample_idx)
+ */
+__global__ void cu_sampleSpherical(glm::vec3 *o_coords, float *o_depths, glm::vec3 origin,
+ const glm::vec3 *dirs, uint nRays, uint nSamples,
+ glm::vec2 range)
+{
+ DEFINE_IDX2(rayIdx, sampleIdx);
+ if (rayIdx >= nRays)
+ return;
+ uint idx = rayIdx * nSamples + sampleIdx;
+ float z = 1.f / ((range.y - range.x) / (nSamples - 1) * sampleIdx + range.x);
+ glm::vec3 p = origin + dirs[rayIdx] * z;
+ float r_reci = 1. / glm::length(p);
+ float theta = _getAngle(p.x, p.z);
+ float phi = acos(p.y * r_reci);
+ o_coords[idx] = {r_reci, theta, phi};
+ o_depths[idx] = z;
+}
+
+/**
+ * Dispatch with block_size=(*, n_samples), grid_size=(nRays/*, 1)
+ * Index with (ray_idx, sample_idx)
+ */
+__global__ void cu_sampleSphere(float *o_coords, float *o_depths, glm::vec3 origin,
+ const glm::vec3 *dirs, uint nRays, uint nSamples,
+ glm::vec2 range, bool withRadius)
+{
+ DEFINE_IDX2(rayIdx, sampleIdx);
+ if (rayIdx >= nRays)
+ return;
+ uint idx = rayIdx * nSamples + sampleIdx;
+ float r_reci = (range.y - range.x) / (nSamples - 1) * sampleIdx + range.x;
+ float r = 1.f / r_reci;
+ glm::vec3 p = _raySphereIntersect(origin, dirs[rayIdx], r, o_depths[idx]);
+ float theta = _getAngle(p.x, p.z);
+ float phi = acos(p.y * r_reci);
+ if (withRadius)
+ ((glm::vec3 *)o_coords)[idx] = {r_reci, theta, phi};
+ else
+ ((glm::vec2 *)o_coords)[idx] = {theta, phi};
+}
+
+namespace modules
+{
+ void Sampler::operator()(darray<float> o_coords, darray<float> o_depths, glm::vec3 origin,
+ const darray<glm::vec3> dirs)
+ {
+ auto n = dirs->n();
+ dim3 blkSize(1024 / _nSamples, _nSamples);
+ dim3 grdSize((uint)ceil(n / (float)blkSize.y), 1);
+ if (_mode == "xyz_disp")
+ CU_INVOKE(cu_sampleXyzDisp)((glm::vec3 *)*o_coords, *o_depths, origin, *dirs, n,
+ _nSamples, 1.f / _range);
+ else if (_mode == "spherical")
+ CU_INVOKE(cu_sampleSpherical)((glm::vec3 *)*o_coords, *o_depths, origin, *dirs, n,
+ _nSamples, 1.f / _range);
+ else if (_mode == "spherical_radius")
+ CU_INVOKE(cu_sampleSphere)(*o_coords, *o_depths, origin, *dirs, n, _nSamples,
+ 1.f / _range, _withRadius);
+ else
+ CU_INVOKE(cu_sampleXyz)((glm::vec3 *)*o_coords, *o_depths, origin, *dirs, n,
+ _nSamples, _range);
+ CHECK_EX(cudaGetLastError());
+ }
+}
\ No newline at end of file
diff --git a/cpp/modules/Sampler.h b/cpp/modules/Sampler.h
new file mode 100644
index 0000000..9649bd4
--- /dev/null
+++ b/cpp/modules/Sampler.h
@@ -0,0 +1,20 @@
+#pragma once
+#include "../utils/common.h"
+
+namespace modules
+{
+ class Sampler
+ {
+ public:
+ Sampler(glm::vec2 range, std::string mode, uint nSamples, bool withRadius) : _range(range), _mode(mode), _nSamples(nSamples), _withRadius(withRadius) {}
+
+ void operator()(darray<float> o_coords, darray<float> o_depths, glm::vec3 origin,
+ const darray<glm::vec3> dirs);
+
+ private:
+ glm::vec2 _range;
+ std::string _mode;
+ uint _nSamples;
+ bool _withRadius;
+ };
+}
\ No newline at end of file
diff --git a/cpp/fnr_core/Voxels.cu b/cpp/modules/Voxels.cu
similarity index 100%
rename from cpp/fnr_core/Voxels.cu
rename to cpp/modules/Voxels.cu
diff --git a/cpp/fnr_core/Voxels.h b/cpp/modules/Voxels.h
similarity index 100%
rename from cpp/fnr_core/Voxels.h
rename to cpp/modules/Voxels.h
diff --git a/cpp/bakes/barbershop_0/fovea.png b/cpp/resources/bakes/barbershop_0/fovea.png
similarity index 100%
rename from cpp/bakes/barbershop_0/fovea.png
rename to cpp/resources/bakes/barbershop_0/fovea.png
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similarity index 100%
rename from cpp/nets/barbershop/periph.onnx
rename to cpp/resources/nets/barbershop/periph.onnx
diff --git a/cpp/nets/barbershop/periph.trt b/cpp/resources/nets/barbershop/periph.trt
similarity index 100%
rename from cpp/nets/barbershop/periph.trt
rename to cpp/resources/nets/barbershop/periph.trt
diff --git a/cpp/nets/classroom/fovea.onnx b/cpp/resources/nets/classroom/fovea.onnx
similarity index 100%
rename from cpp/nets/classroom/fovea.onnx
rename to cpp/resources/nets/classroom/fovea.onnx
diff --git a/cpp/nets/classroom/fovea.trt b/cpp/resources/nets/classroom/fovea.trt
similarity index 100%
rename from cpp/nets/classroom/fovea.trt
rename to cpp/resources/nets/classroom/fovea.trt
diff --git a/cpp/nets/classroom/periph.onnx b/cpp/resources/nets/classroom/periph.onnx
similarity index 100%
rename from cpp/nets/classroom/periph.onnx
rename to cpp/resources/nets/classroom/periph.onnx
diff --git a/cpp/nets/classroom/periph.trt b/cpp/resources/nets/classroom/periph.trt
similarity index 100%
rename from cpp/nets/classroom/periph.trt
rename to cpp/resources/nets/classroom/periph.trt
diff --git a/cpp_old/nets/fovea.mask b/cpp/resources/nets/fovea.mask
similarity index 100%
rename from cpp_old/nets/fovea.mask
rename to cpp/resources/nets/fovea.mask
diff --git a/cpp/nets/lobby/fovea.onnx b/cpp/resources/nets/lobby/fovea.onnx
similarity index 100%
rename from cpp/nets/lobby/fovea.onnx
rename to cpp/resources/nets/lobby/fovea.onnx
diff --git a/cpp/nets/lobby/fovea.trt b/cpp/resources/nets/lobby/fovea.trt
similarity index 100%
rename from cpp/nets/lobby/fovea.trt
rename to cpp/resources/nets/lobby/fovea.trt
diff --git a/cpp/nets/lobby/periph.onnx b/cpp/resources/nets/lobby/periph.onnx
similarity index 100%
rename from cpp/nets/lobby/periph.onnx
rename to cpp/resources/nets/lobby/periph.onnx
diff --git a/cpp/nets/lobby/periph.trt b/cpp/resources/nets/lobby/periph.trt
similarity index 100%
rename from cpp/nets/lobby/periph.trt
rename to cpp/resources/nets/lobby/periph.trt
diff --git a/cpp_old/nets/mid.mask b/cpp/resources/nets/mid.mask
similarity index 100%
rename from cpp_old/nets/mid.mask
rename to cpp/resources/nets/mid.mask
diff --git a/cpp_old/nets/old/fovea_mono/cat.trt b/cpp/resources/nets/old/fovea_mono/cat.trt
similarity index 100%
rename from cpp_old/nets/old/fovea_mono/cat.trt
rename to cpp/resources/nets/old/fovea_mono/cat.trt
diff --git a/cpp_old/nets/old/fovea_mono/fc1.trt b/cpp/resources/nets/old/fovea_mono/fc1.trt
similarity index 100%
rename from cpp_old/nets/old/fovea_mono/fc1.trt
rename to cpp/resources/nets/old/fovea_mono/fc1.trt
diff --git a/cpp_old/nets/old/fovea_mono/fc2.trt b/cpp/resources/nets/old/fovea_mono/fc2.trt
similarity index 100%
rename from cpp_old/nets/old/fovea_mono/fc2.trt
rename to cpp/resources/nets/old/fovea_mono/fc2.trt
diff --git a/cpp_old/nets/old/fovea_mono/msl.trt b/cpp/resources/nets/old/fovea_mono/msl.trt
similarity index 100%
rename from cpp_old/nets/old/fovea_mono/msl.trt
rename to cpp/resources/nets/old/fovea_mono/msl.trt
diff --git a/cpp_old/nets/old/fovea_stereo/cat.trt b/cpp/resources/nets/old/fovea_stereo/cat.trt
similarity index 100%
rename from cpp_old/nets/old/fovea_stereo/cat.trt
rename to cpp/resources/nets/old/fovea_stereo/cat.trt
diff --git a/cpp_old/nets/old/fovea_stereo/fc1.trt b/cpp/resources/nets/old/fovea_stereo/fc1.trt
similarity index 100%
rename from cpp_old/nets/old/fovea_stereo/fc1.trt
rename to cpp/resources/nets/old/fovea_stereo/fc1.trt
diff --git a/cpp_old/nets/old/fovea_stereo/fc2.trt b/cpp/resources/nets/old/fovea_stereo/fc2.trt
similarity index 100%
rename from cpp_old/nets/old/fovea_stereo/fc2.trt
rename to cpp/resources/nets/old/fovea_stereo/fc2.trt
diff --git a/cpp_old/nets/old/periph/cat.trt b/cpp/resources/nets/old/periph/cat.trt
similarity index 100%
rename from cpp_old/nets/old/periph/cat.trt
rename to cpp/resources/nets/old/periph/cat.trt
diff --git a/cpp_old/nets/old/periph/fc1.trt b/cpp/resources/nets/old/periph/fc1.trt
similarity index 100%
rename from cpp_old/nets/old/periph/fc1.trt
rename to cpp/resources/nets/old/periph/fc1.trt
diff --git a/cpp_old/nets/old/periph/fc2.trt b/cpp/resources/nets/old/periph/fc2.trt
similarity index 100%
rename from cpp_old/nets/old/periph/fc2.trt
rename to cpp/resources/nets/old/periph/fc2.trt
diff --git a/cpp_old/nets/old/periph/msl.trt b/cpp/resources/nets/old/periph/msl.trt
similarity index 100%
rename from cpp_old/nets/old/periph/msl.trt
rename to cpp/resources/nets/old/periph/msl.trt
diff --git a/cpp/nets/stones/fovea.onnx b/cpp/resources/nets/stones/fovea.onnx
similarity index 100%
rename from cpp/nets/stones/fovea.onnx
rename to cpp/resources/nets/stones/fovea.onnx
diff --git a/cpp/nets/stones/fovea.trt b/cpp/resources/nets/stones/fovea.trt
similarity index 100%
rename from cpp/nets/stones/fovea.trt
rename to cpp/resources/nets/stones/fovea.trt
diff --git a/cpp/nets/stones/periph.onnx b/cpp/resources/nets/stones/periph.onnx
similarity index 100%
rename from cpp/nets/stones/periph.onnx
rename to cpp/resources/nets/stones/periph.onnx
diff --git a/cpp/nets/stones/periph.trt b/cpp/resources/nets/stones/periph.trt
similarity index 100%
rename from cpp/nets/stones/periph.trt
rename to cpp/resources/nets/stones/periph.trt
diff --git a/cpp/utils/Eye.h b/cpp/utils/Eye.h
index 2969487..77e8145 100644
--- a/cpp/utils/Eye.h
+++ b/cpp/utils/Eye.h
@@ -1,5 +1,2 @@
#pragma once
-enum Eye {
- Eye_Left, Eye_Right
-};
\ No newline at end of file
diff --git a/cpp/utils/Logger.h b/cpp/utils/Logger.h
deleted file mode 100644
index be92e92..0000000
--- a/cpp/utils/Logger.h
+++ /dev/null
@@ -1,93 +0,0 @@
-#pragma once
-#include <stdarg.h>
-#include <iostream>
-#include <string>
-#include <sstream>
-#include <cuda_runtime_api.h>
-#include <NvInfer.h>
-
-namespace nv = nvinfer1;
-
-
-typedef void(*ExternalLogFuncPtr)(int severity, const char*);
-
-
-class Logger : public nv::ILogger {
-public:
- ExternalLogFuncPtr externalLogFunc = nullptr;
- int logLevel = 1;
- static Logger instance;
-
- void verbose(const char* fmt, ...) {
- va_list args;
- va_start(args, fmt);
- logf(nv::ILogger::Severity::kVERBOSE, fmt, args);
- va_end(args);
- }
-
- void info(const char* fmt, ...) {
- va_list args;
- va_start(args, fmt);
- logf(nv::ILogger::Severity::kINFO, fmt, args);
- va_end(args);
- }
-
- void warning(const char* fmt, ...) {
- va_list args;
- va_start(args, fmt);
- logf(nv::ILogger::Severity::kWARNING, fmt, args);
- va_end(args);
- }
-
- void error(const char* fmt, ...) {
- va_list args;
- va_start(args, fmt);
- logf(nv::ILogger::Severity::kERROR, fmt, args);
- va_end(args);
- }
-
- bool checkErr(cudaError_t err, const char* file, int line) {
- if (err == cudaSuccess)
- return true;
- error("Cuda error %s at %s (Line %d): %s", cudaGetErrorName(err), file, line,
- cudaGetErrorString(err));
- return false;
- }
-
- virtual void log(nv::ILogger::Severity severity, const char* msg) noexcept {
- if ((int)severity > logLevel)
- return;
- if (externalLogFunc == nullptr) {
- switch (severity) {
- case nv::ILogger::Severity::kVERBOSE:
- std::cout << "[VERBOSE] " << msg << std::endl;
- break;
- case nv::ILogger::Severity::kINFO:
- std::cout << "[INFO] " << msg << std::endl;
- break;
- case nv::ILogger::Severity::kWARNING:
- std::cerr << "[WARNING] " << msg << std::endl;
- break;
- case nv::ILogger::Severity::kERROR:
- std::cerr << "[ERROR] " << msg << std::endl;
- break;
- case nv::ILogger::Severity::kINTERNAL_ERROR:
- std::cerr << "[ERROR] " << msg << std::endl;
- break;
- }
- return;
- }
- externalLogFunc((int)severity, msg);
- }
-
- void logf(nv::ILogger::Severity severity, const char* fmt, va_list args) {
- char buffer[4096];
- vsprintf(buffer, fmt, args);
- log(severity, buffer);
- }
-
-};
-
-
-#define CHECK(__ERR_CODE__) do { if (!Logger::instance.checkErr((__ERR_CODE__), __FILE__, __LINE__)) return false; } while (0)
-#define CHECK_EX(__ERR_CODE__) do { if (!Logger::instance.checkErr((__ERR_CODE__), __FILE__, __LINE__)) throw std::exception(); } while (0)
diff --git a/cpp/utils/Resource.h b/cpp/utils/Resource.h
deleted file mode 100644
index 624a23f..0000000
--- a/cpp/utils/Resource.h
+++ /dev/null
@@ -1,144 +0,0 @@
-#pragma once
-#include <map>
-#include <vector>
-
-class Resource {
-public:
- virtual ~Resource() {}
-
- virtual void *getBuffer() const = 0;
-
- virtual size_t size() const = 0;
-};
-
-class CudaBuffer : public Resource {
-public:
- CudaBuffer(void *buffer = nullptr, size_t size = 0)
- : _buffer(buffer), _ownBuffer(false), _size(size) {}
- CudaBuffer(size_t size) : _buffer(nullptr), _ownBuffer(true), _size(size) {
- CHECK_EX(cudaMalloc(&_buffer, size));
- }
- CudaBuffer(const CudaBuffer &rhs) = delete;
-
- virtual ~CudaBuffer() {
- if (!_ownBuffer || _buffer == nullptr)
- return;
- try {
- CHECK_EX(cudaFree(_buffer));
- } catch (std::exception &ex) {
- Logger::instance.warning("Exception raised in destructor: %s", ex.what());
- }
- _buffer = nullptr;
- _ownBuffer = false;
- }
-
- virtual void *getBuffer() const { return _buffer; }
- template <class T> T *getBuffer() const { return (T *)getBuffer(); }
-
- virtual size_t size() const { return _size; }
-
-private:
- void *_buffer;
- bool _ownBuffer;
- size_t _size;
-};
-
-template <typename T> class CudaArray : public CudaBuffer {
-public:
- CudaArray(size_t n) : CudaBuffer(n * sizeof(T)) {}
- CudaArray(T *buffer, size_t n) : CudaBuffer(buffer, n * sizeof(T)) {}
- CudaArray(const std::vector<T> &hostArray) : CudaBuffer(hostArray.size() * sizeof(T)) {
- cudaMemcpy(getBuffer(), hostArray.data(), size(), cudaMemcpyHostToDevice);
- }
- CudaArray(const CudaArray<T> &rhs) = delete;
-
- size_t n() const { return size() / sizeof(T); }
-
- operator T *() { return (T *)getBuffer(); }
- CudaArray<T> *subArray(size_t offset, size_t n = -1) {
- if (n == -1)
- n = this->n() - offset;
- return new CudaArray<T>(*this + offset, n);
- }
-};
-
-class GraphicsResource : public Resource {
-public:
- cudaGraphicsResource_t getHandler() { return _res; }
-
- virtual ~GraphicsResource() {
- if (_res == nullptr)
- return;
- try {
- CHECK_EX(cudaGraphicsUnregisterResource(_res));
- } catch (std::exception &ex) {
- Logger::instance.warning("Exception raised in destructor: %s", ex.what());
- }
- _res = nullptr;
- }
-
- virtual size_t size() const { return _size; }
-
-protected:
- cudaGraphicsResource_t _res;
- size_t _size;
-
- GraphicsResource() : _res(nullptr), _size(0) {}
-};
-
-template <typename T> class GlTextureResource : public GraphicsResource {
-public:
- GlTextureResource(GLuint textureID, glm::uvec2 textureSize) {
- CHECK_EX(cudaGraphicsGLRegisterImage(&_res, textureID, GL_TEXTURE_2D,
- cudaGraphicsRegisterFlagsWriteDiscard));
- _size = textureSize.x * textureSize.y * sizeof(T);
- _textureSize = textureSize;
- }
-
- virtual ~GlTextureResource() { cudaGraphicsUnmapResources(1, &_res, 0); }
-
- virtual void *getBuffer() const {
- cudaArray_t buffer;
- try {
- CHECK_EX(cudaGraphicsSubResourceGetMappedArray(&buffer, _res, 0, 0));
- } catch (...) {
- return nullptr;
- }
- return buffer;
- }
-
- operator T *() { return (T *)getBuffer(); }
-
- glm::uvec2 textureSize() { return _textureSize; }
-
-private:
- glm::uvec2 _textureSize;
-};
-
-class Resources {
-public:
- std::map<std::string, Resource *> resources;
- std::vector<cudaGraphicsResource_t> graphicsResources;
-
- void addResource(const std::string &name, Resource *res) {
- auto gres = dynamic_cast<GraphicsResource *>(res);
- if (gres != nullptr)
- graphicsResources.push_back(gres->getHandler());
- resources[name] = res;
- }
-
- void clear() {
- resources.clear();
- graphicsResources.clear();
- }
-};
-
-template <typename T, typename T2 = T>
-void dumpArray(std::ostream &so, CudaArray<T> &arr, size_t maxDumpRows = 0,
- size_t elemsPerRow = 1) {
- int chns = sizeof(T) / sizeof(T2);
- T2 *hostArr = new T2[arr.n() * chns];
- cudaMemcpy(hostArr, arr.getBuffer(), arr.n() * sizeof(T), cudaMemcpyDeviceToHost);
- dumpHostBuffer<T2>(so, hostArr, arr.n() * sizeof(T), chns * elemsPerRow, maxDumpRows);
- delete[] hostArr;
-}
\ No newline at end of file
diff --git a/cpp/utils/common.h b/cpp/utils/common.h
index b73624d..9080ad6 100644
--- a/cpp/utils/common.h
+++ b/cpp/utils/common.h
@@ -8,32 +8,34 @@
#include <string>
#include <sstream>
#include <GL/glew.h>
-#include <cuda_gl_interop.h>
-#include <glm/glm.hpp>
-#include "Logger.h"
-#include "Eye.h"
+#include "common/logger.h"
+#include "common/fmt.h"
#ifdef WIN32
typedef unsigned int uint;
#endif
-#ifndef _countof
-#define _countof(x) (sizeof(x)/sizeof((x)[0]))
+#ifndef COUNTOF
+#define COUNTOF(__x__) (sizeof(__x__) / sizeof((__x__)[0]))
#endif
+#ifndef CEILDIV
+#define CEILDIV(__x__, __y__) (uint) ceil((__x__) / (float)(__y__))
+#endif
+#define INTERVAL(__start__, __end__) (((__end__) - (__start__)) / (float)CLOCKS_PER_SEC * 1000)
-inline unsigned int getElementSize(nv::DataType t) {
+inline uint getElementSize(nv::DataType t) {
switch (t) {
case nv::DataType::kINT32:
- case nv::DataType::kFLOAT:
+ case nv::DataType::kFLOAT:
return 4;
case nv::DataType::kHALF:
return 2;
case nv::DataType::kBOOL:
case nv::DataType::kINT8:
return 1;
- default:
- throw std::runtime_error("Invalid DataType.");
- }
+ default:
+ throw std::runtime_error("Invalid DataType.");
+ }
}
template <typename T> void dumpRow(std::ostream &os, T *buf, size_t n) {
@@ -71,50 +73,6 @@ void dumpHostBuffer(std::ostream &os, T *buf, size_t bufSize, size_t rowCount,
}
}
-class CudaStream {
-public:
- CudaStream() { cudaStreamCreate(&stream); }
-
- operator cudaStream_t() { return stream; }
-
- virtual ~CudaStream() { cudaStreamDestroy(stream); }
-
-private:
- cudaStream_t stream;
-};
-
-class CudaEvent {
-public:
- CudaEvent() { cudaEventCreate(&mEvent); }
-
- operator cudaEvent_t() { return mEvent; }
-
- virtual ~CudaEvent() { cudaEventDestroy(mEvent); }
-
-private:
- cudaEvent_t mEvent;
-};
-
-struct CudaMapScope {
- std::vector<cudaGraphicsResource_t> resources_;
- cudaStream_t stream_;
-
- CudaMapScope(const std::vector<cudaGraphicsResource_t> &resources,
- cudaStream_t stream = nullptr)
- : resources_(resources), stream_(stream) {}
-
- ~CudaMapScope() {
- if (!resources_.empty())
- cudaGraphicsUnmapResources((int)resources_.size(), resources_.data(), stream_);
- }
-
- cudaError_t map() {
- if (!resources_.empty())
- return cudaGraphicsMapResources((int)resources_.size(), resources_.data(), stream_);
- return cudaSuccess;
- }
-};
-
template <typename T> struct Destroy {
void operator()(T *t) {
if (t != nullptr)
@@ -122,28 +80,9 @@ template <typename T> struct Destroy {
}
};
-class Range {
-public:
- Range(glm::vec2 bound, uint steps) :
- _start(bound.x),
- _step((bound.y - bound.x) / (steps - 1)),
- _steps(steps) {}
-
- __host__ __device__ float get(uint i) { return _start + i * _step; }
- __host__ __device__ float start() { return _start; }
- __host__ __device__ float stop() { return _start + _step * _steps; }
- __host__ __device__ uint steps() { return _steps; }
-
-private:
- float _start;
- float _step;
- uint _steps;
-};
+#include "Formatter.h"
template <class T> using uptr = std::unique_ptr<T, ::Destroy<T>>;
template <class T> using sptr = std::shared_ptr<T>;
-#define INTERVAL(__start__, __end__) (((__end__) - (__start__)) / (float)CLOCKS_PER_SEC * 1000)
-
-#include "Resource.h"
-#include "Formatter.h"
\ No newline at end of file
+enum Eye { Eye_Left, Eye_Right };
\ No newline at end of file
diff --git a/cpp/utils/common/fmt.h b/cpp/utils/common/fmt.h
new file mode 100644
index 0000000..370712d
--- /dev/null
+++ b/cpp/utils/common/fmt.h
@@ -0,0 +1,24 @@
+#include <string>
+
+namespace utils::common {
+ template <typename... Args> static std::string fmt(const std::string &format, Args... args) {
+ auto size_buf = std::snprintf(nullptr, 0, format.c_str(), args...) + 1;
+ std::unique_ptr<char[]> buf(new (std::nothrow) char[size_buf]);
+
+ if (!buf)
+ return std::string("");
+
+ std::snprintf(buf.get(), size_buf, format.c_str(), args...);
+ return std::string(buf.get(), buf.get() + size_buf - 1);
+ }
+ template <typename... Args> static std::wstring fmt(const std::wstring &format, Args... args) {
+ auto size_buf = std::snprintf(nullptr, 0, format.c_str(), args...) + 1;
+ std::unique_ptr<char[]> buf(new (std::nothrow) char[size_buf]);
+
+ if (!buf)
+ return std::wstring("");
+
+ std::snprintf(buf.get(), size_buf, format.c_str(), args...);
+ return std::wstring(buf.get(), buf.get() + size_buf - 1);
+ }
+} // namespace utils::common
diff --git a/cpp/utils/common/logger.cpp b/cpp/utils/common/logger.cpp
new file mode 100644
index 0000000..0f7bfd8
--- /dev/null
+++ b/cpp/utils/common/logger.cpp
@@ -0,0 +1,68 @@
+#include "logger.h"
+#include <iostream>
+#include <sstream>
+#include <string>
+
+namespace utils::common {
+ Logger Logger::instance;
+
+ void Logger::verbose(const char *fmt, ...) {
+ va_list args;
+ va_start(args, fmt);
+ logf(nvinfer1::ILogger::Severity::kVERBOSE, fmt, args);
+ va_end(args);
+ }
+
+ void Logger::info(const char *fmt, ...) {
+ va_list args;
+ va_start(args, fmt);
+ logf(nvinfer1::ILogger::Severity::kINFO, fmt, args);
+ va_end(args);
+ }
+
+ void Logger::warning(const char *fmt, ...) {
+ va_list args;
+ va_start(args, fmt);
+ logf(nvinfer1::ILogger::Severity::kWARNING, fmt, args);
+ va_end(args);
+ }
+
+ void Logger::error(const char *fmt, ...) {
+ va_list args;
+ va_start(args, fmt);
+ logf(nvinfer1::ILogger::Severity::kERROR, fmt, args);
+ va_end(args);
+ }
+
+ void Logger::log(nvinfer1::ILogger::Severity severity, const char *msg) noexcept {
+ if ((int)severity > logLevel)
+ return;
+ if (externalLogFunc == nullptr) {
+ switch (severity) {
+ case nvinfer1::ILogger::Severity::kVERBOSE:
+ std::cout << "[VERBOSE] " << msg << std::endl;
+ break;
+ case nvinfer1::ILogger::Severity::kINFO:
+ std::cout << "[INFO] " << msg << std::endl;
+ break;
+ case nvinfer1::ILogger::Severity::kWARNING:
+ std::cerr << "[WARNING] " << msg << std::endl;
+ break;
+ case nvinfer1::ILogger::Severity::kERROR:
+ std::cerr << "[ERROR] " << msg << std::endl;
+ break;
+ case nvinfer1::ILogger::Severity::kINTERNAL_ERROR:
+ std::cerr << "[ERROR] " << msg << std::endl;
+ break;
+ }
+ return;
+ }
+ externalLogFunc((int)severity, msg);
+ }
+
+ void Logger::logf(nvinfer1::ILogger::Severity severity, const char *fmt, va_list args) {
+ char buffer[4096];
+ vsprintf(buffer, fmt, args);
+ log(severity, buffer);
+ }
+} // namespace utils::common
diff --git a/cpp/utils/common/logger.h b/cpp/utils/common/logger.h
new file mode 100644
index 0000000..88ea465
--- /dev/null
+++ b/cpp/utils/common/logger.h
@@ -0,0 +1,26 @@
+#pragma once
+#include <NvInfer.h>
+#include <stdarg.h>
+
+typedef void (*ExternalLogFuncPtr)(int severity, const char *);
+
+namespace utils::common {
+ class Logger : public nvinfer1::ILogger {
+ public:
+ ExternalLogFuncPtr externalLogFunc = nullptr;
+ int logLevel = 1;
+ static Logger instance;
+
+ void verbose(const char *fmt, ...);
+
+ void info(const char *fmt, ...);
+
+ void warning(const char *fmt, ...);
+
+ void error(const char *fmt, ...);
+
+ virtual void log(nvinfer1::ILogger::Severity severity, const char *msg) noexcept;
+
+ void logf(nvinfer1::ILogger::Severity severity, const char *fmt, va_list args);
+ };
+} // namespace utils::common
diff --git a/cpp/utils/cuda.h b/cpp/utils/cuda.h
index 2bd5588..d1de2ef 100644
--- a/cpp/utils/cuda.h
+++ b/cpp/utils/cuda.h
@@ -1,4 +1,11 @@
-#include "thread_index.h"
+#include "cuda/index.h"
+#include "cuda/error.h"
+#include "cuda/event.h"
+#include "cuda/stream.h"
+#include "cuda/map_resources_scope.h"
+#include "cuda/array.h"
+#include "cuda/gl_texture.h"
+#include "cuda/resources.h"
#ifdef __INTELLISENSE__
#define CU_INVOKE(__func__) __func__
@@ -8,4 +15,3 @@
#define CU_INVOKE1(__func__, __grdSize__, __blkSize__) __func__<<<__grdSize__, __blkSize__>>>
#endif
-inline unsigned int ceilDiv(unsigned int a, unsigned int b) { return (unsigned int)ceil(a / (float)b); }
\ No newline at end of file
diff --git a/cpp/utils/cuda/array.h b/cpp/utils/cuda/array.h
new file mode 100644
index 0000000..82c5640
--- /dev/null
+++ b/cpp/utils/cuda/array.h
@@ -0,0 +1,23 @@
+#include "resource.h"
+#include <vector>
+
+namespace utils::cuda {
+ template <typename T> class Array : public BufferResource {
+ public:
+ Array(size_t n) : CudaBuffer(n * sizeof(T)) {}
+ Array(T *buffer, size_t n) : CudaBuffer(buffer, n * sizeof(T)) {}
+ Array(const std::vector<T> &hostArray) : CudaBuffer(hostArray.size() * sizeof(T)) {
+ cudaMemcpy(getBuffer(), hostArray.data(), size(), cudaMemcpyHostToDevice);
+ }
+
+ size_t n() const { return size() / sizeof(T); }
+
+ operator T *() { return (T *)getBuffer(); }
+
+ Array<T> subArray(size_t offset, size_t n = -1) {
+ if (n == -1)
+ n = this->n() - offset;
+ return Array<T>((T *)*this + offset, n);
+ }
+ };
+} // namespace utils::cuda
diff --git a/cpp/utils/cuda/error.cpp b/cpp/utils/cuda/error.cpp
new file mode 100644
index 0000000..86d8299
--- /dev/null
+++ b/cpp/utils/cuda/error.cpp
@@ -0,0 +1,12 @@
+#include "error.h"
+#include "../common/logger.h"
+
+namespace utils::cuda {
+ bool checkErr(cudaError_t err, const char *file, int line) {
+ if (err == cudaSuccess)
+ return true;
+ common::Logger::instance.error("Cuda error %s at %s (Line %d): %s", cudaGetErrorName(err),
+ file, line, cudaGetErrorString(err));
+ return false;
+ }
+} // namespace utils::cuda
diff --git a/cpp/utils/cuda/error.h b/cpp/utils/cuda/error.h
new file mode 100644
index 0000000..4ebf660
--- /dev/null
+++ b/cpp/utils/cuda/error.h
@@ -0,0 +1,16 @@
+#include <cuda_runtime.h>
+
+namespace utils::cuda {
+ bool checkErr(cudaError_t err, const char *file, int line);
+} // namespace utils::cuda
+
+#define RET_IF_FAILED(__ERR_CODE__) \
+ do { \
+ if (!utils::cuda::checkErr((__ERR_CODE__), __FILE__, __LINE__)) \
+ return false; \
+ } while (0)
+#define THROW_IF_FAILED(__ERR_CODE__) \
+ do { \
+ if (!utils::cuda::checkErr((__ERR_CODE__), __FILE__, __LINE__)) \
+ throw std::exception(); \
+ } while (0)
diff --git a/cpp/utils/cuda/event.h b/cpp/utils/cuda/event.h
new file mode 100644
index 0000000..41a88ac
--- /dev/null
+++ b/cpp/utils/cuda/event.h
@@ -0,0 +1,19 @@
+#include <cuda_runtime.h>
+#include <memory>
+
+namespace utils::cuda {
+ class Event {
+ public:
+ Event() : _p_event(std::make_shared<cudaEvent_t>()) { cudaEventCreate(_p_event.get()); }
+
+ virtual ~Event() {
+ if (_p_event.use_count() == 1)
+ cudaEventDestroy(*_p_event);
+ }
+
+ operator cudaEvent_t() { return *_p_event; }
+
+ private:
+ std::shared_ptr<cudaEvent_t> _p_event;
+ };
+} // namespace utils::cuda
diff --git a/cpp/utils/cuda/gl_texture.h b/cpp/utils/cuda/gl_texture.h
new file mode 100644
index 0000000..4a69fa6
--- /dev/null
+++ b/cpp/utils/cuda/gl_texture.h
@@ -0,0 +1,33 @@
+#include <cuda_gl_interop.h>
+#include "resource.h"
+
+namespace utils::cuda {
+ template <typename T> class GlTextureResource : public GraphicsResource {
+ public:
+ GlTextureResource(GLuint textureID, glm::uvec2 textureSize) {
+ CHECK_EX(cudaGraphicsGLRegisterImage(&_res, textureID, GL_TEXTURE_2D,
+ cudaGraphicsRegisterFlagsWriteDiscard));
+ _size = textureSize.x * textureSize.y * sizeof(T);
+ _textureSize = textureSize;
+ }
+
+ virtual ~GlTextureResource() { cudaGraphicsUnmapResources(1, &_res, 0); }
+
+ virtual void *getBuffer() const {
+ cudaArray_t buffer;
+ try {
+ CHECK_EX(cudaGraphicsSubResourceGetMappedArray(&buffer, _res, 0, 0));
+ } catch (...) {
+ return nullptr;
+ }
+ return buffer;
+ }
+
+ operator T *() { return (T *)getBuffer(); }
+
+ glm::uvec2 textureSize() { return _textureSize; }
+
+ private:
+ glm::uvec2 _textureSize;
+ };
+} // namespace utils::cuda
diff --git a/cpp/utils/cuda/index.h b/cpp/utils/cuda/index.h
new file mode 100644
index 0000000..a044f11
--- /dev/null
+++ b/cpp/utils/cuda/index.h
@@ -0,0 +1,34 @@
+#include <device_launch_parameters.h>
+#include <glm/glm.hpp>
+
+#define T_IDX threadIdx.x
+#define T_IDX2 glm::uvec2(threadIdx.x, threadIdx.y)
+#define T_IDX3 glm::uvec3(threadIdx.x, threadIdx.y, threadIdx.z)
+#define B_IDX blockIdx.x
+#define B_IDX2 glm::uvec2(blockIdx.x, blockIdx.y)
+#define B_IDX3 glm::uvec3(blockIdx.x, blockIdx.y, blockIdx.z)
+#define IDX blockIdx.x *blockDim.x + threadIdx.x
+#define IDX2 glm::uvec2(blockIdx.x *blockDim.x + threadIdx.x, blockIdx.y * blockDim.y + threadIdx.y)
+#define IDX3 \
+ glm::uvec3(blockIdx.x *blockDim.x + threadIdx.x, blockIdx.y * blockDim.y + threadIdx.y, \
+ blockIdx.z * blockDim.z + threadIdx.z)
+#define FLAT_INDEX utils::cuda::flattenIdx(IDX3)
+#define DEFINE_IDX(__var1__) uint __var1__ = blockIdx.x * blockDim.x + threadIdx.x;
+#define DEFINE_IDX2(__var1__, __var2__) \
+ uint __var1__ = blockIdx.x * blockDim.x + threadIdx.x; \
+ uint __var2__ = blockIdx.y * blockDim.y + threadIdx.y;
+#define DEFINE_IDX3(__var1__, __var2__, __var3__) \
+ uint __var1__ = blockIdx.x * blockDim.x + threadIdx.x; \
+ uint __var2__ = blockIdx.y * blockDim.y + threadIdx.y; \
+ uint __var3__ = blockIdx.z * blockDim.z + threadIdx.z;
+#define DEFINE_FLAT_INDEX(__var1__) uint __var1__ = FLAT_INDEX;
+
+namespace utils::cuda {
+ __device__ __forceinline__ uint flattenIdx(glm::uvec3 idx3) {
+ return idx3.x + idx3.y * blockDim.x * gridDim.x +
+ idx3.z * blockDim.x * gridDim.x * blockDim.y * gridDim.y;
+ }
+ __device__ __forceinline__ uint flattenIdx(glm::uvec2 idx2) {
+ return idx2.x + idx2.y * blockDim.x * gridDim.x;
+ }
+} // namespace utils::cuda
diff --git a/cpp/utils/cuda/map_resources_scope.h b/cpp/utils/cuda/map_resources_scope.h
new file mode 100644
index 0000000..ab7bdf5
--- /dev/null
+++ b/cpp/utils/cuda/map_resources_scope.h
@@ -0,0 +1,25 @@
+#include <cuda_runtime.h>
+#include <vector>
+#include "error.h"
+
+namespace utils::cuda {
+ class MapResourcesScope {
+ public:
+ MapResourcesScope(const std::vector<cudaGraphicsResource_t> &resources,
+ cudaStream_t stream = nullptr)
+ : _resources(resources), _stream(stream) {
+ if (!_resources.empty())
+ THROW_IF_FAILED(
+ cudaGraphicsMapResources((int)_resources.size(), _resources.data(), _stream));
+ }
+
+ ~MapResourcesScope() {
+ if (!_resources.empty())
+ cudaGraphicsUnmapResources((int)_resources.size(), _resources.data(), _stream);
+ }
+
+ private:
+ std::vector<cudaGraphicsResource_t> _resources;
+ cudaStream_t _stream;
+ };
+} // namespace utils::cuda
diff --git a/cpp/utils/cuda/resource.h b/cpp/utils/cuda/resource.h
new file mode 100644
index 0000000..6a07298
--- /dev/null
+++ b/cpp/utils/cuda/resource.h
@@ -0,0 +1,70 @@
+#pragma once
+#include <cuda_runtime.h>
+#include "../common.h"
+#include "error.h"
+
+namespace utils::cuda {
+ class Resource {
+ public:
+ virtual ~Resource() {}
+
+ virtual void *getBuffer() const = 0;
+
+ virtual size_t size() const = 0;
+ };
+
+ class BufferResource : public Resource {
+ public:
+ BufferResource(void *buffer = nullptr, size_t size = 0)
+ : _p_buffer(buffer), _ownBuffer(false), _size(size) {}
+ BufferResource(size_t size) : _ownBuffer(true), _size(size) {
+ void *p_buffer;
+ THROW_IF_FAILED(cudaMalloc(&p_buffer, size));
+ _p_buffer = std::shared_ptr<void>(p_buffer);
+ }
+
+ virtual ~BufferResource() {
+ if (!_ownBuffer || _p_buffer.use_count() > 1)
+ return;
+ try {
+ THROW_IF_FAILED(cudaFree(_p_buffer.get()));
+ } catch (std::exception &ex) {
+ common::Logger::instance.warning("Exception raised in destructor: %s", ex.what());
+ }
+ }
+
+ virtual void *getBuffer() const { return _p_buffer.get(); }
+
+ virtual size_t size() const { return _size; }
+
+ private:
+ std::shared_ptr<void> _p_buffer;
+ bool _ownBuffer;
+ size_t _size;
+ };
+
+ class GraphicsResource : public Resource {
+ public:
+ cudaGraphicsResource_t getHandler() { return _res; }
+
+ virtual ~GraphicsResource() {
+ if (_res == nullptr)
+ return;
+ try {
+ THROW_IF_FAILED(cudaGraphicsUnregisterResource(_res));
+ } catch (std::exception &ex) {
+ common::Logger::instance.warning("Exception raised in destructor: %s", ex.what());
+ }
+ _res = nullptr;
+ }
+
+ virtual size_t size() const { return _size; }
+
+ protected:
+ cudaGraphicsResource_t _res;
+ size_t _size;
+
+ GraphicsResource() : _res(nullptr), _size(0) {}
+ };
+
+} // namespace utils::cuda
diff --git a/cpp/utils/cuda/resources.h b/cpp/utils/cuda/resources.h
new file mode 100644
index 0000000..0131b4a
--- /dev/null
+++ b/cpp/utils/cuda/resources.h
@@ -0,0 +1,18 @@
+#include "resource.h"
+#include <map>
+#include <vector>
+
+namespace utils::cuda {
+ class Resources : public std::map<std::string, Resource &> {
+ public:
+ std::vector<cudaGraphicsResource_t> getGraphicsResourceHandlers() {
+ std::vector<cudaGraphicsResource_t> handlers;
+ for (auto &&item : *this) {
+ auto gres = dynamic_cast<GraphicsResource *>(&item.second);
+ if (gres != nullptr)
+ handlers.push_back(gres->getHandler());
+ }
+ return handlers;
+ }
+ };
+} // namespace utils::cuda
diff --git a/cpp/utils/cuda/stream.h b/cpp/utils/cuda/stream.h
new file mode 100644
index 0000000..7884dd4
--- /dev/null
+++ b/cpp/utils/cuda/stream.h
@@ -0,0 +1,21 @@
+#include <cuda_runtime.h>
+#include <memory>
+
+namespace utils::cuda {
+ class Stream {
+ public:
+ Stream() : _p_stream(std::make_shared<cudaStream_t>()) {
+ cudaStreamCreate(_p_stream.get());
+ }
+
+ virtual ~Stream() {
+ if (_p_stream.use_count() == 1)
+ cudaStreamDestroy(*_p_stream);
+ }
+
+ operator cudaStream_t() { return *_p_stream; }
+
+ private:
+ std::shared_ptr<cudaStream_t> _p_stream;
+ };
+} // namespace utils::cuda
diff --git a/cpp/utils/debug.h b/cpp/utils/debug.h
new file mode 100644
index 0000000..6187caf
--- /dev/null
+++ b/cpp/utils/debug.h
@@ -0,0 +1,9 @@
+template <typename T, typename T2 = T>
+void dumpArray(std::ostream &so, CudaArray<T> &arr, size_t maxDumpRows = 0,
+ size_t elemsPerRow = 1) {
+ int chns = sizeof(T) / sizeof(T2);
+ T2 *hostArr = new T2[arr.n() * chns];
+ cudaMemcpy(hostArr, arr.getBuffer(), arr.n() * sizeof(T), cudaMemcpyDeviceToHost);
+ dumpHostBuffer<T2>(so, hostArr, arr.n() * sizeof(T), chns * elemsPerRow, maxDumpRows);
+ delete[] hostArr;
+}
\ No newline at end of file
diff --git a/cpp_old/msl_infer/Nmsl2.h b/cpp_old/msl_infer/Nmsl2.h
deleted file mode 100755
index 8d73476..0000000
--- a/cpp_old/msl_infer/Nmsl2.h
+++ /dev/null
@@ -1,25 +0,0 @@
-#pragma once
-#include "Msl.h"
-
-class Nmsl2 : public Msl
-{
-public:
- sptr<Resource> resRaw1;
- sptr<Resource> resRaw2;
- Net *fcNet1;
- Net *fcNet2;
- Net *catNet;
- unsigned int batchSize;
- unsigned int samples;
-
- Nmsl2(int batchSize, int samples);
-
- virtual bool load(const std::string &netDir);
-
- virtual void bindResources(Resource *resEncoded, Resource *resDepths, Resource *resColors);
-
- virtual bool infer();
-
- virtual void dispose();
-
-};
diff --git a/cpp_old/msl_infer/Sampler.h b/cpp_old/msl_infer/Sampler.h
deleted file mode 100755
index 2457ebb..0000000
--- a/cpp_old/msl_infer/Sampler.h
+++ /dev/null
@@ -1,15 +0,0 @@
-#pragma once
-#include "../utils/common.h"
-
-class Sampler {
-public:
- Sampler(glm::vec2 depthRange, unsigned int samples, bool outputRadius)
- : _dispRange(1.0f / depthRange, samples), _outputRadius(outputRadius) {}
-
- void sampleOnRays(sptr<CudaArray<float>> o_coords, sptr<CudaArray<float>> o_depths,
- sptr<CudaArray<glm::vec3>> rays, glm::vec3 rayCenter);
-
-private:
- Range _dispRange;
- bool _outputRadius;
-};
\ No newline at end of file
diff --git a/cpp_old/old/msl_infer/Msl.cpp b/cpp_old/old/msl_infer/Msl.cpp
deleted file mode 100644
index e966669..0000000
--- a/cpp_old/old/msl_infer/Msl.cpp
+++ /dev/null
@@ -1,28 +0,0 @@
-#include "Msl.h"
-#include <time.h>
-
-Msl::Msl() : net(nullptr) {}
-
-bool Msl::load(const std::string &netPath) {
- net = new Net();
- if (net->load(netPath))
- return true;
- dispose();
- return false;
-}
-
-void Msl::bindResources(Resource *resEncoded, Resource *resDepths, Resource *resColors) {
- net->bindResource("Encoded", resEncoded);
- net->bindResource("Depths", resDepths);
- net->bindResource("Colors", resColors);
-}
-
-bool Msl::infer() { return net->infer(); }
-
-void Msl::dispose() {
- if (net != nullptr) {
- net->dispose();
- delete net;
- net = nullptr;
- }
-}
diff --git a/cpp_old/old/msl_infer/Msl.h b/cpp_old/old/msl_infer/Msl.h
deleted file mode 100644
index 0a9e3e7..0000000
--- a/cpp_old/old/msl_infer/Msl.h
+++ /dev/null
@@ -1,15 +0,0 @@
-#pragma once
-#include "../utils/common.h"
-#include "Net.h"
-
-class Msl {
-public:
- Net *net;
-
- Msl();
-
- virtual bool load(const std::string &netDir);
- virtual void bindResources(Resource *resEncoded, Resource *resDepths, Resource *resColors);
- virtual bool infer();
- virtual void dispose();
-};
diff --git a/cpp_old/old/msl_infer/Nmsl2.cpp b/cpp_old/old/msl_infer/Nmsl2.cpp
deleted file mode 100644
index 5d41d0f..0000000
--- a/cpp_old/old/msl_infer/Nmsl2.cpp
+++ /dev/null
@@ -1,65 +0,0 @@
-#include "Nmsl2.h"
-#include <time.h>
-
-Nmsl2::Nmsl2(int batchSize, int samples)
- : batchSize(batchSize),
- samples(samples),
- resRaw1(nullptr),
- resRaw2(nullptr),
- fcNet1(nullptr),
- fcNet2(nullptr),
- catNet(nullptr) {}
-
-bool Nmsl2::load(const std::string &netDir) {
- fcNet1 = new Net();
- fcNet2 = new Net();
- catNet = new Net();
- if (!fcNet1->load(netDir + "fc1.trt") || !fcNet2->load(netDir + "fc2.trt") ||
- !catNet->load(netDir + "cat.trt"))
- return false;
- resRaw1 = sptr<Resource>(new CudaBuffer(batchSize * samples / 2 * sizeof(float4)));
- resRaw2 = sptr<Resource>(new CudaBuffer(batchSize * samples / 2 * sizeof(float4)));
- return true;
-}
-
-void Nmsl2::bindResources(Resource *resEncoded, Resource *resDepths, Resource *resColors) {
- fcNet1->bindResource("Encoded", resEncoded);
- fcNet1->bindResource("Raw", resRaw1.get());
- fcNet2->bindResource("Encoded", resEncoded);
- fcNet2->bindResource("Raw", resRaw2.get());
- catNet->bindResource("Raw1", resRaw1.get());
- catNet->bindResource("Raw2", resRaw2.get());
- catNet->bindResource("Depths", resDepths);
- catNet->bindResource("Colors", resColors);
-}
-
-bool Nmsl2::infer() {
- // CudaStream stream1, stream2;
- if (!fcNet1->infer())
- return false;
- if (!fcNet2->infer())
- return false;
- if (!catNet->infer())
- return false;
- return true;
-}
-
-void Nmsl2::dispose() {
- if (fcNet1 != nullptr) {
- fcNet1->dispose();
- delete fcNet1;
- fcNet1 = nullptr;
- }
- if (fcNet2 != nullptr) {
- fcNet2->dispose();
- delete fcNet2;
- fcNet2 = nullptr;
- }
- if (catNet != nullptr) {
- catNet->dispose();
- delete catNet;
- catNet = nullptr;
- }
- resRaw1 = nullptr;
- resRaw2 = nullptr;
-}
diff --git a/cpp_old/old/msl_infer/Renderer.cu b/cpp_old/old/msl_infer/Renderer.cu
deleted file mode 100644
index 29c35cc..0000000
--- a/cpp_old/old/msl_infer/Renderer.cu
+++ /dev/null
@@ -1,28 +0,0 @@
-#include "Renderer.h"
-#include "../utils/cuda.h"
-
-/// Dispatch (n_rays, -)
-__global__ void cu_render(glm::vec4 *o_colors, glm::vec4 *layeredColors, uint samples, uint nRays) {
- glm::uvec3 idx3 = IDX3;
- uint rayIdx = idx3.x;
- if (rayIdx >= nRays)
- return;
- glm::vec4 outColor;
- for (int si = samples - 1; si >= 0; --si) {
- glm::vec4 c = layeredColors[rayIdx * samples + si];
- outColor = outColor * (1 - c.a) + c * c.a;
- }
- outColor.a = 1.0f;
- o_colors[idx3.x] = outColor;
-}
-
-Renderer::Renderer() {}
-
-void Renderer::render(sptr<CudaArray<glm::vec4>> o_colors,
- sptr<CudaArray<glm::vec4>> layeredColors) {
- dim3 blkSize(1024);
- dim3 grdSize(ceilDiv(o_colors->n(), blkSize.x));
- CU_INVOKE(cu_render)
- (*o_colors, *layeredColors, layeredColors->n() / o_colors->n(), o_colors->n());
- CHECK_EX(cudaGetLastError());
-}
\ No newline at end of file
diff --git a/cpp_old/old/msl_infer/Renderer.h b/cpp_old/old/msl_infer/Renderer.h
deleted file mode 100644
index 4e48a0e..0000000
--- a/cpp_old/old/msl_infer/Renderer.h
+++ /dev/null
@@ -1,15 +0,0 @@
-#pragma once
-#include "../utils/common.h"
-
-class Renderer {
-public:
- Renderer();
-
- /**
- * @brief
- *
- * @param o_colors
- * @param layeredColors
- */
- void render(sptr<CudaArray<glm::vec4>> o_colors, sptr<CudaArray<glm::vec4>> layeredColors);
-};
\ No newline at end of file
diff --git a/cpp_old/utils/Logger.cpp b/cpp_old/utils/Logger.cpp
deleted file mode 100755
index 17f3d8c..0000000
--- a/cpp_old/utils/Logger.cpp
+++ /dev/null
@@ -1,3 +0,0 @@
-#include "Logger.h"
-
-Logger Logger::instance;
diff --git a/cpp_old/utils/thread_index.h b/cpp_old/utils/thread_index.h
deleted file mode 100755
index d8b0faa..0000000
--- a/cpp_old/utils/thread_index.h
+++ /dev/null
@@ -1,15 +0,0 @@
-#include <device_launch_parameters.h>
-#include <glm/glm.hpp>
-
-#define IDX2 glm::uvec2 { blockIdx.x * blockDim.x + threadIdx.x, blockIdx.y * blockDim.y + threadIdx.y }
-#define IDX3 glm::uvec3 { blockIdx.x * blockDim.x + threadIdx.x, blockIdx.y * blockDim.y + threadIdx.y, blockIdx.z * blockDim.z + threadIdx.z }
-
-__device__ __forceinline__ unsigned int flattenIdx(glm::uvec3 idx3)
-{
- return idx3.x + idx3.y * blockDim.x * gridDim.x + idx3.z * blockDim.x * gridDim.x * blockDim.y * gridDim.y;
-}
-
-__device__ __forceinline__ unsigned int flattenIdx()
-{
- return flattenIdx(IDX3);
-}
\ No newline at end of file
diff --git a/data/__init__.py b/data/__init__.py
index 5e8ac37..a35bc07 100644
--- a/data/__init__.py
+++ b/data/__init__.py
@@ -1,3 +1,2 @@
-from .utils import *
-from .dataset_factory import *
-from .loader import *
\ No newline at end of file
+from .dataset import DataDesc, Dataset
+from .loader import RaysLoader, MultiScaleDataLoader
diff --git a/data/dataset.py b/data/dataset.py
index 1fb4372..dbd9666 100644
--- a/data/dataset.py
+++ b/data/dataset.py
@@ -1,93 +1,267 @@
+import json
import torch
+import torch.utils.data
+import torch.nn.functional as nn_f
+from typing import Union
from operator import itemgetter
-from typing import Tuple, Union
-from pathlib import Path
-from utils import view
-from .utils import get_data_path
+try:
+ from ..utils import view, img, math
+ from ..utils.types import *
+ from ..utils.misc import calculate_autosize
+except ImportError:
+ from utils import view, img, math
+ from utils.types import *
+ from utils.misc import calculate_autosize
-class Dataset(object):
- desc: dict
- desc_path: Path
- device: torch.device
+class DataDesc(dict[str, Any]):
+ path: Path
@property
- def name(self):
- return self.desc_path.stem
+ def name(self) -> str:
+ return self.path.stem
@property
- def root(self):
- return self.desc_path.parent
+ def root(self) -> Path:
+ return self.path.parent
@property
- def n_views(self):
- return self.centers.size(0)
+ def coord_sys(self) -> str:
+ return "gl" if self.get("gl_coord") else "dx"
+
+ def __init__(self, path: PathLike):
+ path = DataDesc.get_json_path(path)
+ with open(path, 'r', encoding='utf-8') as file:
+ data = json.loads(file.read())
+ super().__init__(data)
+ self.path = path
+
+ @staticmethod
+ def get_json_path(path: PathLike) -> Path:
+ path = Path(path)
+ if path.suffix != ".json":
+ path = Path(f"{path}.json")
+ return path.absolute()
+
+ def get(self, key: str, fn=lambda x: x, default=None) -> Any | None:
+ if key in self:
+ return fn(self[key])
+ return default
+
+ def get_as_tensor(self, key: str, fn=lambda x: x, default=None, dtype=torch.float, device=None,
+ shape=None) -> torch.Tensor | None:
+ raw_value = self.get(key, fn, default)
+ if raw_value is None:
+ return raw_value
+ tensor_value = torch.tensor(raw_value, dtype=dtype, device=device)
+ if shape is not None:
+ tensor_value = tensor_value.reshape(shape)
+ return tensor_value
+
+ def get_path(self, name: str) -> str | None:
+ path_pattern = self.get(f"{name}_file")
+ if not path_pattern:
+ return None
+ if "/" not in path_pattern:
+ path_pattern = f"{self.name}/{path_pattern}"
+ return str(self.root / path_pattern)
+
+
+class Dataset(torch.utils.data.Dataset):
+
+ root: Path
+ """`Path` Root directory of the dataset"""
+ name: str
+ """`str` Name of the dataset"""
+ color_mode: Color
+ """`Color` Color mode of images in the dataset"""
+ white_bg: bool
+ """`bool` Images in the dataset should have white background"""
+ level: int
+ """`int` Level of this dataset"""
+ res: Resolution
+ """`Resolution` Resolution of each view as (rows, columns)"""
+ coord_sys: str
+ """`str` Coordinate system, must be 'dx' or 'gl'"""
+ device: torch.device
+ """`device` Device of tensors"""
+ cam: view.Camera
+ """`Camera?` Camera object"""
+ depth_range: tuple[float, float] | None
+ """`(float, float)?` Depth range of the scene as a guide to sampling"""
+ bbox: tuple[tuple[float, ...], tuple[float, ...]] | None
+ """`((float,...), (float,...))?` Bounding box of the scene as a guide to sampling"""
+ trans_range: tuple[tuple[float, ...], tuple[float, ...]] | None
+ """`((float,...), (float,...))?` Acceptable Translation (and optional rotation) range"""
+ color_path: str | None
+ """`str?` Path of image data"""
+ depth_path: str | None
+ """`str?` Path of depth data"""
+ indices: torch.Tensor
+ """`Tensor(N)` Indices for loading specific subset of views in the dataset"""
+ centers: torch.Tensor
+ """`Tensor(N, 3)` Center positions of views"""
+ rots: torch.Tensor | None
+ """`Tensor(N, 3, 3)?` Rotation matrices of views"""
@property
- def n_pixels_per_view(self):
- return self.res[0] * self.res[1]
+ def disparity_range(self) -> tuple[float, float] | None:
+ return self.depth_range and (1 / self.depth_range[0], 1 / self.depth_range[1])
@property
- def n_pixels(self):
- return self.n_views * self.n_pixels_per_view
+ def pixels_per_view(self) -> int:
+ return self.cam.local_rays.shape[0]
+
+ @property
+ def tot_pixels(self) -> int:
+ return len(self) * self.pixels_per_view
+
+ @overload
+ def __init__(self, desc: DataDesc, *,
+ res: Resolution | tuple[int, int] | None = None,
+ views_to_load: IndexSelector | None = None,
+ color_mode: Color = Color.rgb,
+ coord_sys: str = "gl",
+ device: torch.device = None) -> None:
+ ...
- def __init__(self, desc: dict, desc_path: Path, *,
- res: Tuple[int, int] = None,
- views_to_load: Union[range, torch.Tensor] = None,
- device: torch.device = None, **kwargs) -> None:
+ @overload
+ def __init__(self, dataset: "Dataset", *,
+ views_to_load: IndexSelector | None = None) -> None:
+ ...
+
+ def __init__(self, dataset_or_desc: Union["Dataset", DataDesc, PathLike], *,
+ res: tuple[int, int] = None,
+ views_to_load: IndexSelector = None,
+ color_mode: Color = Color.rgb,
+ coord_sys: str = "gl",
+ device: torch.device = None) -> None:
super().__init__()
- self.desc = desc
- self.desc_path = desc_path.absolute()
- self.device = device
- self._load_desc(res, views_to_load, **kwargs)
+ if isinstance(dataset_or_desc, Dataset):
+ self._init_from_dataset(dataset_or_desc, views_to_load=views_to_load)
+ else:
+ self._init_from_desc(dataset_or_desc, res=res, views_to_load=views_to_load,
+ color_mode=color_mode, coord_sys=coord_sys, device=device)
- def get_data(self):
+ def __getitem__(self, index: int | torch.Tensor | slice) -> dict[str, torch.Tensor]:
+ if isinstance(index, torch.Tensor) and len(index.shape) == 0:
+ index = index.item()
+ view_index = self.indices[index]
data = {
- 'indices': self.indices,
- 'centers': self.centers
+ "t": self.centers[index]
}
if self.rots is not None:
- data['rots'] = self.rots
+ data["r"] = self.rots[index]
+ for image_type in ["color", "depth"]:
+ image = self.load_images(image_type, view_index)
+ if image is not None:
+ data[image_type] = self.cam.get_pixels(image)
+ if isinstance(index, int):
+ data[image_type].squeeze_(0)
return data
- def _get_data_path(self, name: str) -> str:
- path_pattern = self.desc.get(f"{name}_file_pattern", None)
- return path_pattern and get_data_path(self.desc_path, path_pattern)
-
- def _load_desc(self, res: Tuple[int, int], views_to_load: Union[range, torch.Tensor],
- **kwargs):
- self.level = self.desc.get('level', 0)
- self.res = res or itemgetter("y", "x")(self.desc['view_res'])
- self.cam = view.CameraParam(self.desc['cam_params'], self.res, device=self.device)\
- if 'cam_params' in self.desc else None
- self.depth_range = itemgetter("min", "max")(self.desc['depth_range']) \
- if 'depth_range' in self.desc else None
- self.range = itemgetter("min", "max")(self.desc['range']) if 'range' in self.desc else None
- self.bbox = self.desc.get('bbox')
- self.samples = self.desc.get('samples')
- self.centers = torch.tensor(self.desc['view_centers'], device=self.device) # (N, 3)
- self.rots = torch.tensor(
- [
- view.euler_to_matrix([rot[1] if self.desc.get('gl_coord') else -rot[1], rot[0], 0])
- for rot in self.desc['view_rots']
- ]
- if len(self.desc['view_rots'][0]) == 2 else self.desc['view_rots'],
- device=self.device).view(-1, 3, 3) if 'view_rots' in self.desc else None # (N, 3, 3)
- self.indices = torch.tensor(self.desc.get('views') or [*range(self.centers.size(0))],
- device=self.device)
+ def __len__(self):
+ return self.indices.shape[0]
+
+ def load_images(self, type: str, indices: int | torch.Tensor | list[int]) -> torch.Tensor:
+ if not getattr(self, f"{type}_path"):
+ return None
+ if isinstance(indices, int):
+ raw_images = img.load(getattr(self, f"{type}_path") % indices)
+ elif isinstance(indices, torch.Tensor) and len(indices.shape) == 0:
+ raw_images = img.load(getattr(self, f"{type}_path") % indices.item())
+ else:
+ raw_images = img.load(*[getattr(self, f"{type}_path") % i for i in indices])
+ raw_images = raw_images.to(device=self.device)
+ if self.res != list(raw_images.shape[-2:]):
+ raw_images = nn_f.interpolate(raw_images, self.res)
+ if type == "image":
+ return Color.cvt(raw_images, Color.rgb, self.color_mode)
+ elif type == "depth":
+ return math.lerp(1 - raw_images, self.disparity_range).reciprocal()
+ return raw_images
+
+ def split(self, *views: int) -> list["Dataset"]:
+ views, _ = calculate_autosize(len(self), *views)
+ sub_datasets: list["Dataset"] = []
+ offset = 0
+ for i in range(len(views)):
+ end = offset + views[i]
+ sub_datasets.append(Dataset(self, views_to_load=slice(offset, end)))
+ offset = end
+ return sub_datasets
+
+ def _init_from_desc(self, desc_or_path: DataDesc | PathLike, res: tuple[int, int] | None,
+ views_to_load: IndexSelector | None, color_mode: Color,
+ coord_sys: str, device: torch.device) -> None:
+ desc = desc_or_path if isinstance(desc_or_path, DataDesc) else DataDesc(desc_or_path)
+ self.root = desc.root
+ self.name = desc.name
+ self.color_mode = color_mode
+ self.white_bg = desc.get("white_bg", default=False)
+ self.level = desc.get('level', default=0)
+ self.color_path = desc.get_path("color")
+ self.depth_path = desc.get_path("depth")
+ self.res = Resolution(*res) if res else Resolution.from_str(desc["res"])
+ self.coord_sys = coord_sys
+ self.device = device
+
+ self.cam = view.Camera.create(desc["cam"], self.res, coord_sys=self.coord_sys, device=device)
+ self.depth_range = desc.get("depth_range")
+ self.bbox = desc.get("bbox", lambda val: (tuple(val[:len(val) // 2]),
+ tuple(val[len(val) // 2:])))
+ self.trs_range = desc.get("trs_range")
+ self.rot_range = desc.get("rot_range")
+ self.centers = desc.get_as_tensor("centers", device=device)
+ self.rots = desc.get_as_tensor("rots", lambda rots: [
+ view.euler_to_matrix(rot[1] if desc.coord_sys == "gl" else -rot[1], rot[0], 0)
+ for rot in rots
+ ] if len(rots[0]) == 2 else rots, shape=(-1, 3, 3), device=device)
+ self.indices = desc.get_as_tensor("views", default=list(range(self.centers.shape[0])),
+ dtype=torch.long, device=device)
if views_to_load is not None:
+ if isinstance(views_to_load, list):
+ views_to_load = torch.tensor(views_to_load, device=device)
+ self.indices = self.indices[views_to_load]
self.centers = self.centers[views_to_load]
self.rots = self.rots[views_to_load] if self.rots is not None else None
- self.indices = self.indices[views_to_load]
- if self.desc.get('gl_coord'):
- print('Convert from OGL coordinate to DX coordinate (i.e. flip z axis)')
+ if desc.coord_sys != self.coord_sys:
self.centers[:, 2] *= -1
- if self.cam is not None:
- if not self.desc['cam_params'].get('fov'):
- self.cam.f[1] *= -1
if self.rots is not None:
self.rots[:, 2] *= -1
self.rots[..., 2] *= -1
+
+ def _init_from_dataset(self, dataset: "Dataset", views_to_load: IndexSelector | None) -> None:
+ """
+ Clone or get subset of an existed dataset
+
+ :param dataset `Dataset`: _description_
+ :param views_to_load `IndexSelector?`: _description_, defaults to None
+ """
+ self.root = dataset.root
+ self.name = dataset.name
+ self.color_mode = dataset.color_mode
+ self.level = dataset.level
+ self.res = dataset.res
+ self.coord_sys = dataset.coord_sys
+ self.device = dataset.device
+ self.cam = dataset.cam
+ self.depth_range = dataset.depth_range
+ self.bbox = dataset.bbox
+ self.trs_range = dataset.trs_range
+ self.rot_range = dataset.rot_range
+ self.color_path = dataset.color_path
+ self.depth_path = dataset.depth_path
+ if views_to_load is not None:
+ if isinstance(views_to_load, list):
+ views_to_load = torch.tensor(views_to_load, device=dataset.device)
+ self.indices = dataset.indices[views_to_load].clone()
+ self.centers = dataset.centers[views_to_load].clone()
+ self.rots = None if dataset.rots is None else dataset.rots[views_to_load].clone()
+ else:
+ self.indices = dataset.indices.clone()
+ self.centers = dataset.centers.clone()
+ self.rots = None if dataset.rots is None else dataset.rots.clone()
diff --git a/data/dataset_factory.py b/data/dataset_factory.py
deleted file mode 100644
index bf14cd4..0000000
--- a/data/dataset_factory.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import json
-from pathlib import Path
-
-import utils.device
-from .utils import get_dataset_desc_path
-from .pano_dataset import PanoDataset
-from .view_dataset import ViewDataset
-
-
-class DatasetFactory(object):
-
- @staticmethod
- def load(path: Path, device=None, **kwargs):
- device = device or utils.device.default()
- path = get_dataset_desc_path(path)
- with open(path, 'r', encoding='utf-8') as file:
- data_desc: dict = json.loads(file.read())
- if data_desc.get('type') == 'pano':
- dataset_class = PanoDataset
- else:
- dataset_class = ViewDataset
- dataset = dataset_class(data_desc, path.absolute(), device=device, **kwargs)
- return dataset
diff --git a/data/loader.py b/data/loader.py
index 1458075..b923f9a 100644
--- a/data/loader.py
+++ b/data/loader.py
@@ -1,125 +1,106 @@
-import threading
-import torch
-import math
-from logging import *
-from typing import Dict, List
+import torch.utils.data
+from tqdm import tqdm
+from collections import defaultdict
+from .dataset import Dataset
+try:
+ from ..utils import math
+ from ..utils.types import *
+except ImportError:
+ from utils import math
+ from utils.types import *
-class Preloader(object):
-
- def __init__(self, device=None) -> None:
- super().__init__()
- self.stream = torch.cuda.Stream(device)
- self.event_chunk_loaded = None
-
- def preload_chunk(self, chunk):
- if self.event_chunk_loaded is not None:
- self.event_chunk_loaded.wait()
- if chunk.loaded:
- return
- # print(f'Preloader: preload chunk #{chunk.id}')
- self.event_chunk_loaded = threading.Event()
- threading.Thread(target=Preloader._load_chunk, args=(self, chunk)).start()
-
- def _load_chunk(self, chunk):
- with torch.cuda.stream(self.stream):
- chunk.load()
- self.event_chunk_loaded.set()
- # print(f'Preloader: chunk #{chunk.id} is loaded')
+class RaysLoader(object):
-class DataLoader(object):
+ class Iterator(object):
- class Iter(object):
-
- def __init__(self, chunks, batch_size, shuffle, device: torch.device, preloader: Preloader):
+ def __init__(self, loader: "RaysLoader"):
super().__init__()
- self.batch_size = batch_size
- self.chunks = chunks
- self.offset = -1
- self.chunk_idx = -1
- self.current_chunk = None
- self.shuffle = shuffle
- self.device = device
- self.preloader = preloader
+ self.loader = loader
+ self.offset = 0
- def __del__(self):
- #print('DataLoader.Iter: clean chunks')
- if self.preloader is not None and self.preloader.event_chunk_loaded is not None:
- self.preloader.event_chunk_loaded.wait()
- chunks_to_reserve = 1 if self.preloader is None else 2
- for i in range(chunks_to_reserve, len(self.chunks)):
- if self.chunks[i].loaded:
- self.chunks[i].release()
+ # Initialize ray indices
+ #self.ray_indices = torch.randperm(self.loader.tot_pixels, device=self.loader.device)\
+ # if loader.shuffle else torch.arange(self.loader.tot_pixels, device=self.loader.device)
+ self.ray_indices = torch.randperm(self.loader.tot_pixels, device="cpu")\
+ if loader.shuffle else torch.arange(self.loader.tot_pixels, device="cpu")
- def __next__(self):
- if self.offset == -1:
- self._next_chunk()
- stop = min(self.offset + self.batch_size, len(self.current_chunk))
- if self.indices is not None:
- indices = self.indices[self.offset:stop]
- else:
- indices = torch.arange(self.offset, stop, device=self.device)
- self.offset = stop
- if self.offset >= len(self.current_chunk):
- self.offset = -1
- return self.current_chunk[indices]
-
- def _next_chunk(self):
- if self.current_chunk is not None:
- chunks_to_reserve = 1 if self.preloader is None else 2
- if len(self.chunks) > chunks_to_reserve:
- self.current_chunk.release()
- if self.chunk_idx >= len(self.chunks) - 1:
+ def __next__(self) -> Rays:
+ if self.offset >= self.ray_indices.shape[0]:
raise StopIteration()
- self.chunk_idx += 1
- self.current_chunk = self.chunks[self.chunk_idx]
- self.offset = 0
- self.indices = torch.randperm(len(self.current_chunk)).to(device=self.device) \
- if self.shuffle else None
- if self.preloader is not None:
- self.preloader.preload_chunk(self.chunks[(self.chunk_idx + 1) % len(self.chunks)])
-
- def __init__(self, dataset, batch_size, *,
- chunk_max_items=None, shuffle=False, enable_preload=True, **chunk_args):
+ stop = min(self.offset + self.loader.batch_size, self.ray_indices.shape[0])
+ rays = self._get_rays(self.ray_indices[self.offset:stop])
+ self.offset = stop
+ return rays
+
+ def _get_rays(self, indices: torch.Tensor) -> Rays:
+ indices_on_device = indices.to(self.loader.device) # (B)
+ view_idx = torch.div(indices_on_device, self.loader.pixels_per_view, rounding_mode="trunc")
+ pix_idx = indices_on_device % self.loader.pixels_per_view
+ rays_o = self.loader.centers[view_idx] # (B, 3)
+ rays_d = self.loader.local_rays[pix_idx] # (B, 3)
+ if self.loader.rots is not None:
+ rays_d = (self.loader.rots[view_idx] @ rays_d[..., None])[..., 0]
+ rays = Rays({
+ 'level': self.loader.level,
+ 'idx': indices_on_device,
+ 'rays_o': rays_o,
+ 'rays_d': rays_d
+ })
+
+ # "colors" and "depths" are on host memory. Move part of them to device memory
+ indices = indices.to("cpu")
+ for image_type in ["color", "depth"]:
+ if image_type in self.loader.data:
+ rays[image_type] = self.loader.data[image_type][indices].to(
+ self.loader.device, non_blocking=True)
+ return rays
+
+ def __init__(self, dataset: Dataset, batch_size: int, *,
+ shuffle: bool = False, num_workers: int = 8, device: torch.device = None):
super().__init__()
self.dataset = dataset
self.batch_size = batch_size
+ self.device = device
self.shuffle = shuffle
- self.chunk_args = chunk_args
- self.preloader = Preloader(self.dataset.device) if enable_preload else None
- self._init_chunks(chunk_max_items)
+
+ self.level = dataset.level
+ self.n_views = len(dataset)
+ self.pixels_per_view = dataset.pixels_per_view
+ self.tot_pixels = self.n_views * self.pixels_per_view
+
+ self.indices = dataset.indices.to(self.device)
+ self.centers = dataset.centers.to(self.device)
+ self.rots = dataset.rots.to(self.device) if dataset.rots is not None else None
+ self.local_rays = dataset.cam.local_rays.to(self.device)
+
+ # Load views from dataset
+ self.data = defaultdict(list)
+ views_loader = torch.utils.data.DataLoader(dataset, num_workers=num_workers,
+ pin_memory=True)
+ for view_data in tqdm(views_loader, "Loading views", leave=False, dynamic_ncols=True):
+ for key, val in view_data.items():
+ self.data[key].append(val)
+ print(f"{len(dataset)} views loaded.")
+ self.data = {
+ key: torch.cat(val).flatten(0, 1)
+ for key, val in self.data.items()
+ if key == "color" or key == "depth"
+ }
def __iter__(self):
- return DataLoader.Iter(self.chunks, self.batch_size, self.shuffle, self.dataset.device,
- self.preloader)
+ return RaysLoader.Iterator(self)
def __len__(self):
- return sum(math.ceil(len(chunk) / self.batch_size) for chunk in self.chunks)
-
- def _init_chunks(self, chunk_max_items):
- data: Dict[str, torch.Tensor] = self.dataset.get_data()
- if self.shuffle:
- rand_seq = torch.randperm(self.dataset.n_views).to(device=self.dataset.device)
- data = {key: val[rand_seq] for key, val in data.items()}
- self.chunks = []
- n_chunks = 1 if chunk_max_items is None else \
- math.ceil(self.dataset.n_pixels / chunk_max_items)
- views_per_chunk = math.ceil(self.dataset.n_views / n_chunks)
- for offset in range(0, self.dataset.n_views, views_per_chunk):
- sel = slice(offset, offset + views_per_chunk)
- chunk_data = {key: val[sel] for key, val in data.items()}
- self.chunks.append(self.dataset.Chunk(len(self.chunks), self.dataset,
- chunk_data=chunk_data, **self.chunk_args))
- if self.preloader is not None:
- self.preloader.preload_chunk(self.chunks[0])
+ return math.ceil(self.tot_pixels / self.batch_size)
class MultiScaleDataLoader(object):
class Iter(object):
- def __init__(self, sub_loaders: List[DataLoader]):
+ def __init__(self, sub_loaders: list[RaysLoader]):
super().__init__()
self.sub_loaders = sub_loaders
self.end_flags = [False] * len(sub_loaders)
@@ -150,15 +131,13 @@ class MultiScaleDataLoader(object):
return data_frags
- def __init__(self, dataset, batch_size, *,
- chunk_max_items=None, shuffle=False, enable_preload=True, **chunk_args):
+ def __init__(self, dataset: Dataset, batch_size, *,
+ views_per_chunk=8, shuffle=False, num_workers=4, device: torch.device = None):
super().__init__()
self.batch_size = batch_size
self.sub_loaders = [
- DataLoader(sub_dataset, batch_size // len(dataset),
- chunk_max_items=chunk_max_items // len(dataset)
- if chunk_max_items is not None else None,
- shuffle=shuffle, enable_preload=enable_preload, **chunk_args)
+ RaysLoader(sub_dataset, batch_size // len(dataset), views_per_chunk=views_per_chunk,
+ shuffle=shuffle, num_workers=num_workers, device=device)
for sub_dataset in dataset
]
# Sort by datasets' levels
@@ -178,10 +157,12 @@ class MultiScaleDataLoader(object):
if not isinstance(self.active_sub_loaders, list):
self.active_sub_loaders = [self.active_sub_loaders]
+
def get_loader(dataset, batch_size, *,
- chunk_max_items=None, shuffle=False, enable_preload=True, **chunk_args):
+ views_per_chunk=8, shuffle=False, num_workers=4, device: torch.device = None):
if isinstance(dataset, list):
- return MultiScaleDataLoader(dataset, batch_size, chunk_max_items=chunk_max_items,
- shuffle=shuffle, enable_preload=enable_preload, **chunk_args)
- return DataLoader(dataset, batch_size, chunk_max_items=chunk_max_items,
- shuffle=shuffle, enable_preload=enable_preload, **chunk_args)
+ raise NotImplementedError()
+ return MultiScaleDataLoader(dataset, batch_size, views_per_chunk=views_per_chunk,
+ shuffle=shuffle, num_workers=num_workers, device=device)
+ return RaysLoader(dataset, batch_size, views_per_chunk=views_per_chunk,
+ shuffle=shuffle, num_workers=num_workers, device=device)
diff --git a/data/pano_dataset.py b/data/pano_dataset.py
deleted file mode 100644
index 9cd8bd7..0000000
--- a/data/pano_dataset.py
+++ /dev/null
@@ -1,157 +0,0 @@
-import os
-import torch
-import torch.nn.functional as nn_f
-from typing import Dict, Tuple, Union
-from operator import itemgetter
-from pathlib import Path
-
-from utils import img
-from utils import color
-from utils import sphere
-from utils import math
-from utils.mem_profiler import *
-from .dataset import Dataset
-
-
-class PanoDataset(Dataset):
- """
- Data loader for spherical view synthesis task
-
- Attributes
- --------
- data_dir ```str```: the directory of dataset\n
- view_file_pattern ```str```: the filename pattern of view images\n
- cam_params ```object```: camera intrinsic parameters\n
- centers ```Tensor(N, 3)```: centers of views\n
- view_rots ```Tensor(N, 3, 3)```: rotation matrices of views\n
- images ```Tensor(N, 3, H, W)```: images of views\n
- view_depths ```Tensor(N, H, W)```: depths of views\n
- """
-
- class Chunk(object):
-
- @property
- def n_views(self):
- return self.indices.size(0)
-
- @property
- def n_pixels_per_view(self):
- return self.dataset.n_pixels_per_view
-
- def __init__(self, id: int, dataset, chunk_data: Dict[str, torch.Tensor], *,
- color: int, **kwargs):
- """
- [summary]
-
- :param dataset `PanoDataset`: dataset object
- :param indices `Tensor(N)`: indices of views
- :param centers `Tensor(N, 3)`: centers of views
- """
- self.id = id
- self.dataset = dataset
- self.indices = chunk_data['indices']
- self.centers = chunk_data['centers']
- self.color = color
- self.colors_cpu = None
- self.colors = None
- self.loaded = False
-
- def release(self):
- self.colors = None
- self.loaded = False
- MemProfiler.print_memory_stats(f'Chunk #{self.id} released')
-
- def load(self):
- if self.dataset.image_path and self.colors_cpu is None:
- images = color.cvt(img.load(self.dataset.image_path % i for i in self.indices),
- color.RGB, self.color)
- if self.dataset.res != tuple(images.shape[-2:]):
- images = nn_f.interpolate(images, self.dataset.res)
- self.colors_cpu = images.permute(
- 0, 2, 3, 1)[:, self.dataset.pixels[:, 0], self.dataset.pixels[:, 1]].flatten(0, 1)
- if self.colors_cpu is not None:
- self.colors = self.colors_cpu.to(self.dataset.device)
- self.loaded = True
- MemProfiler.print_memory_stats(
- f'Chunk #{self.id} ({self.n_views} views, '
- f'{self.colors.numel() * self.colors.element_size() / 1024 / 1024:.2f}MB) loaded')
-
- def __len__(self):
- return self.n_views * self.n_pixels_per_view
-
- def __getitem__(self, idx):
- if not self.loaded:
- self.load()
- view_idx = idx // self.n_pixels_per_view
- pix_idx = idx % self.n_pixels_per_view
- global_idx = self.indices[view_idx] * self.n_pixels_per_view + pix_idx
- rays_o = self.centers[view_idx]
- rays_d = self.dataset.rays[pix_idx]
- data = {
- 'idx': global_idx,
- 'rays_o': rays_o,
- 'rays_d': rays_d,
- 'level': self.dataset.level
- }
- if self.colors is not None:
- data['color'] = self.colors[idx]
- return data
-
- @property
- def n_pixels_per_view(self):
- return self.pixels.size(0)
-
- def __init__(self, desc: dict, desc_path: Path, *,
- load_images: bool = True,
- res: Tuple[int, int] = None,
- views_to_load: Union[range, torch.Tensor] = None,
- device: torch.device = None,
- **kwargs):
- """
- Initialize data loader for spherical view synthesis task
-
- The dataset description file is a JSON file with following fields:
-
- - view_file_pattern: string, the path pattern of view images
- - view_res: { "x", "y" }, the resolution of view
- - depth_range: { "min", "max" }, the depth range
- - range: { "min": [...], "max": [...] }, the range of translation and rotation
- - centers: [ [ x, y, z ], ... ], centers of views
-
- :param desc_path ```str```: path to the data description file
- :param load_images ```bool```: whether load view images and return in __getitem__()
- :param c ```int```: color space to convert view images to
- :param calculate_rays ```bool```: whether calculate rays
- """
- super().__init__(desc, desc_path, res=res, views_to_load=views_to_load, device=device,
- load_images=load_images)
-
- def get_data(self):
- return {
- 'indices': self.indices,
- 'centers': self.centers
- }
-
- def _load_desc(self, res: Tuple[int, int], views_to_load: Union[range, torch.Tensor],
- load_images: bool):
- super()._load_desc(res, views_to_load)
- self.image_path = load_images and self._get_data_path("view")
- self.pixels, self.rays = self._get_pano_rays()
-
- def _get_pano_rays(self):
- """
- Get unprojected rays of pixels on a panorama
-
- :return `Tensor(N, 2)`: rays' pixel coordinates in pano image
- :return `Tensor(N, 3)`: rays' directions with one unit length
- """
- phi = (torch.arange(self.res[0], device=self.device) + 0.5) / self.res[0] * math.pi # (H)
- length = (phi.sin() * self.res[1] * 0.5).ceil() * 2
- cols = torch.arange(self.res[1], device=self.device)[None, :].expand(*self.res) # (H, W)
- mask = torch.logical_and(cols >= (self.res[1] - length[:, None]) / 2,
- cols < (self.res[1] + length[:, None]) / 2) # (H, W)
- pixs = mask.nonzero() # (N, 2)
- pixs_phi = (0.5 - (pixs[:, 0] + 0.5) / self.res[0]) * math.pi
- pixs_theta = (pixs[:, 1] * 2 + 1 - self.res[1]) / length[pixs[:, 0]] * math.pi
- spher_coords = torch.stack([torch.ones_like(pixs_phi), pixs_theta, pixs_phi], dim=-1)
- return pixs, sphere.spherical2cartesian(spher_coords) # (N, 3)
diff --git a/data/utils.py b/data/utils.py
deleted file mode 100644
index 9a0d5bb..0000000
--- a/data/utils.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from typing import Union
-from pathlib import Path
-
-
-def get_dataset_desc_path(path: Union[Path, str]) -> Path:
- if isinstance(path, str):
- path = Path(path)
- if path.suffix != ".json":
- path = Path(f"{path}.json")
- return path
-
-def get_data_path(dataset_desc_path: Path, path_pattern: str) -> str:
- root = dataset_desc_path.parent
- if "/" not in path_pattern:
- path_pattern = f"{dataset_desc_path.stem}/{path_pattern}"
- return str(root / path_pattern)
\ No newline at end of file
diff --git a/data/view_dataset.py b/data/view_dataset.py
deleted file mode 100644
index 3405dee..0000000
--- a/data/view_dataset.py
+++ /dev/null
@@ -1,156 +0,0 @@
-import torch
-import torch.nn.functional as nn_f
-from typing import Dict, Tuple, Union
-from pathlib import Path
-
-from utils import img
-from utils import color
-from .dataset import Dataset
-
-
-class ViewDataset(Dataset):
- """
- Data loader for spherical view synthesis task
-
- Attributes
- --------
- data_dir ```str```: the directory of dataset\n
- view_file_pattern ```str```: the filename pattern of view images\n
- cam ```object```: camera intrinsic parameters\n
- view_centers ```Tensor(N, 3)```: centers of views\n
- view_rots ```Tensor(N, 3, 3)```: rotation matrices of views\n
- view_images ```Tensor(N, 3, H, W)```: images of views\n
- view_depths ```Tensor(N, H, W)```: depths of views\n
- """
-
- class Chunk(object):
-
- def __init__(self, id: int, dataset, chunk_data: Dict[str, torch.Tensor], *,
- color: int, **kwargs):
- """
- [summary]
-
- :param dataset `ViewDataset`: dataset object
- :param indices `Tensor(N)`: indices of views
- :param centers `Tensor(N, 3)`: centers of views
- """
- self.id = id
- self.dataset = dataset
- self.indices = chunk_data['indices']
- self.centers = chunk_data['centers']
- self.rots = chunk_data['rots']
- self.color = color
- self.n_views = self.indices.size(0)
- self.n_pixels_per_view = self.dataset.res[0] * self.dataset.res[1]
- self.colors = self.depths = self.bins = None
- self.colors_cpu = self.depths_cpu = self.bins_cpu = None
- self.loaded = False
-
- def release(self):
- self.colors = self.depths = self.bins = None
- self.loaded = False
-
- def load(self):
- #print("chunk load")
- try:
- if self.dataset.image_path and self.colors_cpu is None:
- images = color.cvt(img.load(self.dataset.image_path % i for i in self.indices),
- color.RGB, self.color)
- if self.dataset.res != list(images.shape[-2:]):
- images = nn_f.interpolate(images, self.dataset.res)
- self.colors_cpu = images.permute(0, 2, 3, 1).flatten(0, 2)
- if self.colors_cpu is not None:
- self.colors = self.colors_cpu.to(self.dataset.device, non_blocking=True)
-
- if self.dataset.depth_path and self.depths_cpu is None:
- depths = self.dataset._decode_depth_images(
- img.load(self.depth_path % i for i in self.indices))
- if self.dataset.res != list(depths.shape[-2:]):
- depths = nn_f.interpolate(depths, self.dataset.res)
- self.depths_cpu = depths.flatten(0, 2)
- if self.depths_cpu is not None:
- self.depths = self.depths_cpu.to(self.dataset.device, non_blocking=True)
-
- if self.dataset.bins_path and self.bins_cpu is None:
- bins = img.load([self.dataset.bins_path % i for i in self.indices])
- if self.dataset.res != list(bins.shape[-2:]):
- bins = nn_f.interpolate(bins, self.dataset.res)
- self.bins_cpu = bins.permute(0, 2, 3, 1).flatten(0, 2)
- if self.bins_cpu is not None:
- self.bins = self.bins_cpu.to(self.dataset.device, non_blocking=True)
-
- torch.cuda.current_stream(self.dataset.device).synchronize()
- self.loaded = True
- except Exception as ex:
- print(ex)
- exit(-1)
-
- def __len__(self):
- return self.n_views * self.n_pixels_per_view
-
- def __getitem__(self, idx):
- if not self.loaded:
- self.load()
- view_idx = idx // self.n_pixels_per_view
- pix_idx = idx % self.n_pixels_per_view
- global_idx = self.indices[view_idx] * self.n_pixels_per_view + pix_idx
- rays_o = self.centers[view_idx]
- rays_d = self.dataset.cam_rays[pix_idx][:, None] # (N, 1, 3)
- r = self.rots[view_idx].movedim(-1, -2) # (N, 3, 3)
- rays_d = torch.matmul(rays_d, r)[:, 0] # (N, 3)
- data = {
- 'idx': global_idx,
- 'rays_o': rays_o,
- 'rays_d': rays_d,
- 'level': self.dataset.level
- }
- if self.colors is not None:
- data['color'] = self.colors[idx]
- if self.depths is not None:
- data['depth'] = self.depths[idx]
- if self.bins is not None:
- data['bin'] = self.bins[idx]
- #data['view_idx'] = view_idx
- #data['pix_idx'] = pix_idx
- return data
-
- def __init__(self, desc: dict, desc_path: Path, *,
- load_images: bool = True,
- load_depths: bool = False,
- load_bins: bool = False,
- res: Tuple[int, int] = None,
- views_to_load: Union[range, torch.Tensor] = None,
- device: torch.device = None,
- **kwargs):
- """
- Initialize data loader for spherical view synthesis task
-
- The dataset description file is a JSON file with following fields:
-
- - view_file_pattern: string, the path pattern of view images
- - view_res: { "x", "y" }, the resolution of view images
- - cam: { "fx", "fy", "cx", "cy" }, the focal and center of camera (in normalized image space)
- - view_centers: [ [ x, y, z ], ... ], centers of views
- - view_rots: [ [ m00, m01, ..., m22 ], ... ], rotation matrices of views
-
- :param dataset_desc_path ```str```: path to the data description file
- :param load_images ```bool```: whether load view images and return in __getitem__()
- :param load_depths ```bool```: whether load depth images and return in __getitem__()
- :param c ```int```: color space to convert view images to
- :param calculate_rays ```bool```: whether calculate rays
- """
- super().__init__(desc, desc_path, res=res, views_to_load=views_to_load, device=device,
- load_images=load_images, load_depths=load_depths, load_bins=load_bins)
-
- def _decode_depth_images(self, input):
- disp_range = (1 / self.depth_range[0], 1 / self.depth_range[1])
- disp_val = (1 - input[..., 0, :, :]) * (disp_range[1] - disp_range[0]) + disp_range[0]
- return torch.reciprocal(disp_val)
-
- def _load_desc(self, res: Tuple[int, int], views_to_load: Union[range, torch.Tensor],
- load_images: bool, load_depths: bool, load_bins: bool):
- super()._load_desc(res, views_to_load)
- self.image_path = load_images and self._get_data_path("view")
- self.depth_path = load_depths and self._get_data_path("depth")
- self.bins_path = load_bins and self._get_data_path("bins")
- self.cam_rays = self.cam.get_local_rays(flatten=True)
diff --git a/fntest.py b/fntest.py
deleted file mode 100644
index 5f5a879..0000000
--- a/fntest.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from math import ceil
-
-cdf = [2.2, 3.5, 3.6, 3.7, 4.0]
-bins = []
-part = 1
-offset = 0
-for i in range(len(cdf)):
- if cdf[i] >= part:
- bins.append(i + 1 - offset)
- offset = i + 1
- part = int(cdf[i]) + 1
-print(bins)
\ No newline at end of file
diff --git a/fovea_net.png b/fovea_net.png
new file mode 100644
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diff --git a/fovea_params_optim.py b/fovea_params_optim.py
new file mode 100644
index 0000000..c38ee4a
--- /dev/null
+++ b/fovea_params_optim.py
@@ -0,0 +1,62 @@
+import itertools
+import torch
+from utils import math
+from tqdm import tqdm
+
+mar0 = 1. / 48.
+mar_slope = 0.0275
+weights = torch.tensor([1., .25, .25], device="cuda") # (L) Also define levels here
+
+# VR configuration
+res = (1440, 1600) # (hor, ver)
+fov = 110 # degrees
+distance = .5 * res[1] / math.tan(.5 * math.radians(fov))
+ratio = res[0] / res[1] # hor / ver
+
+K = 360. / math.pi / distance
+L = len(weights)
+
+min_sum = math.inf
+x_of_min_sum = None
+e_of_min_sum = None
+s_of_min_sum = None
+D_of_min_sum = None
+
+for x1 in tqdm(itertools.product(*([range(1, res[0] - 2)] * (L - 3))),
+ total=int(math.pow(res[0] - 1, L - 3))):
+ if any([x1[i] <= x1[i - 1] for i in range(1, len(x1))]):
+ continue
+ if not x1:
+ x2 = torch.stack(torch.meshgrid(
+ [torch.arange(1, res[0], device="cuda")] * 2), -1).flatten(0, 1)
+ x = x2[(x2[:, 1:] > x2[:, :-1]).any(-1)]
+ else:
+ x2 = torch.stack(torch.meshgrid(
+ [torch.arange(x1[-1] + 1, res[0], device="cuda")] * 2), -1).flatten(0, 1)
+ x = torch.cat([
+ torch.tensor([x1], device="cuda").expand(x2.shape[0], -1),
+ x2[(x2[:, 1:] <= x2[:, :-1]).any(-1)]
+ ], -1)
+ tan_e = x / distance # (N, L - 1)
+ e = tan_e.arctan().rad2deg() # (N, L - 1)
+ mar = mar0 + mar_slope * e # (N, L - 1)
+ s = torch.cat([e.new_ones(e.shape[0], 1), mar * (1. + tan_e.pow(2.)) / K], -1) # (N, L)
+ D = torch.cat([x * 2. / s[:, :-1], res[1] / s[:, -1:]], -1) # (N, L)
+ P = D * D
+ P[:, -1] *= ratio
+ weighted_sum = (P * weights).sum(-1)
+ min_value, min_indice = weighted_sum.min(0)
+ min_value = min_value.item()
+ min_indice = min_indice.item()
+ if min_value < min_sum:
+ min_sum = min_value
+ x_of_min_sum = x[min_indice]
+ e_of_min_sum = e[min_indice]
+ s_of_min_sum = s[min_indice]
+ D_of_min_sum = D[min_indice]
+
+print(min_sum)
+print("x:", x_of_min_sum)
+print("e:", e_of_min_sum)
+print("s:", s_of_min_sum)
+print("D:", D_of_min_sum)
diff --git a/gt.png b/gt.png
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literal 0
HcmV?d00001
diff --git a/key_test.py b/key_test.py
deleted file mode 100644
index f9ee9fa..0000000
--- a/key_test.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import sys
-import tty
-import termios
-import select
-import time
-
-
-def readchar():
- r, w, e = select.select([sys.stdin], [], [])
- if sys.stdin in r:
- ch = sys.stdin.read(1)
- return ch
-
-
-fd = sys.stdin.fileno()
-oldtty = termios.tcgetattr(fd)
-newtty = termios.tcgetattr(fd)
-try:
- termios.tcsetattr(fd, termios.TCSANOW, newtty)
- tty.setraw(fd)
- tty.setcbreak(fd)
- while True:
- print('Wait')
- time.sleep(0.1)
- key = readchar()
- print('%d' % ord(key))
- if key == 'w':
- print('w')
- if key == 'q':
- break
-finally:
- termios.tcsetattr(fd, termios.TCSADRAIN, oldtty)
\ No newline at end of file
diff --git a/model/__common__.py b/model/__common__.py
index 547233d..d92baf6 100644
--- a/model/__common__.py
+++ b/model/__common__.py
@@ -1,8 +1,5 @@
-import torch
-from torch import Tensor
-from typing import List, Dict, Union, Optional, Any
-
+from configargparse import ArgumentParser
from modules import *
-from utils.type import InputData, NetInput, NetOutput, ReturnData
-from utils.perf import perf
-from utils.samples import Samples
+from utils import math, config, nn
+from utils.types import *
+from utils.profile import profile
\ No newline at end of file
diff --git a/model/__init__.py b/model/__init__.py
index 129aea3..3b97052 100644
--- a/model/__init__.py
+++ b/model/__init__.py
@@ -1,15 +1,15 @@
-import importlib
-import os
-from .utils import *
-
-
-# Automatically import model files this directory
-package_dir = os.path.dirname(__file__)
-package = os.path.basename(package_dir)
-for file in os.listdir(package_dir):
- path = os.path.join(package_dir, file)
- if file.startswith('_') or file.startswith('.') or file == "utils.py":
- continue
- if file.endswith('.py') or os.path.isdir(path):
- model_name = file[:-3] if file.endswith('.py') else file
- importlib.import_module(f'{package}.{model_name}')
+import sys
+from inspect import isclass
+
+from .model import Model, model_classes
+from .nerf import NeRF
+from .fs_nerf import FsNeRF
+
+__all__ = ["Model", "NeRF", "FsNeRF"]
+
+
+# Register all model classes
+for item in __all__:
+ var = getattr(sys.modules[__name__], item)
+ if isclass(var) and issubclass(var, Model):
+ model_classes[item] = var
\ No newline at end of file
diff --git a/model/__old/__common__.py b/model/__old/__common__.py
new file mode 100644
index 0000000..c47fa20
--- /dev/null
+++ b/model/__old/__common__.py
@@ -0,0 +1,6 @@
+import torch
+from torch import Tensor
+
+from modules import *
+from utils.type import *
+from utils.profile import profile
\ No newline at end of file
diff --git a/model/__old/__init__.py b/model/__old/__init__.py
new file mode 100644
index 0000000..129aea3
--- /dev/null
+++ b/model/__old/__init__.py
@@ -0,0 +1,15 @@
+import importlib
+import os
+from .utils import *
+
+
+# Automatically import model files this directory
+package_dir = os.path.dirname(__file__)
+package = os.path.basename(package_dir)
+for file in os.listdir(package_dir):
+ path = os.path.join(package_dir, file)
+ if file.startswith('_') or file.startswith('.') or file == "utils.py":
+ continue
+ if file.endswith('.py') or os.path.isdir(path):
+ model_name = file[:-3] if file.endswith('.py') else file
+ importlib.import_module(f'{package}.{model_name}')
diff --git a/model/__old/bg_net.py b/model/__old/__old/bg_net.py
similarity index 100%
rename from model/__old/bg_net.py
rename to model/__old/__old/bg_net.py
diff --git a/model/__old/nerf_depth.py b/model/__old/__old/nerf_depth.py
similarity index 100%
rename from model/__old/nerf_depth.py
rename to model/__old/__old/nerf_depth.py
diff --git a/model/__old/oracle.py b/model/__old/__old/oracle.py
similarity index 100%
rename from model/__old/oracle.py
rename to model/__old/__old/oracle.py
diff --git a/model/base.py b/model/__old/base.py
similarity index 85%
rename from model/base.py
rename to model/__old/base.py
index cbb0464..dcff495 100644
--- a/model/base.py
+++ b/model/__old/base.py
@@ -1,13 +1,10 @@
import json
-from typing import Optional
from torch import Tensor
from utils import color
-from utils.misc import print_and_log
-from utils.samples import Samples
-from utils.module import Module
-from utils.type import NetInput, NetOutput, InputData, ReturnData
-from utils.perf import perf
+from utils.nn import Module
+from utils.types import *
+from utils.profile import profile
model_classes = {}
@@ -39,11 +36,10 @@ class BaseModel(Module, metaclass=BaseModelMeta):
self._preprocess_args()
self._init_chns()
- def chns(self, name: str, value: int = None) -> Optional[int]:
+ def chns(self, name: str, value: int = None) -> int:
if value is not None:
self._chns[name] = value
- else:
- return self._chns.get(name, 1)
+ return self._chns.get(name, 1)
def input(self, samples: Samples, *whats: str) -> NetInput:
all = ["x", "d", "f"]
@@ -65,7 +61,7 @@ class BaseModel(Module, metaclass=BaseModelMeta):
"""
raise NotImplementedError()
- @perf
+ @profile
def forward(self, data: InputData, *outputs: str, **extra_args) -> ReturnData:
"""
Perform rendering for given rays.
@@ -82,7 +78,7 @@ class BaseModel(Module, metaclass=BaseModelMeta):
return ret
def print_config(self):
- print_and_log(json.dumps(self.args))
+ return json.dumps(self.args)
def _preprocess_args(self):
pass
@@ -93,7 +89,7 @@ class BaseModel(Module, metaclass=BaseModelMeta):
self._chns["color"] = color.chns(self.color)
self._chns.update(chns)
- def _input(self, samples: Samples, what: str) -> Optional[Tensor]:
+ def _input(self, samples: Samples, what: str) -> Tensor | None:
raise NotImplementedError()
def _sample(self, data: InputData, **extra_args) -> Samples:
diff --git a/model/cnerf.py b/model/__old/cnerf.py
similarity index 91%
rename from model/cnerf.py
rename to model/__old/cnerf.py
index 2560838..553ff5b 100644
--- a/model/cnerf.py
+++ b/model/__old/cnerf.py
@@ -4,7 +4,7 @@ from .base import BaseModel
from typing import Callable
from .nerf import NeRF
-from utils.voxels import trilinear_interp
+from utils.voxels import linear_interp
class CNeRF(BaseModel):
@@ -19,17 +19,17 @@ class CNeRF(BaseModel):
self.corner_indices, self.corners = space.get_corners(vidxs)
self.feats_on_corners = feats_fn(self.corners)
- @perf
+ @profile
def interp(self, samples: Samples) -> Tensor:
- with perf("Prepare for coarse interpolation"):
+ with profile("Prepare for coarse interpolation"):
voxels = self.space.voxels[samples.interp_vidxs]
cidxs = self.corner_indices[samples.interp_vidxs] # (N, 8)
feats_on_corners = self.feats_on_corners[cidxs] # (N, 8, X)
# (N, 3) normed-coords in voxel
p = (samples.pts - voxels) / self.space.voxel_size + .5
- with perf("Interpolate features"):
- return trilinear_interp(p, feats_on_corners)
+ with profile("Interpolate features"):
+ return linear_interp(p, feats_on_corners)
@property
def stage(self):
@@ -62,21 +62,22 @@ class CNeRF(BaseModel):
self.model(stage).space = self.model(stage - 1).space.clone()
self.args0["stage"] = stage
- @perf
+ @profile
def infer(self, *outputs: str, samples: Samples, inputs: NetInput = None, **kwargs) -> NetOutput:
inputs = inputs or self.input(samples)
return self.model(samples.level).infer(*outputs, samples=samples, inputs=inputs, **kwargs)
def print_config(self):
+ s = f"{len(self.sub_models)} levels:\n"
for i, model in enumerate(self.sub_models):
- print(f"Model {i} =====>")
- model.print_config()
+ s += f"Model {i}: {model.print_config()}\n"
+ return s
@torch.no_grad()
def split(self):
return self.model(self.stage).split()
- def _input(self, samples: Samples, what: str) -> Optional[Tensor]:
+ def _input(self, samples: Samples, what: str) -> Tensor | None:
if what == "f":
if samples.level == 0:
return None
@@ -86,7 +87,7 @@ class CNeRF(BaseModel):
else:
return self.model(samples.level)._input(samples, what)
- @perf
+ @profile
def _sample(self, data: InputData, **extra_args) -> Samples:
samples: Samples = self.model(data["level"])._sample(data, **extra_args)
samples.level = data["level"]
@@ -95,7 +96,7 @@ class CNeRF(BaseModel):
# samples.voxel_indices, data["rays_d"])
return samples
- @perf
+ @profile
def _render(self, samples: Samples, *outputs: str, **extra_args) -> ReturnData:
self._prepare_interp(samples, on_coarse=self.args.get("interp_on_coarse"))
return self.model(samples.level).renderer(self, samples, *outputs, **{
diff --git a/model/mnerf.py b/model/__old/mnerf.py
similarity index 98%
rename from model/mnerf.py
rename to model/__old/mnerf.py
index ab9e4a2..e1b964a 100644
--- a/model/mnerf.py
+++ b/model/__old/mnerf.py
@@ -22,7 +22,7 @@ class MNeRF(NeRF):
in_chns = self.x_encoder.out_dim + core_params['nf']
return MultiNerf(nets)
- @perf
+ @profile
def _sample(self, data: InputData, **extra_args) -> Samples:
samples = super()._sample(data, **extra_args)
samples.level = data["level"]
diff --git a/model/mnerf_advance.py b/model/__old/mnerf_advance.py
similarity index 90%
rename from model/mnerf_advance.py
rename to model/__old/mnerf_advance.py
index de55b95..c418833 100644
--- a/model/mnerf_advance.py
+++ b/model/__old/mnerf_advance.py
@@ -1,8 +1,6 @@
from .__common__ import *
from .mnerf import MNeRF
-from utils.misc import merge
-
class MNeRFAdvance(MNeRF):
"""
@@ -19,7 +17,7 @@ class MNeRFAdvance(MNeRF):
return super().split()
def _sample(self, data: InputData, **extra_args) -> Samples:
- return super()._sample(data, **merge(extra_args, n_samples=self.n_samples(data["level"]) + 1))
+ return super()._sample(data, **(extra_args | {"n_samples": self.n_samples(data["level"]) + 1}))
def _render(self, samples: Samples, *outputs: str, **extra_args) -> ReturnData:
L = samples.level
diff --git a/model/__old/nerf.py b/model/__old/nerf.py
new file mode 100644
index 0000000..68a5d3a
--- /dev/null
+++ b/model/__old/nerf.py
@@ -0,0 +1,212 @@
+from pathlib import Path
+from operator import itemgetter
+
+from modules.input_encoder import FreqEncoder
+from .__common__ import *
+from .base import BaseModel
+from utils import math
+from utils.misc import masked_scatter
+
+
+class NeRF(BaseModel):
+
+ TrainerClass = "TrainWithSpace"
+ SamplerClass = None
+ RendererClass = None
+
+ space: Space | Voxels | Octree
+
+ @property
+ def multi_nets(self) -> int:
+ return self.args.get("multi_nets", 1)
+
+ def __init__(self, args0: dict, args1: dict = None):
+ """
+ Initialize a NeRF model
+
+ :param args0 `dict`: basic arguments
+ :param args1 `dict`: extra arguments, defaults to {}
+ """
+ super().__init__(args0, args1)
+
+ # Initialize components
+
+ self._init_space()
+ self._init_encoders()
+ self._init_core()
+ self._init_sampler()
+ self._init_renderer()
+
+ @profile
+ def infer(self, *outputs: str, samples: Samples, inputs: NetInput = None, **kwargs) -> NetOutput:
+ inputs = inputs or self.input(samples)
+ if len(self.cores) == 1:
+ return self.cores[0](inputs, *outputs, samples=samples, **kwargs)
+ return self._multi_infer(inputs, *outputs, samples=samples, **kwargs)
+
+ @torch.no_grad()
+ def split(self):
+ ret = self.space.split()
+ if 'n_samples' in self.args0:
+ self.args0['n_samples'] *= 2
+ if 'voxel_size' in self.args0:
+ self.args0['voxel_size'] /= 2
+ if "sample_step_ratio" in self.args0:
+ self.args1["sample_step"] = self.args0["voxel_size"] \
+ * self.args0["sample_step_ratio"]
+ if 'sample_step' in self.args0:
+ self.args0['sample_step'] /= 2
+ return ret
+
+ def export_onnx(self, path: str | Path, batch_size: int = None):
+ self.cores[0].get_exporter().export_onnx(path / "core_0.onnx", batch_size)
+
+ def _preprocess_args(self):
+ if "sample_step_ratio" in self.args0:
+ self.args1["sample_step"] = self.args0["voxel_size"] * self.args0["sample_step_ratio"]
+ if self.args0.get("spherical"):
+ sample_range = [
+ 1 / self.args0['depth_range'][0],
+ 1 / self.args0['depth_range'][1]
+ ] if 'depth_range' in self.args0 else [1, 0]
+ rot_range = [[-180, -90], [180, 90]]
+ self.args1['bbox'] = [
+ [sample_range[0], math.radians(rot_range[0][0]), math.radians(rot_range[0][1])],
+ [sample_range[1], math.radians(rot_range[1][0]), math.radians(rot_range[1][1])]
+ ]
+ self.args1['sample_range'] = sample_range
+ if not self.args.get("multi_nets"):
+ if not self.args.get("net_bounds"):
+ self.register_temp("net_bounds", None)
+ self.args1["multi_nets"] = 1
+ else:
+ self.register_temp("net_bounds", torch.tensor(self.args["net_bounds"]))
+ self.args1["multi_nets"] = self.net_bounds.size(0)
+
+ def _init_chns(self, **chns):
+ super()._init_chns(**{
+ "x": self.args.get('n_featdim') or 3,
+ "d": 3 if self.args.get('encode_d') else 0,
+ **chns
+ })
+
+ def _init_space(self):
+ self.space = Space.create(self.args)
+ if self.args.get('n_featdim'):
+ self.space.create_embedding(self.args['n_featdim'])
+
+ def _init_encoders(self):
+ if isinstance(self.args["encode_x"], list):
+ self.x_encoder = InputEncoder.create(self.chns("x"), *self.args["encode_x"])
+ else:
+ self.x_encoder = FreqEncoder(self.chns("x"), self.args['encode_x'], cat_input=True)
+ if self.args.get("encode_d"):
+ if isinstance(self.args["encode_d"], list):
+ self.d_encoder = InputEncoder.create(self.chns("d"), *self.args["encode_d"])
+ else:
+ self.d_encoder = FreqEncoder(self.chns("d"), self.args['encode_d'], angular=True)
+ else:
+ self.d_encoder = None
+
+ def _init_core(self):
+ self.cores = self.create_multiple(self._create_core_unit, self.args.get("multi_nets", 1))
+
+ def _init_sampler(self):
+ if self.SamplerClass is None:
+ SamplerClass = Sampler
+ else:
+ SamplerClass = self.SamplerClass
+ self.sampler = SamplerClass(**self.args)
+
+ def _init_renderer(self):
+ if self.RendererClass is None:
+ if self.args.get("core") == "nerfadv":
+ RendererClass = DensityFirstVolumnRenderer
+ else:
+ RendererClass = VolumnRenderer
+ else:
+ RendererClass = self.RendererClass
+ self.renderer = RendererClass(**self.args)
+
+ def _create_core_unit(self, core_params: dict = None, **args):
+ core_params = core_params or self.args["core_params"]
+ if self.args.get("core") == "nerfadv":
+ return NerfAdvCore(**{
+ "x_chns": self.x_encoder.out_dim,
+ "d_chns": self.d_encoder.out_dim,
+ "density_chns": self.chns('density'),
+ "color_chns": self.chns('color'),
+ **core_params,
+ **args
+ })
+ else:
+ return NerfCore(**{
+ "x_chns": self.x_encoder.out_dim,
+ "density_chns": self.chns('density'),
+ "color_chns": self.chns('color'),
+ "d_chns": self.d_encoder.out_dim if self.d_encoder else 0,
+ **core_params,
+ **args
+ })
+
+ @profile
+ def _sample(self, data: InputData, **extra_args) -> Samples:
+ return self.sampler(*itemgetter("rays_o", "rays_d")(data), self.space,
+ **self.args | extra_args)
+
+ @profile
+ def _render(self, samples: Samples, *outputs: str, **extra_args) -> ReturnData:
+ if len(samples.size) == 1:
+ return self.infer(*outputs, samples=samples)
+ return self.renderer(self, samples, *outputs, **self.args | extra_args)
+
+ def _input(self, samples: Samples, what: str) -> torch.Tensor | None:
+ if what == "x":
+ if self.args.get('n_featdim'):
+ return self._encode("emb", self.space.extract_embedding(
+ samples.pts, samples.voxel_indices))
+ else:
+ return self._encode("x", samples.pts)
+ elif what == "d":
+ if self.d_encoder and samples.dirs is not None:
+ return self._encode("d", samples.dirs)
+ else:
+ return None
+ elif what == "f":
+ return None
+ else:
+ ValueError(f"Don't know how to process input \"{what}\"")
+
+ def _encode(self, what: str, val: torch.Tensor) -> torch.Tensor:
+ if what == "x":
+ # Normalize x according to the encoder's range requirement using space's bounding box
+ if self.space.bbox is not None:
+ val = (val - self.space.bbox[0]) / (self.space.bbox[1] - self.space.bbox[0])
+ val = val * (self.x_encoder.in_range[1] - self.x_encoder.in_range[0])\
+ + self.x_encoder.in_range[0]
+ return self.x_encoder(val)
+ elif what == "emb":
+ return self.x_encoder(val)
+ elif what == "d":
+ return self.d_encoder(val)
+ else:
+ ValueError(f"Don't know how to encode \"{what}\"")
+
+ @profile
+ def _multi_infer(self, inputs: NetInput, *outputs: str, samples: Samples, **kwargs) -> NetOutput:
+ ret: NetOutput = {}
+ for i, core in enumerate(self.cores):
+ selector = (samples.pts >= self.net_bounds[i, 0]
+ and samples.pts < self.net_bounds[i, 1]).all(-1)
+ partial_ret: NetOutput = core(inputs[selector], *outputs, samples=samples[selector],
+ **kwargs)
+ for key, value in partial_ret.items():
+ if key not in ret:
+ ret[key] = value.new_zeros(*inputs.shape, value.shape[-1])
+ ret[key] = masked_scatter(selector, value, ret[key])
+ return ret
+
+
+class NSVF(NeRF):
+
+ SamplerClass = VoxelSampler
diff --git a/model/snerf_fast.py b/model/__old/snerf_fast.py
similarity index 66%
rename from model/snerf_fast.py
rename to model/__old/snerf_fast.py
index d343789..7293fe1 100644
--- a/model/snerf_fast.py
+++ b/model/__old/snerf_fast.py
@@ -1,17 +1,14 @@
from .__common__ import *
from .nerf import NeRF
-from utils.misc import merge
-
class SnerfFast(NeRF):
def infer(self, *outputs: str, samples: Samples, inputs: NetInput = None, chunk_id: int, **kwargs) -> NetOutput:
inputs = inputs or self.input(samples)
- ret = self.cores[chunk_id](inputs, *outputs)
return {
- key: value.reshape(*inputs.shape, -1)
- for key, value in ret.items()
+ key: value.reshape(*samples.size, -1)
+ for key, value in self.cores[chunk_id](inputs, *outputs).items()
}
def _preprocess_args(self):
@@ -28,7 +25,7 @@ class SnerfFast(NeRF):
density_chns=self.chns('density') * self.samples_per_part,
color_chns=self.chns('color') * self.samples_per_part)
- def _input(self, samples: Samples, what: str) -> Optional[torch.Tensor]:
+ def _input(self, samples: Samples, what: str) -> torch.Tensor | None:
if what == "x":
return self._encode("x", samples.pts[..., -self.chns("x"):]).flatten(1, 2)
elif what == "d":
@@ -37,17 +34,25 @@ class SnerfFast(NeRF):
else:
return super()._input(samples, what)
+ def _encode(self, what: str, val: torch.Tensor) -> torch.Tensor:
+ if what == "x":
+ # Normalize x according to the encoder's range requirement using space's bounding box
+ bbox = self.space.bbox[:, -self.chns("x"):]
+ val = (val - bbox[0]) / (bbox[1] - bbox[0])
+ val = val * (self.x_encoder.in_range[1] - self.x_encoder.in_range[0])\
+ + self.x_encoder.in_range[0]
+ return self.x_encoder(val)
+ return super()._encode(what, val)
+
def _render(self, samples: Samples, *outputs: str, **extra_args) -> ReturnData:
- return self._render(samples, *outputs,
- **merge(extra_args,
- raymarching_chunk_size_or_sections=[self.samples_per_part]))
+ return super()._render(samples, *outputs,
+ **extra_args |
+ {"raymarching_chunk_size_or_sections", [self.samples_per_part]})
- def _multi_infer(self, inputs: NetInput, *outputs: str, samples: Samples, chunk_id: int, **kwargs) -> NetOutput:
- ret = self.cores[chunk_id](inputs, *outputs)
- return {
- key: value.reshape(*samples.size, -1)
- for key, value in ret.items()
- }
+ def _sample(self, data: InputData, **extra_args) -> Samples:
+ samples = super()._sample(data, **extra_args)
+ samples.voxel_indices = 0
+ return samples
class SnerfFastExport(torch.nn.Module):
diff --git a/model/snerf_x.py b/model/__old/snerf_x.py
similarity index 84%
rename from model/snerf_x.py
rename to model/__old/snerf_x.py
index e4f7692..9df2234 100644
--- a/model/snerf_x.py
+++ b/model/__old/snerf_x.py
@@ -2,7 +2,6 @@ from .__common__ import *
from .nerf import NeRF
from .utils import load
-from utils.misc import merge
class SNeRFX(NeRF):
@@ -23,12 +22,12 @@ class SNeRFX(NeRF):
self.args0['net_samples'] = [val * 2 for val in self.args0['net_samples']]
return ret
- @perf
+ @profile
def _render(self, samples: Samples, *outputs: str, **extra_args) -> ReturnData:
return super()._render(samples, *outputs,
- **merge(extra_args,
- raymarching_chunk_size_or_sections=self.args["net_samples"]))
+ **extra_args |
+ {"raymarching_chunk_size_or_sections": self.args["net_samples"]})
- @perf
+ @profile
def _multi_infer(self, inputs: NetInput, *outputs: str, chunk_id: int, **kwargs) -> NetOutput:
return self.cores[chunk_id](inputs, *outputs, **kwargs)
diff --git a/model/utils.py b/model/__old/utils.py
similarity index 86%
rename from model/utils.py
rename to model/__old/utils.py
index 964b541..1f0a74a 100644
--- a/model/utils.py
+++ b/model/__old/utils.py
@@ -1,10 +1,9 @@
from pathlib import Path
-from typing import Optional, Union
from .base import model_classes, BaseModel
from utils import netio
-def get_class(model_class_name: str) -> Optional[type]:
+def get_class(model_class_name: str) -> type | None:
return model_classes.get(model_class_name)
@@ -31,5 +30,5 @@ def serialize(model: BaseModel) -> dict:
}
-def load(path: Union[str, Path]) -> BaseModel:
+def load(path: str | Path) -> BaseModel:
return deserialize(netio.load_checkpoint(path)[0])
diff --git a/model/vnerf.py b/model/__old/vnerf.py
similarity index 89%
rename from model/vnerf.py
rename to model/__old/vnerf.py
index 8c30c84..c643683 100644
--- a/model/vnerf.py
+++ b/model/__old/vnerf.py
@@ -14,7 +14,7 @@ class VNeRF(NeRF):
def _create_core_unit(self):
return super()._create_core_unit(x_chns=self.x_encoder.out_dim + self.args['n_featdim'])
- def _input(self, samples: Samples, what: str) -> Optional[torch.Tensor]:
+ def _input(self, samples: Samples, what: str) -> torch.Tensor | None:
if what == "x":
return torch.cat([
self.space.extract_voxel_embedding(samples.voxel_indices),
diff --git a/model/__todo/cnerf.py b/model/__todo/cnerf.py
new file mode 100644
index 0000000..553ff5b
--- /dev/null
+++ b/model/__todo/cnerf.py
@@ -0,0 +1,129 @@
+from utils.misc import dump_tensors_to_csv
+from .__common__ import *
+from .base import BaseModel
+from typing import Callable
+
+from .nerf import NeRF
+from utils.voxels import linear_interp
+
+
+class CNeRF(BaseModel):
+
+ TrainerClass = "TrainMultiScale"
+
+ class InterpSpace(object):
+
+ def __init__(self, space: Voxels, vidxs: Tensor, feats_fn: Callable[[Any], Tensor]) -> None:
+ super().__init__()
+ self.space = space
+ self.corner_indices, self.corners = space.get_corners(vidxs)
+ self.feats_on_corners = feats_fn(self.corners)
+
+ @profile
+ def interp(self, samples: Samples) -> Tensor:
+ with profile("Prepare for coarse interpolation"):
+ voxels = self.space.voxels[samples.interp_vidxs]
+ cidxs = self.corner_indices[samples.interp_vidxs] # (N, 8)
+ feats_on_corners = self.feats_on_corners[cidxs] # (N, 8, X)
+ # (N, 3) normed-coords in voxel
+ p = (samples.pts - voxels) / self.space.voxel_size + .5
+
+ with profile("Interpolate features"):
+ return linear_interp(p, feats_on_corners)
+
+ @property
+ def stage(self):
+ return self.args.get("stage", 0)
+
+ def __init__(self, args0: dict, args1: dict = None):
+ super().__init__(args0, args1)
+ self.sub_models = []
+ args0_for_submodel = {
+ key: value for key, value in args0.items()
+ if key != "sub_models" and key != "interp_on_coarse"
+ }
+ for i in range(len(self.args["sub_models"])):
+ self.args["sub_models"][i] = {
+ **args0_for_submodel,
+ **self.args["sub_models"][i]
+ }
+ self.sub_models.append(NeRF(self.args["sub_models"][i], args1))
+ self.sub_models = torch.nn.ModuleList(self.sub_models)
+ for i in range(self.stage):
+ print(f"__init__: freeze model {i}")
+ self.model(i).freeze()
+
+ def model(self, level: int) -> NeRF:
+ return self.sub_models[level]
+
+ def trigger_stage(self, stage: int):
+ print(f"trigger_stage: freeze model {stage - 1}")
+ self.model(stage - 1).freeze()
+ self.model(stage).space = self.model(stage - 1).space.clone()
+ self.args0["stage"] = stage
+
+ @profile
+ def infer(self, *outputs: str, samples: Samples, inputs: NetInput = None, **kwargs) -> NetOutput:
+ inputs = inputs or self.input(samples)
+ return self.model(samples.level).infer(*outputs, samples=samples, inputs=inputs, **kwargs)
+
+ def print_config(self):
+ s = f"{len(self.sub_models)} levels:\n"
+ for i, model in enumerate(self.sub_models):
+ s += f"Model {i}: {model.print_config()}\n"
+ return s
+
+ @torch.no_grad()
+ def split(self):
+ return self.model(self.stage).split()
+
+ def _input(self, samples: Samples, what: str) -> Tensor | None:
+ if what == "f":
+ if samples.level == 0:
+ return None
+ if samples.interp_space is None:
+ return self._infer_features(pts=samples.pts, level=samples.level - 1)
+ return samples.interp_space.interp(samples)
+ else:
+ return self.model(samples.level)._input(samples, what)
+
+ @profile
+ def _sample(self, data: InputData, **extra_args) -> Samples:
+ samples: Samples = self.model(data["level"])._sample(data, **extra_args)
+ samples.level = data["level"]
+ # TODO remove below
+ #dump_tensors_to_csv(f"/home/dengnc/dvs/data/classroom/_nets/ms_train_t0.8/_cnerf_ioc/{'train' if self.training else 'test'}.csv",
+ # samples.voxel_indices, data["rays_d"])
+ return samples
+
+ @profile
+ def _render(self, samples: Samples, *outputs: str, **extra_args) -> ReturnData:
+ self._prepare_interp(samples, on_coarse=self.args.get("interp_on_coarse"))
+ return self.model(samples.level).renderer(self, samples, *outputs, **{
+ **self.model(samples.level).args, **extra_args})
+
+ def _infer_features(self, samples: Samples = None, **sample_data) -> NetOutput:
+ samples = samples or Samples(**sample_data)
+ if self.args.get("interp_on_coarse"):
+ self._prepare_interp(samples, on_coarse=True)
+ inputs = self.input(samples, "x", "f")
+ return self.infer("features", samples=samples, inputs=inputs)["features"]
+
+ def _prepare_interp(self, samples: Samples, on_coarse: bool):
+ if samples.level == 0:
+ return
+ if on_coarse:
+ interp_space = self.model(samples.level - 1).space
+ samples.interp_vidxs = interp_space.get_voxel_indices(samples.pts)
+ else:
+ interp_space = self.model(samples.level).space
+ samples.interp_vidxs = samples.voxel_indices
+ samples.interp_space = CNeRF.InterpSpace(interp_space, samples.interp_vidxs,
+ lambda corners: self._infer_features(
+ pts=corners, level=samples.level - 1))
+
+ def _after_load_state_dict(self) -> None:
+ a: torch.Tensor = None
+ return
+ print(list(self.model(0).named_parameters())[2])
+ exit()
diff --git a/model/__todo/mnerf.py b/model/__todo/mnerf.py
new file mode 100644
index 0000000..e1b964a
--- /dev/null
+++ b/model/__todo/mnerf.py
@@ -0,0 +1,29 @@
+import torch
+from .__common__ import *
+from .nerf import NeRF
+
+
+class MNeRF(NeRF):
+ """
+ Multi-scale NeRF
+ """
+
+ TrainerClass = "TrainMultiScale"
+
+ def freeze(self, level: int):
+ for core in self.cores:
+ core.set_frozen(level, True)
+
+ def _create_core_unit(self):
+ nets = []
+ in_chns = self.x_encoder.out_dim
+ for core_params in self.args['core_params']:
+ nets.append(super()._create_core_unit(core_params, x_chns=in_chns))
+ in_chns = self.x_encoder.out_dim + core_params['nf']
+ return MultiNerf(nets)
+
+ @profile
+ def _sample(self, data: InputData, **extra_args) -> Samples:
+ samples = super()._sample(data, **extra_args)
+ samples.level = data["level"]
+ return samples
diff --git a/model/__todo/mnerf_advance.py b/model/__todo/mnerf_advance.py
new file mode 100644
index 0000000..c418833
--- /dev/null
+++ b/model/__todo/mnerf_advance.py
@@ -0,0 +1,36 @@
+from .__common__ import *
+from .mnerf import MNeRF
+
+
+class MNeRFAdvance(MNeRF):
+ """
+ Advanced Multi-scale NeRF
+ """
+
+ TrainerClass = "TrainMultiScale"
+
+ def n_samples(self, level: int = -1) -> int:
+ return self.args["n_samples_list"][level]
+
+ def split(self):
+ self.args0["n_samples_list"] = [val * 2 for val in self.args["n_samples_list"]]
+ return super().split()
+
+ def _sample(self, data: InputData, **extra_args) -> Samples:
+ return super()._sample(data, **(extra_args | {"n_samples": self.n_samples(data["level"]) + 1}))
+
+ def _render(self, samples: Samples, *outputs: str, **extra_args) -> ReturnData:
+ L = samples.level
+ steps = self.args["n_samples_list"][L] // self.args["n_samples_list"][0]
+ curr_samples = samples[:, ::steps]
+ curr_samples.level = 0
+ curr_samples.features = None
+ for i in range(L):
+ render_out = super()._render(curr_samples, 'features', **extra_args)
+ next_steps = self.args["n_samples_list"][L] // self.args["n_samples_list"][i + 1]
+ next_samples = samples[:, ::next_steps]
+ features = curr_samples.interpolate(next_samples, render_out['features'])
+ curr_samples = next_samples
+ curr_samples.level = i + 1
+ curr_samples.features = features
+ return super()._render(curr_samples, *outputs, **extra_args)
diff --git a/model/__todo/snerf_x.py b/model/__todo/snerf_x.py
new file mode 100644
index 0000000..9df2234
--- /dev/null
+++ b/model/__todo/snerf_x.py
@@ -0,0 +1,33 @@
+from .__common__ import *
+from .nerf import NeRF
+
+from .utils import load
+
+
+class SNeRFX(NeRF):
+
+ def _preprocess_args(self):
+ self.args0["spherical"] = True
+ super()._preprocess_args()
+ if "net_samples" not in self.args:
+ n_nets = self.args.get("multi_nets", 1)
+ cut_by_space = load(self.args['cut_by']).space if "cut_by" in self.args else self.space
+ k = self.args["n_samples"] // cut_by_space.steps[0].item()
+ self.args0["net_samples"] = [val * k for val in cut_by_space.balance_cut(0, n_nets)]
+ self.args1["multi_nets"] = len(self.args["net_samples"])
+
+ @torch.no_grad()
+ def split(self):
+ ret = super().split()
+ self.args0['net_samples'] = [val * 2 for val in self.args0['net_samples']]
+ return ret
+
+ @profile
+ def _render(self, samples: Samples, *outputs: str, **extra_args) -> ReturnData:
+ return super()._render(samples, *outputs,
+ **extra_args |
+ {"raymarching_chunk_size_or_sections": self.args["net_samples"]})
+
+ @profile
+ def _multi_infer(self, inputs: NetInput, *outputs: str, chunk_id: int, **kwargs) -> NetOutput:
+ return self.cores[chunk_id](inputs, *outputs, **kwargs)
diff --git a/model/__todo/vnerf.py b/model/__todo/vnerf.py
new file mode 100644
index 0000000..c643683
--- /dev/null
+++ b/model/__todo/vnerf.py
@@ -0,0 +1,24 @@
+from .__common__ import *
+from .nerf import NeRF
+
+
+class VNeRF(NeRF):
+
+ def _init_chns(self):
+ super()._init_chns(x=3)
+
+ def _init_space(self):
+ self.space = Space.create(self.args)
+ self.space.create_voxel_embedding(self.args['n_featdim'])
+
+ def _create_core_unit(self):
+ return super()._create_core_unit(x_chns=self.x_encoder.out_dim + self.args['n_featdim'])
+
+ def _input(self, samples: Samples, what: str) -> torch.Tensor | None:
+ if what == "x":
+ return torch.cat([
+ self.space.extract_voxel_embedding(samples.voxel_indices),
+ self._encode("x", samples.pts)
+ ], dim=-1)
+ else:
+ return super()._input(samples, what)
diff --git a/model/fs_nerf.py b/model/fs_nerf.py
new file mode 100644
index 0000000..0f2dd8d
--- /dev/null
+++ b/model/fs_nerf.py
@@ -0,0 +1,76 @@
+from .__common__ import *
+from .model import Model
+
+
+class FsNeRF(Model):
+
+ class Args(Model.Args):
+ n_samples: int = 64
+ perturb_sampling: bool = False
+ with_radius: bool = False
+ n_fields: int = 1
+ depth: int = 8
+ width: int = 256
+ skips: list[int] = [4]
+ act: str = "relu"
+ ln: bool = False
+ xfreqs: int = 6
+ raw_noise_std: float = 0.
+ near: float = 1.
+ far: float = 10.
+ white_bg: bool = False
+
+ args: Args
+
+ def __init__(self, args: Args):
+ """
+ Initialize a FS-NeRF model
+
+ :param args `Args`: arguments
+ """
+ super().__init__(args)
+
+ # Initialize components
+ self._init_sampler()
+ self._init_encoders()
+ self._init_core()
+ self._init_renderer()
+
+ @profile
+ def forward(self, rays: Rays, *outputs: str, **args) -> ReturnData:
+ samples = self.sample(rays, **args)
+ x = self.encode(samples)
+ rgbd = self.infer(x)
+ return self.render(samples, rgbd, *outputs, **args)
+
+ def sample(self, rays: Rays, **kwargs) -> Samples:
+ args = self.args.merge_with(kwargs)
+ return self.sampler(rays, None, range=(args.near, args.far), mode="spherical_radius",
+ n_samples=args.n_samples,
+ perturb=args.perturb_sampling if self.training else False)
+
+ def encode(self, samples: Samples) -> torch.Tensor:
+ return self.x_encoder(samples.pts[..., -self.x_encoder.in_chns:])
+
+ def infer(self, x: torch.Tensor) -> torch.Tensor:
+ return self.core(x)
+
+ def render(self, samples: Samples, rgbd: torch.Tensor, *outputs: str, **kwargs) -> ReturnData:
+ args = self.args.merge_with(kwargs)
+ return self.renderer(samples, rgbd, *outputs, white_bg=args.white_bg,
+ raw_noise_std=args.raw_noise_std if self.training else 0.)
+
+ def _init_encoders(self):
+ self.x_encoder = FreqEncoder(self.sampler.out_chns["x"] - (not self.args.with_radius),
+ self.args.xfreqs, False)
+
+ def _init_core(self):
+ self.core = core.FsNeRF(self.x_encoder.out_chns, self.color.chns,
+ self.args.depth, self.args.width, self.args.skips,
+ self.args.act, self.args.ln, self.args.n_samples, self.args.n_fields)
+
+ def _init_sampler(self):
+ self.sampler = UniformSampler()
+
+ def _init_renderer(self):
+ self.renderer = VolumnRenderer()
diff --git a/model/model.py b/model/model.py
new file mode 100644
index 0000000..5850d95
--- /dev/null
+++ b/model/model.py
@@ -0,0 +1,48 @@
+from operator import itemgetter
+from .__common__ import *
+from utils import netio
+from utils.args import BaseArgs
+
+
+model_classes: dict[str, "Model"] = {}
+
+
+class Model(nn.Module):
+ class Args(BaseArgs):
+ color: str = "rgb"
+ coord: str = "gl"
+ args: Args
+ color: Color
+
+ def __init__(self, args: Args):
+ super().__init__()
+ self.args = args
+ self.color = Color[self.args.color]
+
+ # stub method
+ def __call__(self, rays: Rays, *outputs: str, **args) -> ReturnData:
+ ...
+
+ def forward(self, rays: Rays, *outputs: str, **args) -> ReturnData:
+ raise NotImplementedError()
+
+ @staticmethod
+ def get_class(typename: str) -> Type["Model"] | None:
+ return model_classes.get(typename)
+
+ @staticmethod
+ def create(typename: str, args: dict|Args) -> "Model":
+ ModelCls = Model.get_class(typename)
+ if ModelCls is None:
+ raise ValueError(f"Model {typename} is not found")
+ if isinstance(args, dict):
+ args = ModelCls.Args(**args)
+ return ModelCls(args)
+
+ @staticmethod
+ def load(path: PathLike) -> "Model":
+ ckpt = netio.load_checkpoint(Path(path))[0]
+ model_type, model_args = itemgetter("model", "model_args")(ckpt["args"])
+ model = Model.create(model_type, model_args)
+ model.load_state_dict(ckpt["states"]["model"])
+ return model
diff --git a/model/nerf.py b/model/nerf.py
index 32f2bc7..1b1d128 100644
--- a/model/nerf.py
+++ b/model/nerf.py
@@ -1,198 +1,113 @@
from .__common__ import *
-from .base import BaseModel
-from operator import itemgetter
-
-from utils import math
-from utils.misc import masked_scatter, merge
-
-
-class NeRF(BaseModel):
-
- TrainerClass = "TrainWithSpace"
- SamplerClass = None
- RendererClass = None
-
- space: Union[Space, Voxels, Octree]
-
- @property
- def multi_nets(self) -> int:
- return self.args.get("multi_nets", 1)
-
- def __init__(self, args0: dict, args1: dict = None):
+from .model import Model
+
+
+class NeRF(Model):
+ class Args(Model.Args):
+ n_samples: int = 64
+ sample_mode: str = "xyz"
+ perturb_sampling: bool = False
+ depth: int = 8
+ width: int = 256
+ skips: list[int] = [4]
+ act: str = "relu"
+ ln: bool = False
+ color_decoder: str = "NeRF"
+ n_importance: int = 0
+ fine_depth: int = 8
+ fine_width: int = 256
+ fine_skips: list[int] = [4]
+ xfreqs: int = 10
+ dfreqs: int = 4
+ raw_noise_std: float = 0.
+ near: float = 1.
+ far: float = 10.
+ white_bg: bool = False
+
+ args: Args
+
+ def __init__(self, args: Args):
"""
Initialize a NeRF model
- :param args0 `dict`: basic arguments
- :param args1 `dict`: extra arguments, defaults to {}
+ :param args `dict`: arguments
"""
- super().__init__(args0, args1)
+ super().__init__(args)
+ if args.sample_mode == "xyz" or args.sample_mode == "xyz_disp":
+ args.near = 0.1
# Initialize components
-
- self._init_space()
+ self._init_sampler()
self._init_encoders()
self._init_core()
- self._init_sampler()
self._init_renderer()
- @perf
- def infer(self, *outputs: str, samples: Samples, inputs: NetInput = None, **kwargs) -> NetOutput:
- inputs = inputs or self.input(samples)
- if len(self.cores) == 1:
- return self.cores[0](inputs, *outputs, samples=samples, **kwargs)
- return self._multi_infer(inputs, *outputs, samples=samples, **kwargs)
-
- @torch.no_grad()
- def split(self):
- ret = self.space.split()
- if 'n_samples' in self.args0:
- self.args0['n_samples'] *= 2
- if 'voxel_size' in self.args0:
- self.args0['voxel_size'] /= 2
- if "sample_step_ratio" in self.args0:
- self.args1["sample_step"] = self.args0["voxel_size"] \
- * self.args0["sample_step_ratio"]
- if 'sample_step' in self.args0:
- self.args0['sample_step'] /= 2
- return ret
-
- def _preprocess_args(self):
- if "sample_step_ratio" in self.args0:
- self.args1["sample_step"] = self.args0["voxel_size"] * self.args0["sample_step_ratio"]
- if self.args0.get("spherical"):
- sample_range = [1 / self.args0['depth_range'][0], 1 / self.args0['depth_range'][1]] \
- if self.args0.get('depth_range') else [1, 0]
- rot_range = [[-180, -90], [180, 90]]
- self.args1['bbox'] = [
- [sample_range[0], math.radians(rot_range[0][0]), math.radians(rot_range[0][1])],
- [sample_range[1], math.radians(rot_range[1][0]), math.radians(rot_range[1][1])]
- ]
- self.args1['sample_range'] = sample_range
- if not self.args.get("net_bounds"):
- self.register_temp("net_bounds", None)
- self.args1["multi_nets"] = 1
- else:
- self.register_temp("net_bounds", torch.tensor(self.args["net_bounds"]))
- self.args1["multi_nets"] = self.net_bounds.size(0)
-
- def _init_chns(self, **chns):
- super()._init_chns(**{
- "x": self.args.get('n_featdim') or 3,
- "d": 3 if self.args.get('encode_d') else 0,
- **chns
- })
-
- def _init_space(self):
- self.space = Space.create(self.args)
- if self.args.get('n_featdim'):
- self.space.create_embedding(self.args['n_featdim'])
+ if self.args.n_importance > 0:
+ self._init_cascade()
+
+ @profile
+ def forward(self, rays: Rays, *outputs: str, **args) -> ReturnData:
+ samples = self.sample(rays, **args)
+ x, d = self.encode(samples)
+ rgbd = self.infer(x, d)
+ return self.render(rays, samples, rgbd, *outputs, cascade=True, **args)
+
+ def sample(self, rays: Rays, **kwargs) -> Samples:
+ args = self.args.merge_with(kwargs)
+ return self.sampler(rays, None, range=(args.near, args.far),
+ mode=args.sample_mode, n_samples=args.n_samples,
+ perturb=args.perturb_sampling if self.training else False)
+
+ def encode(self, samples: Samples) -> tuple[torch.Tensor, torch.Tensor]:
+ return self.x_encoder(samples.pts), self.d_encoder(math.normalize(samples.dirs))
+
+ def infer(self, x: torch.Tensor, d: torch.Tensor, *, fine: bool = False) -> torch.Tensor:
+ if self.args.n_importance > 0 and fine:
+ return self.fine_core(x, d)
+ return self.core(x, d)
+
+ def render(self, rays: Rays, samples: Samples, rgbd: torch.Tensor, *outputs: str,
+ cascade: bool = False, **kwargs) -> ReturnData:
+ args = self.args.merge_with(kwargs)
+ if args.n_importance > 0 and cascade:
+ coarse_outputs = [item[7:] for item in outputs if item.startswith("coarse_")]
+ coarse_ret = self.renderer(samples, rgbd, "weights", *coarse_outputs,
+ white_bg=args.white_bg,
+ raw_noise_std=args.raw_noise_std if self.training else 0.)
+ samples = self.pdf_sampler(rays, None, samples.t_vals, coarse_ret["weights"][..., 0],
+ mode=args.sample_mode,
+ n_importance=args.n_importance,
+ perturb=args.perturb_sampling if self.training else False,
+ include_existed_samples=True)
+ x, d = self.encode(samples)
+ fine_rgbd = self.infer(x, d, fine=True)
+ return self.renderer(samples, fine_rgbd, *outputs, white_bg=args.white_bg,
+ raw_noise_std=args.raw_noise_std if self.training else 0.) | {
+ f"coarse_{key}": coarse_ret[key]
+ for key in coarse_outputs
+ if key in coarse_ret
+ }
+ return self.renderer(samples, rgbd, *outputs, white_bg=args.white_bg,
+ raw_noise_std=args.raw_noise_std if self.training else 0.)
def _init_encoders(self):
- self.x_encoder = InputEncoder(self.chns("x"), self.args['encode_x'], cat_input=True)
- self.d_encoder = InputEncoder(self.chns("d"), self.args['encode_d'])\
- if self.chns("d") > 0 else None
+ self.x_encoder = FreqEncoder(self.sampler.out_chns["x"], self.args.xfreqs, True)
+ self.d_encoder = FreqEncoder(self.sampler.out_chns["d"], self.args.dfreqs, True)
def _init_core(self):
- self.cores = self.create_multiple(self._create_core_unit, self.args.get("multi_nets", 1))
+ self.core = core.NeRF(self.x_encoder.out_chns, self.d_encoder.out_chns, self.color.chns,
+ self.args.depth, self.args.width, self.args.skips,
+ self.args.act, self.args.ln, self.args.color_decoder)
def _init_sampler(self):
- if self.SamplerClass is None:
- SamplerClass = Sampler
- else:
- SamplerClass = self.SamplerClass
- self.sampler = SamplerClass(**self.args)
+ self.sampler = UniformSampler()
+
+ def _init_cascade(self):
+ self.pdf_sampler = PdfSampler()
+ self.fine_core = core.NeRF(self.x_encoder.out_chns, self.d_encoder.out_chns, self.color.chns,
+ self.args.fine_depth, self.args.fine_width,
+ self.args.fine_skips, self.args.act, self.args.ln,
+ self.args.color_decoder)
def _init_renderer(self):
- if self.RendererClass is None:
- if self.args.get("core") == "nerfadv":
- RendererClass = DensityFirstVolumnRenderer
- else:
- RendererClass = VolumnRenderer
- else:
- RendererClass = self.RendererClass
- self.renderer = RendererClass(**self.args)
-
- def _create_core_unit(self, core_params: dict = None, **args):
- core_params = core_params or self.args["core_params"]
- if self.args.get("core") == "nerfadv":
- return NerfAdvCore(**{
- "x_chns": self.x_encoder.out_dim,
- "d_chns": self.d_encoder.out_dim,
- "density_chns": self.chns('density'),
- "color_chns": self.chns('color'),
- **core_params,
- **args
- })
- else:
- return NerfCore(**{
- "x_chns": self.x_encoder.out_dim,
- "density_chns": self.chns('density'),
- "color_chns": self.chns('color'),
- "d_chns": self.d_encoder.out_dim if self.d_encoder else 0,
- **core_params,
- **args
- })
-
- @perf
- def _sample(self, data: InputData, **extra_args) -> Samples:
- return self.sampler(*itemgetter("rays_o", "rays_d")(data), self.space,
- **merge(self.args, extra_args))
-
- @perf
- def _render(self, samples: Samples, *outputs: str, **extra_args) -> ReturnData:
- if len(samples.size) == 1:
- return self.infer(*outputs, samples=samples)
- return self.renderer(self, samples, *outputs, **merge(self.args, extra_args))
-
- def _input(self, samples: Samples, what: str) -> Optional[torch.Tensor]:
- if what == "x":
- if self.args.get('n_featdim'):
- return self._encode("emb", self.space.extract_embedding(
- samples.pts, samples.voxel_indices))
- else:
- return self._encode("x", samples.pts)
- elif what == "d":
- if self.d_encoder and samples.dirs is not None:
- return self._encode("d", samples.dirs)
- else:
- return None
- elif what == "f":
- return None
- else:
- ValueError(f"Don't know how to process input \"{what}\"")
-
- def _encode(self, what: str, val: torch.Tensor) -> torch.Tensor:
- if what == "x":
- if self.args.get("spherical"):
- sr = self.args['sample_range']
- # scale val.r: [sr[0], sr[1]] -> [-PI/2, PI/2]
- val = val.clone()
- val[..., 0] = ((val[..., 0] - sr[0]) / (sr[1] - sr[0]) - .5) * math.pi
- return self.x_encoder(val)
- else:
- return self.x_encoder(val * math.pi)
- elif what == "emb":
- return self.x_encoder(val * math.pi)
- elif what == "d":
- return self.d_encoder(val, angular=True)
- else:
- ValueError(f"Don't know how to encode \"{what}\"")
-
- @perf
- def _multi_infer(self, inputs: NetInput, *outputs: str, samples: Samples, **kwargs) -> NetOutput:
- ret: NetOutput = {}
- for i, core in enumerate(self.cores):
- selector = (samples.pts >= self.net_bounds[i, 0]
- and samples.pts < self.net_bounds[i, 1]).all(-1)
- partial_ret: NetOutput = core(inputs[selector], *outputs, samples=samples[selector],
- **kwargs)
- for key, value in partial_ret.items():
- if key not in ret:
- ret[key] = value.new_zeros(*inputs.shape, value.shape[-1])
- ret[key] = masked_scatter(selector, value, ret[key])
- return ret
-
-
-class NSVF(NeRF):
-
- SamplerClass = VoxelSampler
+ self.renderer = VolumnRenderer()
diff --git a/modules/__common__.py b/modules/__common__.py
new file mode 100644
index 0000000..bfaf835
--- /dev/null
+++ b/modules/__common__.py
@@ -0,0 +1,5 @@
+import sys
+from utils import math, nn
+from utils.types import *
+from utils.misc import union, split
+from utils.profile import profile
diff --git a/modules/__init__.py b/modules/__init__.py
index 898abaf..e7ab6a9 100644
--- a/modules/__init__.py
+++ b/modules/__init__.py
@@ -1,5 +1,5 @@
-from .sampler import Sampler, PdfSampler, VoxelSampler
-from .input_encoder import InputEncoder, IntegratedPosEncoder
-from .renderer import VolumnRenderer, DensityFirstVolumnRenderer
-from .space import Space, Voxels, Octree
-from .core import NerfCore, NerfAdvCore, MultiNerf
\ No newline at end of file
+from .core import *
+from .sampler import *
+from .input_encoder import *
+from .renderer import *
+from .space import *
diff --git a/modules/core.py b/modules/__old/core.py
similarity index 87%
rename from modules/core.py
rename to modules/__old/core.py
index 2fb68de..299e8db 100644
--- a/modules/core.py
+++ b/modules/__old/core.py
@@ -1,28 +1,19 @@
-import re
-import torch
-from typing import Iterable, Tuple
-
-from .generic import *
-from utils.misc import union, split
-from utils.type import NetInput, NetOutput
-from utils.module import Module
-from utils.samples import Samples
-
-
-class NerfCore(Module):
+class NeRF(Module):
def __init__(self, *, x_chns, density_chns, color_chns, nf, n_layers,
d_chns=0, d_nf=0, act='relu', skips=[], with_layer_norm=False,
- density_out_act='relu', color_out_act='sigmoid', f_chns=0):
+ density_out_act='relu', color_out_act='sigmoid', feature_layer=False):
super().__init__()
- self.input_f = f_chns > 0
- self.core_field = FcBlock(in_chns=x_chns + f_chns, out_chns=None, nf=nf, n_layers=n_layers,
- skips=skips, act=act, with_ln=with_layer_norm)
+ self.x_chns = x_chns
+ self.d_chns = d_chns
+ self.field = FcBlock(in_chns=x_chns, out_chns=None, nf=nf, n_layers=n_layers,
+ skips=skips, act=act, with_ln=with_layer_norm)
self.density_out = FcLayer(nf, density_chns, density_out_act, with_ln=False) \
if density_chns > 0 else None
if color_chns == 0:
self.color_out = None
elif d_chns > 0:
+ self.feature_layer = feature_layer and FcLayer(nf, nf, with_ln=False)
self.color_out = FcBlock(in_chns=nf + d_chns, out_chns=color_chns,
nf=d_nf or nf // 2, n_layers=1,
act=act, out_act=color_out_act, with_ln=with_layer_norm)
@@ -31,20 +22,30 @@ class NerfCore(Module):
self.color_out = FcLayer(nf, color_chns, color_out_act, with_ln=False)
self.with_dir = False
- def forward(self, inputs: NetInput, *outputs: str, features: torch.Tensor = None, **kwargs) -> NetOutput:
- ret = {}
- if features is None:
- features = self.core_field(union(inputs.x, inputs.f) if self.input_f else inputs.x)
- if 'features' in outputs:
- ret['features'] = features
+ def forward(self, inputs: NetInput, *outputs: str, field_out: torch.Tensor = None, **kwargs) -> NetOutput:
+ ret = NetOutput()
+ if field_out is None:
+ field_out = self.field(inputs.x)
+ if 'field_out' in outputs:
+ ret.field_out = field_out
if 'densities' in outputs and self.density_out:
- ret['densities'] = self.density_out(features)
+ ret.densities = self.density_out(field_out)
if 'colors' in outputs and self.color_out:
if self.with_dir:
- features = union(features, inputs.d)
- ret['colors'] = self.color_out(features)
+ if self.feature_layer:
+ h = self.feature_layer(field_out)
+ h = union(h, inputs.d)
+ else:
+ h = field_out
+ ret.colors = self.color_out(h)
return ret
+ def get_exporter(self):
+ return ModelExporter(self.infer, "densities", "colors", x=[self.x_chns], d=[self.d_chns])
+
+ def infer(self, x: torch.Tensor, d: torch.Tensor = None, f: torch.Tensor = None):
+ return tuple(self._forward(NetInput(x, d, f), "colors", "densities").values())
+
class NerfAdvCore(Module):
@@ -129,7 +130,7 @@ class NerfAdvCore(Module):
if 'colors' in outputs or 'specluars' in outputs or 'diffuses' in outputs:
if 'densities' in ret:
valid_mask = ret['densities'][..., 0].detach() >= 1e-4
- indices: Tuple[torch.Tensor, ...] = valid_mask.nonzero(as_tuple=True)
+ indices: tuple[torch.Tensor, ...] = valid_mask.nonzero(as_tuple=True)
inputs, features = inputs[indices], features[indices]
else:
indices = None
diff --git a/modules/__old/input_encoder.py b/modules/__old/input_encoder.py
new file mode 100644
index 0000000..652282d
--- /dev/null
+++ b/modules/__old/input_encoder.py
@@ -0,0 +1,417 @@
+class IntegratedPosEncoder(InputEncoder):
+
+ def __init__(self, chns, L, shape: str, cat_input=False):
+ super().__init__(chns)
+ self.shape = shape
+
+ def _lift_gaussian(self, d: torch.Tensor, t_mean: torch.Tensor, t_var: torch.Tensor,
+ r_var: torch.Tensor, diag: bool):
+ """Lift a Gaussian defined along a ray to 3D coordinates."""
+ mean = d[..., None, :] * t_mean[..., None]
+ d_sq = d**2
+
+ d_mag_sq = torch.sum(d_sq, -1, keepdim=True).clamp_min(1e-10)
+
+ if diag:
+ d_outer_diag = d_sq
+ null_outer_diag = 1 - d_outer_diag / d_mag_sq
+ t_cov_diag = t_var[..., None] * d_outer_diag[..., None, :]
+ xy_cov_diag = r_var[..., None] * null_outer_diag[..., None, :]
+ cov_diag = t_cov_diag + xy_cov_diag
+ return mean, cov_diag
+ else:
+ d_outer = d[..., :, None] * d[..., None, :]
+ eye = torch.eye(d.shape[-1], device=d.device)
+ null_outer = eye - d[..., :, None] * (d / d_mag_sq)[..., None, :]
+ t_cov = t_var[..., None, None] * d_outer[..., None, :, :]
+ xy_cov = r_var[..., None, None] * null_outer[..., None, :, :]
+ cov = t_cov + xy_cov
+ return mean, cov
+
+ def _conical_frustum_to_gaussian(self, d: torch.Tensor, t0: float, t1: float, base_radius: float,
+ diag: bool, stable: bool = True):
+ """Approximate a conical frustum as a Gaussian distribution (mean+cov).
+
+ Assumes the ray is originating from the origin, and base_radius is the
+ radius at dist=1. Doesn't assume `d` is normalized.
+
+ Args:
+ d: torch.float32 3-vector, the axis of the cone
+ t0: float, the starting distance of the frustum.
+ t1: float, the ending distance of the frustum.
+ base_radius: float, the scale of the radius as a function of distance.
+ diag: boolean, whether or the Gaussian will be diagonal or full-covariance.
+ stable: boolean, whether or not to use the stable computation described in
+ the paper (setting this to False will cause catastrophic failure).
+
+ Returns:
+ a Gaussian (mean and covariance).
+ """
+ if stable:
+ mu = (t0 + t1) / 2
+ hw = (t1 - t0) / 2
+ t_mean = mu + (2 * mu * hw**2) / (3 * mu**2 + hw**2)
+ t_var = (hw**2) / 3 - (4 / 15) * ((hw**4 * (12 * mu**2 - hw**2)) /
+ (3 * mu**2 + hw**2)**2)
+ r_var = base_radius**2 * ((mu**2) / 4 + (5 / 12) * hw**2 - 4 / 15 *
+ (hw**4) / (3 * mu**2 + hw**2))
+ else:
+ t_mean = (3 * (t1**4 - t0**4)) / (4 * (t1**3 - t0**3))
+ r_var = base_radius**2 * (3 / 20 * (t1**5 - t0**5) / (t1**3 - t0**3))
+ t_mosq = 3 / 5 * (t1**5 - t0**5) / (t1**3 - t0**3)
+ t_var = t_mosq - t_mean**2
+ return self._lift_gaussian(d, t_mean, t_var, r_var, diag)
+
+ def _cylinder_to_gaussian(self, d: torch.Tensor, t0: float, t1: float, radius: float, diag: bool):
+ """Approximate a cylinder as a Gaussian distribution (mean+cov).
+
+ Assumes the ray is originating from the origin, and radius is the
+ radius. Does not renormalize `d`.
+
+ Args:
+ d: torch.float32 3-vector, the axis of the cylinder
+ t0: float, the starting distance of the cylinder.
+ t1: float, the ending distance of the cylinder.
+ radius: float, the radius of the cylinder
+ diag: boolean, whether or the Gaussian will be diagonal or full-covariance.
+
+ Returns:
+ a Gaussian (mean and covariance).
+ """
+ t_mean = (t0 + t1) / 2
+ r_var = radius**2 / 4
+ t_var = (t1 - t0)**2 / 12
+ return self._lift_gaussian(d, t_mean, t_var, r_var, diag)
+
+ def cast_rays(self, t_vals: torch.Tensor, rays_o: torch.Tensor, rays_d: torch.Tensor,
+ rays_r: torch.Tensor, diag: bool = True):
+ """Cast rays (cone- or cylinder-shaped) and featurize sections of it.
+
+ Args:
+ t_vals: float array, the "fencepost" distances along the ray.
+ rays_o: float array, the ray origin coordinates.
+ rays_d: float array, the ray direction vectors.
+ radii: float array, the radii (base radii for cones) of the rays.
+ ray_shape: string, the shape of the ray, must be 'cone' or 'cylinder'.
+ diag: boolean, whether or not the covariance matrices should be diagonal.
+
+ Returns:
+ a tuple of arrays of means and covariances.
+ """
+ t0 = t_vals[..., :-1]
+ t1 = t_vals[..., 1:]
+ if self.shape == 'cone':
+ gaussian_fn = self._conical_frustum_to_gaussian
+ elif self.shape == 'cylinder':
+ gaussian_fn = self._cylinder_to_gaussian
+ else:
+ assert False
+ means, covs = gaussian_fn(rays_d, t0, t1, rays_r, diag)
+ means = means + rays_o[..., None, :]
+ return means, covs
+
+ def integrated_pos_enc(x_coord: tuple[torch.Tensor, torch.Tensor], min_deg: int, max_deg: int,
+ diag: bool = True):
+ """Encode `x` with sinusoids scaled by 2^[min_deg:max_deg-1].
+
+ Args:
+ x_coord: a tuple containing: x, torch.ndarray, variables to be encoded. Should
+ be in [-pi, pi]. x_cov, torch.ndarray, covariance matrices for `x`.
+ min_deg: int, the min degree of the encoding.
+ max_deg: int, the max degree of the encoding.
+ diag: bool, if true, expects input covariances to be diagonal (full
+ otherwise).
+
+ Returns:
+ encoded: torch.ndarray, encoded variables.
+ """
+ if diag:
+ x, x_cov_diag = x_coord
+ scales = torch.tensor([2**i for i in range(min_deg, max_deg)], device=x.device)[:, None]
+ shape = list(x.shape[:-1]) + [-1]
+ y = torch.reshape(x[..., None, :] * scales, shape)
+ y_var = torch.reshape(x_cov_diag[..., None, :] * scales**2, shape)
+ else:
+ x, x_cov = x_coord
+ num_dims = x.shape[-1]
+ basis = torch.cat([
+ 2**i * torch.eye(num_dims, device=x.device)
+ for i in range(min_deg, max_deg)
+ ], 1)
+ y = torch.matmul(x, basis)
+ # Get the diagonal of a covariance matrix (ie, variance). This is equivalent
+ # to jax.vmap(torch.diag)((basis.T @ covs) @ basis).
+ y_var = (torch.matmul(x_cov, basis) * basis).sum(-2)
+
+ return math.expected_sin(
+ torch.cat([y, y + 0.5 * math.pi], -1),
+ torch.cat([y_var] * 2, -1))[0]
+
+
+# @torch.jit.script
+def intepolate_calc_weight(x, corners):
+ return x * corners * 2 - x - corners + 1
+
+
+class MultiresHashEncoder(InputEncoder):
+ fast_op = True
+ t_ind: torch.dtype
+
+ layers: int
+ coarse_levels: int
+ layers_hashsize: list[int]
+
+ layers_res: torch.Tensor
+ """Tensor(L, D)"""
+ local_corners: torch.Tensor
+ """Tensor(C, D)"""
+ layers_hashoffset: torch.Tensor
+ """Tensor(L+1)"""
+ hashtable: torch.nn.parameter.Parameter
+ """Parameter(T, F)"""
+
+ def __init__(self, chns: int, layers: int, log2_hashsize: int, features: int,
+ res0: int | list[int], scale_up: float = 2.0):
+ super().__init__(chns, layers * features, (0., 1.))
+
+ res0 = torch.tensor([res0] * chns if isinstance(res0, int) else res0)
+
+ self.layers = layers
+ self.features = features
+ self.scale_up = scale_up
+ self.max_hashsize = 2 ** log2_hashsize
+ self.t_ind = torch.int if self.fast_op else torch.long
+
+ layers_res: list[torch.Tensor] = []
+ self.layers_hashsize: list[int] = []
+ self.coarse_levels = 0
+ layers_hashoffset: list[int] = [0]
+ for i in range(layers):
+ layers_res.append((res0 * scale_up ** i).to(self.t_ind))
+ if layers_res[-1].max() > self.max_hashsize ** (1 / 3)\
+ or layers_res[-1].prod() > self.max_hashsize:
+ self.layers_hashsize.append(self.max_hashsize)
+ else:
+ self.layers_hashsize.append(layers_res[-1].prod().item())
+ self.coarse_levels = i + 1
+ layers_hashoffset.append(layers_hashoffset[-1] + self.layers_hashsize[-1])
+ self.register_temp("layers_res", torch.stack(layers_res, 0))
+ self.register_temp("layers_hashoffset", torch.tensor(layers_hashoffset, dtype=self.t_ind))
+ self.register_temp("local_corners", split_voxels_local(1, 2, dims=chns) + .5)
+
+ # Initialize the hash table entries using the uniform distribution U(−10^−4, 10^−4) to provide
+ # a small amount of randomness while encouraging initial predictions close to zero [muller2022instant]
+ self.hashtable = torch.nn.parameter.Parameter(
+ (torch.rand(layers_hashoffset[-1], features, device=self.device) - .5))
+
+ @profile
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """
+ Encode inputs using multi-resolution hash encoder [muller2022instant]
+
+ :param x `Tensor(N..., D)`: D-dim inputs
+ :return `Tensor(N..., LF)`: encoded outputs
+ """
+ if self.fast_op:
+ N_, D = x.shape[:-1], x.shape[-1]
+ return multires_hash_encode(self.layers, self.coarse_levels, self.layers_res,
+ self.layers_hashoffset, x.reshape(-1, D), self.hashtable)\
+ .transpose(0, 1).reshape(*N_, -1)
+
+ @profile("Calculate corners")
+ def calc_corners(x) -> tuple[torch.Tensor, torch.Tensor]:
+ grid_pos = x.unsqueeze(-2) * (self.layers_res - 1) # (N..., L, D)
+ grid_pos.unsqueeze_(-2) # (N..., L, 1, D)
+ grid_lo = torch.floor(grid_pos)
+ grid_pos.sub_(grid_lo)
+ corners = (grid_lo + self.local_corners).long().min(self.layers_res.unsqueeze(-2) - 1)
+ # (N..., L, C, D)
+ return grid_pos, corners
+
+ grid_pos, corners = calc_corners(x)
+ # (N..., L, 1, D), (N..., L, C, D)
+
+ @profile("Calculate encoded")
+ def calc_encoded(level: int) -> torch.Tensor:
+ if level < self.coarse_levels:
+ idx = to_flat_indices(corners[..., level, :, :], self.layers_res[level, None])
+ else:
+ idx = self._fast_hash(corners[..., level, :, :]) % self.max_hashsize
+ idx.add_(self.layers_hashoffset[level, None])
+ return self._linear_interp(grid_pos[..., level, :, :], self.hashtable[idx])
+
+ result = torch.stack([calc_encoded(level) for level in range(self.layers)], dim=-2)
+ # (N..., L, X)
+
+ return result.flatten(-2)
+
+ def _linear_interp(self, x: torch.Tensor, corner_values: torch.Tensor) -> torch.Tensor:
+ """
+ [summary]
+
+ :param x `Tensor(N..., L, 1, D)`: [description]
+ :param corner_values `Tensor(N..., L, C, X)`: [description]
+ :return `Tensor(N..., L, X): [description]
+ :rtype: [type]
+ """
+ weights = (x * self.local_corners * 2 - x - self.local_corners + 1).prod(-1, keepdim=True)
+ # (N..., L, C, 1)
+ return (weights * corner_values).sum(-2) # (N..., L, X)
+
+ def extra_repr(self) -> str:
+ return f"{self.in_chns} -> {self.out_chns}({self.layers}x{self.features})"\
+ f", resolution={self.layers_res[0].tolist()}*{self.scale_up}^L"\
+ f", max_hashsize={self.max_hashsize}"
+
+ @profile
+ def _fast_hash(self, grid_pos: torch.Tensor) -> torch.Tensor:
+ """
+ Perform fast hash according to instant-ngp
+
+ :param grid_pos `Tensor(N..., D)`: integer grid positions
+ :return `Tensor(N...)`: hash values
+ """
+ if grid_pos.shape[-1] > 7:
+ raise ValueError("fast_hash can only hash up to 7 dimensions.")
+
+ # While 1 is technically not a good prime for hashing (or a prime at all), it helps memory coherence
+ # and is sufficient for our use case of obtaining a uniformly colliding index from high-dimensional
+ # coordinates. [muller2022instant]
+ primes = [1, 2654435761, 805459861, 3674653429, 2097192037, 1434869437, 2165219737]
+ result = grid_pos[..., 0] * primes[0]
+ for i in range(1, grid_pos.shape[-1]):
+ result.bitwise_xor_(grid_pos[..., i] * primes[i])
+ return result
+
+
+class LayeredMultiresHashEncoder(InputEncoder):
+ use_cpp = False
+
+ layers: int
+ coarse_levels: int
+ layers_res: torch.Tensor
+ """Tensor(L, D)"""
+ local_corners: torch.Tensor
+ """Tensor(C, D)"""
+ layers_hashsize: list[int]
+ layers_hashoffset: list[int]
+ t_ind: torch.dtype
+
+ def __init__(self, chns: int, layers: int, log2_hashsize: int, features: int,
+ res0: int | list[int], scale_up: float = 2.0, parts: int = 64):
+ super().__init__(chns, layers * features, (0., 1.))
+
+ res0 = torch.tensor([res0] * chns if isinstance(res0, int) else res0)
+
+ self.layers = layers
+ self.features = features
+ self.scale_up = scale_up
+ self.max_hashsize = 2 ** log2_hashsize // parts
+ self.t_ind = torch.int if self.use_cpp else torch.long
+
+ layers_res: list[torch.Tensor] = []
+ self.layers_hashsize: list[int] = []
+ self.layers_usehash: list[bool] = []
+ self.coarse_levels = 0
+ layers_hashoffset: list[int] = [0]
+ for i in range(layers):
+ layers_res.append(res0 if i == 0 else (layers_res[-1] * scale_up).to(self.t_ind))
+ if layers_res[-1].max() > self.max_hashsize ** (1 / 3)\
+ or layers_res[-1].prod() > self.max_hashsize:
+ self.layers_hashsize.append(self.max_hashsize)
+ else:
+ self.layers_hashsize.append(layers_res[-1].prod().item())
+ self.coarse_levels = i + 1
+ layers_hashoffset.append(layers_hashoffset[-1] + self.layers_hashsize[-1])
+ self.register_temp("layers_res", torch.stack(layers_res, 0))
+ self.register_temp("layers_hashoffset", torch.tensor(layers_hashoffset, dtype=self.t_ind))
+
+ # Initialize the hash table entries using the uniform distribution U(−10^−4, 10^−4) to provide
+ # a small amount of randomnesddaddadwss while encouraging initial predictions close to zero [muller2022instant]
+ self.hashtable = torch.nn.parameter.Parameter(
+ (torch.rand(parts, layers_hashoffset[-1], features, device=self.device) - .5))
+
+ self.register_temp("local_corners", split_voxels_local(1, 2, dims=chns) + .5)
+
+ @profile
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """
+ Encode inputs using multi-resolution hash encoder [muller2022instant]
+
+ :param x `Tensor(N..., P, D)`: D-dim inputs
+ :return `Tensor(N..., P, LF)`: encoded outputs
+ """
+ if self.use_cpp:
+ N_, P, D = x.shape[:-2], x.shape[-2], x.shape[-1]
+ return torch.stack([
+ multires_hash_encode(self.layers, self.coarse_levels, self.layers_res,
+ self.layers_hashoffset, x[..., i, :].reshape(-1, D),
+ self.hashtable[i]).reshape(*N_, -1)
+ for i in range(P)
+ ], dim=-2)
+
+ @profile("Calculate corners")
+ def calc_corners(x) -> tuple[torch.Tensor, torch.Tensor]:
+ grid_pos = x.unsqueeze(-2) * (self.layers_res - 1) # (N..., P, L, D)
+ grid_pos.unsqueeze_(-2) # (N..., P, L, 1, D)
+ grid_lo = torch.floor(grid_pos)
+ grid_pos.sub_(grid_lo)
+ corners = (grid_lo + self.local_corners).long().min(self.layers_res.unsqueeze(-2) - 1)
+ # (N..., L, C, D)
+ return grid_pos, corners
+
+ grid_pos, corners = calc_corners(x)
+ # (N..., P, L, 1, D), (N..., P, L, C, D)
+
+ @profile("Calculate encoded")
+ def calc_encoded(level: int) -> torch.Tensor:
+ if level < self.coarse_levels:
+ idx = to_flat_indices(corners[..., level, :, :], self.layers_res[level, None])
+ else:
+ idx = self._fast_hash(corners[..., level, :, :]) % self.max_hashsize
+ idx.add_(self.layers_hashoffset[level, None])
+ part_idx = torch.arange(x.shape[-2], device=x.device)[:, None].broadcast_to(idx.shape)
+ return self._linear_interp(grid_pos[..., level, :, :], self.hashtable[part_idx, idx])
+
+ result = torch.stack([calc_encoded(level) for level in range(self.layers)], dim=-2)
+ # (N..., L, X)
+
+ return result.flatten(-2)
+
+ def _linear_interp(self, x: torch.Tensor, corner_values: torch.Tensor) -> torch.Tensor:
+ """
+ [summary]
+
+ :param x `Tensor(N..., L, 1, D)`: [description]
+ :param corner_values `Tensor(N..., L, C, X)`: [description]
+ :return `Tensor(N..., L, X): [description]
+ :rtype: [type]
+ """
+ weights = (x * self.local_corners * 2 - x - self.local_corners + 1).prod(-1, keepdim=True)
+ # (N..., L, C, 1)
+ return (weights * corner_values).sum(-2) # (N..., L, X)
+
+ def extra_repr(self) -> str:
+ return f"{self.in_chns} -> {self.out_chns}({self.layers}x{self.features})"\
+ f", resolution={self.layers_res[0].tolist()}*{self.scale_up}^L"\
+ f", max_hashsize={self.max_hashsize}"
+
+ @profile
+ def _fast_hash(self, grid_pos: torch.Tensor) -> torch.Tensor:
+ """
+ Perform fast hash according to instant-ngp
+
+ :param grid_pos `Tensor(N..., D)`: integer grid positions
+ :return `Tensor(N...)`: hash values
+ """
+ if grid_pos.shape[-1] > 7:
+ raise ValueError("fast_hash can only hash up to 7 dimensions.")
+
+ # While 1 is technically not a good prime for hashing (or a prime at all), it helps memory coherence
+ # and is sufficient for our use case of obtaining a uniformly colliding index from high-dimensional
+ # coordinates. [muller2022instant]
+ primes = [1, 2654435761, 805459861, 3674653429, 2097192037, 1434869437, 2165219737]
+ result = grid_pos[..., 0] * primes[0]
+ for i in range(1, grid_pos.shape[-1]):
+ result.bitwise_xor_(grid_pos[..., i] * primes[i])
+ return result
diff --git a/modules/__old/renderer.py b/modules/__old/renderer.py
new file mode 100644
index 0000000..013d102
--- /dev/null
+++ b/modules/__old/renderer.py
@@ -0,0 +1,364 @@
+from itertools import cycle
+from typing import Set
+
+from ..__common__ import *
+from model.model import BaseModel
+
+
+def density2energy(densities: torch.Tensor, dists: torch.Tensor, raw_noise_std: float = 0):
+ """
+ Calculate energies from densities inferred by model.
+
+ :param densities `Tensor(N..., 1)`: model's output densities
+ :param dists `Tensor(N...)`: integration times
+ :param raw_noise_std `float`: the noise std used to egularize network during training (prevents
+ floater artifacts), defaults to 0, means no noise is added
+ :return `Tensor(N..., 1)`: energies which block light rays
+ """
+ if raw_noise_std > 0:
+ # Add noise to model's predictions for density. Can be used to
+ # regularize network during training (prevents floater artifacts).
+ densities = densities + torch.normal(0.0, raw_noise_std, densities.size())
+ return densities * dists[..., None]
+
+
+def density2alpha(densities: torch.Tensor, dists: torch.Tensor, raw_noise_std: float = 0):
+ """
+ Calculate alphas from densities inferred by model.
+
+ :param densities `Tensor(N..., 1)`: model's output densities
+ :param dists `Tensor(N...)`: integration times
+ :param raw_noise_std `float`: the noise std used to egularize network during training (prevents
+ floater artifacts), defaults to 0, means no noise is added
+ :return `Tensor(N..., 1)`: alphas
+ """
+ energies = density2energy(densities, dists, raw_noise_std)
+ return 1.0 - torch.exp(-energies)
+
+
+class AlphaComposition(Module):
+
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, colors, alphas, bg=None):
+ """
+ [summary]
+
+ :param colors `Tensor(N, P, C)`: [description]
+ :param alphas `Tensor(N, P, 1)`: [description]
+ :param bg `Tensor([N, ]C)`: [description], defaults to None
+ :return `Tensor(N, C)`: [description]
+ """
+ # Compute weight for RGB of each sample along each ray. A cumprod() is
+ # used to express the idea of the ray not having reflected up to this
+ # sample yet.
+ one_minus_alpha = torch.cumprod(1 - alphas[..., :-1, :] + math.tiny, dim=-2)
+ one_minus_alpha = torch.cat([
+ torch.ones_like(one_minus_alpha[..., :1, :]),
+ one_minus_alpha
+ ], dim=-2)
+ weights = alphas * one_minus_alpha # (N, P, 1)
+
+ # (N, C), computed weighted color of each sample along each ray.
+ final_color = torch.sum(weights * colors, dim=-2)
+
+ # To composite onto a white background, use the accumulated alpha map.
+ if bg is not None:
+ # Sum of weights along each ray. This value is in [0, 1] up to numerical error.
+ acc_map = torch.sum(weights, -1)
+ final_color = final_color + bg * (1. - acc_map[..., None])
+
+ return {
+ 'color': final_color,
+ 'weights': weights,
+ }
+
+
+class VolumnRenderer(Module):
+
+ class States:
+ kernel: BaseModel
+ samples: Samples
+ early_stop_tolerance: float
+ outputs: Set[str]
+ hit_mask: torch.Tensor
+ N: int
+ P: int
+ device: torch.device
+
+ colors: torch.Tensor
+ densities: torch.Tensor
+ energies: torch.Tensor
+ weights: torch.Tensor
+ cum_energies: torch.Tensor
+ exp_energies: torch.Tensor
+
+ tot_evaluations: dict[str, int]
+
+ chunk: tuple[slice, slice]
+ cum_chunk: tuple[slice, slice]
+ cum_last: tuple[slice, slice]
+ chunk_id: int
+
+ @property
+ def start(self) -> int:
+ return self.chunk[1].start
+
+ @property
+ def end(self) -> int:
+ return self.chunk[1].stop
+
+ def __init__(self, kernel: BaseModel, samples: Samples, early_stop_tolerance: float,
+ outputs: Set[str]) -> None:
+ self.kernel = kernel
+ self.samples = samples
+ self.early_stop_tolerance = early_stop_tolerance
+ self.outputs = outputs
+
+ N, P = samples.size
+ self.device = self.samples.device
+ self.hit_mask = samples.voxel_indices != -1 # (N, P) | bool
+ self.colors = torch.zeros(N, P, kernel.chns('color'), device=samples.device)
+ self.densities = torch.zeros(N, P, 1, device=samples.device)
+ self.energies = torch.zeros(N, P, 1, device=samples.device)
+ self.weights = torch.zeros(N, P, 1, device=samples.device)
+ self.cum_energies = torch.zeros(N, P + 1, 1, device=samples.device)
+ self.exp_energies = torch.ones(N, P + 1, 1, device=samples.device)
+ self.tot_evaluations = {}
+ self.N, self.P = N, P
+ self.chunk_id = -1
+
+ def n_hits(self, index: int | slice = None) -> int:
+ if not isinstance(self.hit_mask, torch.Tensor):
+ if index is not None:
+ return self.N * self.colors[:, index].shape[1]
+ return self.N * self.P
+ if index is None:
+ return self.hit_mask.count_nonzero().item()
+ return self.hit_mask[:, index].count_nonzero().item()
+
+ def accumulate_tot_evaluations(self, key: str, n: int):
+ if key not in self.tot_evaluations:
+ self.tot_evaluations[key] = 0
+ self.tot_evaluations[key] += n
+
+ def next_chunk(self, *, length=None, end=None):
+ start = 0 if not hasattr(self, "chunk") else self.end
+ length = length or self.P
+ end = min(end or start + length, self.P)
+ self.chunk = slice(None), slice(start, end)
+ self.cum_chunk = slice(None), slice(start + 1, end + 1)
+ self.cum_last = slice(None), slice(start, start + 1)
+ self.chunk_id += 1
+ return self
+
+ def put(self, key: str, values: torch.Tensor, indices: tuple[torch.Tensor, torch.Tensor] | tuple[slice, slice]):
+ if not hasattr(self, key):
+ new_tensor = torch.zeros(self.N, self.P, values.shape[-1], device=self.device)
+ setattr(self, key, new_tensor)
+ tensor: torch.Tensor = getattr(self, key)
+ # if isinstance(indices[0], torch.Tensor):
+ # tensor.index_put_(indices, values)
+ # else:
+ tensor[indices] = values
+
+ def __init__(self, **kwargs):
+ super().__init__()
+
+ @profile
+ def forward(self, kernel: BaseModel, samples: Samples, *outputs: str,
+ raymarching_early_stop_tolerance: float = 0,
+ raymarching_chunk_size_or_sections: int | list[int] = None,
+ **kwargs) -> ReturnData:
+ """
+ Perform volumn rendering.
+
+ :param kernel `BaseModel`: render kernel
+ :param samples `Samples(N, P)`: samples
+ :param outputs `str...`: items should be contained in the result dict.
+ Optional values include 'color', 'depth', 'layers', 'states' and attribute names in class `States` (e.g. 'weights'). Defaults to []
+ :param raymarching_early_stop_tolerance `float`: tolerance of raymarching early stop.
+ Should between 0 and 1 (0 means no early stop). Defaults to 0
+ :param raymarching_chunk_size_or_sections `int|list[int]`: indicates how to split raymarching process.
+ Use a list of integers to specify samples of every chunk, or a positive integer to specify number of chunks.
+ Use a negative interger to split by number of hits in chunks, and the absolute value means maximum number of hits in a chunk.
+ 0 and `None` means not splitting the raymarching process. Defaults to `None`
+ :return `dict`: render result { 'color'[, 'depth', 'layers', 'states', ...] }
+ """
+ if samples.size[1] == 0:
+ print("VolumnRenderer.forward(): # of samples is zero")
+ return None
+
+ infer_outputs = set()
+ for key in outputs:
+ if key == "color":
+ infer_outputs.add("colors")
+ infer_outputs.add("densities")
+ elif key == "specular":
+ infer_outputs.add("speculars")
+ infer_outputs.add("densities")
+ elif key == "diffuse":
+ infer_outputs.add("diffuses")
+ infer_outputs.add("densities")
+ elif key == "depth":
+ infer_outputs.add("densities")
+ else:
+ infer_outputs.add(key)
+
+ with profile("Prepare states object"):
+ s = VolumnRenderer.States(kernel, samples, raymarching_early_stop_tolerance,
+ infer_outputs)
+
+ if not raymarching_chunk_size_or_sections:
+ raymarching_chunk_size_or_sections = [s.P]
+ elif isinstance(raymarching_chunk_size_or_sections, int) and \
+ raymarching_chunk_size_or_sections > 0:
+ raymarching_chunk_size_or_sections = [
+ math.ceil(s.P / raymarching_chunk_size_or_sections)
+ ]
+
+ with profile("Run forward chunks"):
+ if isinstance(raymarching_chunk_size_or_sections, list):
+ chunk_sections = raymarching_chunk_size_or_sections
+ for chunk_samples in cycle(chunk_sections):
+ self._forward_chunk(s.next_chunk(length=chunk_samples))
+ if s.end >= s.P:
+ break
+ else:
+ chunk_size = -raymarching_chunk_size_or_sections
+ chunk_hits = s.n_hits(0)
+ for i in range(1, s.P):
+ n_hits = s.n_hits(i)
+ if chunk_hits + n_hits > chunk_size:
+ self._forward_chunk(s.next_chunk(end=i))
+ n_hits = s.n_hits(i)
+ chunk_hits = 0
+ chunk_hits += n_hits
+ self._forward_chunk(s.next_chunk())
+
+ with profile("Set return data"):
+ ret = {}
+ for key in outputs:
+ if key == 'color':
+ ret['color'] = torch.sum(s.colors * s.weights, 1)
+ elif key == 'depth':
+ ret['depth'] = torch.sum(s.samples.depths[..., None] * s.weights, 1)
+ elif key == 'diffuse' and hasattr(s, "diffuses"):
+ ret['diffuse'] = torch.sum(s.diffuses * s.weights, 1)
+ elif key == 'specular' and hasattr(s, "speculars"):
+ ret['specular'] = torch.sum(s.speculars * s.weights, 1)
+ elif key == 'layers':
+ ret['layers'] = torch.cat([s.colors, 1 - torch.exp(-s.energies)], dim=-1)
+ elif key == 'states':
+ ret['states'] = s
+ else:
+ if hasattr(s, key):
+ ret[key] = getattr(s, key)
+
+ return ret
+
+ @profile
+ def _calc_weights(self, s: States):
+ """
+ Calculate weights of samples in composited outputs
+
+ :param s `States`: states
+ :param start `int`: chunk's start
+ :param end `int`: chunk's end
+ """
+ s.energies[s.chunk] = density2energy(s.densities[s.chunk], s.samples.dists[s.chunk])
+ s.cum_energies[s.cum_chunk] = torch.cumsum(s.energies[s.chunk], 1) \
+ + s.cum_energies[s.cum_last]
+ s.exp_energies[s.cum_chunk] = (-s.cum_energies[s.cum_chunk]).exp()
+ s.weights[s.chunk] = s.exp_energies[s.chunk] - s.exp_energies[s.cum_chunk]
+
+ @profile
+ def _apply_early_stop(self, s: States):
+ """
+ Stop rays whose accumulated opacity are larger than a threshold
+
+ :param s `States`: s
+ :param end `int`: chunk's end
+ """
+ if s.end < s.P and s.early_stop_tolerance > 0 and isinstance(s.hit_mask, torch.Tensor):
+ rays_to_stop = s.exp_energies[:, s.end, 0] < s.early_stop_tolerance
+ s.hit_mask[rays_to_stop, s.end:] = 0
+
+ @profile
+ def _forward_chunk(self, s: States) -> int:
+ if isinstance(s.hit_mask, torch.Tensor):
+ fi_idxs: tuple[torch.Tensor, ...] = s.hit_mask[s.chunk].nonzero(as_tuple=True)
+ if fi_idxs[0].size(0) == 0:
+ s.cum_energies[s.cum_chunk] = s.cum_energies[s.cum_last]
+ s.exp_energies[s.cum_chunk] = s.exp_energies[s.cum_last]
+ return
+ fi_idxs[1].add_(s.start)
+ s.accumulate_tot_evaluations("colors", fi_idxs[0].size(0))
+ else:
+ fi_idxs = s.chunk
+ fi_outputs = s.kernel.infer(*s.outputs, samples=s.samples[fi_idxs], chunk_id=s.chunk_id)
+ for key, value in fi_outputs.items():
+ s.put(key, value, fi_idxs)
+
+ self._calc_weights(s)
+ self._apply_early_stop(s)
+
+
+class DensityFirstVolumnRenderer(VolumnRenderer):
+
+ def __init__(self, **kwargs):
+ super().__init__(**kwargs)
+
+ def _forward_chunk(self, s: VolumnRenderer.States) -> int:
+ fi_idxs: tuple[torch.Tensor, ...] = s.hit_mask[s.chunk].nonzero(as_tuple=True) # (N')
+ fi_idxs[1].add_(s.start)
+
+ if fi_idxs[0].size(0) == 0:
+ s.cum_energies[s.cum_chunk] = s.cum_energies[s.cum_last]
+ s.exp_energies[s.cum_chunk] = s.exp_energies[s.cum_last]
+ return
+
+ # fi_* means "filtered" by hit mask
+ fi_samples = s.samples[fi_idxs] # N -> N'
+
+ # For all valid samples: encode X
+ density_inputs = s.kernel.input(fi_samples, "x", "f") # (N', Ex)
+
+ # Infer densities (shape)
+ density_outputs = s.kernel.infer('densities', 'features', samples=fi_samples,
+ inputs=density_inputs, chunk_id=s.chunk_id)
+ s.put('densities', density_outputs['densities'], fi_idxs)
+ s.accumulate_tot_evaluations("densities", fi_idxs[0].size(0))
+
+ self._calc_weights(s)
+ self._apply_early_stop(s)
+
+ # Remove samples whose weights are less than a threshold
+ s.hit_mask[s.chunk][s.weights[s.chunk][..., 0] < 0.01] = 0
+
+ # Update "filtered" tensors
+ fi_mask = s.hit_mask[fi_idxs]
+ fi_idxs = (fi_idxs[0][fi_mask], fi_idxs[1][fi_mask]) # N' -> N"
+ fi_samples = s.samples[fi_idxs] # N -> N"
+ fi_features = density_outputs['features'][fi_mask]
+ color_inputs = s.kernel.input(fi_samples, "d") # (N")
+ color_inputs.x = density_inputs.x[fi_mask]
+
+ # Infer colors (appearance)
+ outputs = s.outputs.copy()
+ if 'densities' in outputs:
+ outputs.remove('densities')
+ color_outputs = s.kernel.infer(*outputs, samples=fi_samples, inputs=color_inputs,
+ chunk_id=s.chunk_id, features=fi_features)
+ # if s.chunk_id == 0:
+ # fi_colors[:] *= fi_colors.new_tensor([1, 0, 0])
+ # elif s.chunk_id == 1:
+ # fi_colors[:] *= fi_colors.new_tensor([0, 1, 0])
+ # elif s.chunk_id == 2:
+ # fi_colors[:] *= fi_colors.new_tensor([0, 0, 1])
+ # else:
+ # fi_colors[:] *= fi_colors.new_tensor([1, 1, 0])
+ for key, value in color_outputs.items():
+ s.put(key, value, fi_idxs)
+ s.accumulate_tot_evaluations("colors", fi_idxs[0].size(0))
diff --git a/modules/__old/sampler.py b/modules/__old/sampler.py
new file mode 100644
index 0000000..591c0c5
--- /dev/null
+++ b/modules/__old/sampler.py
@@ -0,0 +1,112 @@
+class VoxelSampler(Module):
+
+ def __init__(self, *, sample_step: float, **kwargs):
+ """
+ Initialize a VoxelSampler module
+
+ :param perturb_sample: perturb the sample depths
+ :param step_size: step size
+ """
+ super().__init__()
+ self.sample_step = sample_step
+
+ def _forward(self, rays_o: torch.Tensor, rays_d: torch.Tensor, space_module: Space, *,
+ perturb_sample: bool, **kwargs) -> tuple[Samples, torch.Tensor]:
+ """
+ [summary]
+
+ :param rays_o `Tensor(N, 3)`: rays' origin positions
+ :param rays_d `Tensor(N, 3)`: rays' directions
+ :param step_size `float`: gap between samples along a ray
+ :return `Samples(N', P)`: samples along valid rays (which hit at least one voxel)
+ :return `Tensor(N)`: valid rays mask
+ """
+ intersections = space_module.ray_intersect(rays_o, rays_d, 100)
+ valid_rays_mask = intersections.hits > 0
+ rays_o = rays_o[valid_rays_mask]
+ rays_d = rays_d[valid_rays_mask]
+ intersections = intersections[valid_rays_mask] # (N) -> (N')
+ n_rays = rays_o.size(0)
+ ray_index_list = torch.arange(n_rays, device=rays_o.device, dtype=torch.long) # (N')
+
+ hits = intersections.hits
+ min_depths = intersections.min_depths
+ max_depths = intersections.max_depths
+ voxel_indices = intersections.voxel_indices
+
+ rays_near_depth = min_depths[:, :1] # (N', 1)
+ rays_far_depth = max_depths[ray_index_list, hits - 1][:, None] # (N', 1)
+ rays_length = rays_far_depth - rays_near_depth
+ rays_steps = (rays_length / self.sample_step).ceil().long()
+ rays_step_size = rays_length / rays_steps
+ max_steps = rays_steps.max().item()
+ rays_step = torch.arange(max_steps, device=rays_o.device,
+ dtype=torch.float)[None].repeat(n_rays, 1) # (N', P)
+ invalid_samples_mask = rays_step >= rays_steps
+ samples_min_depth = rays_near_depth + rays_step * rays_step_size
+ samples_depth = samples_min_depth + rays_step_size \
+ * (torch.rand_like(samples_min_depth) if perturb_sample else 0.5) # (N', P)
+ samples_dist = rays_step_size.repeat(1, max_steps) # (N', 1) -> (N', P)
+ samples_voxel_index = voxel_indices[
+ ray_index_list[:, None],
+ torch.searchsorted(max_depths, samples_depth)
+ ] # (N', P)
+ samples_depth[invalid_samples_mask] = math.huge
+ samples_dist[invalid_samples_mask] = 0
+ samples_voxel_index[invalid_samples_mask] = -1
+
+ rays_o, rays_d = rays_o[:, None], rays_d[:, None]
+ return Samples(
+ pts=rays_o + rays_d * samples_depth[..., None],
+ dirs=rays_d.expand(-1, max_steps, -1),
+ depths=samples_depth,
+ dists=samples_dist,
+ voxel_indices=samples_voxel_index
+ ), valid_rays_mask
+
+ @profile
+ def forward(self, rays_o: torch.Tensor, rays_d: torch.Tensor,
+ space: Space, *, perturb_sample: bool, **kwargs) -> tuple[Samples, torch.Tensor]:
+ """
+ [summary]
+
+ :param rays_o `Tensor(N, 3)`: [description]
+ :param rays_d `Tensor(N, 3)`: [description]
+ :param step_size `float`: [description]
+ :return `Samples(N, P)`: [description]
+ """
+ with profile("Ray intersect"):
+ intersections = space.ray_intersect(rays_o, rays_d, 100)
+ valid_rays_mask = intersections.hits > 0
+ rays_o = rays_o[valid_rays_mask]
+ rays_d = rays_d[valid_rays_mask]
+ intersections = intersections[valid_rays_mask] # (N) -> (N')
+
+ if intersections.size == 0:
+ return None, valid_rays_mask
+ else:
+ with profile("Inverse CDF sampling"):
+ min_depth = intersections.min_depths
+ max_depth = intersections.max_depths
+ pts_idx = intersections.voxel_indices
+ dists = max_depth - min_depth
+ tot_dists = dists.sum(dim=-1, keepdim=True) # (N, 1)
+ probs = dists / tot_dists
+ steps = tot_dists[:, 0] / self.sample_step
+
+ # sample points and use middle point approximation
+ sampled_indices, sampled_depths, sampled_dists = inverse_cdf_sampling(
+ pts_idx, min_depth, max_depth, probs, steps, -1, not perturb_sample)
+ sampled_indices = sampled_indices.long()
+ invalid_idx_mask = sampled_indices.eq(-1)
+ sampled_dists.clamp_min_(0).masked_fill_(invalid_idx_mask, 0)
+ sampled_depths.masked_fill_(invalid_idx_mask, math.huge)
+
+ rays_o, rays_d = rays_o[:, None], rays_d[:, None]
+ return Samples(
+ pts=rays_o + rays_d * sampled_depths[..., None],
+ dirs=rays_d.expand(-1, sampled_depths.size(1), -1),
+ depths=sampled_depths,
+ dists=sampled_dists,
+ voxel_indices=sampled_indices
+ ), valid_rays_mask
diff --git a/modules/core/__init__.py b/modules/core/__init__.py
new file mode 100644
index 0000000..cf3da1f
--- /dev/null
+++ b/modules/core/__init__.py
@@ -0,0 +1,4 @@
+from .nerf import NeRF
+from .fs_nerf import FsNeRF
+
+__all__ = ["NeRF", "FsNeRF"]
diff --git a/modules/core/color_decoder.py b/modules/core/color_decoder.py
new file mode 100644
index 0000000..cec94fc
--- /dev/null
+++ b/modules/core/color_decoder.py
@@ -0,0 +1,41 @@
+from ..__common__ import *
+
+__all__ = ["ColorDecoder", "BasicColorDecoder", "NeRFColorDecoder"]
+
+
+class ColorDecoder(nn.Module):
+ def __init__(self, f_chns: int, d_chns: int, color_chns: int):
+ super().__init__({"f": f_chns, "d": d_chns}, {"color": color_chns})
+
+ # stub method for type hint
+ def __call__(self, f: torch.Tensor, d: torch.Tensor) -> torch.Tensor:
+ ...
+
+ def forward(self, f: torch.Tensor, d: torch.Tensor) -> torch.Tensor:
+ raise NotImplementedError()
+
+ @staticmethod
+ def create(f_chns: int, d_chns: int, color_chns: int, type: str, args: dict[str, Any]) -> "ColorDecoder":
+ return getattr(sys.modules[__name__], f"{type}ColorDecoder")(
+ f_chns=f_chns, d_chns=d_chns, color_chns=color_chns, **args)
+
+
+class BasicColorDecoder(ColorDecoder):
+ def __init__(self, f_chns: int, color_chns: int, out_act: str = "sigmoid", **kwargs):
+ super().__init__(f_chns, 0, color_chns)
+ self.net = nn.FcLayer(f_chns, color_chns, out_act)
+
+ def forward(self, f: torch.Tensor, d: torch.Tensor) -> torch.Tensor:
+ return self.net(f)
+
+
+class NeRFColorDecoder(ColorDecoder):
+ def __init__(self, f_chns: int, d_chns: int, color_chns: int, act: str = "relu",
+ out_act: str = "sigmoid", with_ln: bool = False, **kwargs):
+ super().__init__(f_chns, d_chns, color_chns)
+ self.feature_layer = nn.FcLayer(f_chns, f_chns, with_ln=with_ln)
+ self.net = nn.FcBlock(f_chns + d_chns, color_chns, 1,
+ f_chns // 2, [], act, out_act, with_ln)
+
+ def forward(self, f: torch.Tensor, d: torch.Tensor) -> torch.Tensor:
+ return self.net(union(self.feature_layer(f), d))
diff --git a/modules/core/density_decoder.py b/modules/core/density_decoder.py
new file mode 100644
index 0000000..778cf18
--- /dev/null
+++ b/modules/core/density_decoder.py
@@ -0,0 +1,16 @@
+from ..__common__ import *
+
+__all__ = ["DensityDecoder"]
+
+
+class DensityDecoder(nn.Module):
+ def __init__(self, f_chns: int, density_chns: int, **kwargs):
+ super().__init__({"f": f_chns}, {"density": density_chns})
+ self.net = nn.FcLayer(f_chns, density_chns)
+
+ # stub method for type hint
+ def __call__(self, f: torch.Tensor) -> torch.Tensor:
+ ...
+
+ def forward(self, f: torch.Tensor) -> torch.Tensor:
+ return self.net(f)
diff --git a/modules/core/field.py b/modules/core/field.py
new file mode 100644
index 0000000..73b7a97
--- /dev/null
+++ b/modules/core/field.py
@@ -0,0 +1,19 @@
+from ..__common__ import *
+
+__all__ = ["Field"]
+
+
+class Field(nn.Module):
+ def __init__(self, x_chns: int, shape: list[int], skips: list[int] = [],
+ act: str = 'relu', with_ln: bool = False):
+ super().__init__({"x": x_chns}, {"f": shape[1]})
+ self.net = nn.FcBlock(x_chns, 0, *shape, skips, act, with_ln=with_ln)
+
+ # stub method for type hint
+ def __call__(self, x: torch.Tensor) -> torch.Tensor:
+ ...
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ return self.net(x)
+
+
diff --git a/modules/core/fs_nerf.py b/modules/core/fs_nerf.py
new file mode 100644
index 0000000..4a657d7
--- /dev/null
+++ b/modules/core/fs_nerf.py
@@ -0,0 +1,58 @@
+from ..__common__ import *
+from .field import *
+from .color_decoder import *
+from .density_decoder import *
+
+
+class FsNeRF(nn.Module):
+
+ def __init__(self, x_chns: int, color_chns: int, depth: int, width: int,
+ skips: list[int], act: str, ln: bool, n_samples: int, n_fields: int):
+ """
+ Initialize a FS-NeRF core module.
+
+ :param x_chns `int`: channels of input positions (D_x)
+ :param d_chns `int`: channels of input directions (D_d)
+ :param color_chns `int`: channels of output colors (D_c)
+ :param depth `int`: number of layers in field network
+ :param width `int`: width of each layer in field network
+ :param skips `[int]`: skip connections from input to specific layers in field network
+ :param act `str`: activation function in field network and color decoder
+ :param ln `bool`: whether enable layer normalization in field network and color decoder
+ :param color_decoder_type `str`: type of color decoder
+ """
+ super().__init__({"x": x_chns}, {"rgbd": 1 + color_chns})
+ self.n_fields = n_fields
+ self.samples_per_field = n_samples // n_fields
+ self.subnets = torch.nn.ModuleList()
+ for _ in range(n_fields):
+ field = Field(x_chns * self.samples_per_field, [depth, width], skips, act, ln)
+ density_decoder = DensityDecoder(field.out_chns, self.samples_per_field)
+ color_decoder = BasicColorDecoder(field.out_chns, color_chns * self.samples_per_field)
+ self.subnets.append(torch.nn.ModuleDict({
+ "field": field,
+ "density_decoder": density_decoder,
+ "color_decoder": color_decoder
+ }))
+
+ # stub method for type hint
+ def __call__(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+ """
+ Inference colors and densities from input samples
+
+ :param x `Tensor(B..., P, D_x)`: input positions
+ :return `Tensor(B..., P, D_c + D_σ)`: output colors and densities
+ """
+ ...
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ densities = []
+ colors = []
+ for i in range(self.n_fields):
+ f = self.subnets[i]["field"](
+ x[..., i * self.samples_per_field:(i + 1) * self.samples_per_field, :].flatten(-2))
+ densities.append(self.subnets[i]["density_decoder"](f)
+ .unflatten(-1, (self.samples_per_field, -1)))
+ colors.append(self.subnets[i]["color_decoder"](f, None)
+ .unflatten(-1, (self.samples_per_field, -1)))
+ return torch.cat([torch.cat(colors, -2), torch.cat(densities, -2)], -1)
\ No newline at end of file
diff --git a/modules/core/nerf.py b/modules/core/nerf.py
new file mode 100644
index 0000000..503adf4
--- /dev/null
+++ b/modules/core/nerf.py
@@ -0,0 +1,45 @@
+from ..__common__ import *
+from .field import *
+from .color_decoder import *
+from .density_decoder import *
+
+
+class NeRF(nn.Module):
+
+ def __init__(self, x_chns: int, d_chns: int, color_chns: int, depth: int, width: int,
+ skips: list[int], act: str, ln: bool, color_decoder_type: str):
+ """
+ Initialize a NeRF core module.
+
+ :param x_chns `int`: channels of input positions (D_x)
+ :param d_chns `int`: channels of input directions (D_d)
+ :param color_chns `int`: channels of output colors (D_c)
+ :param depth `int`: number of layers in field network
+ :param width `int`: width of each layer in field network
+ :param skips `[int]`: skip connections from input to specific layers in field network
+ :param act `str`: activation function in field network and color decoder
+ :param ln `bool`: whether enable layer normalization in field network and color decoder
+ :param color_decoder_type `str`: type of color decoder
+ """
+ super().__init__({"x": x_chns, "d": d_chns}, {"density": 1, "color": color_chns})
+ self.field = Field(x_chns, [depth, width], skips, act, ln)
+ self.density_decoder = DensityDecoder(self.field.out_chns, 1)
+ self.color_decoder = ColorDecoder.create(self.field.out_chns, d_chns, color_chns,
+ color_decoder_type, {"act": act, "with_ln": ln})
+
+ # stub method for type hint
+ def __call__(self, x: torch.Tensor, d: torch.Tensor) -> torch.Tensor:
+ """
+ Inference colors and densities from input samples
+
+ :param x `Tensor(B..., D_x)`: input positions
+ :param d `Tensor(B..., D_d)`: input directions
+ :return `Tensor(B..., D_c + D_σ)`: output colors and densities
+ """
+ ...
+
+ def forward(self, x: torch.Tensor, d: torch.Tensor) -> torch.Tensor:
+ f = self.field(x)
+ densities = self.density_decoder(f)
+ colors = self.color_decoder(f, d)
+ return torch.cat([colors, densities], -1)
diff --git a/modules/generic/__init__.py b/modules/generic/__init__.py
deleted file mode 100644
index 12ccca8..0000000
--- a/modules/generic/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .linear import *
diff --git a/modules/generic/linear.py b/modules/generic/linear.py
deleted file mode 100644
index c064cb4..0000000
--- a/modules/generic/linear.py
+++ /dev/null
@@ -1,104 +0,0 @@
-from typing import List
-from .weight_init import *
-from .fn import *
-
-
-class BatchLinear(nn.Linear):
- '''
- A linear meta-layer that can deal with batched weight matrices and biases,
- as for instance output by a hypernetwork.
- '''
- __doc__ = nn.Linear.__doc__
-
- def forward(self, input, params=None):
- # if params is None:
- # params = OrderedDict(self.named_parameters())
-
- bias = params.get('bias', None)
- weight = params['weight']
-
- output = input.matmul(weight.permute(*[i for i in range(len(weight.shape) - 2)], -1, -2))
- output += bias.unsqueeze(-2)
- return output
-
-
-class FcLayer(nn.Module):
-
- def __init__(self, in_chns: int, out_chns: int, act: str = 'linear', skip_chns: int = 0,
- with_ln: bool = True):
- super().__init__()
- nls_and_inits = {
- 'sine': (Sine, init_weights_sine),
- 'relu': (nn.ReLU, init_weights_relu),
- 'leakyrelu': (nn.LeakyReLU, init_weights_leakyrelu),
- 'sigmoid': (nn.Sigmoid, init_weights_xavier),
- 'tanh': (nn.Tanh, init_weights_xavier),
- 'selu': (nn.SELU, init_weights_selu),
- 'softplus': (nn.Softplus, init_weights_trunc_normal),
- 'elu': (nn.ELU, init_weights_elu),
- 'softmax': (nn.Softmax, init_weights_softmax),
- 'logsoftmax': (nn.LogSoftmax, init_weights_softmax),
- 'mise': (Mise, init_weights_xavier),
- 'linear': (nn.Identity, init_weights_xavier)
- }
- nl_cls, weight_init_fn = nls_and_inits[act]
-
- self.net = [nn.Linear(in_chns + skip_chns, out_chns)]
- if with_ln:
- self.net += [nn.LayerNorm([out_chns])]
- self.net += [nl_cls()]
- self.net = nn.Sequential(*self.net)
- self.skip = skip_chns != 0
- self.with_ln = with_ln
- self.net.apply(weight_init_fn)
-
- def forward(self, x: torch.Tensor, x0: torch.Tensor = None) -> torch.Tensor:
- return self.net(torch.cat([x0, x], -1) if self.skip else x)
-
- def __repr__(self) -> str:
- s = f"{self.net[0].in_features} -> {self.net[0].out_features}, "\
- + ", ".join(module.__class__.__name__ for module in self.net[1:])
- return f"{self._get_name()}({s})"
-
-
-class FcBlock(nn.Module):
-
- def __init__(self, *, in_chns: int, out_chns: int, nf: int, n_layers: int,
- skips: List[int] = [], act: str = 'relu', out_act='linear', with_ln=True):
- """
- Initialize a full-connection net
-
- :kwarg in_chns: channels of input
- :kwarg out_chns: channels of output
- :kwarg nf: number of features in each hidden layer
- :kwarg n_layers: number of layers
- :kwarg skips: create skip connections from input to layers in this list
- """
- super().__init__()
-
- self.layers = nn.ModuleList([
- FcLayer(in_chns, nf, act, with_ln=with_ln)] + [
- FcLayer(nf, nf, act, skip_chns=in_chns if i in skips else 0, with_ln=with_ln)
- for i in range(1, n_layers)
- ])
- if out_chns:
- self.layers.append(FcLayer(nf, out_chns, out_act, with_ln=False))
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x0 = x
- for layer in self.layers:
- x = layer(x, x0)
- return x
-
- def __repr__(self):
- lines = []
- for key, module in self.layers._modules.items():
- mod_str = repr(module)
- mod_str = nn.modules.module._addindent(mod_str, 2)
- lines.append('(' + key + '): ' + mod_str)
-
- main_str = self._get_name() + '('
- if lines:
- main_str += '\n ' + '\n '.join(lines) + '\n'
- main_str += ')'
- return main_str
diff --git a/modules/input_encoder.py b/modules/input_encoder.py
index 8b7d5c5..b57ee35 100644
--- a/modules/input_encoder.py
+++ b/modules/input_encoder.py
@@ -1,184 +1,78 @@
-from typing import Tuple
-import torch
+from .__common__ import *
-from .generic import *
-from utils import math
-from utils.module import Module
+__all__ = ["InputEncoder", "LinearEncoder", "FreqEncoder"]
-class InputEncoder(Module):
+class InputEncoder(nn.Module):
+ """
+ Base class for input encoder.
+ """
- def __init__(self, chns, L, cat_input=False):
- super().__init__()
- emb = torch.exp(torch.arange(L, dtype=torch.float) * math.log(2.))
+ def __init__(self, in_chns: int, out_chns: int):
+ super().__init__({"_": in_chns}, {"_": out_chns})
- self.emb = nn.Parameter(emb, requires_grad=False)
- self.in_dim = chns
- self.out_dim = chns * (L * 2 + cat_input)
- self.cat_input = cat_input
+ # stub method for type hint
+ def __call__(self, x: torch.Tensor) -> torch.Tensor:
+ """
+ Encode the input tensor.
- def forward(self, x: torch.Tensor, angular=False):
- sizes = x.size()
- x0 = x
+ :param x `Tensor(N..., D)`: D-dim inputs
+ :return `Tensor(N..., E)`: encoded outputs
+ """
+ ...
- if angular:
- x = torch.acos(x.clamp(-1, 1))
- x = x[..., None] @ self.emb[None]
- x = torch.cat([torch.sin(x), torch.cos(x)], -1)
- x = x.flatten(-2)
- if self.cat_input:
- x = torch.cat([x0, x], -1)
- return x
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ raise NotImplementedError()
- def extra_repr(self) -> str:
- return f'in={self.in_dim}, out={self.out_dim}, cat_input={self.cat_input}'
-
-
-class IntegratedPosEncoder(Module):
-
- def __init__(self, chns, L, shape: str, cat_input=False):
- super.__init__()
- self.shape = shape
-
- def _lift_gaussian(self, d: torch.Tensor, t_mean: torch.Tensor, t_var: torch.Tensor,
- r_var: torch.Tensor, diag: bool):
- """Lift a Gaussian defined along a ray to 3D coordinates."""
- mean = d[..., None, :] * t_mean[..., None]
- d_sq = d**2
-
- d_mag_sq = torch.sum(d_sq, -1, keepdim=True).clamp_min(1e-10)
-
- if diag:
- d_outer_diag = d_sq
- null_outer_diag = 1 - d_outer_diag / d_mag_sq
- t_cov_diag = t_var[..., None] * d_outer_diag[..., None, :]
- xy_cov_diag = r_var[..., None] * null_outer_diag[..., None, :]
- cov_diag = t_cov_diag + xy_cov_diag
- return mean, cov_diag
- else:
- d_outer = d[..., :, None] * d[..., None, :]
- eye = torch.eye(d.shape[-1], device=d.device)
- null_outer = eye - d[..., :, None] * (d / d_mag_sq)[..., None, :]
- t_cov = t_var[..., None, None] * d_outer[..., None, :, :]
- xy_cov = r_var[..., None, None] * null_outer[..., None, :, :]
- cov = t_cov + xy_cov
- return mean, cov
-
- def _conical_frustum_to_gaussian(self, d: torch.Tensor, t0: float, t1: float, base_radius: float,
- diag: bool, stable: bool = True):
- """Approximate a conical frustum as a Gaussian distribution (mean+cov).
-
- Assumes the ray is originating from the origin, and base_radius is the
- radius at dist=1. Doesn't assume `d` is normalized.
-
- Args:
- d: torch.float32 3-vector, the axis of the cone
- t0: float, the starting distance of the frustum.
- t1: float, the ending distance of the frustum.
- base_radius: float, the scale of the radius as a function of distance.
- diag: boolean, whether or the Gaussian will be diagonal or full-covariance.
- stable: boolean, whether or not to use the stable computation described in
- the paper (setting this to False will cause catastrophic failure).
-
- Returns:
- a Gaussian (mean and covariance).
+ @staticmethod
+ def create(chns: int, type: str, args: dict[str, Any]) -> "InputEncoder":
"""
- if stable:
- mu = (t0 + t1) / 2
- hw = (t1 - t0) / 2
- t_mean = mu + (2 * mu * hw**2) / (3 * mu**2 + hw**2)
- t_var = (hw**2) / 3 - (4 / 15) * ((hw**4 * (12 * mu**2 - hw**2)) /
- (3 * mu**2 + hw**2)**2)
- r_var = base_radius**2 * ((mu**2) / 4 + (5 / 12) * hw**2 - 4 / 15 *
- (hw**4) / (3 * mu**2 + hw**2))
- else:
- t_mean = (3 * (t1**4 - t0**4)) / (4 * (t1**3 - t0**3))
- r_var = base_radius**2 * (3 / 20 * (t1**5 - t0**5) / (t1**3 - t0**3))
- t_mosq = 3 / 5 * (t1**5 - t0**5) / (t1**3 - t0**3)
- t_var = t_mosq - t_mean**2
- return self._lift_gaussian(d, t_mean, t_var, r_var, diag)
-
- def _cylinder_to_gaussian(self, d: torch.Tensor, t0: float, t1: float, radius: float, diag: bool):
- """Approximate a cylinder as a Gaussian distribution (mean+cov).
-
- Assumes the ray is originating from the origin, and radius is the
- radius. Does not renormalize `d`.
-
- Args:
- d: torch.float32 3-vector, the axis of the cylinder
- t0: float, the starting distance of the cylinder.
- t1: float, the ending distance of the cylinder.
- radius: float, the radius of the cylinder
- diag: boolean, whether or the Gaussian will be diagonal or full-covariance.
-
- Returns:
- a Gaussian (mean and covariance).
- """
- t_mean = (t0 + t1) / 2
- r_var = radius**2 / 4
- t_var = (t1 - t0)**2 / 12
- return self._lift_gaussian(d, t_mean, t_var, r_var, diag)
-
- def cast_rays(self, t_vals: torch.Tensor, rays_o: torch.Tensor, rays_d: torch.Tensor,
- rays_r: torch.Tensor, diag: bool = True):
- """Cast rays (cone- or cylinder-shaped) and featurize sections of it.
-
- Args:
- t_vals: float array, the "fencepost" distances along the ray.
- rays_o: float array, the ray origin coordinates.
- rays_d: float array, the ray direction vectors.
- radii: float array, the radii (base radii for cones) of the rays.
- ray_shape: string, the shape of the ray, must be 'cone' or 'cylinder'.
- diag: boolean, whether or not the covariance matrices should be diagonal.
-
- Returns:
- a tuple of arrays of means and covariances.
- """
- t0 = t_vals[..., :-1]
- t1 = t_vals[..., 1:]
- if self.shape == 'cone':
- gaussian_fn = self._conical_frustum_to_gaussian
- elif self.shape == 'cylinder':
- gaussian_fn = self._cylinder_to_gaussian
- else:
- assert False
- means, covs = gaussian_fn(rays_d, t0, t1, rays_r, diag)
- means = means + rays_o[..., None, :]
- return means, covs
-
- def integrated_pos_enc(x_coord: Tuple[torch.Tensor, torch.Tensor], min_deg: int, max_deg: int,
- diag: bool = True):
- """Encode `x` with sinusoids scaled by 2^[min_deg:max_deg-1].
-
- Args:
- x_coord: a tuple containing: x, torch.ndarray, variables to be encoded. Should
- be in [-pi, pi]. x_cov, torch.ndarray, covariance matrices for `x`.
- min_deg: int, the min degree of the encoding.
- max_deg: int, the max degree of the encoding.
- diag: bool, if true, expects input covariances to be diagonal (full
- otherwise).
-
- Returns:
- encoded: torch.ndarray, encoded variables.
+ Create an input encoder of `type` with `args`.
+
+ :param chns `int`: input channels
+ :param type `str`: type of input encoder, without suffix "Encoder"
+ :param args `{str:Any}`: arguments for initializing the input encoder
+ :return `InputEncoder`: the created input encoder
"""
- if diag:
- x, x_cov_diag = x_coord
- scales = torch.tensor([2**i for i in range(min_deg, max_deg)], device=x.device)[:, None]
- shape = list(x.shape[:-1]) + [-1]
- y = torch.reshape(x[..., None, :] * scales, shape)
- y_var = torch.reshape(x_cov_diag[..., None, :] * scales**2, shape)
- else:
- x, x_cov = x_coord
- num_dims = x.shape[-1]
- basis = torch.cat([
- 2**i * torch.eye(num_dims, device=x.device)
- for i in range(min_deg, max_deg)
- ], 1)
- y = torch.matmul(x, basis)
- # Get the diagonal of a covariance matrix (ie, variance). This is equivalent
- # to jax.vmap(torch.diag)((basis.T @ covs) @ basis).
- y_var = (torch.matmul(x_cov, basis) * basis).sum(-2)
-
- return math.expected_sin(
- torch.cat([y, y + 0.5 * math.pi], -1),
- torch.cat([y_var] * 2, -1))[0]
+ return getattr(sys.modules[__name__], f"{type}Encoder")(chns, **args)
+
+
+class LinearEncoder(InputEncoder):
+ """
+ The linear encoder: D -> D.
+ """
+
+ def __init__(self, chns):
+ super().__init__(chns, chns)
+
+ def forward(self, x: torch.Tensor):
+ return x
+
+ def extra_repr(self) -> str:
+ return f"{self.in_chns} -> {self.out_chns}"
+
+
+class FreqEncoder(InputEncoder):
+ """
+ The frequency encoder introduced in [mildenhall2020nerf]: D -> 2LD[+D].
+ """
+
+ freq_bands: torch.Tensor
+ """
+ `Tensor(L)` Frequency bands (1, 2, ..., 2^(L-1))
+ """
+
+ def __init__(self, chns, freqs: int, include_input: bool):
+ super().__init__(chns, chns * (freqs * 2 + include_input))
+ self.include_input = include_input
+ self.freqs = freqs
+ self.register_temp("freq_bands", (2. ** torch.arange(freqs))[:, None].expand(-1, chns))
+
+ def forward(self, x: torch.Tensor):
+ x_ = x.unsqueeze(-2) * self.freq_bands
+ result = union(torch.sin(x_), torch.cos(x_)).flatten(-2)
+ return union(x, result) if self.include_input else result
+
+ def extra_repr(self) -> str:
+ return f"{self.in_chns} -> {self.out_chns}"\
+ f"(2x{self.freqs}x{self.in_chns}{f'+{self.in_chns}' * self.include_input})"
diff --git a/modules/renderer.py b/modules/renderer.py
index 08f7649..e1d3668 100644
--- a/modules/renderer.py
+++ b/modules/renderer.py
@@ -1,373 +1,83 @@
-import torch
-from itertools import cycle
-from typing import Dict, Set, Tuple, Union
+from .__common__ import *
+import torch.nn.functional as F
-from utils.type import NetInput, ReturnData
+__all__ = ["density2energy", "density2alpha", "VolumnRenderer"]
-from .generic import *
-from model.base import BaseModel
-from utils import math
-from utils.module import Module
-from utils.perf import checkpoint, perf
-from utils.samples import Samples
-
-def density2energy(densities: torch.Tensor, dists: torch.Tensor, raw_noise_std: float = 0):
+def density2energy(densities: torch.Tensor, dists: torch.Tensor, raw_noise_std: float = 0) -> torch.Tensor:
"""
Calculate energies from densities inferred by model.
- :param densities `Tensor(N..., 1)`: model's output densities
+ :param densities `Tensor(N...)`: model's output densities
:param dists `Tensor(N...)`: integration times
:param raw_noise_std `float`: the noise std used to egularize network during training (prevents
floater artifacts), defaults to 0, means no noise is added
- :return `Tensor(N..., 1)`: energies which block light rays
+ :return `Tensor(N...)`: energies which block light rays
"""
if raw_noise_std > 0:
# Add noise to model's predictions for density. Can be used to
# regularize network during training (prevents floater artifacts).
- densities = densities + torch.normal(0.0, raw_noise_std, densities.size())
- return densities * dists[..., None]
+ densities = densities + torch.normal(0.0, raw_noise_std, densities.shape,
+ device=densities.device)
+ return F.relu(densities) * dists
-def density2alpha(densities: torch.Tensor, dists: torch.Tensor, raw_noise_std: float = 0):
+def energy2alpha(energies: torch.Tensor) -> torch.Tensor:
"""
- Calculate alphas from densities inferred by model.
+ Convert energies to alphas.
- :param densities `Tensor(N..., 1)`: model's output densities
- :param dists `Tensor(N...)`: integration times
- :param raw_noise_std `float`: the noise std used to egularize network during training (prevents
- floater artifacts), defaults to 0, means no noise is added
- :return `Tensor(N..., 1)`: alphas
+ :param energies `Tensor(N...)`: energies (calculated from densities)
+ :return `Tensor(N...)`: alphas
"""
- energies = density2energy(densities, dists, raw_noise_std)
return 1.0 - torch.exp(-energies)
-class AlphaComposition(Module):
-
- def __init__(self):
- super().__init__()
-
- def forward(self, colors, alphas, bg=None):
- """
- [summary]
-
- :param colors `Tensor(N, P, C)`: [description]
- :param alphas `Tensor(N, P, 1)`: [description]
- :param bg `Tensor([N, ]C)`: [description], defaults to None
- :return `Tensor(N, C)`: [description]
- """
- # Compute weight for RGB of each sample along each ray. A cumprod() is
- # used to express the idea of the ray not having reflected up to this
- # sample yet.
- one_minus_alpha = torch.cumprod(1 - alphas[..., :-1, :] + math.tiny, dim=-2)
- one_minus_alpha = torch.cat([
- torch.ones_like(one_minus_alpha[..., :1, :]),
- one_minus_alpha
- ], dim=-2)
- weights = alphas * one_minus_alpha # (N, P, 1)
-
- # (N, C), computed weighted color of each sample along each ray.
- final_color = torch.sum(weights * colors, dim=-2)
-
- # To composite onto a white background, use the accumulated alpha map.
- if bg is not None:
- # Sum of weights along each ray. This value is in [0, 1] up to numerical error.
- acc_map = torch.sum(weights, -1)
- final_color = final_color + bg * (1. - acc_map[..., None])
-
- return {
- 'color': final_color,
- 'weights': weights,
- }
-
-
-class VolumnRenderer(Module):
-
- class States:
- kernel: BaseModel
- samples: Samples
- early_stop_tolerance: float
- outputs: Set[str]
- hit_mask: torch.Tensor
- N: int
- P: int
- device: torch.device
-
- colors: torch.Tensor
- densities: torch.Tensor
- energies: torch.Tensor
- weights: torch.Tensor
- cum_energies: torch.Tensor
- exp_energies: torch.Tensor
-
- tot_evaluations: Dict[str, int]
-
- chunk: Tuple[slice, slice]
- cum_chunk: Tuple[slice, slice]
- cum_last: Tuple[slice, slice]
- chunk_id: int
-
- @property
- def start(self) -> int:
- return self.chunk[1].start
-
- @property
- def end(self) -> int:
- return self.chunk[1].stop
-
- def __init__(self, kernel: BaseModel, samples: Samples, early_stop_tolerance: float,
- outputs: Set[str]) -> None:
- self.kernel = kernel
- self.samples = samples
- self.early_stop_tolerance = early_stop_tolerance
- self.outputs = outputs
-
- N, P = samples.size
- self.device = self.samples.device
- self.hit_mask = samples.voxel_indices != -1 # (N, P) | bool
- self.colors = torch.zeros(N, P, kernel.chns('color'), device=samples.device)
- self.densities = torch.zeros(N, P, 1, device=samples.device)
- self.energies = torch.zeros(N, P, 1, device=samples.device)
- self.weights = torch.zeros(N, P, 1, device=samples.device)
- self.cum_energies = torch.zeros(N, P + 1, 1, device=samples.device)
- self.exp_energies = torch.ones(N, P + 1, 1, device=samples.device)
- self.tot_evaluations = {}
- self.N, self.P = N, P
- self.chunk_id = -1
-
- def n_hits(self, index: Union[int, slice] = None) -> int:
- if not isinstance(self.hit_mask, torch.Tensor):
- if index is not None:
- return self.N * self.colors[:, index].shape[1]
- return self.N * self.P
- if index is None:
- return self.hit_mask.count_nonzero().item()
- return self.hit_mask[:, index].count_nonzero().item()
+def density2alpha(densities: torch.Tensor, dists: torch.Tensor, raw_noise_std: float = 0) -> torch.Tensor:
+ """
+ Calculate alphas from densities inferred by model.
- def accumulate_tot_evaluations(self, key: str, n: int):
- if key not in self.tot_evaluations:
- self.tot_evaluations[key] = 0
- self.tot_evaluations[key] += n
+ :param densities `Tensor(N...)`: model's output densities
+ :param dists `Tensor(N...)`: integration times
+ :param raw_noise_std `float`: the noise std used to regularize network during training (prevents
+ floater artifacts), defaults to 0, means no noise is added
+ :return `Tensor(N...)`: alphas
+ """
+ return energy2alpha(density2energy(densities, dists, raw_noise_std))
- def next_chunk(self, *, length=None, end=None):
- start = 0 if not hasattr(self, "chunk") else self.end
- length = length or self.P
- end = min(end or start + length, self.P)
- self.chunk = slice(None), slice(start, end)
- self.cum_chunk = slice(None), slice(start + 1, end + 1)
- self.cum_last = slice(None), slice(start, start + 1)
- self.chunk_id += 1
- return self
- def put(self, key: str, values: torch.Tensor, indices: Union[Tuple[torch.Tensor, torch.Tensor], Tuple[slice, slice]]):
- if not hasattr(self, key):
- new_tensor = torch.zeros(self.N, self.P, values.shape[-1], device=self.device)
- setattr(self, key, new_tensor)
- tensor: torch.Tensor = getattr(self, key)
- # if isinstance(indices[0], torch.Tensor):
- # tensor.index_put_(indices, values)
- # else:
- tensor[indices] = values
+class VolumnRenderer(nn.Module):
- def __init__(self, **kwargs):
+ def __init__(self):
super().__init__()
- @perf
- def forward(self, kernel: BaseModel, samples: Samples, *outputs: str,
- raymarching_early_stop_tolerance: float = 0,
- raymarching_chunk_size_or_sections: Union[int, List[int]] = None,
- **kwargs) -> ReturnData:
+ # stub method
+ def __call__(self, samples: Samples, densities: torch.Tensor, colors: torch.Tensor, *outputs: str,
+ white_bg: bool, raw_noise_std: float) -> ReturnData:
"""
Perform volumn rendering.
- :param kernel `BaseModel`: render kernel
- :param samples `Samples(N, P)`: samples
+ :param samples `Samples(B, P)`: samples
+ :param rgbd `Tensor(B, P, C+1)`: colors and densities
:param outputs `str...`: items should be contained in the result dict.
Optional values include 'color', 'depth', 'layers', 'states' and attribute names in class `States` (e.g. 'weights'). Defaults to []
- :param raymarching_early_stop_tolerance `float`: tolerance of raymarching early stop.
- Should between 0 and 1 (0 means no early stop). Defaults to 0
- :param raymarching_chunk_size_or_sections `int|list[int]`: indicates how to split raymarching process.
- Use a list of integers to specify samples of every chunk, or a positive integer to specify number of chunks.
- Use a negative interger to split by number of hits in chunks, and the absolute value means maximum number of hits in a chunk.
- 0 and `None` means not splitting the raymarching process. Defaults to `None`
- :return `dict`: render result { 'color'[, 'depth', 'layers', 'states', ...] }
+ :return `ReturnData`: render result { 'color'[, 'depth', 'layers', 'states', ...] }
"""
- if samples.size[1] == 0:
- print("VolumnRenderer.forward(): # of samples is zero")
- return None
-
- infer_outputs = set()
- for key in outputs:
- if key == "color":
- infer_outputs.add("colors")
- infer_outputs.add("densities")
- elif key == "specular":
- infer_outputs.add("speculars")
- infer_outputs.add("densities")
- elif key == "diffuse":
- infer_outputs.add("diffuses")
- infer_outputs.add("densities")
- elif key == "depth":
- infer_outputs.add("densities")
- else:
- infer_outputs.add(key)
- s = VolumnRenderer.States(kernel, samples, raymarching_early_stop_tolerance, infer_outputs)
-
- checkpoint("Prepare states object")
-
- if not raymarching_chunk_size_or_sections:
- raymarching_chunk_size_or_sections = [s.P]
- elif isinstance(raymarching_chunk_size_or_sections, int) and \
- raymarching_chunk_size_or_sections > 0:
- raymarching_chunk_size_or_sections = [
- math.ceil(s.P / raymarching_chunk_size_or_sections)
- ]
-
- if isinstance(raymarching_chunk_size_or_sections, list):
- chunk_sections = raymarching_chunk_size_or_sections
- for chunk_samples in cycle(chunk_sections):
- self._forward_chunk(s.next_chunk(length=chunk_samples))
- if s.end >= s.P:
- break
- else:
- chunk_size = -raymarching_chunk_size_or_sections
- chunk_hits = s.n_hits(0)
- for i in range(1, s.P):
- n_hits = s.n_hits(i)
- if chunk_hits + n_hits > chunk_size:
- self._forward_chunk(s.next_chunk(end=i))
- n_hits = s.n_hits(i)
- chunk_hits = 0
- chunk_hits += n_hits
- self._forward_chunk(s.next_chunk())
-
- checkpoint("Run forward chunks")
-
- ret = {}
- for key in outputs:
- if key == 'color':
- ret['color'] = torch.sum(s.colors * s.weights, 1)
- elif key == 'depth':
- ret['depth'] = torch.sum(s.samples.depths[..., None] * s.weights, 1)
- elif key == 'diffuse' and hasattr(s, "diffuses"):
- ret['diffuse'] = torch.sum(s.diffuses * s.weights, 1)
- elif key == 'specular' and hasattr(s, "speculars"):
- ret['specular'] = torch.sum(s.speculars * s.weights, 1)
- elif key == 'layers':
- ret['layers'] = torch.cat([s.colors, 1 - torch.exp(-s.energies)], dim=-1)
- elif key == 'states':
- ret['states'] = s
- else:
- if hasattr(s, key):
- ret[key] = getattr(s, key)
-
- checkpoint("Set return data")
-
- return ret
-
- @perf
- def _calc_weights(self, s: States):
- """
- Calculate weights of samples in composited outputs
-
- :param s `States`: states
- :param start `int`: chunk's start
- :param end `int`: chunk's end
- """
- s.energies[s.chunk] = density2energy(s.densities[s.chunk], s.samples.dists[s.chunk])
- s.cum_energies[s.cum_chunk] = torch.cumsum(s.energies[s.chunk], 1) \
- + s.cum_energies[s.cum_last]
- s.exp_energies[s.cum_chunk] = (-s.cum_energies[s.cum_chunk]).exp()
- s.weights[s.chunk] = s.exp_energies[s.chunk] - s.exp_energies[s.cum_chunk]
-
- @perf
- def _apply_early_stop(self, s: States):
- """
- Stop rays whose accumulated opacity are larger than a threshold
-
- :param s `States`: s
- :param end `int`: chunk's end
- """
- if s.end < s.P and s.early_stop_tolerance > 0 and isinstance(s.hit_mask, torch.Tensor):
- rays_to_stop = s.exp_energies[:, s.end, 0] < s.early_stop_tolerance
- s.hit_mask[rays_to_stop, s.end:] = 0
-
- @perf
- def _forward_chunk(self, s: States) -> int:
- if isinstance(s.hit_mask, torch.Tensor):
- fi_idxs: Tuple[torch.Tensor, ...] = s.hit_mask[s.chunk].nonzero(as_tuple=True)
- if fi_idxs[0].size(0) == 0:
- s.cum_energies[s.cum_chunk] = s.cum_energies[s.cum_last]
- s.exp_energies[s.cum_chunk] = s.exp_energies[s.cum_last]
- return
- fi_idxs[1].add_(s.start)
- s.accumulate_tot_evaluations("colors", fi_idxs[0].size(0))
- else:
- fi_idxs = s.chunk
-
- fi_outputs = s.kernel.infer(*s.outputs, samples=s.samples[fi_idxs], chunk_id=s.chunk_id)
- for key, value in fi_outputs.items():
- s.put(key, value, fi_idxs)
-
- self._calc_weights(s)
- self._apply_early_stop(s)
-
-
-class DensityFirstVolumnRenderer(VolumnRenderer):
-
- def __init__(self, **kwargs):
- super().__init__(**kwargs)
-
- def _forward_chunk(self, s: VolumnRenderer.States) -> int:
- fi_idxs: Tuple[torch.Tensor, ...] = s.hit_mask[s.chunk].nonzero(as_tuple=True) # (N')
- fi_idxs[1].add_(s.start)
-
- if fi_idxs[0].size(0) == 0:
- s.cum_energies[s.cum_chunk] = s.cum_energies[s.cum_last]
- s.exp_energies[s.cum_chunk] = s.exp_energies[s.cum_last]
- return
-
- # fi_* means "filtered" by hit mask
- fi_samples = s.samples[fi_idxs] # N -> N'
-
- # For all valid samples: encode X
- density_inputs = s.kernel.input(fi_samples, "x", "f") # (N', Ex)
-
- # Infer densities (shape)
- density_outputs = s.kernel.infer('densities', 'features', samples=fi_samples,
- inputs=density_inputs, chunk_id=s.chunk_id)
- s.put('densities', density_outputs['densities'], fi_idxs)
- s.accumulate_tot_evaluations("densities", fi_idxs[0].size(0))
-
- self._calc_weights(s)
- self._apply_early_stop(s)
-
- # Remove samples whose weights are less than a threshold
- s.hit_mask[s.chunk][s.weights[s.chunk][..., 0] < 0.01] = 0
-
- # Update "filtered" tensors
- fi_mask = s.hit_mask[fi_idxs]
- fi_idxs = (fi_idxs[0][fi_mask], fi_idxs[1][fi_mask]) # N' -> N"
- fi_samples = s.samples[fi_idxs] # N -> N"
- fi_features = density_outputs['features'][fi_mask]
- color_inputs = s.kernel.input(fi_samples, "d") # (N")
- color_inputs.x = density_inputs.x[fi_mask]
-
- # Infer colors (appearance)
- outputs = s.outputs.copy()
- if 'densities' in outputs:
- outputs.remove('densities')
- color_outputs = s.kernel.infer(*outputs, samples=fi_samples, inputs=color_inputs,
- chunk_id=s.chunk_id, features=fi_features)
- # if s.chunk_id == 0:
- # fi_colors[:] *= fi_colors.new_tensor([1, 0, 0])
- # elif s.chunk_id == 1:
- # fi_colors[:] *= fi_colors.new_tensor([0, 1, 0])
- # elif s.chunk_id == 2:
- # fi_colors[:] *= fi_colors.new_tensor([0, 0, 1])
- # else:
- # fi_colors[:] *= fi_colors.new_tensor([1, 1, 0])
- for key, value in color_outputs.items():
- s.put(key, value, fi_idxs)
- s.accumulate_tot_evaluations("colors", fi_idxs[0].size(0))
+ ...
+
+ @profile
+ def forward(self, samples: Samples, rgbd: torch.Tensor, *outputs: str,
+ white_bg: bool, raw_noise_std: float) -> ReturnData:
+ energies = density2energy(rgbd[..., -1], samples.dists, raw_noise_std) # (B, P)
+ alphas = energy2alpha(energies) # (B, P)
+ weights = (alphas * torch.cumprod(union(1, 1. - alphas + 1e-10), -1)[..., :-1])[..., None]
+ output_fn = {
+ "color": lambda: torch.sum(weights * rgbd[..., :-1], -2) + (1. - torch.sum(weights, -2)
+ if white_bg else 0.),
+ "depth": lambda: torch.sum(weights * samples.depths[..., None], -2),
+ "colors": lambda: rgbd[..., :-1],
+ "densities": lambda: rgbd[..., -1:],
+ "alphas": lambda: alphas[..., None],
+ "energies": lambda: energies[..., None],
+ "weights": lambda: weights
+ }
+ return ReturnData({key: output_fn[key]() for key in outputs if key in output_fn})
diff --git a/modules/sampler.py b/modules/sampler.py
index 5f51657..6df284f 100644
--- a/modules/sampler.py
+++ b/modules/sampler.py
@@ -1,346 +1,224 @@
-import torch
-from typing import Tuple
-
-from .generic import *
+from .__common__ import *
from .space import Space
from clib import *
-from utils import device
from utils import sphere
-from utils import misc
-from utils import math
-from utils.module import Module
-from utils.samples import Samples
-from utils.perf import perf, checkpoint
-
-
-class Bins(object):
-
- @property
- def up(self):
- return self.bounds[1:]
+from utils.misc import grid2d
- @property
- def lo(self):
- return self.bounds[:-1]
+__all__ = ["Sampler", "UniformSampler", "PdfSampler"]
- def __init__(self, vals: torch.Tensor):
- self.vals = vals
- self.bounds = torch.cat([
- self.vals[:1],
- 0.5 * (self.vals[1:] + self.vals[:-1]),
- self.vals[-1:]
- ])
- @staticmethod
- def linspace(val_range: Tuple[float, float], N: int, device: torch.device = None):
- return Bins(torch.linspace(*val_range, N, device=device))
+class Sampler(nn.Module):
+ _samples_indices_cached: torch.Tensor | None
- def to(self, device: torch.device):
- self.vals = self.vals.to(device)
- self.bounds = self.bounds.to(device)
-
-
-class Sampler(Module):
-
- def __init__(self, **kwargs):
+ def __init__(self, x_chns: int, d_chns: int):
"""
Initialize a Sampler module
"""
- super().__init__()
+ super().__init__({}, {"x": x_chns, "d": d_chns})
self._samples_indices_cached = None
- def _sample(self, range: Tuple[float, float], n_rays: int, n_samples: int, perturb: bool,
- device: torch.device) -> torch.Tensor:
- """
- [summary]
+ # stub method for type hint
+ def __call__(self, rays: Rays, space: Space, **kwargs) -> Samples:
+ ...
- :param t_range `float, float`: sampling range
- :param n_rays `int`: number of rays (B)
- :param n_samples `int`: number of samples per ray (P)
- :param perturb `bool`: whether perturb sampling
- :param device `torch.device`: the device used to create tensors
- :return `Tensor(B, P+1)`: sampling bounds of t
+ def _get_samples_indices(self, pts: torch.Tensor) -> torch.Tensor:
"""
- bounds = torch.linspace(*range, n_samples + 1, device=device) # (P+1)
- if perturb:
- rand_bounds = torch.cat([
- bounds[:1],
- 0.5 * (bounds[1:] + bounds[:-1]),
- bounds[-1:]
- ])
- rand_vals = torch.rand(n_rays, n_samples + 1, device=device)
- bounds = rand_bounds[:-1] * (1 - rand_vals) + rand_bounds[1:] * rand_vals
- else:
- bounds = bounds[None].expand(n_rays, -1)
- return bounds
+ Get 2D indices of samples. The first value is the index of ray, while the second value is
+ the index of sample in a ray.
- def _get_samples_indices(self, pts: torch.Tensor):
+ :param pts `Tensor(B, P, 3)`: the sample points
+ :return `Tensor(B, P)`: the 2D indices of samples
+ """
if self._samples_indices_cached is None\
+ or self._samples_indices_cached.device != pts.device\
or self._samples_indices_cached.shape[0] < pts.shape[0]\
or self._samples_indices_cached.shape[1] < pts.shape[1]:
- self._samples_indices_cached = misc.meshgrid(
- *pts.shape[:2], swap_dim=True, device=pts.device)
+ self._samples_indices_cached = grid2d(*pts.shape[:2], indexing="ij", device=pts.device)
return self._samples_indices_cached[:pts.shape[0], :pts.shape[1]]
- @perf
- def forward(self, rays_o: torch.Tensor, rays_d: torch.Tensor, space_: Space, *,
- sample_range: Tuple[float, float], n_samples: int, lindisp: bool = False,
- perturb_sample: bool = True, spherical: bool = False,
- **kwargs) -> Tuple[Samples, torch.Tensor]:
+ def _get_samples(self, rays: Rays, space: Space, t_vals: torch.Tensor, mode: str) -> Samples:
"""
- Sample points along rays.
+ Get samples along rays at sample steps specified by `t_vals`.
:param rays_o `Tensor(B, 3)`: rays' origin
:param rays_d `Tensor(B, 3)`: rays' direction
- :param sample_range `float, float`: sampling range
- :param n_samples `int`: number of samples per ray
- :param lindisp `bool`: whether sample linearly in disparity space (1/depth)
- :param perturb_sample `bool`: whether perturb sampling
+ :param t_vals `Tensor(B, P)`: sample steps
+ :param mode `str`: sample mode, one of "xyz", "xyz_disp", "spherical", "spherical_radius"
:return `Samples(B, P)`: samples
"""
- if spherical:
- t_bounds = self._sample(sample_range, rays_o.shape[0], n_samples, perturb_sample,
- rays_o.device)
- t0, t1 = t_bounds[:, :-1], t_bounds[:, 1:] # (B, P)
- t = (t0 + t1) * .5
-
- p, z = sphere.ray_sphere_intersect(rays_o, rays_d, t.reciprocal())
- p = sphere.cartesian2spherical(p, inverse_r=True)
- vidxs = space_.get_voxel_indices(p)
- return Samples(
- pts=p,
- dirs=rays_d[:, None].expand(-1, n_samples, -1),
- depths=z,
- dists=(t1 + math.tiny).reciprocal() - t0.reciprocal(),
- voxel_indices=vidxs,
- indices=self._get_samples_indices(p),
- t=t
- )
+ if mode == "xyz":
+ z_vals = t_vals
+ pts = rays.get_points(z_vals)
+ elif mode == "xyz_disp":
+ z_vals = t_vals.reciprocal()
+ pts = rays.get_points(z_vals)
+ elif mode == "spherical":
+ z_vals = t_vals.reciprocal()
+ pts = sphere.cartesian2spherical(rays.get_points(z_vals), inverse_r=True)
+ elif mode == "spherical_radius":
+ z_vals = sphere.ray_sphere_intersect(rays, t_vals.reciprocal())
+ pts = sphere.cartesian2spherical(rays.get_points(z_vals), inverse_r=True)
else:
- sample_range = (1 / sample_range[0], 1 / sample_range[1]) if lindisp else sample_range
- z_bounds = self._sample(sample_range, rays_o.shape[0], n_samples, perturb_sample,
- rays_o.device)
- if lindisp:
- z_bounds = z_bounds.reciprocal()
- z0, z1 = z_bounds[:, :-1], z_bounds[:, 1:] # (B, P)
- z = (z0 + z1) * .5
- p = rays_o[:, None] + rays_d[:, None] * z[..., None]
- vidxs = space_.get_voxel_indices(p)
- return Samples(
- pts=p,
- dirs=rays_d[:, None].expand(-1, n_samples, -1),
- depths=z,
- dists=z1 - z0,
- voxel_indices=vidxs,
- indices=self._get_samples_indices(p),
- t=z
- )
+ raise ValueError(f"Unknown mode: {mode}")
+ rays_d = rays.rays_d.unsqueeze(1) # (B, 1, 3)
+ dists = union(z_vals[..., 1:] - z_vals[..., :-1], math.huge) # (B, P)
+ dists *= torch.norm(rays_d, dim=-1)
+ return Samples(
+ pts=pts,
+ dirs=rays_d.expand(*pts.shape[:2], -1),
+ depths=z_vals,
+ t_vals=t_vals,
+ dists=dists,
+ voxel_indices=space.get_voxel_indices(pts) if space else 0,
+ indices=self._get_samples_indices(pts)
+ )
-class PdfSampler(Module):
- def __init__(self, *, depth_range: Tuple[float, float], n_samples: int, perturb_sample: bool,
- spherical: bool, lindisp: bool, **kwargs):
- """
- Initialize a Sampler module
+class UniformSampler(Sampler):
+ """
+ This module expands NeRF's code of uniform sampling to support our spherical sampling and enable
+ the trace of samples' indices.
+ """
- :param depth_range: depth range for sampler
- :param n_samples: count to sample along ray
- :param perturb_sample: perturb the sample depths
- :param lindisp: If True, sample linearly in inverse depth rather than in depth
- """
- super().__init__()
- self.lindisp = lindisp
- self.perturb_sample = perturb_sample
- self.spherical = spherical
- self.n_samples = n_samples
- self.s_range = (1 / depth_range[0], 1 / depth_range[1]) if self.lindisp else depth_range
+ def __init__(self):
+ super().__init__(3, 3)
- def forward(self, rays_o, rays_d, *, weights, s_vals=None, include_s_vals=False, **kwargs):
+ def _sample(self, range: tuple[float, float], n_rays: int, n_samples: int, perturb: bool) -> torch.Tensor:
"""
- Sample points along rays. return Spherical or Cartesian coordinates,
- specified by `self.shperical`
+ Generate sample steps along rays in the specified range.
- :param rays_o `Tensor(B, 3)`: rays' origin
- :param rays_d `Tensor(B, 3)`: rays' direction
- :param weights `Tensor(B, M)`: weights of sample bins
- :param s_vals `Tensor(B, M)`: (optional) center of sample bins
- :param include_s_vals `bool`: (default to `False`) include `s_vals` in the sample array
- :return `Tensor(B, N, 3)`: sampled points
- :return `Tensor(B, N)`: corresponding depths along rays
+ :param range `float, float`: sampling range
+ :param n_rays `int`: number of rays (B)
+ :param n_samples `int`: number of samples per ray (P)
+ :param perturb `bool`: whether perturb sampling
+ :return `Tensor(B, P)`: sampled "t"s along rays
"""
- if s_vals is None:
- s_vals = torch.linspace(*self.s_range, self.n_samples, device=device.default())
- s = self.sample_pdf(Bins(s_vals).bounds, weights, self.n_samples, det=self.perturb_sample)
- if include_s_vals:
- s = torch.cat([s, s_vals], dim=-1)
- s = torch.sort(s, descending=self.lindisp)[0]
- z = torch.reciprocal(s) if self.lindisp else s
- if self.spherical:
- pts, depths = sphere.ray_sphere_intersect(rays_o, rays_d, z)
- sphers = sphere.cartesian2spherical(pts, inverse_r=self.lindisp)
- return sphers, depths, s, pts
+ t_vals = torch.linspace(*range, n_samples, device=self.device) # (P)
+ if perturb:
+ mids = .5 * (t_vals[..., 1:] + t_vals[..., :-1])
+ upper = union(mids, t_vals[..., -1:])
+ lower = union(t_vals[..., :1], mids)
+ # stratified samples in those intervals
+ t_vals = t_vals.expand(n_rays, -1)
+ t_vals = lower + (upper - lower) * torch.rand_like(t_vals)
else:
- return rays_o[..., None, :] + rays_d[..., None, :] * z[..., None], z, s
-
- def sample_pdf(self, bins: torch.Tensor, weights: torch.Tensor, N: int, det=True):
- '''
- :param bins `Tensor(..., M+1)`: bounds of bins
- :param weights `Tensor(..., M)`: weights of bins
- :param N `int`: # of samples along each ray
- :param det `bool`: (default to `True`) perform deterministic sampling or not
- :return `Tensor(..., N)`: samples
- '''
- # Get pdf
- weights = weights + math.tiny # prevent nans
- pdf = weights / torch.sum(weights, dim=-1, keepdim=True) # [..., M]
- cdf = torch.cat([
- torch.zeros_like(pdf[..., :1]),
- torch.cumsum(pdf, dim=-1)
- ], dim=-1) # [..., M+1]
-
- # Take uniform samples
- dots_sh = list(weights.shape[:-1])
- M = weights.shape[-1]
+ t_vals = t_vals.expand(n_rays, -1)
+ return t_vals
- u = torch.linspace(0, 1, N, device=bins.device).expand(dots_sh + [N]) \
- if det else torch.rand(dots_sh + [N], device=bins.device) # [..., N]
-
- # Invert CDF
- # [..., N, 1] >= [..., 1, M] ----> [..., N, M] ----> [..., N,]
- above_inds = torch.sum(u[..., None] >= cdf[..., None, :-1], dim=-1).long()
-
- # random sample inside each bin
- below_inds = torch.clamp(above_inds - 1, min=0)
- inds_g = torch.stack((below_inds, above_inds), dim=-1) # [..., N, 2]
-
- cdf = cdf[..., None, :].expand(dots_sh + [N, M + 1]) # [..., N, M+1]
- cdf_g = torch.gather(cdf, dim=-1, index=inds_g) # [..., N, 2]
-
- bins = bins[..., None, :].expand(dots_sh + [N, M + 1]) # [..., N, M+1]
- bins_g = torch.gather(bins, dim=-1, index=inds_g) # [..., N, 2]
-
- # fix numeric issue
- denom = cdf_g[..., 1] - cdf_g[..., 0] # [..., N]
- denom = torch.where(denom < math.tiny, torch.ones_like(denom), denom)
- t = (u - cdf_g[..., 0]) / denom
-
- samples = bins_g[..., 0] + t * (bins_g[..., 1] - bins_g[..., 0] + math.tiny)
-
- return samples
-
-
-class VoxelSampler(Module):
-
- def __init__(self, *, sample_step: float, **kwargs):
- """
- Initialize a VoxelSampler module
-
- :param perturb_sample: perturb the sample depths
- :param step_size: step size
+ # stub method for type hint
+ def __call__(self, rays: Rays, space: Space, *,
+ range: tuple[float, float],
+ mode: str,
+ n_samples: int,
+ perturb: bool) -> Samples:
"""
- super().__init__()
- self.sample_step = sample_step
+ Sample points along rays.
- def _forward(self, rays_o: torch.Tensor, rays_d: torch.Tensor, space_module: Space, *,
- perturb_sample: bool, **kwargs) -> Tuple[Samples, torch.Tensor]:
+ :param rays `Rays(B)`: rays
+ :param space `Space`: sample space
+ :param range `float, float`: sampling range
+ :param mode `str`: sample mode, one of "xyz", "xyz_disp", "spherical", "spherical_radius"
+ :param n_samples `int`: number of samples per ray
+ :param perturb `bool`: whether perturb sampling, defaults to `False`
+ :return `Samples(B, P)`: samples
"""
- [summary]
+ ...
- :param rays_o `Tensor(N, 3)`: rays' origin positions
- :param rays_d `Tensor(N, 3)`: rays' directions
- :param step_size `float`: gap between samples along a ray
- :return `Samples(N', P)`: samples along valid rays (which hit at least one voxel)
- :return `Tensor(N)`: valid rays mask
- """
- intersections = space_module.ray_intersect(rays_o, rays_d, 100)
- valid_rays_mask = intersections.hits > 0
- rays_o = rays_o[valid_rays_mask]
- rays_d = rays_d[valid_rays_mask]
- intersections = intersections[valid_rays_mask] # (N) -> (N')
- n_rays = rays_o.size(0)
- ray_index_list = torch.arange(n_rays, device=rays_o.device, dtype=torch.long) # (N')
+ @profile
+ def forward(self, rays: Rays, space: Space, *,
+ range: tuple[float, float],
+ mode: str,
+ n_samples: int,
+ perturb: bool) -> Samples:
+ t_range = range if mode == "xyz" else (1. / range[0], 1. / range[1])
+ t_vals = self._sample(t_range, rays.shape[0], n_samples, perturb) # (B, P)
+ return self._get_samples(rays, space, t_vals, mode)
- hits = intersections.hits
- min_depths = intersections.min_depths
- max_depths = intersections.max_depths
- voxel_indices = intersections.voxel_indices
- rays_near_depth = min_depths[:, :1] # (N', 1)
- rays_far_depth = max_depths[ray_index_list, hits - 1][:, None] # (N', 1)
- rays_length = rays_far_depth - rays_near_depth
- rays_steps = (rays_length / self.sample_step).ceil().long()
- rays_step_size = rays_length / rays_steps
- max_steps = rays_steps.max().item()
- rays_step = torch.arange(max_steps, device=rays_o.device,
- dtype=torch.float)[None].repeat(n_rays, 1) # (N', P)
- invalid_samples_mask = rays_step >= rays_steps
- samples_min_depth = rays_near_depth + rays_step * rays_step_size
- samples_depth = samples_min_depth + rays_step_size \
- * (torch.rand_like(samples_min_depth) if perturb_sample else 0.5) # (N', P)
- samples_dist = rays_step_size.repeat(1, max_steps) # (N', 1) -> (N', P)
- samples_voxel_index = voxel_indices[
- ray_index_list[:, None],
- torch.searchsorted(max_depths, samples_depth)
- ] # (N', P)
- samples_depth[invalid_samples_mask] = math.huge
- samples_dist[invalid_samples_mask] = 0
- samples_voxel_index[invalid_samples_mask] = -1
+class PdfSampler(Sampler):
+ """
+ Hierarchical sampling (section 5.2 of NeRF)
+ """
- rays_o, rays_d = rays_o[:, None], rays_d[:, None]
- return Samples(
- pts=rays_o + rays_d * samples_depth[..., None],
- dirs=rays_d.expand(-1, max_steps, -1),
- depths=samples_depth,
- dists=samples_dist,
- voxel_indices=samples_voxel_index
- ), valid_rays_mask
+ def __init__(self):
+ super().__init__(3, 3)
- @perf
- def forward(self, rays_o: torch.Tensor, rays_d: torch.Tensor,
- space: Space, *, perturb_sample: bool, **kwargs) -> Tuple[Samples, torch.Tensor]:
+ def _sample(self, t_vals: torch.Tensor, weights: torch.Tensor, n_importance: int,
+ perturb: bool, include_existed: bool, sort_descending: bool) -> torch.Tensor:
"""
- [summary]
+ Generate sample steps by PDF according to existed sample steps and their weights.
- :param rays_o `Tensor(N, 3)`: [description]
- :param rays_d `Tensor(N, 3)`: [description]
- :param step_size `float`: [description]
- :return `Samples(N, P)`: [description]
+ :param t_vals `Tensor(B, P)`: existed sample steps
+ :param weights `Tensor(B, P)`: weights of existed sample steps
+ :param n_importance `int`: number of samples to generate for each ray
+ :param perturb `bool`: whether perturb sampling
+ :param include_existed `bool`: whether to include existed samples in the output
+ :return `Tensor(B, P'[+P])`: the output sample steps
"""
- intersections = space.ray_intersect(rays_o, rays_d, 100)
- valid_rays_mask = intersections.hits > 0
- rays_o = rays_o[valid_rays_mask]
- rays_d = rays_d[valid_rays_mask]
- intersections = intersections[valid_rays_mask] # (N) -> (N')
+ bins = .5 * (t_vals[..., 1:] + t_vals[..., :-1]) # (B, P - 1)
+ weights = weights[..., 1:-1] + math.tiny # (B, P - 2)
- checkpoint("Ray intersect")
+ # Get PDF
+ pdf = weights / torch.sum(weights, -1, keepdim=True)
+ cdf = union(0., torch.cumsum(pdf, -1)) # (B, P - 1)
- if intersections.size == 0:
- return None, valid_rays_mask
+ # Take uniform samples
+ if perturb:
+ u = torch.rand(*cdf.shape[:-1], n_importance, device=self.device)
else:
- min_depth = intersections.min_depths
- max_depth = intersections.max_depths
- pts_idx = intersections.voxel_indices
- dists = max_depth - min_depth
- tot_dists = dists.sum(dim=-1, keepdim=True) # (N, 1)
- probs = dists / tot_dists
- steps = tot_dists[:, 0] / self.sample_step
-
- # sample points and use middle point approximation
- sampled_indices, sampled_depths, sampled_dists = inverse_cdf_sampling(
- pts_idx, min_depth, max_depth, probs, steps, -1, not perturb_sample)
- sampled_indices = sampled_indices.long()
- invalid_idx_mask = sampled_indices.eq(-1)
- sampled_dists.clamp_min_(0).masked_fill_(invalid_idx_mask, 0)
- sampled_depths.masked_fill_(invalid_idx_mask, math.huge)
+ u = torch.linspace(0., 1., steps=n_importance, device=self.device).\
+ expand(*cdf.shape[:-1], -1)
- checkpoint("Inverse CDF sampling")
-
- rays_o, rays_d = rays_o[:, None], rays_d[:, None]
- return Samples(
- pts=rays_o + rays_d * sampled_depths[..., None],
- dirs=rays_d.expand(-1, sampled_depths.size(1), -1),
- depths=sampled_depths,
- dists=sampled_dists,
- voxel_indices=sampled_indices
- ), valid_rays_mask
+ # Invert CDF
+ u = u.contiguous() # (B, P')
+ inds = torch.searchsorted(cdf, u, right=True) # (B, P')
+ inds_g = torch.stack([
+ (inds - 1).clamp_min(0), # below
+ inds.clamp_max(cdf.shape[-1] - 1) # above
+ ], -1) # (B, P', 2)
+
+ matched_shape = [inds_g.shape[0], inds_g.shape[1], cdf.shape[-1]] # [B, P', P - 1]
+ cdf_g = torch.gather(cdf.unsqueeze(1).expand(matched_shape), 2, inds_g) # (B, P', 2)
+ bins_g = torch.gather(bins.unsqueeze(1).expand(matched_shape), 2, inds_g) # (B, P', 2)
+
+ denom = cdf_g[..., 1] - cdf_g[..., 0]
+ denom = torch.where(denom < math.tiny, torch.ones_like(denom), denom)
+ t = (u - cdf_g[..., 0]) / denom
+ t_samples = (bins_g[..., 0] + t * (bins_g[..., 1] - bins_g[..., 0])).detach()
+ if include_existed:
+ return torch.sort(union(t_vals, t_samples), -1, descending=sort_descending)[0]
+ else:
+ return t_samples
+
+ # stub method for type hint
+ def __call__(self, rays: Rays, space: Space, t_vals: torch.Tensor, weights: torch.Tensor, *,
+ mode: str,
+ n_importance: int,
+ perturb: bool,
+ include_existed_samples: bool) -> Samples:
+ """
+ Sample points along rays using PDF sampling based on existed samples.
+
+ :param rays `Rays(B)`: rays
+ :param space `Space`: sample space
+ :param t_vals `Tensor(B, P)`: existed sample steps
+ :param weights `Tensor(B, P)`: weights of existed sample steps
+ :param mode `str`: sample mode, one of "xyz", "xyz_disp", "spherical", "spherical_radius"
+ :param n_importance `int`: number of samples to generate using PDF sampling for each ray
+ :param perturb `bool`: whether perturb sampling, defaults to `False`
+ :param include_existed_samples `bool`: whether to include existed samples in the output,
+ defaults to `True`
+ :return `Samples(B, P'[+P])`: samples
+ """
+ ...
+
+ @profile
+ def forward(self, rays: Rays, space: Space, t_vals: torch.Tensor, weights: torch.Tensor, *,
+ mode: str,
+ n_importance: int,
+ perturb: bool,
+ include_existed_samples: bool) -> Samples:
+ t_vals = self._sample(t_vals, weights, n_importance, perturb, include_existed_samples,
+ mode != "xyz")
+ return self._get_samples(rays, space, t_vals, mode)
diff --git a/modules/space.py b/modules/space.py
index 3d46c53..821144f 100644
--- a/modules/space.py
+++ b/modules/space.py
@@ -1,13 +1,11 @@
-import torch
-from typing import Dict, List, Optional, Tuple, Union
-
+from .__common__ import *
from clib import *
-from model.utils import load
-from utils.module import Module
+#from model.utils import load
+from utils.nn import Parameter
from utils.geometry import *
from utils.voxels import *
-from utils.perf import perf
-from utils.env import get_env
+
+__all__ = ["Space", "Voxels", "Octree"]
class Intersections:
@@ -24,8 +22,8 @@ class Intersections:
"""`Tensor(N)` Number of hits"""
@property
- def size(self):
- return self.hits.size(0)
+ def shape(self):
+ return self.hits.shape
def __init__(self, min_depths: torch.Tensor, max_depths: torch.Tensor,
voxel_indices: torch.Tensor, hits: torch.Tensor) -> None:
@@ -42,9 +40,9 @@ class Intersections:
hits=self.hits[index])
-class Space(Module):
- bbox: Optional[torch.Tensor]
- """`Tensor(2, 3)` Bounding box"""
+class Space(nn.Module):
+ bbox: torch.Tensor | None
+ """`Tensor(2, D)` Bounding box"""
@property
def dims(self) -> int:
@@ -52,16 +50,18 @@ class Space(Module):
return self.bbox.shape[1] if self.bbox is not None else 3
@staticmethod
- def create(args: dict) -> 'Space':
- if 'space' not in args:
- return Space(**args)
- if args['space'] == 'octree':
- return Octree(**args)
- if args['space'] == 'voxels':
- return Voxels(**args)
- return load(args['space']).space
-
- def __init__(self, clone_src: "Space" = None, *, bbox: List[float] = None, **kwargs):
+ def create(type: str, args: dict[str, Any]) -> 'Space':
+ match type:
+ case "Space":
+ return Space(**args)
+ case "Octree":
+ return Octree(**args)
+ case "Voxels":
+ return Voxels(**args)
+ case _:
+ return load(type).space
+
+ def __init__(self, clone_src: "Space" = None, *, bbox: list[float] = None, **kwargs):
super().__init__()
if clone_src:
self.device = clone_src.device
@@ -69,10 +69,30 @@ class Space(Module):
else:
self.register_temp('bbox', None if not bbox else torch.tensor(bbox).reshape(2, -1))
- def ray_intersect(self, rays_o: torch.Tensor, rays_d: torch.Tensor, n_max_hits: int) -> Intersections:
- raise NotImplementedError
+ def ray_intersect_with_bbox(self, rays_o: torch.Tensor, rays_d: torch.Tensor) -> Intersections:
+ """
+ [summary]
- def get_voxel_indices(self, pts: torch.Tensor) -> torch.Tensor:
+ :param rays_o `Tensor(N..., D)`: rays' origin
+ :param rays_d `Tensor(N..., D)`: rays' direction
+ :param max_hits `int?`: max number of hits of each ray, have no effect for this method
+ :return `Intersect(N...)`: rays' intersection with the bounding box
+ """
+ if self.bbox is None:
+ raise RuntimeError("The space has no bounding box")
+ inv_d = rays_d.reciprocal().unsqueeze(-2)
+ t = (self.bbox - rays_o.unsqueeze(-2)) * inv_d # (N..., 2, D)
+ t0 = t.min(dim=-2)[0].max(dim=-1, keepdim=True)[0].clamp(min=1e-4) # (N..., 1)
+ t1 = t.max(dim=-2)[0].min(dim=-1, keepdim=True)[0]
+ miss = t1 <= t0
+ t0[miss], t1[miss] = -1., -1.
+ hit = torch.logical_not(miss).long()
+ return Intersections(t0, t1, hit - 1, hit.squeeze(-1))
+
+ def ray_intersect(self, rays_o: torch.Tensor, rays_d: torch.Tensor, max_hits: int) -> Intersections:
+ return self.ray_intersect_with_bbox(rays_o, rays_d)
+
+ def get_voxel_indices(self, pts: torch.Tensor) -> int | torch.Tensor:
if self.bbox is None:
return 0
voxel_indices = torch.zeros_like(pts[..., 0], dtype=torch.long)
@@ -81,19 +101,22 @@ class Space(Module):
return voxel_indices
@torch.no_grad()
- def prune(self, keeps: torch.Tensor) -> Tuple[int, int]:
+ def prune(self, keeps: torch.Tensor) -> tuple[int, int]:
raise NotImplementedError()
@torch.no_grad()
- def split(self) -> Tuple[int, int]:
+ def split(self) -> tuple[int, int]:
raise NotImplementedError()
@torch.no_grad()
def clone(self):
- return Space(self)
+ return self.__class__(self)
class Voxels(Space):
+ bbox: torch.Tensor
+ """`Tensor(2, D)` Bounding box"""
+
steps: torch.Tensor
"""`Tensor(3)` Steps along each dimension"""
@@ -131,42 +154,43 @@ class Voxels(Space):
@property
def voxel_size(self) -> torch.Tensor:
"""`Tensor(3)` Voxel size"""
+ if self.bbox is None:
+ raise RuntimeError("Cannot get property 'voxel_size' of a space which "
+ "doesn't have bounding box")
return (self.bbox[1] - self.bbox[0]) / self.steps
@property
- def corner_embeddings(self) -> Dict[str, torch.nn.Embedding]:
+ def corner_embeddings(self) -> dict[str, torch.nn.Embedding]:
return {name[4:]: emb for name, emb in self.named_modules() if name.startswith("emb_")}
@property
- def voxel_embeddings(self) -> Dict[str, torch.nn.Embedding]:
+ def voxel_embeddings(self) -> dict[str, torch.nn.Embedding]:
return {name[5:]: emb for name, emb in self.named_modules() if name.startswith("vemb_")}
- def __init__(self, clone_src: "Voxels" = None, *, bbox: List[float] = None,
- voxel_size: float = None, steps: Union[torch.Tensor, Tuple[int, ...]] = None,
+ def __init__(self, clone_src: "Voxels" = None, *, bbox: list[float] = None,
+ voxel_size: float = None, steps: torch.Tensor | tuple[int, ...] = None,
**kwargs) -> None:
- super().__init__(clone_src, bbox=bbox, **kwargs)
if clone_src:
+ super().__init__(clone_src)
self.register_buffer('steps', clone_src.steps)
self.register_buffer('voxels', clone_src.voxels)
self.register_buffer("corners", clone_src.corners)
self.register_buffer("corner_indices", clone_src.corner_indices)
self.register_buffer('voxel_indices_in_grid', clone_src.voxel_indices_in_grid)
else:
- if self.bbox is None:
+ if bbox is None:
raise ValueError("Missing argument 'bbox'")
- if voxel_size is not None:
- self.register_buffer('steps', get_grid_steps(self.bbox, voxel_size))
- else:
+ super().__init__(bbox=bbox)
+ if steps is not None:
self.register_buffer('steps', torch.tensor(steps, dtype=torch.long))
+ else:
+ self.register_buffer('steps', get_grid_steps(self.bbox, voxel_size))
self.register_buffer('voxels', init_voxels(self.bbox, self.steps))
corners, corner_indices = get_corners(self.voxels, self.bbox, self.steps)
self.register_buffer("corners", corners)
self.register_buffer("corner_indices", corner_indices)
self.register_buffer('voxel_indices_in_grid', torch.arange(-1, self.n_voxels))
- def clone(self):
- return Voxels(self)
-
def to_vi(self, gi: torch.Tensor) -> torch.Tensor:
return self.voxel_indices_in_grid[gi + 1]
@@ -208,7 +232,7 @@ class Voxels(Space):
voxels = self.voxels[voxel_indices] # (N, 3)
corner_indices = self.corner_indices[voxel_indices] # (N, 8)
p = (pts - voxels) / self.voxel_size + .5 # (N, 3) normed-coords in voxel
- return trilinear_interp(p, emb(corner_indices))
+ return linear_interp(p, emb(corner_indices))
def create_voxel_embedding(self, n_dims: int, name: str = 'default') -> torch.nn.Embedding:
"""
@@ -245,7 +269,7 @@ class Voxels(Space):
raise KeyError(f"Embedding '{name}' doesn't exist")
return emb(voxel_indices)
- @perf
+ @profile
def ray_intersect(self, rays_o: torch.Tensor, rays_d: torch.Tensor, n_max_hits: int) -> Intersections:
"""
Calculate intersections of rays and voxels.
@@ -277,7 +301,7 @@ class Voxels(Space):
hits=hits[0]
)
- @perf
+ @profile
def get_voxel_indices(self, pts: torch.Tensor) -> torch.Tensor:
"""
Get voxel indices of points.
@@ -290,8 +314,8 @@ class Voxels(Space):
gi = to_grid_indices(pts, self.bbox, self.steps)
return self.to_vi(gi)
- @perf
- def get_corners(self, vidxs: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+ @profile
+ def get_corners(self, vidxs: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
vidxs = vidxs.unique()
if vidxs[0] == -1:
vidxs = vidxs[1:]
@@ -303,7 +327,7 @@ class Voxels(Space):
return fi_corner_indices, fi_corners
@torch.no_grad()
- def split(self) -> Tuple[int, int]:
+ def split(self) -> tuple[int, int]:
"""
Split voxels into smaller voxels with half size.
"""
@@ -336,7 +360,7 @@ class Voxels(Space):
return self.n_voxels // 8, self.n_voxels
@torch.no_grad()
- def prune(self, keeps: torch.Tensor) -> Tuple[int, int]:
+ def prune(self, keeps: torch.Tensor) -> tuple[int, int]:
self.voxels = self.voxels[keeps]
self.corner_indices = self.corner_indices[keeps]
self._update_gi2vi()
@@ -351,7 +375,7 @@ class Voxels(Space):
new_emb = self.set_voxel_embedding(update_fn(emb.weight), name)
self._update_optimizer(emb.weight, new_emb.weight, update_fn)
- def _update_optimizer(self, old_param: nn.Parameter, new_param: nn.Parameter, update_fn):
+ def _update_optimizer(self, old_param: Parameter, new_param: Parameter, update_fn):
optimizer = get_env()["trainer"].optimizer
if isinstance(optimizer, (torch.optim.Adam, torch.optim.AdamW)):
# Update related states in optimizer
@@ -384,7 +408,7 @@ class Voxels(Space):
sum_dims = [val for val in range(self.dims) if val != dim]
return self.voxel_indices_in_grid[1:].reshape(*self.steps).ne(-1).sum(sum_dims)
- def balance_cut(self, dim: int, n_parts: int) -> List[int]:
+ def balance_cut(self, dim: int, n_parts: int) -> list[int]:
n_voxels_list = self.n_voxels_along_dim(dim)
cdf = (n_voxels_list.cumsum(0) / self.n_voxels * n_parts).tolist()
bins = []
@@ -398,7 +422,7 @@ class Voxels(Space):
bins.append(len(cdf) - offset)
return bins
- def sample(self, S: int, perturb: bool = False, include_border: bool = True) -> Tuple[torch.Tensor, torch.Tensor]:
+ def sample(self, S: int, perturb: bool = False, include_border: bool = True) -> tuple[torch.Tensor, torch.Tensor]:
"""
For each voxel, sample `S^3` points uniformly, with small perturb if `perturb` is `True`.
@@ -419,7 +443,7 @@ class Voxels(Space):
pts += (torch.rand_like(pts) - .5) * self.voxel_size / S
return pts.reshape(-1, 3), voxel_indices.flatten()
- def _ray_intersect(self, rays_o: torch.Tensor, rays_d: torch.Tensor, n_max_hits: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ def _ray_intersect(self, rays_o: torch.Tensor, rays_d: torch.Tensor, n_max_hits: int) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
return aabb_ray_intersect(self.voxel_size, n_max_hits, self.voxels, rays_o, rays_d)
def _update_gi2vi(self):
@@ -456,7 +480,7 @@ class Octree(Voxels):
self.nodes_cached = None
self.tree_cached = None
- def get(self) -> Tuple[torch.Tensor, torch.Tensor]:
+ def get(self) -> tuple[torch.Tensor, torch.Tensor]:
if self.nodes_cached is None:
self.nodes_cached, self.tree_cached = build_easy_octree(
self.voxels, 0.5 * self.voxel_size)
@@ -477,7 +501,7 @@ class Octree(Voxels):
return ret
@torch.no_grad()
- def prune(self, keeps: torch.Tensor) -> Tuple[int, int]:
+ def prune(self, keeps: torch.Tensor) -> tuple[int, int]:
ret = super().prune(keeps)
self.clear()
return ret
diff --git a/notebook/__demo/layers/mc_0071(0).png b/notebook/__demo/layers/mc_0071(0).png
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diff --git a/notebook/dynamic_bar.ipynb b/notebook/dynamic_bar.ipynb
new file mode 100644
index 0000000..d9475fb
--- /dev/null
+++ b/notebook/dynamic_bar.ipynb
@@ -0,0 +1,393 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import plotly_express as px # import plotly.express as px\n",
+ "import plotly.graph_objects as go\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import os\n",
+ "\n",
+ "name = [\"NeRF\", \"PANO\", \"OURS+\", \"OURS\"]\n",
+ "foveal = 8.0\n",
+ "mid = 2.8\n",
+ "far = 2.6\n",
+ "blend = 0.1\n",
+ "ours_full = 562\n",
+ "pano = 1e4\n",
+ "nerf = 9e4\n",
+ "\n",
+ "times = {\n",
+ " \"fovea_l\": 0,\n",
+ " \"mid_l\": 0,\n",
+ " \"far_l\": 0,\n",
+ " \"fovea_r\": 0,\n",
+ " \"mid_r\": 0,\n",
+ " \"far_r\": 0,\n",
+ " \"blend\": 0,\n",
+ " \"ours_full\": 0,\n",
+ " \"pano\": 0,\n",
+ " \"nerf\": 0,\n",
+ "}\n",
+ "clip = 0\n",
+ "frame_id = 0\n",
+ "\n",
+ "\n",
+ "def calc_total():\n",
+ " return [\n",
+ " times[\"nerf\"],\n",
+ " times[\"pano\"],\n",
+ " times[\"ours_full\"],\n",
+ " times[\"fovea_l\"] + times[\"mid_l\"] + times[\"far_l\"] +\n",
+ " times[\"fovea_r\"] + times[\"mid_r\"] + times[\"far_r\"] + times[\"blend\"]\n",
+ " ]\n",
+ "\n",
+ "\n",
+ "def draw_frame(*, xlim=None, **kwargs):\n",
+ " global frame_id\n",
+ " for key in kwargs:\n",
+ " times[key] = kwargs[key]\n",
+ " tot = calc_total()\n",
+ " data = {\n",
+ " \"fovea_l\": [0, 0, 0, times[\"fovea_l\"]],\n",
+ " \"mid_l\": [0, 0, 0, times[\"mid_l\"]],\n",
+ " \"far_l\": [0, 0, 0, times[\"far_l\"]],\n",
+ " \"fovea_r\": [0, 0, 0, times[\"fovea_r\"]],\n",
+ " \"mid_r\": [0, 0, 0, times[\"mid_r\"]],\n",
+ " \"far_r\": [0, 0, 0, times[\"far_r\"]],\n",
+ " \"blend\": [0, 0, 0, times[\"blend\"]],\n",
+ " \"ours_full\": [0, 0, times[\"ours_full\"], 0],\n",
+ " \"pano\": [0, times[\"pano\"], 0, 0],\n",
+ " \"nerf\": [times[\"nerf\"], 0, 0, 0],\n",
+ " }\n",
+ " if xlim is None or xlim < max(tot) * 1.1:\n",
+ " xlim = max(tot) * 1.1\n",
+ " \n",
+ " fig = go.Figure()\n",
+ " times_keys = list(times.keys())\n",
+ " for key in times_keys:\n",
+ " if key == times_keys[-1]:\n",
+ " fig.add_trace(go.Bar(\n",
+ " y=name,\n",
+ " x=data[key],\n",
+ " name=key,\n",
+ " orientation='h',\n",
+ " text=[\"\" if item == 0 else f\"{item:.1f}\" if item < 1000 else f\"{item:.1e}\" for item in tot],\n",
+ " textposition=\"outside\"\n",
+ " ))\n",
+ " else:\n",
+ " fig.add_trace(go.Bar(\n",
+ " y=name,\n",
+ " x=data[key],\n",
+ " name=key,\n",
+ " orientation='h',\n",
+ " ))\n",
+ " fig.update_traces(width=0.5)\n",
+ " fig.update_layout(barmode='stack', showlegend=False,\n",
+ " yaxis_visible=False, yaxis_showticklabels=False, xaxis_range=[0, xlim])\n",
+ " \n",
+ " # fig.show()\n",
+ " fig.write_image(f\"dynamic_bar/clip_{clip}/{frame_id:04d}.png\", width=1920 // 2, height=1080 // 2, scale=2)\n",
+ " frame_id = frame_id + 1\n",
+ "\n",
+ "def add_animation(*, frames, xlim=None, **kwargs):\n",
+ " if frames == 1:\n",
+ " draw_frame(**kwargs, xlim=xlim)\n",
+ " return\n",
+ " data = {\n",
+ " key: np.linspace(times[key], kwargs[key], frames)\n",
+ " for key in kwargs\n",
+ " }\n",
+ " for i in range(frames):\n",
+ " draw_frame(**{key: data[key][i] for key in data}, xlim=xlim)\n",
+ "\n",
+ "def new_clip():\n",
+ " global clip, frame_id\n",
+ " clip += 1\n",
+ " frame_id = 0\n",
+ " os.system(f\"mkdir dynamic_bar/clip_{clip}\")\n",
+ "\n",
+ "os.system('rm -f -r dynamic_bar')\n",
+ "os.system('mkdir dynamic_bar')\n",
+ "\n",
+ "# ours mono\n",
+ "new_clip()\n",
+ "add_animation(fovea_l=foveal, frames=48, xlim=30) # Step 1: grow foveal\n",
+ "add_animation(mid_l=mid, frames=16, xlim=30) # Step 2: grow mid\n",
+ "add_animation(far_l=far, frames=16, xlim=30) # Step 3: grow far\n",
+ "add_animation(blend=blend, frames=1, xlim=30) # Step 4: grow blend\n",
+ "\n",
+ "# ours stereo\n",
+ "new_clip()\n",
+ "add_animation(fovea_r=foveal, frames=24, xlim=30) # Step 1: grow foveal\n",
+ "add_animation(mid_r=mid, frames=8, xlim=30) # Step 2: grow mid\n",
+ "add_animation(far_r=far, frames=8, xlim=30) # Step 3: grow far\n",
+ "\n",
+ "# ours stereo adapt\n",
+ "new_clip()\n",
+ "add_animation(mid_r=0, far_r=0, frames=24, xlim=30)\n",
+ "\n",
+ "# other series\n",
+ "new_clip()\n",
+ "add_animation(ours_full=ours_full, frames=48, xlim=30)\n",
+ "new_clip()\n",
+ "add_animation(pano=pano, frames=48, xlim=30)\n",
+ "new_clip()\n",
+ "add_animation(nerf=nerf, frames=48, xlim=30)\n",
+ "\n",
+ "#os.system(f'ffmpeg -y -r 24 -i dynamic_bar/%04d.png dynamic_bar.avi')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = px.bar(\n",
+ " df1, # 绘图数æ®\n",
+ " x=list(times.keys()), # yè½´\n",
+ " y=\"name\", # xè½´\n",
+ " orientation='h', # 水平柱状图\n",
+ " #text=[[\"a\", \"tot\"], \"tot1\", \"tot2\", {\"fovea_l\": \"\", \"blend_r\": 13.5}] # 需è¦æ˜¾ç¤ºçš„æ•°æ®\n",
+ ")\n",
+ "fig.update_traces(textposition=\"outside\", showlegend=False, text=[[\"a\"]*11, [\"b\"]*11, [\"c\"]*11, [\"d\"]*11,[\"e\"]*11])\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>country</th>\n",
+ " <th>continent</th>\n",
+ " <th>year</th>\n",
+ " <th>lifeExp</th>\n",
+ " <th>pop</th>\n",
+ " <th>gdpPercap</th>\n",
+ " <th>iso_alpha</th>\n",
+ " <th>iso_num</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Afghanistan</td>\n",
+ " <td>Asia</td>\n",
+ " <td>1952</td>\n",
+ " <td>28.801</td>\n",
+ " <td>8425333</td>\n",
+ " <td>779.445314</td>\n",
+ " <td>AFG</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Afghanistan</td>\n",
+ " <td>Asia</td>\n",
+ " <td>1957</td>\n",
+ " <td>30.332</td>\n",
+ " <td>9240934</td>\n",
+ " <td>820.853030</td>\n",
+ " <td>AFG</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>Afghanistan</td>\n",
+ " <td>Asia</td>\n",
+ " <td>1962</td>\n",
+ " <td>31.997</td>\n",
+ " <td>10267083</td>\n",
+ " <td>853.100710</td>\n",
+ " <td>AFG</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>Afghanistan</td>\n",
+ " <td>Asia</td>\n",
+ " <td>1967</td>\n",
+ " <td>34.020</td>\n",
+ " <td>11537966</td>\n",
+ " <td>836.197138</td>\n",
+ " <td>AFG</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>Afghanistan</td>\n",
+ " <td>Asia</td>\n",
+ " <td>1972</td>\n",
+ " <td>36.088</td>\n",
+ " <td>13079460</td>\n",
+ " <td>739.981106</td>\n",
+ " <td>AFG</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1699</th>\n",
+ " <td>Zimbabwe</td>\n",
+ " <td>Africa</td>\n",
+ " <td>1987</td>\n",
+ " <td>62.351</td>\n",
+ " <td>9216418</td>\n",
+ " <td>706.157306</td>\n",
+ " <td>ZWE</td>\n",
+ " <td>716</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1700</th>\n",
+ " <td>Zimbabwe</td>\n",
+ " <td>Africa</td>\n",
+ " <td>1992</td>\n",
+ " <td>60.377</td>\n",
+ " <td>10704340</td>\n",
+ " <td>693.420786</td>\n",
+ " <td>ZWE</td>\n",
+ " <td>716</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1701</th>\n",
+ " <td>Zimbabwe</td>\n",
+ " <td>Africa</td>\n",
+ " <td>1997</td>\n",
+ " <td>46.809</td>\n",
+ " <td>11404948</td>\n",
+ " <td>792.449960</td>\n",
+ " <td>ZWE</td>\n",
+ " <td>716</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1702</th>\n",
+ " <td>Zimbabwe</td>\n",
+ " <td>Africa</td>\n",
+ " <td>2002</td>\n",
+ " <td>39.989</td>\n",
+ " <td>11926563</td>\n",
+ " <td>672.038623</td>\n",
+ " <td>ZWE</td>\n",
+ " <td>716</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1703</th>\n",
+ " <td>Zimbabwe</td>\n",
+ " <td>Africa</td>\n",
+ " <td>2007</td>\n",
+ " <td>43.487</td>\n",
+ " <td>12311143</td>\n",
+ " <td>469.709298</td>\n",
+ " <td>ZWE</td>\n",
+ " <td>716</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>1704 rows × 8 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " country continent year lifeExp pop gdpPercap iso_alpha \\\n",
+ "0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG \n",
+ "1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG \n",
+ "2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG \n",
+ "3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG \n",
+ "4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG \n",
+ "... ... ... ... ... ... ... ... \n",
+ "1699 Zimbabwe Africa 1987 62.351 9216418 706.157306 ZWE \n",
+ "1700 Zimbabwe Africa 1992 60.377 10704340 693.420786 ZWE \n",
+ "1701 Zimbabwe Africa 1997 46.809 11404948 792.449960 ZWE \n",
+ "1702 Zimbabwe Africa 2002 39.989 11926563 672.038623 ZWE \n",
+ "1703 Zimbabwe Africa 2007 43.487 12311143 469.709298 ZWE \n",
+ "\n",
+ " iso_num \n",
+ "0 4 \n",
+ "1 4 \n",
+ "2 4 \n",
+ "3 4 \n",
+ "4 4 \n",
+ "... ... \n",
+ "1699 716 \n",
+ "1700 716 \n",
+ "1701 716 \n",
+ "1702 716 \n",
+ "1703 716 \n",
+ "\n",
+ "[1704 rows x 8 columns]"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = px.data.gapminder()\n",
+ "df"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.0 ('dvs')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.0"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "4469b029896260c1221afa6e0e6159922aafd2738570e75b7bc15e28db242604"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebook/gen/gen_crop.ipynb b/notebook/gen/gen_crop.ipynb
new file mode 100644
index 0000000..615cc15
--- /dev/null
+++ b/notebook/gen/gen_crop.ipynb
@@ -0,0 +1,83 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "import os\n",
+ "import torch\n",
+ "import torch.nn.functional as nn_f\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "rootdir = os.path.abspath(sys.path[0] + '/../../')\n",
+ "sys.path.append(rootdir)\n",
+ "\n",
+ "from utils import img\n",
+ "from utils.view import *\n",
+ "\n",
+ "datadir = f\"{rootdir}/data/__thesis/__demo/compare\"\n",
+ "figs = ['fsnerf', 'gt', 'nerf']\n",
+ "crops = {\n",
+ " 'barbershop': [[406, 117, 100], [209, 170, 100]],\n",
+ " 'gas': [[195, 69, 100], [7, 305, 100]],\n",
+ " 'mc': [[395, 128, 100], [97, 391, 100]],\n",
+ " 'pabellon': [[208, 115, 100], [22, 378, 100]]\n",
+ "}\n",
+ "colors = torch.tensor([[0, 1, 0], [1, 1, 0]], dtype=torch.float)\n",
+ "border = 3\n",
+ "\n",
+ "for scene in crops:\n",
+ " images = img.load([f\"{datadir}/origin/{scene}_{fig}.png\" for fig in figs])\n",
+ " halfw = images.size(-1) // 2\n",
+ " halfh = images.size(-2) // 2\n",
+ " overlay = torch.zeros(1, 4, *images.shape[2:])\n",
+ " mask = torch.zeros(len(crops[scene]), *images.shape[2:], dtype=torch.bool)\n",
+ " for i, crop in enumerate(crops[scene]):\n",
+ " patches = images[..., crop[1]: crop[1] + crop[2], crop[0]: crop[0] + crop[2]].clone()\n",
+ " patches[..., :border, :] = colors[i, :, None, None]\n",
+ " patches[..., -border:, :] = colors[i, :, None, None]\n",
+ " patches[..., :, :border] = colors[i, :, None, None]\n",
+ " patches[..., :, -border:] = colors[i, :, None, None]\n",
+ " img.save(patches, [f\"{datadir}/crop/{scene}_{i}_{fig}.png\" for fig in figs])\n",
+ " mask[i,\n",
+ " crop[1] - border: crop[1] + crop[2] + border,\n",
+ " crop[0] - border: crop[0] + crop[2] + border] = True\n",
+ " mask[i,\n",
+ " crop[1]: crop[1] + crop[2],\n",
+ " crop[0]: crop[0] + crop[2]] = False\n",
+ " images[:, :, mask[i]] = colors[i, :, None]\n",
+ " img.save(images, [f\"{datadir}/overlay/{scene}_{fig}.png\" for fig in figs])\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.0 ('dvs')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.0"
+ },
+ "orig_nbformat": 2,
+ "vscode": {
+ "interpreter": {
+ "hash": "4469b029896260c1221afa6e0e6159922aafd2738570e75b7bc15e28db242604"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebook/gen_demo_mono.ipynb b/notebook/gen/gen_demo_mono.ipynb
similarity index 57%
rename from notebook/gen_demo_mono.ipynb
rename to notebook/gen/gen_demo_mono.ipynb
index 5968a13..47953f1 100644
--- a/notebook/gen_demo_mono.ipynb
+++ b/notebook/gen/gen_demo_mono.ipynb
@@ -2,8 +2,11 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
+ "%matplotlib inline\n",
"import sys\n",
"import os\n",
"import torch\n",
@@ -12,25 +15,25 @@
"\n",
"rootdir = os.path.abspath(sys.path[0] + '/../')\n",
"sys.path.append(rootdir)\n",
- "torch.cuda.set_device(0)\n",
+ "\n",
+ "torch.cuda.set_device(3)\n",
"print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"torch.autograd.set_grad_enabled(False)\n",
"\n",
- "from configs.spherical_view_syn import SphericalViewSynConfig\n",
- "from utils import netio\n",
- "from utils import img\n",
- "from utils import device\n",
+ "import model\n",
+ "from data import Dataset\n",
+ "from utils import netio, img, device\n",
"from utils.view import *\n",
+ "from utils.type import PathLike\n",
"from components.fnr import FoveatedNeuralRenderer\n",
+ "from components.render import render\n",
"\n",
"\n",
- "def load_net(path):\n",
- " config = SphericalViewSynConfig()\n",
- " config.from_id(os.path.splitext(path)[0])\n",
- " config.sa['perturb_sample'] = False\n",
- " net = config.create_net().to(device.default())\n",
- " netio.load(path, net)\n",
- " return net\n",
+ "def load_model(model_path: PathLike):\n",
+ " return model.deserialize(netio.load_checkpoint(model_path)[0],\n",
+ " raymarching_early_stop_tolerance=0.01,\n",
+ " raymarching_chunk_size_or_sections=None,\n",
+ " perturb_sample=False).eval().to(device.default())\n",
"\n",
"\n",
"def find_file(prefix):\n",
@@ -40,6 +43,16 @@
" return None\n",
"\n",
"\n",
+ "def create_renderer(*nets, fov_scale=1.):\n",
+ " fov_list = [20, 45, 110]\n",
+ " for i in range(len(fov_list)):\n",
+ " fov_list[i] = length2fov(fov2length(fov_list[i]) * fov_scale)\n",
+ " res_list = [(256, 256), (256, 256), (256, 230)]\n",
+ " res_full = (1600, 1440)\n",
+ " return FoveatedNeuralRenderer(fov_list, res_list, nn.ModuleList(nets), res_full,\n",
+ " device=device.default())\n",
+ "\n",
+ "\n",
"def plot_images(images):\n",
" plt.figure(figsize=(12, 4))\n",
" plt.subplot(131)\n",
@@ -49,64 +62,50 @@
" plt.subplot(133)\n",
" img.plot(images['layers_img'][2])\n",
" #plt.figure(figsize=(12, 12))\n",
- " #img.plot(images['overlaid'])\n",
+ " # img.plot(images['overlaid'])\n",
" #plt.figure(figsize=(12, 12))\n",
- " #img.plot(images['blended_raw'])\n",
+ " # img.plot(images['blended_raw'])\n",
" plt.figure(figsize=(12, 12))\n",
" img.plot(images['blended'])\n",
"\n",
"\n",
+ "def save_images(images, scene, i):\n",
+ " outputdir = '../__demo/mono/'\n",
+ " os.makedirs(outputdir, exist_ok=True)\n",
+ " for layer in range(len(images[\"layers_img\"])):\n",
+ " img.save(images['layers_img'][layer], f'{outputdir}{scene}_{i:04d}({layer}).png')\n",
+ " img.save(images['blended'], f'{outputdir}{scene}_{i:04d}.png')\n",
+ " if \"overlaid\" in images:\n",
+ " img.save(images['overlaid'], f'{outputdir}{scene}_{i:04d}_overlaid.png')\n",
+ " if \"blended_raw\" in images:\n",
+ " img.save(images['blended_raw'], f'{outputdir}{scene}_{i:04d}_noCE.png')\n",
+ " if \"nerf\" in images:\n",
+ " img.save(images['nerf'], f'{outputdir}{scene}_{i:04d}_nerf.png')\n",
+ "\n",
+ "\n",
"scenes = {\n",
- " 'classroom': 'classroom_all',\n",
- " 'stones': 'stones_all',\n",
- " 'barbershop': 'barbershop_all',\n",
- " 'lobby': 'lobby_all'\n",
+ " 'classroom': '__new/classroom_all',\n",
+ " 'stones': '__new/stones_all',\n",
+ " 'barbershop': '__new/barbershop_all',\n",
+ " 'lobby': '__new/lobby_all',\n",
+ " \"bedroom2\": \"__captured/bedroom2\"\n",
"}\n",
"\n",
- "fov_list = [20, 45, 110]\n",
- "res_list = [(256, 256), (256, 256), (400, 360)]\n",
- "res_full = (1600, 1440)"
- ],
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Set CUDA:0 as current device.\n"
- ]
- }
- ],
- "metadata": {}
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "source": [
- "scene = 'barbershop'\n",
- "os.chdir(f'{rootdir}/data/__new/{scenes[scene]}')\n",
+ "\n",
+ "scene = \"bedroom2\"\n",
+ "os.chdir(f'{rootdir}/data/{scenes[scene]}')\n",
"print('Change working directory to ', os.getcwd())\n",
"\n",
- "fovea_net = load_net(find_file('fovea'))\n",
- "periph_net = load_net(find_file('periph'))\n",
- "renderer = FoveatedNeuralRenderer(fov_list, res_list, nn.ModuleList([fovea_net, periph_net, periph_net]),\n",
- " res_full, device=device.default())"
- ],
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Change working directory to /home/dengnc/dvs/data/__new/barbershop_all\n",
- "Load net from fovea200@snerffast4-rgb_e6_fc512x4_d1.20-6.00_s64_~p.pth ...\n",
- "Load net from periph200@snerffast2-rgb_e6_fc256x4_d1.20-6.00_s32_~p.pth ...\n"
- ]
- }
- ],
- "metadata": {}
+ "fovea_net = load_model(find_file('fovea'))\n",
+ "periph_net = load_model(find_file('periph'))\n",
+ "nerf_net = load_model(find_file(\"nerf\"))"
+ ]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"params = {\n",
" 'classroom': [\n",
@@ -165,7 +164,7 @@
"\n",
"for i, param in enumerate(params[scene]):\n",
" view = Trans(torch.tensor(param[:3], device=device.default()),\n",
- " torch.tensor(euler_to_matrix([-param[4], param[3], 0]), device=device.default()).view(3, 3))\n",
+ " torch.tensor(euler_to_matrix(-param[4], param[3], 0), device=device.default()).view(3, 3))\n",
" images = renderer(view, param[-2:], using_mask=False, ret_raw=True)\n",
" images['overlaid'] = renderer.foveation.synthesis(images['layers_raw'], param[-2:], do_blend=False)\n",
" if True:\n",
@@ -179,49 +178,45 @@
" #img.save(images['blended_raw'], f'{outputdir}{scene}_{i}.png')\n",
" else:\n",
" images = plot_images(images)\n"
- ],
- "outputs": [],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
- "def load_views(data_desc_file) -> Trans:\n",
- " with open(data_desc_file, 'r', encoding='utf-8') as file:\n",
- " data_desc = json.loads(file.read())\n",
- " view_centers = torch.tensor(\n",
- " data_desc['view_centers'], device=device.default()).view(-1, 3)\n",
- " view_rots = torch.tensor(\n",
- " data_desc['view_rots'], device=device.default()).view(-1, 3, 3)\n",
- " return Trans(view_centers, view_rots)\n",
- "\n",
- "\n",
- "views = load_views('for_panorama_cvt.json')\n",
- "print('Dataset loaded.')\n",
- "for view_idx in range(views.size()[0]):\n",
- " center = (0, 0)\n",
- " images = renderer(views.get(view_idx), center, using_mask=True)\n",
- " outputdir = 'panorama'\n",
- " os.makedirs(outputdir, exist_ok=True)\n",
- " img.save(images['blended'], f'{outputdir}/{view_idx:04d}.png')"
- ],
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Dataset loaded.\n"
- ]
- }
- ],
- "metadata": {}
+ "def load_views(data_desc_file) -> tuple[list[int], Trans]:\n",
+ " dataset = Dataset(data_desc_file)\n",
+ " return dataset.indices.tolist(),\\\n",
+ " Trans(dataset.centers, dataset.rots).to(device.default())\n",
+ "\n",
+ "\n",
+ "demos = [ # view_idx, center_x, center_y, fov_scale\n",
+ " [220, 30, 25, 0.7],\n",
+ " [235, 0, 130, 0.7],\n",
+ " [239, 70, 140, 0.7],\n",
+ " [841, -100, 160, 0.7]\n",
+ "]\n",
+ "indices, views = load_views('images.json')\n",
+ "for demo_idx in [0]:\n",
+ " view_idx = demos[demo_idx][0]\n",
+ " i = indices.index(view_idx)\n",
+ " center = tuple(demos[demo_idx][1:3])\n",
+ " renderer = create_renderer(fovea_net, periph_net, periph_net, fov_scale=demos[demo_idx][3])\n",
+ " images = renderer(views.get(i), center, using_mask=False)\n",
+ " #nerf_fovea = render(nerf_net, renderer.cam, views.get(i), None, batch_size=16384)[\"color\"]\n",
+ " #images[\"nerf\"] = nerf_fovea\n",
+ " plot_images(images)\n",
+ " #save_images(images, scene, view_idx)\n"
+ ]
}
],
"metadata": {
"kernelspec": {
- "name": "python3",
- "display_name": "Python 3.8.5 64-bit ('base': conda)"
+ "display_name": "Python 3.10.0 ('dvs')",
+ "language": "python",
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -233,17 +228,19 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.10.0"
},
"metadata": {
"interpreter": {
"hash": "82066b63b621a9e3d15e3b7c11ca76da6238eff3834294910d715044bd0561e5"
}
},
- "interpreter": {
- "hash": "82066b63b621a9e3d15e3b7c11ca76da6238eff3834294910d715044bd0561e5"
+ "vscode": {
+ "interpreter": {
+ "hash": "4469b029896260c1221afa6e0e6159922aafd2738570e75b7bc15e28db242604"
+ }
}
},
"nbformat": 4,
"nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/notebook/gen_demo_stereo.ipynb b/notebook/gen/gen_demo_stereo.ipynb
similarity index 51%
rename from notebook/gen_demo_stereo.ipynb
rename to notebook/gen/gen_demo_stereo.ipynb
index 138950d..933944a 100644
--- a/notebook/gen_demo_stereo.ipynb
+++ b/notebook/gen/gen_demo_stereo.ipynb
@@ -2,14 +2,17 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Set CUDA:0 as current device.\n"
+ "Set CUDA:0 as current device.\n",
+ "Change working directory to /home/dengnc/Work/fov_nerf/data/__thesis/barbershop\n",
+ "Load model fovea.tar\n",
+ "Load model periph.tar\n"
]
}
],
@@ -20,29 +23,25 @@
"import torch.nn as nn\n",
"import matplotlib.pyplot as plt\n",
"\n",
- "rootdir = os.path.abspath(sys.path[0] + '/../')\n",
+ "rootdir = os.path.abspath(sys.path[0] + '/../../')\n",
"sys.path.append(rootdir)\n",
"\n",
"torch.cuda.set_device(0)\n",
"print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"torch.autograd.set_grad_enabled(False)\n",
"\n",
- "from data.spherical_view_syn import *\n",
- "from configs.spherical_view_syn import SphericalViewSynConfig\n",
- "from utils import netio\n",
- "from utils import img\n",
- "from utils import device\n",
+ "from model import Model\n",
+ "from data import Dataset\n",
+ "from utils import netio, img, device\n",
"from utils.view import *\n",
+ "from utils.types import PathLike\n",
"from components.fnr import FoveatedNeuralRenderer\n",
+ "from components.render import render\n",
"\n",
"\n",
- "def load_net(path):\n",
- " config = SphericalViewSynConfig()\n",
- " config.from_id(os.path.splitext(path)[0])\n",
- " config.sa['perturb_sample'] = False\n",
- " net = config.create_net().to(device.default())\n",
- " netio.load(path, net)\n",
- " return net\n",
+ "def load_model(model_path: PathLike):\n",
+ " print(\"Load model\", model_path)\n",
+ " return Model.load(model_path).eval().to(device.default())\n",
"\n",
"\n",
"def find_file(prefix):\n",
@@ -52,6 +51,16 @@
" return None\n",
"\n",
"\n",
+ "def create_renderer(*nets, fov_scale=1.):\n",
+ " fov_list = [20, 45, 110]\n",
+ " for i in range(len(fov_list)):\n",
+ " fov_list[i] = length2fov(fov2length(fov_list[i]) * fov_scale)\n",
+ " res_list = [(256, 256), (256, 256), (256, 230)]\n",
+ " res_full = (1600, 1440)\n",
+ " return FoveatedNeuralRenderer(fov_list, res_list, nn.ModuleList(nets), res_full,\n",
+ " device=device.default())\n",
+ "\n",
+ "\n",
"def load_views(data_desc_file) -> Trans:\n",
" with open(data_desc_file, 'r', encoding='utf-8') as file:\n",
" data_desc = json.loads(file.read())\n",
@@ -124,38 +133,29 @@
"scenes = {\n",
" 'classroom': 'classroom_all',\n",
" 'stones': 'stones_all',\n",
- " 'barbershop': 'barbershop_all',\n",
+ " 'barbershop': '__thesis/barbershop',\n",
" 'lobby': 'lobby_all'\n",
"}\n",
"\n",
"\n",
- "fov_list = [20, 45, 110]\n",
- "res_list = [(256, 256), (256, 256), (256, 230)]\n",
- "res_full = (1600, 1440)\n"
+ "scene = \"barbershop\"\n",
+ "os.chdir(f'{rootdir}/data/{scenes[scene]}')\n",
+ "print('Change working directory to ', os.getcwd())\n",
+ "\n",
+ "fovea_net = load_model(find_file('fovea'))\n",
+ "periph_net = load_model(find_file('periph'))\n",
+ "renderer = create_renderer(fovea_net, periph_net, periph_net)"
]
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Change working directory to /home/dengnc/dvs/data/__new/barbershop_all\n",
- "Load net from fovea200@snerffast4-rgb_e6_fc512x4_d1.20-6.00_s64_~p.pth ...\n",
- "Load net from periph200@snerffast2-rgb_e6_fc256x4_d1.20-6.00_s32_~p.pth ...\n",
- "barbershop 0 Saved\n",
- "barbershop 1 Saved\n",
- "barbershop 2 Saved\n",
- "barbershop 3 Saved\n",
- "barbershop 4 Saved\n",
- "barbershop 0 Saved\n",
- "barbershop 1 Saved\n",
- "barbershop 2 Saved\n",
- "barbershop 3 Saved\n",
- "barbershop 4 Saved\n",
"barbershop 0 Saved\n",
"barbershop 1 Saved\n",
"barbershop 2 Saved\n",
@@ -180,60 +180,46 @@
" [(0, 0, 0, 0, 0), (21, 150), (12, 150)]\n",
" ],\n",
" 'barbershop': [\n",
- " #[(0, 0, 0, 0, 0), (106, -67), (90, -67)],\n",
+ " #[(-0.08247789757018531, -0.17165164843400083, -0.20644832805536045, -66.00281344384992, -4.354400833888114), (0, 0), (0, 0)],\n",
" #[(0, 0, 0, 0, 0), (-114, 10), (-126, 10)],\n",
- " [(0, 0, 0, 25, 20), (189, -45), (173, -45)],\n",
- " [(0, 0, 0, 25, 20), (-148, 130), (-163, 130)],\n",
- " [(0.15, 0.15, 0, 43, 2), (9, 0), (-9, 0)],\n",
- " [(0.15, 0, 0.15, -13, -5), (6, 0), (-6, 0)],\n",
- " [(-0.15, 0.15, 0.15, -53, -21), (3, 0), (-3, 0)]\n",
+ " [(0, 0, 0, -25, 20), (189, -45), (173, -45)],\n",
+ " [(0, 0, 0, -25, 20), (-148, 130), (-163, 130)],\n",
+ " [(0.15, 0.15, 0, -43, 2), (9, 0), (-9, 0)],\n",
+ " [(0.15, 0, -0.15, 13, -5), (6, 0), (-6, 0)],\n",
+ " [(-0.15, 0.15, -0.15, 53, -21), (3, 0), (-3, 0)]\n",
" ]\n",
"}\n",
"\n",
- "#for scene in ['classroom', 'lobby', 'barbershop']:\n",
- "for scene in ['barbershop']:\n",
- " os.chdir(f'{rootdir}/data/__new/{scenes[scene]}')\n",
- " print('Change working directory to ', os.getcwd())\n",
- "\n",
- " fovea_net = load_net(find_file('fovea'))\n",
- " periph_net = load_net(find_file('periph'))\n",
- " renderer = FoveatedNeuralRenderer(fov_list, res_list,\n",
- " nn.ModuleList([fovea_net, periph_net, periph_net]),\n",
- " res_full, device=device.default())\n",
- "\n",
- " for mono_periph in range(0,4):\n",
- " for i, param in enumerate(params[scene]):\n",
- " view = Trans(torch.tensor(param[0][:3], device=device.default()),\n",
- " torch.tensor(euler_to_matrix([-param[0][4], param[0][3], 0]),\n",
- " device=device.default()).view(3, 3))\n",
- " eye_offset = torch.tensor([0.03, 0, 0], device=device.default())\n",
- " left_view = Trans(view.trans_point(-eye_offset), view.r)\n",
- " right_view = Trans(view.trans_point(eye_offset), view.r)\n",
- " left_images, right_images = renderer(view, param[1], param[2],\n",
- " stereo_disparity=0.06,\n",
- " using_mask=True,\n",
- " mono_periph_mode=mono_periph,\n",
- " ret_raw=False)\n",
- " if True:\n",
- " outputdir = '../__demo/stereo_m%d' % mono_periph if mono_periph else '../__demo/stereo'\n",
- " os.makedirs(outputdir, exist_ok=True)\n",
- " img.save(torch.cat([\n",
- " left_images['blended'],\n",
- " right_images['blended']\n",
- " ], dim=-1), '%s/%s_%d.png' % (outputdir, scene, i))\n",
- " img.save(left_images['blended'], '%s/%s_%d_l.png' % (outputdir, scene, i))\n",
- " img.save(right_images['blended'], '%s/%s_%d_r.png' % (outputdir, scene, i))\n",
- " stereo_overlap = torch.cat([\n",
- " left_images['blended'][:, 0:1],\n",
- " right_images['blended'][:, 1:3]\n",
- " ], dim=1)\n",
- " img.save(stereo_overlap, '%s/%s_%d_stereo.png' % (outputdir, scene, i))\n",
- " #os.makedirs(outputdir + '/mid', exist_ok=True)\n",
- " #img.save(left_images['layers_img'][1], '%s/mid/%s_%d_l.png' % (outputdir, scene, i))\n",
- " #img.save(right_images['layers_img'][1], '%s/mid/%s_%d_r.png' % (outputdir, scene, i))\n",
- " print(\"%s %d Saved\" % (scene, i))\n",
- " else:\n",
- " plot_figures(left_images, right_images, param[1], param[2])\n"
+ "for mono_periph in range(3, 5):\n",
+ " for i, param in enumerate(params[scene]):\n",
+ " view = Trans(torch.tensor(param[0][:3], device=device.default()),\n",
+ " torch.tensor(euler_to_matrix(param[0][4], param[0][3], 0),\n",
+ " device=device.default()).view(3, 3))\n",
+ " left_images, right_images = renderer(view, param[1], param[2],\n",
+ " stereo_disparity=0.06,\n",
+ " using_mask=True,\n",
+ " mono_periph_mode=mono_periph,\n",
+ " ret_raw=False)\n",
+ " if True:\n",
+ " outputdir = '../__demo/stereo_m%d' % mono_periph if mono_periph else '../__demo/stereo'\n",
+ " os.makedirs(outputdir, exist_ok=True)\n",
+ " img.save(torch.cat([\n",
+ " left_images['blended'],\n",
+ " right_images['blended']\n",
+ " ], dim=-1), '%s/%s_%d.png' % (outputdir, scene, i))\n",
+ " img.save(left_images['blended'], '%s/%s_%d_l.png' % (outputdir, scene, i))\n",
+ " img.save(right_images['blended'], '%s/%s_%d_r.png' % (outputdir, scene, i))\n",
+ " stereo_overlap = torch.cat([\n",
+ " left_images['blended'][:, 0:1],\n",
+ " right_images['blended'][:, 1:3]\n",
+ " ], dim=1)\n",
+ " img.save(stereo_overlap, '%s/%s_%d_stereo.png' % (outputdir, scene, i))\n",
+ " #os.makedirs(outputdir + '/mid', exist_ok=True)\n",
+ " #img.save(left_images['layers_img'][1], '%s/mid/%s_%d_l.png' % (outputdir, scene, i))\n",
+ " #img.save(right_images['layers_img'][1], '%s/mid/%s_%d_r.png' % (outputdir, scene, i))\n",
+ " print(\"%s %d Saved\" % (scene, i))\n",
+ " else:\n",
+ " plot_figures(left_images, right_images, param[1], param[2])\n"
]
},
{
@@ -245,11 +231,8 @@
}
],
"metadata": {
- "interpreter": {
- "hash": "82066b63b621a9e3d15e3b7c11ca76da6238eff3834294910d715044bd0561e5"
- },
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3.10.0 ('dvs')",
"language": "python",
"name": "python3"
},
@@ -263,7 +246,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.10.0"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "4469b029896260c1221afa6e0e6159922aafd2738570e75b7bc15e28db242604"
+ }
}
},
"nbformat": 4,
diff --git a/notebook/gen_for_eval.ipynb b/notebook/gen/gen_for_eval.ipynb
similarity index 100%
rename from notebook/gen_for_eval.ipynb
rename to notebook/gen/gen_for_eval.ipynb
diff --git a/notebook/gen/gen_layers.ipynb b/notebook/gen/gen_layers.ipynb
new file mode 100644
index 0000000..ec37fab
--- /dev/null
+++ b/notebook/gen/gen_layers.ipynb
@@ -0,0 +1,118 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "import sys\n",
+ "import os\n",
+ "import torch\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "rootdir = os.path.abspath(sys.path[0] + '/../../')\n",
+ "sys.path.append(rootdir)\n",
+ "\n",
+ "torch.autograd.set_grad_enabled(False)\n",
+ "\n",
+ "from model import Model\n",
+ "from data import Dataset\n",
+ "from utils import netio, img, device\n",
+ "from utils.view import *\n",
+ "from utils.types import *\n",
+ "from components.render import render\n",
+ "\n",
+ "\n",
+ "model: Model = None\n",
+ "dataset: Dataset = None\n",
+ "\n",
+ "\n",
+ "def load_model(path: PathLike):\n",
+ " ckpt_path = netio.find_checkpoint(Path(path))\n",
+ " ckpt = torch.load(ckpt_path)\n",
+ " model = Model.create(ckpt[\"args\"][\"model\"], ckpt[\"args\"][\"model_args\"])\n",
+ " model.load_state_dict(ckpt[\"states\"][\"model\"])\n",
+ " model.to(device.default()).eval()\n",
+ " return model\n",
+ "\n",
+ "\n",
+ "def load_dataset(path: PathLike):\n",
+ " return Dataset(path, color_mode=model.color, coord_sys=model.args.coord,\n",
+ " device=device.default())\n",
+ "\n",
+ "\n",
+ "def plot_images(images, rows, cols):\n",
+ " plt.figure(figsize=(20, int(20 / cols * rows)))\n",
+ " for r in range(rows):\n",
+ " for c in range(cols):\n",
+ " plt.subplot(rows, cols, r * cols + c + 1)\n",
+ " img.plot(images[r * cols + c])\n",
+ "\n",
+ "\n",
+ "def save_images(images, scene, i):\n",
+ " outputdir = f'{rootdir}/data/__demo/layers/'\n",
+ " os.makedirs(outputdir, exist_ok=True)\n",
+ " for layer in range(len(images)):\n",
+ " img.save(images[layer], f'{outputdir}{scene}_{i:04d}({layer}).png')\n",
+ "\n",
+ "scene = \"gas\"\n",
+ "model_path = f\"{rootdir}/data/__thesis/{scene}/_nets/train/snerf_fast\"\n",
+ "dataset_path = f\"{rootdir}/data/__thesis/{scene}/test.json\"\n",
+ "\n",
+ "\n",
+ "model = load_model(model_path)\n",
+ "dataset = load_dataset(dataset_path)\n",
+ "\n",
+ "\n",
+ "i = 6\n",
+ "cam = dataset.cam\n",
+ "view = Trans(dataset.centers[i], dataset.rots[i])\n",
+ "output = render(model, dataset.cam, view, \"colors\", \"weights\")\n",
+ "output_colors = output.colors * output.weights\n",
+ "\n",
+ "samples_per_layer = 4#model.core.samples_per_field\n",
+ "n_samples = model.args.n_samples\n",
+ "output_layers = [\n",
+ " output_colors[..., offset:offset+samples_per_layer, :].sum(-2)\n",
+ " for offset in range(0, n_samples, samples_per_layer)\n",
+ "]\n",
+ " \n",
+ "plot_images(output_layers, 8, 2)\n",
+ "#save_images(output_layers, scene, i)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.0 ('dvs')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.0"
+ },
+ "metadata": {
+ "interpreter": {
+ "hash": "82066b63b621a9e3d15e3b7c11ca76da6238eff3834294910d715044bd0561e5"
+ }
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "4469b029896260c1221afa6e0e6159922aafd2738570e75b7bc15e28db242604"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebook/gen_prebake.ipynb b/notebook/gen/gen_prebake.ipynb
similarity index 99%
rename from notebook/gen_prebake.ipynb
rename to notebook/gen/gen_prebake.ipynb
index c8f7733..e9aed19 100644
--- a/notebook/gen_prebake.ipynb
+++ b/notebook/gen/gen_prebake.ipynb
@@ -60,7 +60,7 @@
" enable_ce = True, output_res = None):\n",
" ipd = 0.06\n",
" layers_cam = [\n",
- " CameraParam({\n",
+ " Camera({\n",
" 'fov': 110,\n",
" 'cx': 0.5,\n",
" 'cy': 0.5,\n",
diff --git a/notebook/gen_teaser.ipynb b/notebook/gen/gen_teaser.ipynb
similarity index 100%
rename from notebook/gen_teaser.ipynb
rename to notebook/gen/gen_teaser.ipynb
diff --git a/notebook/gen_test.ipynb b/notebook/gen/gen_test.ipynb
similarity index 96%
rename from notebook/gen_test.ipynb
rename to notebook/gen/gen_test.ipynb
index cd6197d..7196353 100644
--- a/notebook/gen_test.ipynb
+++ b/notebook/gen/gen_test.ipynb
@@ -31,7 +31,7 @@
"from utils import device\n",
"from utils import view\n",
"from components.gen_final import GenFinal\n",
- "from utils.perf import Perf\n",
+ "from utils.profile import Profiler\n",
"\n",
"\n",
"def load_net(path):\n",
@@ -135,15 +135,15 @@
" torch.tensor([[0.0, 0.0, 0.0]], device=device.default()),\n",
" torch.tensor([[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]], device=device.default())\n",
")\n",
- "perf = Perf(True, True)\n",
+ "profile = Profiler(True, True)\n",
"rays_o, rays_d = gen.layer_cams[0].get_global_rays(test_view, True)\n",
- "perf.checkpoint(\"GetRays\")\n",
+ "profile.checkpoint(\"GetRays\")\n",
"rays_o = rays_o.view(-1, 3)\n",
"rays_d = rays_d.view(-1, 3)\n",
"coords, pts, depths = fovea_net.sampler(rays_o, rays_d)\n",
- "perf.checkpoint(\"Sample\")\n",
+ "profile.checkpoint(\"Sample\")\n",
"encoded = fovea_net.input_encoder(coords)\n",
- "perf.checkpoint(\"Encode\")\n",
+ "profile.checkpoint(\"Encode\")\n",
"print(\"Rays:\", rays_d)\n",
"print(\"Spherical coords:\", coords)\n",
"print(\"Depths:\", depths)\n",
diff --git a/notebook/gen_user_study_images.ipynb b/notebook/gen/gen_user_study_images.ipynb
similarity index 98%
rename from notebook/gen_user_study_images.ipynb
rename to notebook/gen/gen_user_study_images.ipynb
index 4518b11..fceb675 100644
--- a/notebook/gen_user_study_images.ipynb
+++ b/notebook/gen/gen_user_study_images.ipynb
@@ -140,7 +140,7 @@
"\n",
"# Load Dataset\n",
"views = load_views('views.json')\n",
- "#ref_dataset = SphericalViewSynDataset('ref.json', load_images=False, calculate_rays=False)\n",
+ "#ref_dataset = SphericalViewSynDataset('ref.json', load_colors=False, calculate_rays=False)\n",
"print('Dataset loaded.')\n",
"\n",
"print('views:', views.size())\n",
@@ -226,4 +226,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/notebook/gen_video.ipynb b/notebook/gen/gen_video.ipynb
similarity index 98%
rename from notebook/gen_video.ipynb
rename to notebook/gen/gen_video.ipynb
index df80a3c..c157092 100644
--- a/notebook/gen_video.ipynb
+++ b/notebook/gen/gen_video.ipynb
@@ -44,7 +44,7 @@
" return None\n",
"\n",
"\n",
- "def load_views(data_desc_file) -> Tuple[view.Trans, torch.Tensor]:\n",
+ "def load_views(data_desc_file) -> tuple[view.Trans, torch.Tensor]:\n",
" with open(data_desc_file, 'r', encoding='utf-8') as file:\n",
" lines = file.readlines()\n",
" n = len(lines) // 7\n",
diff --git a/notebook/gen_crop.ipynb b/notebook/gen_crop.ipynb
deleted file mode 100644
index e7f6a9b..0000000
--- a/notebook/gen_crop.ipynb
+++ /dev/null
@@ -1,103 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "source": [
- "import sys\n",
- "import os\n",
- "import torch\n",
- "import torch.nn.functional as nn_f\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "rootdir = os.path.abspath(sys.path[0] + '/../')\n",
- "sys.path.append(rootdir)\n",
- "torch.cuda.set_device(0)\n",
- "print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
- "torch.autograd.set_grad_enabled(False)\n",
- "\n",
- "from utils import img\n",
- "from utils.view import *\n",
- "\n",
- "datadir = f\"{rootdir}/data/__new/__demo/for_crop\"\n",
- "figs = ['our', 'gt', 'nerf', 'fgt']\n",
- "crops = {\n",
- " 'classroom_0': [[720, 790, 100], [370, 1160, 200]],\n",
- " 'lobby_1': [[570, 1000, 100], [1300, 1000, 200]],\n",
- " 'stones_2': [[720, 800, 100], [680, 1317, 200]],\n",
- " 'barbershop_3': [[745, 810, 100], [950, 900, 200]]\n",
- "}\n",
- "colors = torch.tensor([[0, 1, 0, 1], [1, 1, 0, 1]], dtype=torch.float)\n",
- "border = 10\n",
- "\n",
- "for scene in crops:\n",
- " images = img.load([f\"{datadir}/origin/{scene}_{fig}.png\" for fig in figs])\n",
- " halfw = images.size(-1) // 2\n",
- " halfh = images.size(-2) // 2\n",
- " crop = crops[scene]\n",
- " fovea_patches = images[...,\n",
- " crop[0][1] - crop[0][2] // 2: crop[0][1] + crop[0][2] // 2,\n",
- " crop[0][0] - crop[0][2] // 2: crop[0][0] + crop[0][2] // 2]\n",
- " periph_patches = images[...,\n",
- " crop[1][1] - crop[1][2] // 2: crop[1][1] + crop[1][2] // 2,\n",
- " crop[1][0] - crop[1][2] // 2: crop[1][0] + crop[1][2] // 2]\n",
- " fovea_patches = nn_f.interpolate(fovea_patches, (128, 128))\n",
- " periph_patches = nn_f.interpolate(periph_patches, (128, 128))\n",
- " overlay = torch.zeros(1, 4, 1600, 1440)\n",
- " mask = torch.zeros(2, 1600, 1440, dtype=torch.bool)\n",
- " for i in range(2):\n",
- " mask[i,\n",
- " crop[i][1] - crop[i][2] // 2 - border: crop[i][1] + crop[i][2] // 2 + border,\n",
- " crop[i][0] - crop[i][2] // 2 - border: crop[i][0] + crop[i][2] // 2 + border] = True\n",
- " mask[i,\n",
- " crop[i][1] - crop[i][2] // 2: crop[i][1] + crop[i][2] // 2,\n",
- " crop[i][0] - crop[i][2] // 2: crop[i][0] + crop[i][2] // 2] = False\n",
- " overlay[:, :, mask[0]] = colors[0][..., None]\n",
- " overlay[:, :, mask[1]] = colors[1][..., None]\n",
- " plt.figure(figsize=(12, 6))\n",
- " plt.subplot(1, 2, 1)\n",
- " img.plot(images[0])\n",
- " plt.subplot(1, 2, 2)\n",
- " img.plot(overlay)\n",
- " plt.figure(figsize=(12, 6))\n",
- " for i in range(4):\n",
- " plt.subplot(2, 4, i + 1)\n",
- " img.plot(fovea_patches[i])\n",
- " for i in range(4):\n",
- " plt.subplot(2, 4, i + 5)\n",
- " img.plot(periph_patches[i])\n",
- " img.save(fovea_patches, [f\"{datadir}/fovea/{scene}_{fig}.png\" for fig in figs])\n",
- " img.save(periph_patches, [f\"{datadir}/periph/{scene}_{fig}.png\" for fig in figs])\n",
- " img.save(torch.cat([fovea_patches, periph_patches], dim=-1),\n",
- " [f\"{datadir}/patch/{scene}_{fig}.png\" for fig in figs])\n",
- " img.save(overlay, f\"{datadir}/overlay/{scene}.png\")\n"
- ],
- "outputs": [],
- "metadata": {}
- }
- ],
- "metadata": {
- "interpreter": {
- "hash": "65406b00395a48e1d89cf658ae895e7869e05878f5469716b06a752a3915211c"
- },
- "kernelspec": {
- "name": "python3",
- "display_name": "Python 3.8.5 64-bit ('base': conda)"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.5"
- },
- "orig_nbformat": 2
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/notebook/test/__general.ipynb b/notebook/test/__general.ipynb
new file mode 100644
index 0000000..2278581
--- /dev/null
+++ b/notebook/test/__general.ipynb
@@ -0,0 +1,237 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Performance of Randperm on CPU/GPU"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Node Random perm on host: host duration 6384.1ms, device duration 6384.4ms\n",
+ "Node Random perm on device: host duration 2525.0ms, device duration 2525.0ms\n"
+ ]
+ }
+ ],
+ "source": [
+ "from common import *\n",
+ "from utils.profile import debug_profile\n",
+ "from utils.mem_profiler import MemProfiler\n",
+ "\n",
+ "with debug_profile(\"Random perm on host\"):\n",
+ " torch.randperm(1024 * 1024 * 100)\n",
+ "\n",
+ "with debug_profile(\"Random perm on device\"),\\\n",
+ " MemProfiler(\"Random perm on host\", device=\"cuda:3\"):\n",
+ " torch.randperm(1024 * 1024 * 100, device=\"cuda:3\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# \\_\\_getattribute\\_\\_ Method"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "a.a: 1\n",
+ "a.b: 2\n"
+ ]
+ },
+ {
+ "ename": "AttributeError",
+ "evalue": "'A' object has no attribute 'c'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-3-bdb259af4410>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a.a:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a.b:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a.c:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m<ipython-input-3-bdb259af4410>\u001b[0m in \u001b[0;36m__getattribute__\u001b[0;34m(self, _A__name)\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m<ipython-input-3-bdb259af4410>\u001b[0m in \u001b[0;36m__getattribute__\u001b[0;34m(self, _A__name)\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m__name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mAttributeError\u001b[0m: 'A' object has no attribute 'c'"
+ ]
+ }
+ ],
+ "source": [
+ "class A(object):\n",
+ "\n",
+ "\n",
+ " def __init__(self, a, **extra) -> None:\n",
+ " super().__init__()\n",
+ " self.a = a\n",
+ " self.extra = extra\n",
+ "\n",
+ " def __getattribute__(self, __name: str):\n",
+ " try:\n",
+ " return super().__getattribute__(__name)\n",
+ " except AttributeError as e:\n",
+ " try:\n",
+ " return self.extra[__name]\n",
+ " except KeyError:\n",
+ " pass\n",
+ " err = e\n",
+ " raise err\n",
+ "\n",
+ "\n",
+ "a = A(a=1, b=2)\n",
+ "\n",
+ "\n",
+ "print(\"a.a:\", a.a)\n",
+ "print(\"a.b:\", a.b)\n",
+ "print(\"a.c:\", a.c)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Performance of Various Select/Scatter Methods"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.010159730911254883 tensor(100., device='cuda:0')\n",
+ "0.011237859725952148 tensor(1400., device='cuda:0')\n",
+ "0.032263755798339844 tensor(2700., device='cuda:0')\n",
+ "0.02148723602294922 tensor(4.1723e-07, device='cuda:0')\n",
+ "0.009927511215209961 tensor(4.1723e-07, device='cuda:0')\n",
+ "Mask set 0.02173590660095215\n",
+ "Inplace mask scatter 0.0041882991790771484\n",
+ "Mask scatter 0.00580906867980957\n",
+ "Index set 0.03358888626098633\n",
+ "Index put 0.01044917106628418\n"
+ ]
+ }
+ ],
+ "source": [
+ "from common import *\n",
+ "from time import time\n",
+ "\n",
+ "a = torch.zeros(200000, device=\"cuda\")\n",
+ "i = torch.randint(0, a.shape[0], [500000], device=\"cuda\")\n",
+ "start = time()\n",
+ "for _ in range(100):\n",
+ " a[i] += 1\n",
+ "end = time()\n",
+ "print(end - start, a.max())\n",
+ "start = time()\n",
+ "for _ in range(100):\n",
+ " a.index_add_(0, i, torch.ones_like(i, dtype=torch.float))\n",
+ "end = time()\n",
+ "print(end - start, a.max())\n",
+ "\n",
+ "start = time()\n",
+ "for _ in range(100):\n",
+ " ui, n = i.unique(return_counts=True)\n",
+ " a[ui] += n\n",
+ "end = time()\n",
+ "print(end - start, a.max())\n",
+ "\n",
+ "\n",
+ "a = torch.rand(2000, 2000, device=\"cuda\") - .5\n",
+ "m = a > 0\n",
+ "\n",
+ "start = time()\n",
+ "for _ in range(100):\n",
+ " b = a[m]\n",
+ "end = time()\n",
+ "print(end - start, b.min())\n",
+ "\n",
+ "start = time()\n",
+ "for _1 in range(20):\n",
+ " m1 = m.nonzero(as_tuple=True)\n",
+ " for _ in range(5):\n",
+ " b = a[m1]\n",
+ "end = time()\n",
+ "print(end - start, b.min())\n",
+ "\n",
+ "\n",
+ "c = torch.rand_like(b)\n",
+ "\n",
+ "start = time()\n",
+ "for _ in range(100):\n",
+ " a[m] = c\n",
+ "end = time()\n",
+ "print(\"Mask set\", end - start)\n",
+ "\n",
+ "start = time()\n",
+ "for _ in range(100):\n",
+ " a.masked_scatter_(m, c)\n",
+ "end = time()\n",
+ "print(\"Inplace mask scatter\", end - start)\n",
+ "\n",
+ "\n",
+ "start = time()\n",
+ "for _ in range(100):\n",
+ " a = a.masked_scatter(m, c)\n",
+ "end = time()\n",
+ "print(\"Mask scatter\", end - start)\n",
+ "\n",
+ "start = time()\n",
+ "for _1 in range(20):\n",
+ " m1 = m.nonzero(as_tuple=True)\n",
+ " for _ in range(5):\n",
+ " a[m1] = b\n",
+ "end = time()\n",
+ "print(\"Index set\", end - start)\n",
+ "\n",
+ "\n",
+ "start = time()\n",
+ "for _1 in range(20):\n",
+ " m1 = m.nonzero(as_tuple=True)\n",
+ " for _ in range(5):\n",
+ " a.index_put_(m1, b)\n",
+ "end = time()\n",
+ "print(\"Index put\", end - start)"
+ ]
+ }
+ ],
+ "metadata": {
+ "interpreter": {
+ "hash": "65406b00395a48e1d89cf658ae895e7869e05878f5469716b06a752a3915211c"
+ },
+ "kernelspec": {
+ "display_name": "Python 3.8.12 ('base')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebook/test/common.py b/notebook/test/common.py
new file mode 100644
index 0000000..69fc5d3
--- /dev/null
+++ b/notebook/test/common.py
@@ -0,0 +1,10 @@
+import sys
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+from pathlib import Path
+
+rootdir = Path(sys.path[0]).absolute().parents[1]
+sys.path.append(str(rootdir))
+
+torch.cuda.set_device(0)
\ No newline at end of file
diff --git a/notebook/test_constrast.ipynb b/notebook/test/constrast.ipynb
similarity index 69%
rename from notebook/test_constrast.ipynb
rename to notebook/test/constrast.ipynb
index f548d3e..d820133 100644
--- a/notebook/test_constrast.ipynb
+++ b/notebook/test/constrast.ipynb
@@ -6,21 +6,11 @@
"metadata": {},
"outputs": [],
"source": [
- "import sys\n",
- "import os\n",
- "import torch\n",
- "import matplotlib.pyplot as plt\n",
- "import torchvision.transforms.functional as trans_f\n",
- "\n",
- "rootdir = os.path.abspath(sys.path[0] + '/../')\n",
- "sys.path.append(rootdir)\n",
- "torch.cuda.set_device(2)\n",
- "print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
- "\n",
+ "from common import *\n",
"from components import refine\n",
"from utils import img\n",
"\n",
- "img = img.load(os.path.join(rootdir, \"data/gas_2021.01.04_all_in_one/output/mid_0536.png\"))\n",
+ "img = img.load(rootdir / \"data/gas_2021.01.04_all_in_one/output/mid_0536.png\")\n",
"\n",
"fe = 0.2\n",
"leng_sigma = [0,3,5]\n",
@@ -37,8 +27,7 @@
"img.plot(enhanced)\n",
"plt.title('Enhanced')\n",
"plt.axis('off')\n",
- "plt.show()\n",
- "\n"
+ "plt.show()"
]
}
],
@@ -63,4 +52,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/notebook/test/fisheye_undistort.ipynb b/notebook/test/fisheye_undistort.ipynb
new file mode 100644
index 0000000..9d181de
--- /dev/null
+++ b/notebook/test/fisheye_undistort.ipynb
@@ -0,0 +1,135 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Image size is (3800, 3000)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x7f01e2d35250>"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1152x1152 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1152x1152 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "import cv2\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "raw_im = cv2.imread(\"/home/dengnc/dvs/data/__captured/dalab1/raw_images/1/image001.png\")\n",
+ "im_size = raw_im.shape[:-1][::-1]\n",
+ "print(\"Image size is\", im_size)\n",
+ "\n",
+ "plt.figure()\n",
+ "plt.imshow(raw_im)\n",
+ "\n",
+ "K = np.array([\n",
+ " [1369.757446, 0, 1838.643555],\n",
+ " [ 0, 1369.757446, 1524.068604],\n",
+ " [ 0, 0, 1]\n",
+ "])\n",
+ "newK = np.array([\n",
+ " [1369.26, 0, 1900],\n",
+ " [0, 1369.26, 1500],\n",
+ " [0, 0, 1]\n",
+ "])\n",
+ "D = np.array([[-0.044752], [-0.006285], [0.000000], [0.000000]])\n",
+ "\n",
+ "map1, map2 = cv2.fisheye.initUndistortRectifyMap(K, D, None, K, im_size, cv2.CV_16SC2)\n",
+ "undistorted_im = cv2.remap(raw_im, map1, map2, interpolation=cv2.INTER_LINEAR,\n",
+ " borderMode=cv2.BORDER_CONSTANT)\n",
+ "undistorted_im = cv2.rotate(undistorted_im, cv2.ROTATE_90_COUNTERCLOCKWISE)\n",
+ "plt.figure(figsize=(16,16))\n",
+ "plt.imshow(undistorted_im)\n",
+ "\n",
+ "map1, map2 = cv2.fisheye.initUndistortRectifyMap(K, D, None, newK, im_size, cv2.CV_16SC2)\n",
+ "undistorted_im = cv2.remap(raw_im, map1, map2, interpolation=cv2.INTER_LINEAR,\n",
+ " borderMode=cv2.BORDER_CONSTANT)\n",
+ "undistorted_im = cv2.rotate(undistorted_im, cv2.ROTATE_90_COUNTERCLOCKWISE)\n",
+ "plt.figure(figsize=(16,16))\n",
+ "plt.imshow(undistorted_im)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "interpreter": {
+ "hash": "65406b00395a48e1d89cf658ae895e7869e05878f5469716b06a752a3915211c"
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebook/test_image_filter.ipynb b/notebook/test/image_filter.ipynb
similarity index 78%
rename from notebook/test_image_filter.ipynb
rename to notebook/test/image_filter.ipynb
index 7dce2ab..09fc61f 100644
--- a/notebook/test_image_filter.ipynb
+++ b/notebook/test/image_filter.ipynb
@@ -6,28 +6,17 @@
"metadata": {},
"outputs": [],
"source": [
- "# 图åƒåŽ»å™ªå¹³æ»‘滤波\n",
- "# 使用opencv的自带函数实现，与自编写作比较\n",
- "# 产生椒ç›å™ªå£°ï¼Œé«˜æ–¯å™ªå£°ç‰\n",
- "# 使用ä¸å€¼æ»¤æ³¢ï¼Œå¹³å‡æ»¤æ³¢ï¼Œé«˜æ–¯æ»¤æ³¢ï¼Œæ–¹æ¡†æ»¤æ³¢\n",
- "import sys\n",
- "import os\n",
- "rootdir = os.path.abspath(sys.path[0] + '/../')\n",
- "sys.path.append(rootdir)\n",
- "\n",
- "import numpy as np\n",
- "import math\n",
"import cv2\n",
- "import matplotlib.pyplot as plt\n",
- "import torch.nn.functional as nn_f\n",
- "import torch\n",
- "from loss.perc_loss import *\n",
+ "\n",
+ "from common import *\n",
+ "from utils import math\n",
+ "from loss import *\n",
"\n",
"loss = VGGPerceptualLoss().to('cuda')\n",
"\n",
"def psnr(input, gt):\n",
" input, gt = torch.from_numpy(input / 255).permute(2, 0, 1)[None, :].to('cuda', torch.float32), torch.from_numpy(gt / 255).permute(2, 0, 1)[None, :].to('cuda', torch.float32)\n",
- " rmse = math.sqrt(nn_f.mse_loss(input, gt))\n",
+ " rmse = math.sqrt(mse_loss(input, gt))\n",
" #diff = target / 255 - ref / 255\n",
" #rmse = math.sqrt(np.mean(diff ** 2.))\n",
" #return rmse\n",
@@ -35,8 +24,8 @@
"\n",
"\n",
"for i in range(3):\n",
- " image_gt = cv2.imread(os.path.join (rootdir, 'data/gas_fovea_2020.12.31/train/view_%04d.png' % i))\n",
- " image = cv2.imread(os.path.join (rootdir, 'data/gas_fovea_2020.12.31/new_fovea_rgb@nmsl-rgb_e10_fc128x4_d1-50_s32/output/model-epoch_300/train/out_view_%04d.png' % i))\n",
+ " image_gt = cv2.imread(rootdir / 'data/gas_fovea_2020.12.31/train/view_%04d.png' % i)\n",
+ " image = cv2.imread(rootdir / 'data/gas_fovea_2020.12.31/new_fovea_rgb@nmsl-rgb_e10_fc128x4_d1-50_s32/output/model-epoch_300/train/out_view_%04d.png' % i)\n",
" plt.figure(facecolor='white', figsize=(10,4))\n",
" plt.subplot(2, 3, 1)\n",
" plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
@@ -112,4 +101,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/notebook/test_data_loader.ipynb b/notebook/test/load_dataset.ipynb
similarity index 74%
rename from notebook/test_data_loader.ipynb
rename to notebook/test/load_dataset.ipynb
index 6d30850..4b09f84 100644
--- a/notebook/test_data_loader.ipynb
+++ b/notebook/test/load_dataset.ipynb
@@ -6,29 +6,18 @@
"metadata": {},
"outputs": [],
"source": [
- "import sys\n",
- "import os\n",
- "import torch\n",
+ "%matplotlib inline\n",
"import time\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "\n",
- "rootdir = os.path.abspath('../')\n",
- "sys.path.append(rootdir)\n",
"\n",
+ "from common import *\n",
"from utils import img\n",
"from utils import sphere\n",
"from utils import device\n",
"from utils import misc\n",
"from utils.mem_profiler import *\n",
- "from data.dataset_factory import DatasetFactory\n",
- "from data.loader import DataLoader\n",
- "\n",
- "# Select device\n",
- "torch.cuda.set_device(0)\n",
- "print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
+ "from data import Dataset, RaysLoader\n",
"\n",
- "MemProfiler.enable = True\n"
+ "MemProfiler.enable = False"
]
},
{
@@ -57,27 +46,28 @@
"#DATA_DESC_FILE = f'{rootdir}/data/__nerf/horns/images_4.json'\n",
"#DATA_DESC_FILE = f'{rootdir}/data/__new/city_fovea_r360x80_t5.0/train1.json'\n",
"#DATA_DESC_FILE = f'{rootdir}/data/__captured/room/train.json'\n",
- "DATA_DESC_FILE = f'{rootdir}/data/__pano/stones_fovea_t1.0/train.json'\n",
+ "DATA_DESC_FILE = f'{rootdir}/data/__captured/bedroom/images4_train.json'\n",
"\n",
"MemProfiler.print_memory_stats('Start')\n",
"\n",
- "dataset = DatasetFactory.load(DATA_DESC_FILE)\n",
+ "dataset = Dataset(DATA_DESC_FILE)\n",
"res = dataset.res\n",
- "data_loader = DataLoader(dataset, res[0] * res[1], chunk_max_items=6e8)\n",
+ "data_loader = RaysLoader(dataset, res[0] * res[1], device=torch.device(\"cuda\"))\n",
"\n",
"MemProfiler.print_memory_stats('After dataset loaded')\n",
"\n",
"fig = plt.figure(figsize=(12, 6))\n",
"i = 0\n",
- "for indices, rays_o, rays_d, extras in data_loader:\n",
+ "for data in data_loader:\n",
" if i >= 4:\n",
" break\n",
" plt.subplot(2, 2, i + 1)\n",
- " img.plot(extras['colors'].view(1, res[0], res[1], 3))\n",
+ " img.plot(data['color'].view(1, res[0], res[1], 3))\n",
" MemProfiler.print_memory_stats(f'After view {i} is plotted')\n",
" i += 1\n",
" time.sleep(1)\n",
"\n",
+ "#plt.show()\n",
"MemProfiler.print_memory_stats(f'After all views are plotted')"
]
},
@@ -88,23 +78,23 @@
"outputs": [],
"source": [
"selector = torch.arange(res[0] * res[1]).reshape(res[0], res[1])\n",
- "selector = selector[1024-512:1024+512:10, 2048-512:2048+512:5].flatten()\n",
+ "selector = selector[::3, ::3].flatten()\n",
"idx_range = [0, 4, 20, 24, 62, 100, 104, 120, 124]\n",
- "for r in torch.arange(3, 3.5, 0.1):\n",
+ "for r in torch.arange(11, 50, 5):\n",
" p = None\n",
" centers = None\n",
" pixels = None\n",
" idx = 0\n",
" MemProfiler.print_memory_stats(f'Before iter')\n",
- " for indices, rays_o, rays_d, extras in data_loader:\n",
+ " for data in data_loader:\n",
" if idx > max(idx_range):\n",
" break\n",
" if idx not in idx_range:\n",
" idx += 1\n",
" continue\n",
- " colors = extras['colors'][selector]\n",
- " rays_o = rays_o[selector]\n",
- " rays_d = rays_d[selector]\n",
+ " colors = data['color'][selector]\n",
+ " rays_o = data['rays_o'][selector]\n",
+ " rays_d = data['rays_d'][selector]\n",
" r = torch.tensor([[r]], device=device.default())\n",
" p_ = misc.torch2np(sphere.ray_sphere_intersect(rays_o, rays_d, r)[0].view(-1, 3))\n",
" p = p_ if p is None else np.concatenate((p, p_), axis=0)\n",
@@ -118,7 +108,6 @@
" plt.ylabel('z')\n",
" plt.title('r = %f' % r)\n",
" ax.scatter([0], [0], [0], color=\"k\", s=10)\n",
- " print(p.shape, pixels.shape)\n",
" ax.scatter(p[:, 0], p[:, 2], p[:, 1], color=pixels, s=0.5)\n",
" ax.view_init(elev=0, azim=-90)\n"
]
@@ -126,10 +115,10 @@
],
"metadata": {
"interpreter": {
- "hash": "82066b63b621a9e3d15e3b7c11ca76da6238eff3834294910d715044bd0561e5"
+ "hash": "65406b00395a48e1d89cf658ae895e7869e05878f5469716b06a752a3915211c"
},
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3.8.12 ('base')",
"language": "python",
"name": "python3"
},
@@ -143,7 +132,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.8.12"
}
},
"nbformat": 4,
diff --git a/notebook/test/sphere.ipynb b/notebook/test/sphere.ipynb
new file mode 100644
index 0000000..4f9fc66
--- /dev/null
+++ b/notebook/test/sphere.ipynb
@@ -0,0 +1,142 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Test Ray-Sphere Intersection & Cartesian-Spherical Conversion"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensor([[ 0.0000, -0.3536, 0.3536]])\n",
+ "tensor(0.5000)\n",
+ "tensor([[ 0.0000, -45.0000]])\n",
+ "torch.Size([1, 3])\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from common import *\n",
+ "from utils import sphere\n",
+ "from utils import math\n",
+ "\n",
+ "\n",
+ "def PlotSphere(ax, r):\n",
+ " u, v = np.mgrid[0:2 * math.pi:50j, 0:math.pi:20j]\n",
+ " x = np.cos(u) * np.sin(v) * r\n",
+ " y = np.sin(u) * np.sin(v) * r\n",
+ " z = np.cos(v) * r\n",
+ " ax.plot_surface(x, y, z, rstride=1, cstride=1,\n",
+ " color='b', linewidth=0.5, alpha=0.3)\n",
+ "\n",
+ "\n",
+ "def PlotPlane(ax, r):\n",
+ " # 二元函数定义域平é¢\n",
+ " x = np.linspace(-r, r, 3)\n",
+ " y = np.linspace(-r, r, 3)\n",
+ " X, Y = np.meshgrid(x, y)\n",
+ " ax.plot_wireframe(X, Y, X * 0, color='g', linewidth=1)\n",
+ "\n",
+ "\n",
+ "p = torch.tensor([[0.0, 0.0, 0.0]])\n",
+ "v = torch.tensor([[0.0, -1.0, 1.0]])\n",
+ "r = torch.tensor([[0.5]])\n",
+ "v = v / torch.norm(v) * r * 2\n",
+ "p_on_sphere_ = sphere.ray_sphere_intersect(p, v, r)[0][0]\n",
+ "print(p_on_sphere_)\n",
+ "print(p_on_sphere_.norm())\n",
+ "spher_coord = sphere.cartesian2spherical(p_on_sphere_)\n",
+ "print(spher_coord[..., 1:3].rad2deg())\n",
+ "p_on_sphere = sphere.spherical2cartesian(spher_coord)\n",
+ "print(p_on_sphere_.size())\n",
+ "\n",
+ "fig = plt.figure(figsize=(8, 8))\n",
+ "ax = fig.gca(projection='3d')\n",
+ "plt.xlabel('x')\n",
+ "plt.ylabel('z')\n",
+ "\n",
+ "PlotPlane(ax, r.item())\n",
+ "PlotSphere(ax, r[0, 0].item())\n",
+ "\n",
+ "ax.scatter([0], [0], [0], color=\"g\", s=10) # Center\n",
+ "ax.scatter([p_on_sphere[0, 0].item()],\n",
+ " [p_on_sphere[0, 2].item()],\n",
+ " [p_on_sphere[0, 1].item()],\n",
+ " color=\"r\", s=10) # Ray position\n",
+ "ax.scatter([p_on_sphere_[0, 0].item()],\n",
+ " [p_on_sphere_[0, 2].item()],\n",
+ " [p_on_sphere_[0, 1].item()],\n",
+ " color=\"y\", s=10) # Ray position\n",
+ "\n",
+ "p_ = p + v\n",
+ "ax.plot([p[0, 0].item(), p_[0, 0].item()],\n",
+ " [p[0, 2].item(), p_[0, 2].item()],\n",
+ " [p[0, 1].item(), p_[0, 1].item()],\n",
+ " color=\"r\")\n",
+ "\n",
+ "ax.plot([p_on_sphere_[0, 0].item(), p_on_sphere_[0, 0].item()],\n",
+ " [p_on_sphere_[0, 2].item(), p_on_sphere_[0, 2].item()],\n",
+ " [0, p_on_sphere_[0, 1].item()], color=\"k\", linestyle='--', linewidth=0.5)\n",
+ "\n",
+ "ax.plot([p_on_sphere_[0, 0].item(), 0],\n",
+ " [p_on_sphere_[0, 2].item(), 0],\n",
+ " [0, 0],\n",
+ " linewidth=0.5, linestyle=\"--\", color=\"k\")\n",
+ "\n",
+ "ax.plot([p_on_sphere_[0, 0].item(), 0],\n",
+ " [p_on_sphere_[0, 2].item(), 0],\n",
+ " [p_on_sphere_[0, 1], 0],\n",
+ " linewidth=0.5, linestyle=\"--\", color=\"k\")\n",
+ "\n",
+ "ax.set_xlim(-r.item(), r.item())\n",
+ "ax.set_ylim(-r.item(), r.item())\n",
+ "ax.set_zlim(-r.item(), r.item())\n",
+ "\n",
+ "plt.show()\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "interpreter": {
+ "hash": "65406b00395a48e1d89cf658ae895e7869e05878f5469716b06a752a3915211c"
+ },
+ "kernelspec": {
+ "display_name": "Python 3.8.12 ('base')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebook/test_eccv.ipynb b/notebook/test/test_eccv.ipynb
similarity index 99%
rename from notebook/test_eccv.ipynb
rename to notebook/test/test_eccv.ipynb
index 2aa812a..19abdb7 100644
--- a/notebook/test_eccv.ipynb
+++ b/notebook/test/test_eccv.ipynb
@@ -80,7 +80,7 @@
"\n",
"images = img.load_seq(f'{datadir}/img%02d.png', 16, permute=False)\n",
"res=(int(p[0, 0]), int(p[0, 1]))\n",
- "cam = CameraParam({\"fy\":-p[0, 2], \"fx\": p[0, 2], \"cx\":res[1]//2, \"cy\":res[0]//2}, res)\n",
+ "cam = Camera({\"fy\":-p[0, 2], \"fx\": p[0, 2], \"cx\":res[1]//2, \"cy\":res[0]//2}, res)\n",
"views = Trans(torch.tensor(t, dtype=torch.float), torch.tensor(r, dtype=torch.float))\n",
"_rays_o, _rays_d = cam.get_global_rays(views, flatten=True)\n",
"_patches = images.flatten(1, 2)\n",
diff --git a/notebook/test_foveation.ipynb b/notebook/test/test_foveation.ipynb
similarity index 61%
rename from notebook/test_foveation.ipynb
rename to notebook/test/test_foveation.ipynb
index 677c639..3ecdacf 100644
--- a/notebook/test_foveation.ipynb
+++ b/notebook/test/test_foveation.ipynb
@@ -6,29 +6,28 @@
"metadata": {},
"outputs": [],
"source": [
+ "%matplotlib inline\n",
"import sys\n",
"import os\n",
"import struct\n",
"import torch\n",
- "import torch.nn as nn\n",
"import matplotlib.pyplot as plt\n",
"\n",
"rootdir = os.path.abspath(sys.path[0] + '/../')\n",
"sys.path.append(rootdir)\n",
- "torch.cuda.set_device(0)\n",
- "print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
- "torch.autograd.set_grad_enabled(False)\n",
+ "torch.set_grad_enabled(False)\n",
"\n",
"from utils import img\n",
- "from utils import device\n",
"from utils.view import *\n",
"from components.foveation import Foveation\n",
"\n",
+ "\n",
"def gen_mask_file(layer_mask, filename):\n",
" indices = torch.arange(layer_mask.size(0) * layer_mask.size(1),\n",
" device=layer_mask.device).view_as(layer_mask)\n",
- " indices = indices[layer_mask>=0]\n",
- " inverseIndices = torch.ones(layer_mask.size(0) * layer_mask.size(1), device=layer_mask.device, dtype=torch.long) * -1\n",
+ " indices = indices[layer_mask >= 0]\n",
+ " inverseIndices = torch.ones(layer_mask.size(0) * layer_mask.size(1),\n",
+ " device=layer_mask.device, dtype=torch.long) * -1\n",
" inverseIndices[indices] = torch.arange(indices.size(0), device=layer_mask.device)\n",
" with open(filename, 'wb') as fp:\n",
" fp.write(indices.size(0).to_bytes(4, 'little'))\n",
@@ -36,32 +35,46 @@
" fp.write(inverseIndices.size(0).to_bytes(4, 'little'))\n",
" fp.write(struct.pack(f\"<{inverseIndices.size(0)}i\", *inverseIndices))\n",
"\n",
+ "\n",
"foveation = Foveation([20, 45, 110], [(256, 256), (256, 256), (256, 230)], (1600, 1440))\n",
- "layers_mask = foveation.get_layers_mask()\n",
+ "layers_mask = foveation.get_layers_mask(gaze=(0, 0))\n",
+ "\n",
"plt.figure(figsize=(12, 4))\n",
"for i, mask in enumerate(layers_mask):\n",
" colored_mask = torch.zeros(mask.size(0), mask.size(1), 3, device=mask.device)\n",
- " c = torch.tensor([[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 1, 1]], device=mask.device)\n",
+ " c = torch.tensor([\n",
+ " [1, 1, 1, 1, 1, 1],\n",
+ " [1, 1, 1, 1, 1, 1],\n",
+ " [1, 1, 1, 0, 0, 0]\n",
+ " ], device=mask.device)\n",
" for bi in range(3):\n",
" region = torch.logical_and(mask >= bi, mask < bi + 1)\n",
- " colored_mask[region] = c[bi] + (c[-1] - c[bi]) * (mask[region][..., None] - bi)\n",
+ " colored_mask[region] = c[bi, :3] + (c[bi, 3:] - c[bi, :3]) * (mask[region][..., None] - bi)\n",
" plt.subplot(1, len(layers_mask), i + 1)\n",
" img.plot(colored_mask)\n",
+ " img.save(colored_mask, f\"blend_{i}.png\")\n",
" n_skipped = torch.sum(mask < 0)\n",
" n_tot = len(mask.flatten())\n",
- " print (f\"Layer {i}: {n_skipped}({n_skipped / n_tot * 100:.2f}%) pixels are masked as skipped, {n_tot - n_skipped} pixels need to be inferred\")\n",
+ " print(f\"Layer {i}: {n_skipped}({n_skipped / n_tot * 100:.2f}%) pixels are masked as skipped, \"\n",
+ " f\"{n_tot - n_skipped}({(n_tot - n_skipped) / n_tot * 100:.2f}%) pixels need to be inferred\")\n",
"\n",
- "gen_mask_file(layers_mask[0], 'fovea.mask')\n",
- "gen_mask_file(layers_mask[1], 'mid.mask')"
+ "plt.figure(figsize=(12, 4))\n",
+ "for i, mask in enumerate(layers_mask):\n",
+ " binary_mask = (mask >= 0).float().expand(3, -1, -1)\n",
+ " plt.subplot(1, len(layers_mask), i + 1)\n",
+ " img.plot(binary_mask)\n",
+ " img.save(binary_mask, f\"mask_{i}.png\")\n",
+ "#gen_mask_file(layers_mask[0], 'fovea.mask')\n",
+ "#gen_mask_file(layers_mask[1], 'mid.mask')\n"
]
}
],
"metadata": {
"interpreter": {
- "hash": "82066b63b621a9e3d15e3b7c11ca76da6238eff3834294910d715044bd0561e5"
+ "hash": "65406b00395a48e1d89cf658ae895e7869e05878f5469716b06a752a3915211c"
},
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3.8.12 ('base')",
"language": "python",
"name": "python3"
},
@@ -75,7 +88,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.8.12"
},
"metadata": {
"interpreter": {
diff --git a/notebook/test_mono_gen.ipynb b/notebook/test/test_mono_gen.ipynb
similarity index 92%
rename from notebook/test_mono_gen.ipynb
rename to notebook/test/test_mono_gen.ipynb
index 545ee19..a19723c 100644
--- a/notebook/test_mono_gen.ipynb
+++ b/notebook/test/test_mono_gen.ipynb
@@ -25,18 +25,11 @@
}
],
"source": [
- "import sys\n",
- "import os\n",
- "import torch\n",
+ "from common import *\n",
"import torch.nn as nn\n",
"\n",
- "rootdir = os.path.abspath(sys.path[0] + '/../')\n",
- "sys.path.append(rootdir)\n",
- "torch.cuda.set_device(0)\n",
- "print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"torch.autograd.set_grad_enabled(False)\n",
"\n",
- "from configs.spherical_view_syn import SphericalViewSynConfig\n",
"from utils import netio\n",
"from utils import img\n",
"from utils import device\n",
diff --git a/notebook/test_mono_view.ipynb b/notebook/test/test_mono_view.ipynb
similarity index 99%
rename from notebook/test_mono_view.ipynb
rename to notebook/test/test_mono_view.ipynb
index 323287e..3f83e31 100644
--- a/notebook/test_mono_view.ipynb
+++ b/notebook/test/test_mono_view.ipynb
@@ -137,7 +137,7 @@
"test_view = views.get(*view_coord)\n",
"\n",
"cams = [\n",
- " view.CameraParam({\n",
+ " view.Camera({\n",
" \"fov\": fov_list[i],\n",
" \"cx\": 0.5,\n",
" \"cy\": 0.5,\n",
@@ -147,7 +147,7 @@
"]\n",
"fovea_cam, mid_cam, periph_cam = cams[0], cams[1], cams[2]\n",
"#guide_cam = ref_dataset.cam_params\n",
- "vr_cam = view.CameraParam({\n",
+ "vr_cam = view.Camera({\n",
" 'fov': fov_list[-1],\n",
" 'cx': 0.5,\n",
" 'cy': 0.5,\n",
diff --git a/notebook/test_refinement.ipynb b/notebook/test/test_refinement.ipynb
similarity index 99%
rename from notebook/test_refinement.ipynb
rename to notebook/test/test_refinement.ipynb
index b86b0f8..d911fca 100644
--- a/notebook/test_refinement.ipynb
+++ b/notebook/test/test_refinement.ipynb
@@ -63,7 +63,7 @@
" view_dataset.samples)\n",
"ref_indices = torch.arange(ref_dataset.n_views, device=device.default()).view(\n",
" ref_dataset.samples)\n",
- "cam_params = view.CameraParam({\n",
+ "cam_params = view.Camera({\n",
" \"fov\": 20,\n",
" \"cx\": 0.5,\n",
" \"cy\": 0.5,\n",
@@ -76,7 +76,7 @@
"netio.load(model_path, net)\n",
"print('Net loaded.')\n",
"\n",
- "vr_cam = view.CameraParam({\n",
+ "vr_cam = view.Camera({\n",
" 'fov': 110,\n",
" 'cx': 0.5,\n",
" 'cy': 0.5,\n",
diff --git a/notebook/test_voxel.ipynb b/notebook/test/voxel.ipynb
similarity index 92%
rename from notebook/test_voxel.ipynb
rename to notebook/test/voxel.ipynb
index 99ebe24..f598453 100644
--- a/notebook/test_voxel.ipynb
+++ b/notebook/test/voxel.ipynb
@@ -6,13 +6,7 @@
"metadata": {},
"outputs": [],
"source": [
- "import torch\n",
- "import sys\n",
- "import os\n",
- "\n",
- "rootdir = os.path.abspath(sys.path[0] + '/../')\n",
- "sys.path.append(rootdir)\n",
- "\n",
+ "from common import *\n",
"from utils.voxels import *\n",
"\n",
"bbox, steps = torch.tensor([[-2, -3.14159, 1], [2, 3.14159, 0]]), torch.tensor([2, 3, 3])\n",
@@ -94,7 +88,7 @@
"voxel_indices_of_new_corner = voxel_indices_in_grid[to_flat_indices(to_grid_coords(new_corners, bbox, steps).min(steps - 1), steps) + 1]\n",
"print(voxel_indices_of_new_corner)\n",
"p_of_new_corners = (new_corners - voxels[voxel_indices_of_new_corner]) / voxel_size + .5\n",
- "print(((new_corners - trilinear_interp(p_of_new_corners, emb(corner_indices[voxel_indices_of_new_corner]))) > 1e-6).sum())"
+ "print(((new_corners - linear_interp(p_of_new_corners, emb(corner_indices[voxel_indices_of_new_corner]))) > 1e-6).sum())"
]
}
],
diff --git a/notebook/test_lf_syn.ipynb b/notebook/test_lf_syn.ipynb
deleted file mode 100644
index 418bf92..0000000
--- a/notebook/test_lf_syn.ipynb
+++ /dev/null
@@ -1,134 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import torch\n",
- "import matplotlib.pyplot as plt\n",
- "from data.lf_syn import LightFieldSynDataset\n",
- "from utils import img\n",
- "from utils import math\n",
- "from nets.trans_unet import LatentSpaceTransformer\n",
- "\n",
- "device = torch.device(\"cuda:2\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Test data loader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "DATA_DIR = '../data/lf_syn_2020.12.23'\n",
- "TRAIN_DATA_DESC_FILE = DATA_DIR + '/train.json'\n",
- "\n",
- "train_dataset = LightFieldSynDataset(TRAIN_DATA_DESC_FILE)\n",
- "train_data_loader = torch.utils.data.DataLoader(\n",
- " dataset=train_dataset,\n",
- " batch_size=3,\n",
- " num_workers=8,\n",
- " pin_memory=True,\n",
- " shuffle=True,\n",
- " drop_last=False)\n",
- "print(len(train_data_loader))\n",
- "\n",
- "print(train_dataset.cam_params)\n",
- "print(train_dataset.sparse_view_positions)\n",
- "print(train_dataset.diopter_of_layers)\n",
- "plt.figure()\n",
- "img.plot(train_dataset.sparse_view_images[0])\n",
- "plt.figure()\n",
- "img.plot(train_dataset.sparse_view_depths[0] / 255 * 10)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Test disparity wrapper"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "\n",
- "\n",
- "\n",
- "transformer = LatentSpaceTransformer(train_dataset.sparse_view_images.size()[2],\n",
- " train_dataset.cam_params,\n",
- " train_dataset.diopter_of_layers,\n",
- " train_dataset.sparse_view_positions)\n",
- "novel_views = torch.stack([\n",
- " train_dataset.view_positions[13],\n",
- " train_dataset.view_positions[30],\n",
- " train_dataset.view_positions[57],\n",
- "], dim=0)\n",
- "trans_images = transformer(train_dataset.sparse_view_images.to(device),\n",
- " train_dataset.sparse_view_depths.to(device),\n",
- " novel_views)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "\n",
- "mask = (torch.sum(trans_images[0], 1) > math.tiny).to(dtype=torch.float)\n",
- "blended = torch.sum(trans_images[0], 0)\n",
- "weight = torch.sum(mask, 0)\n",
- "blended = blended / weight.unsqueeze(0)\n",
- "\n",
- "plt.figure(figsize=(6, 6))\n",
- "img.plot(train_dataset.view_images[13])\n",
- "plt.figure(figsize=(6, 6))\n",
- "img.plot(blended)\n",
- "plt.figure(figsize=(12, 6))\n",
- "plt.subplot(2, 4, 1)\n",
- "img.plot(train_dataset.sparse_view_images[0])\n",
- "plt.subplot(2, 4, 2)\n",
- "img.plot(train_dataset.sparse_view_images[1])\n",
- "plt.subplot(2, 4, 3)\n",
- "img.plot(train_dataset.sparse_view_images[2])\n",
- "plt.subplot(2, 4, 4)\n",
- "img.plot(train_dataset.sparse_view_images[3])\n",
- "\n",
- "plt.subplot(2, 4, 5)\n",
- "img.plot(trans_images[0, 0])\n",
- "plt.subplot(2, 4, 6)\n",
- "img.plot(trans_images[0, 1])\n",
- "plt.subplot(2, 4, 7)\n",
- "img.plot(trans_images[0, 2])\n",
- "plt.subplot(2, 4, 8)\n",
- "img.plot(trans_images[0, 3])\n"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3.7.9 64-bit ('pytorch': conda)",
- "name": "python379jvsc74a57bd0660ca2a75467d3af74a68fcc6f40bc78ab96b99ff17d2f100b5ca821fbb183f2"
- },
- "language_info": {
- "name": "python",
- "version": ""
- },
- "orig_nbformat": 2
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebook/test_sphere.ipynb b/notebook/test_sphere.ipynb
deleted file mode 100644
index 09f8f61..0000000
--- a/notebook/test_sphere.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Test Ray-Sphere Intersection & Cartesian-Spherical Conversion"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "tensor([[ 0.0000, -0.3536, 0.3536]])\n",
- "tensor(0.5000)\n",
- "tensor([[ 90., 135.]])\n",
- "tensor([[1.0000, 0.7071]])\n",
- "tensor([[-4.3711e-08, -7.0711e-01]])\n",
- "torch.Size([1, 3])\n"
- ]
- },
- {
- "data": {
- "image/png": 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- "text/plain": [
- "<Figure size 1152x1152 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "import sys\n",
- "import os\n",
- "import torch\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "\n",
- "rootdir = os.path.abspath('../')\n",
- "sys.path.append(rootdir)\n",
- "\n",
- "from utils import sphere\n",
- "from utils import math\n",
- "\n",
- "\n",
- "def PlotSphere(ax, r):\n",
- " u, v = np.mgrid[0:2 * math.pi:50j, 0:math.pi:20j]\n",
- " x = np.cos(u) * np.sin(v) * r\n",
- " y = np.sin(u) * np.sin(v) * r\n",
- " z = np.cos(v) * r\n",
- " ax.plot_surface(x, y, z, rstride=1, cstride=1,\n",
- " color='b', linewidth=0.5, alpha=0.3)\n",
- "\n",
- "\n",
- "def PlotPlane(ax, r):\n",
- " # 二元函数定义域平é¢\n",
- " x = np.linspace(-r, r, 3)\n",
- " y = np.linspace(-r, r, 3)\n",
- " X, Y = np.meshgrid(x, y)\n",
- " ax.plot_wireframe(X, Y, X * 0, color='g', linewidth=1)\n",
- "\n",
- "\n",
- "p = torch.tensor([[0.0, 0.0, 0.0]])\n",
- "v = torch.tensor([[0.0, -1.0, 1.0]])\n",
- "r = torch.tensor([[0.5]])\n",
- "v = v / torch.norm(v) * r * 2\n",
- "p_on_sphere_ = sphere.ray_sphere_intersect(p, v, r)[0][0]\n",
- "print(p_on_sphere_)\n",
- "print(p_on_sphere_.norm())\n",
- "spher_coord = sphere.cartesian2spherical(p_on_sphere_)\n",
- "print(spher_coord[..., 1:3].rad2deg())\n",
- "p_on_sphere = sphere.spherical2cartesian(spher_coord)\n",
- "print(p_on_sphere_.size())\n",
- "\n",
- "fig = plt.figure(figsize=(16, 16))\n",
- "ax = fig.gca(projection='3d')\n",
- "plt.xlabel('x')\n",
- "plt.ylabel('z')\n",
- "\n",
- "PlotPlane(ax, r.item())\n",
- "PlotSphere(ax, r[0, 0].item())\n",
- "\n",
- "ax.scatter([0], [0], [0], color=\"g\", s=10) # Center\n",
- "ax.scatter([p_on_sphere[0, 0].item()],\n",
- " [p_on_sphere[0, 2].item()],\n",
- " [p_on_sphere[0, 1].item()],\n",
- " color=\"r\", s=10) # Ray position\n",
- "ax.scatter([p_on_sphere_[0, 0].item()],\n",
- " [p_on_sphere_[0, 2].item()],\n",
- " [p_on_sphere_[0, 1].item()],\n",
- " color=\"y\", s=10) # Ray position\n",
- "\n",
- "p_ = p + v\n",
- "ax.plot([p[0, 0].item(), p_[0, 0].item()],\n",
- " [p[0, 2].item(), p_[0, 2].item()],\n",
- " [p[0, 1].item(), p_[0, 1].item()],\n",
- " color=\"r\")\n",
- "\n",
- "ax.plot([p_on_sphere_[0, 0].item(), p_on_sphere_[0, 0].item()],\n",
- " [p_on_sphere_[0, 2].item(), p_on_sphere_[0, 2].item()],\n",
- " [0, p_on_sphere_[0, 1].item()], color=\"k\", linestyle='--', linewidth=0.5)\n",
- "\n",
- "ax.plot([p_on_sphere_[0, 0].item(), 0],\n",
- " [p_on_sphere_[0, 2].item(), 0],\n",
- " [0, 0],\n",
- " linewidth=0.5, linestyle=\"--\", color=\"k\")\n",
- "\n",
- "ax.plot([p_on_sphere_[0, 0].item(), 0],\n",
- " [p_on_sphere_[0, 2].item(), 0],\n",
- " [p_on_sphere_[0, 1], 0],\n",
- " linewidth=0.5, linestyle=\"--\", color=\"k\")\n",
- "\n",
- "ax.set_xlim(-r.item(), r.item())\n",
- "ax.set_ylim(-r.item(), r.item())\n",
- "ax.set_zlim(-r.item(), r.item())\n",
- "\n",
- "plt.show()\n"
- ]
- }
- ],
- "metadata": {
- "interpreter": {
- "hash": "82066b63b621a9e3d15e3b7c11ca76da6238eff3834294910d715044bd0561e5"
- },
- "kernelspec": {
- "display_name": "Python 3.8.5 64-bit ('base': conda)",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.5"
- },
- "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebook/test_ycbcr.ipynb b/notebook/test_ycbcr.ipynb
deleted file mode 100644
index 79ad3d8..0000000
--- a/notebook/test_ycbcr.ipynb
+++ /dev/null
@@ -1,60 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import sys\n",
- "import os\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "os.chdir('../')\n",
- "sys.path.append(os.getcwd())\n",
- "\n",
- "from utils import img\n",
- "from utils import color\n",
- "\n",
- "input_img = img.load('data/gas_fovea_2020.12.31/upsampling_test/input/out_view_0000.png')\n",
- "ycbcr = color.rgb2ycbcr(input_img)\n",
- "rgb = color.ycbcr2rgb(ycbcr)\n",
- "\n",
- "plt.figure()\n",
- "img.plot(input_img)\n",
- "plt.figure()\n",
- "plt.subplot(1, 4, 1)\n",
- "img.plot(ycbcr)\n",
- "plt.subplot(1, 4, 2)\n",
- "img.plot(ycbcr[:, 0])\n",
- "plt.subplot(1, 4, 3)\n",
- "img.plot(ycbcr[:, 1])\n",
- "plt.subplot(1, 4, 4)\n",
- "img.plot(ycbcr[:, 2])\n",
- "plt.figure()\n",
- "img.plot(rgb)\n"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3.7.9 64-bit ('pytorch': conda)",
- "name": "python379jvsc74a57bd0660ca2a75467d3af74a68fcc6f40bc78ab96b99ff17d2f100b5ca821fbb183f2"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- },
- "orig_nbformat": 2
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/periph_net.png b/periph_net.png
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diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..ae0e1d0
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,19 @@
+bpy==2.82.1
+ConfigArgParse==1.4
+dash_core_components==1.3.1
+dash_html_components==1.0.1
+glm==0.4.3
+json5==0.9.6
+matplotlib==3.5.1
+numpy==1.21.5
+opencv_python==4.5.5.64
+Pillow==9.1.1
+plotly==5.6.0
+PyGLM==2.5.7
+setuptools==61.2.0
+tensorboardX==2.5.1
+thop==0.1.0.post2206102148
+torch==1.11.0
+torchvision==0.12.0
+tqdm==4.64.0
+typing_extensions==4.2.0
diff --git a/run_lf_syn.py b/run_lf_syn.py
deleted file mode 100644
index 23b3bf1..0000000
--- a/run_lf_syn.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import sys
-import os
-import torch
-import torch.optim
-import torchvision
-from tensorboardX import SummaryWriter
-from utils.loss import PerceptionReconstructionLoss
-from utils import netio
-from utils import misc
-from utils import device
-from utils import img
-from utils.perf import Perf
-from data.lf_syn import LightFieldSynDataset
-from nets.trans_unet import TransUnet
-
-
-torch.cuda.set_device(2)
-print("Set CUDA:%d as current device." % torch.cuda.current_device())
-
-DATA_DIR = os.path.dirname(__file__) + '/data/lf_syn_2020.12.23'
-TRAIN_DATA_DESC_FILE = DATA_DIR + '/train.json'
-OUTPUT_DIR = DATA_DIR + '/output_bat2'
-RUN_DIR = DATA_DIR + '/run_bat2'
-BATCH_SIZE = 8
-TEST_BATCH_SIZE = 10
-NUM_EPOCH = 1000
-MODE = "Silence" # "Perf"
-EPOCH_BEGIN = 600
-
-
-def train():
- # 1. Initialize data loader
- print("Load dataset: " + TRAIN_DATA_DESC_FILE)
- train_dataset = LightFieldSynDataset(TRAIN_DATA_DESC_FILE)
- train_data_loader = torch.utils.data.DataLoader(
- dataset=train_dataset,
- batch_size=BATCH_SIZE,
- pin_memory=True,
- shuffle=True,
- drop_last=False)
- print(len(train_data_loader))
-
- # 2. Initialize components
- model = TransUnet(cam_params=train_dataset.cam_params,
- view_images=train_dataset.sparse_view_images,
- view_depths=train_dataset.sparse_view_depths,
- view_positions=train_dataset.sparse_view_positions,
- diopter_of_layers=train_dataset.diopter_of_layers).to(device.default())
- optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
- loss = PerceptionReconstructionLoss()
-
- if EPOCH_BEGIN > 0:
- netio.load('%s/model-epoch_%d.pth' % (RUN_DIR, EPOCH_BEGIN), model,
- solver=optimizer)
-
- # 3. Train
- model.train()
- epoch = EPOCH_BEGIN
- iters = EPOCH_BEGIN * len(train_data_loader) * BATCH_SIZE
-
- os.makedirs(RUN_DIR, exist_ok=True)
-
- perf = Perf(enable=(MODE == "Perf"), start=True)
- writer = SummaryWriter(RUN_DIR)
-
- print("Begin training...")
- for epoch in range(EPOCH_BEGIN, NUM_EPOCH):
- for _, view_images, _, view_positions in train_data_loader:
-
- view_images = view_images.to(device.default())
-
- perf.checkpoint("Load")
-
- out_view_images = model(view_positions)
-
- perf.checkpoint("Forward")
-
- optimizer.zero_grad()
- loss_value = loss(out_view_images, view_images)
-
- perf.checkpoint("Compute loss")
-
- loss_value.backward()
-
- perf.checkpoint("Backward")
-
- optimizer.step()
-
- perf.checkpoint("Update")
-
- print("Epoch: ", epoch, ", Iter: ", iters,
- ", Loss: ", loss_value.item())
-
- iters = iters + BATCH_SIZE
-
- # Write tensorboard logs.
- writer.add_scalar("loss", loss_value, iters)
- if iters % len(train_data_loader) == 0:
- output_vs_gt = torch.cat([out_view_images, view_images], dim=0)
- writer.add_image("Output_vs_gt", torchvision.utils.make_grid(
- output_vs_gt, scale_each=True, normalize=False)
- .cpu().detach().numpy(), iters)
-
- # Save checkpoint
- if ((epoch + 1) % 50 == 0):
- netio.save('%s/model-epoch_%d.pth' % (RUN_DIR, epoch + 1), model, iters)
-
- print("Train finished")
-
-
-def test(net_file: str):
- # 1. Load train dataset
- print("Load dataset: " + TRAIN_DATA_DESC_FILE)
- train_dataset = LightFieldSynDataset(TRAIN_DATA_DESC_FILE)
- train_data_loader = torch.utils.data.DataLoader(
- dataset=train_dataset,
- batch_size=TEST_BATCH_SIZE,
- pin_memory=True,
- shuffle=False,
- drop_last=False)
-
- # 2. Load trained model
- model = TransUnet(cam_params=train_dataset.cam_params,
- view_images=train_dataset.sparse_view_images,
- view_depths=train_dataset.sparse_view_depths,
- view_positions=train_dataset.sparse_view_positions,
- diopter_of_layers=train_dataset.diopter_of_layers).to(device.default())
- netio.load(net_file, model)
-
- # 3. Test on train dataset
- print("Begin test on train dataset...")
- os.makedirs(OUTPUT_DIR, exist_ok=True)
- for view_idxs, view_images, _, view_positions in train_data_loader:
- out_view_images = model(view_positions)
- img.save(view_images,
- '%s/gt_view%02d.png' % (OUTPUT_DIR, i) for i in view_idxs)
- img.save(out_view_images,
- '%s/out_view%02d.png' % (OUTPUT_DIR, i) for i in view_idxs)
-
-
-if __name__ == "__main__":
- # train()
- test(RUN_DIR + '/model-epoch_1000.pth')
diff --git a/run_spherical_view_syn.py b/run_spherical_view_syn.py
deleted file mode 100644
index 6a02568..0000000
--- a/run_spherical_view_syn.py
+++ /dev/null
@@ -1,764 +0,0 @@
-import os
-import sys
-import argparse
-from typing import Mapping
-import torch
-import torch.optim
-import time
-from tensorboardX import SummaryWriter
-from torch import nn
-from numpy.core.numeric import NaN
-
-parser = argparse.ArgumentParser()
-# Arguments for train >>>
-parser.add_argument('-c', '--config', type=str,
- help='Net config files')
-parser.add_argument('-i', '--config-id', type=str,
- help='Net config id')
-parser.add_argument('-e', '--epochs', type=int, default=200,
- help='Max epochs for train')
-parser.add_argument('-n', '--prev-net', type=str)
-# Arguments for test >>>
-parser.add_argument('-r', '--output-res', type=str,
- help='Output resolution')
-parser.add_argument('-o', '--output', nargs='+', type=str, default=['perf', 'color'],
- help='Specify what to output (perf, color, depth, all)')
-parser.add_argument('--output-type', type=str, default='image',
- help='Specify the output type (image, video, debug)')
-# Other arguments >>>
-parser.add_argument('-t', '--test', action='store_true',
- help='Start in test mode')
-parser.add_argument('-m', '--model', type=str,
- help='The model file to load for continue train or test')
-parser.add_argument('-d', '--device', type=int, default=0,
- help='Which CUDA device to use.')
-parser.add_argument('-l', '--log-redirect', action='store_true',
- help='Is log redirected to file?')
-parser.add_argument('-p', '--prompt', action='store_true',
- help='Interactive prompt mode')
-parser.add_argument('dataset', type=str,
- help='Dataset description file')
-args = parser.parse_args()
-
-
-torch.cuda.set_device(args.device)
-print("Set CUDA:%d as current device." % torch.cuda.current_device())
-
-
-from utils import netio
-from utils import math
-from utils import device
-from utils import img
-from utils import interact
-from utils import color
-from utils.progress_bar import progress_bar
-from utils.perf import Perf
-from data.dataset_factory import *
-from data.loader import DataLoader
-from configs.spherical_view_syn import SphericalViewSynConfig
-from loss.ssim import ssim
-
-
-data_desc_path = args.dataset if args.dataset.endswith('.json') \
- else os.path.join(args.dataset, 'train.json')
-data_desc_name = os.path.splitext(os.path.basename(data_desc_path))[0]
-data_dir = os.path.dirname(data_desc_path) + '/'
-config = SphericalViewSynConfig()
-BATCH_SIZE = 4096
-MAX_CHUNK_ITEMS = 1e8
-SAVE_INTERVAL = 10
-TEST_BATCH_SIZE = 16484
-TEST_MAX_CHUNK_ITEMS = 3e8
-
-# Toggles
-EVAL_TIME_PERFORMANCE = False
-# ========
-#EVAL_TIME_PERFORMANCE = True
-
-
-def get_model_files(datadir):
- model_files = []
- for root, _, files in os.walk(datadir):
- model_files += [
- os.path.join(root, file).replace(datadir, '')
- for file in files if file.endswith('.pth')
- ]
- return model_files
-
-
-def set_outputs(args, outputs_str: str):
- args.output = [s.strip() for s in outputs_str.split(',')]
-
-
-if not args.test:
- print('Start in train mode.')
- if args.prompt: # 2.1 Prompt max epochs
- args.epochs = interact.input_ex('Max epochs:', interact.input_to_int(min=1),
- default=200)
- epochRange = range(1, args.epochs + 1)
- if args.prompt: # 2.2 Prompt continue train
- model_files = get_model_files(data_dir)
- args.model = interact.input_enum('Continue train on model:', model_files,
- err_msg='No such model file', default='')
- if args.model:
- cont_model = os.path.join(data_dir, args.model)
- model_name = os.path.splitext(os.path.basename(cont_model))[0]
- epochRange = range(int(model_name[12:]) + 1, epochRange.stop)
- run_dir = os.path.dirname(cont_model) + '/'
- run_id = os.path.basename(run_dir[:-1])
- config.from_id(run_id)
- else:
- if args.prompt: # 2.3 Prompt config file and additional config items
- config_files = [
- f[:-3] for f in os.listdir('configs')
- if f.endswith('.py') and f != 'spherical_view_syn.py'
- ]
- args.config = interact.input_enum('Specify config file:', config_files,
- err_msg='No such config file', default='')
- args.config_id = interact.input_ex('Specify custom config items:',
- default='')
- if args.config:
- config.load(os.path.join('configs', args.config + '.py'))
- if args.config_id:
- config.from_id(args.config_id)
- run_id = config.to_id()
- run_dir = data_dir + run_id + '/'
- log_dir = run_dir + 'log/'
-else: # Test mode
- print('Start in test mode.')
- if args.prompt: # 3. Prompt test model, output resolution, output mode
- model_files = get_model_files(data_dir)
- args.model = interact.input_enum('Specify test model:', model_files,
- err_msg='No such model file')
- args.output_res = interact.input_ex('Specify output resolution:',
- default='')
- set_outputs(args, interact.input_ex('Specify the outputs | [perf,color,depth,layers]/all:',
- default='perf,color'))
- args.output_type = interact.input_enum('Specify the output type | image/video:',
- ['image', 'video'],
- err_msg='Wrong output type',
- default='image')
- test_model_path = os.path.join(data_dir, args.model)
- test_model_name = os.path.splitext(os.path.basename(test_model_path))[0]
- run_dir = os.path.dirname(test_model_path) + '/'
- run_id = os.path.basename(run_dir[:-1])
- config.from_id(run_id)
- config.sa['perturb_sample'] = False
- args.output_res = tuple(int(s) for s in args.output_res.split('x')) \
- if args.output_res else None
- output_dir = f"{run_dir}output_{int(test_model_name.split('_')[-1])}"
- output_dataset_id = '%s%s' % (
- data_desc_name,
- '_%dx%d' % (args.output_res[0], args.output_res[1]) if args.output_res else '')
- args.output_flags = {
- item: item in args.output or 'all' in args.output
- for item in ['perf', 'color', 'depth', 'layers']
- }
-
-
-config.print()
-print("run dir: ", run_dir)
-
-# Initialize model
-model = config.create_net().to(device.default())
-loss_mse = nn.MSELoss().to(device.default())
-
-
-if args.prev_net:
- prev_net_config_id = os.path.split(args.prev_net)[-2]
- prev_net_config = SphericalViewSynConfig()
- prev_net_config.from_id(prev_net_config_id)
- prev_net = prev_net_config.create_net().to(device.default())
- netio.load(args.prev_net, prev_net)
- model.prev_net = prev_net
-
-
-toggle_show_dir = False
-last_toggle_time = 0
-
-from nets.nerf_depth import NerfDepth
-is_dnerf = isinstance(model, NerfDepth)
-is_cnerf = False
-
-
-def train_loop(data_loader, optimizer, perf, writer, epoch, iters):
- global toggle_show_dir
- global last_toggle_time
- dataset = data_loader.dataset
- sub_iters = 0
- iters_in_epoch = len(data_loader)
- loss_min = 1e5
- loss_max = 0
- loss_avg = 0
- perf1 = Perf(args.log_redirect, True)
- for idx, rays_o, rays_d, extra in data_loader:
- gt = extra['colors']
- if is_dnerf:
- rays_depth = extra.get('depths')
- rays_bins = extra.get('bins')
- perf.checkpoint("Load")
-
- out = model(rays_o, rays_d, rays_depth, rays_bins)
- if isinstance(out, torch.Tensor):
- out = {'color': out}
- if isinstance(out, Mapping):
- out = [out]
- perf.checkpoint("Forward")
-
- optimizer.zero_grad()
- loss_value = loss_mse(out[0]['color'], gt)
- for i in range(1, len(out)):
- loss_value += loss_mse(out[i]['color'], gt)
- elif is_cnerf:
- rays_weights = model.bin_weights.flatten(0, 2)[idx]
- perf.checkpoint("Load")
-
- out = model(rays_o, rays_d, rays_weights)
- if isinstance(out, torch.Tensor):
- out = {'color': out}
- if isinstance(out, Mapping):
- out = [out]
- perf.checkpoint("Forward")
-
- optimizer.zero_grad()
- loss_value = loss_mse(out[0]['color'], gt)
- for i in range(1, len(out)):
- loss_value += loss_mse(out[i]['color'], gt)
- else:
- gt_disp = torch.reciprocal(dataset.patched_depths[idx]) if config.depth_ref else None
- perf.checkpoint("Load")
-
- out = model(rays_o, rays_d, ret_depth=config.depth_ref)
- if isinstance(out, torch.Tensor):
- out = {'color': out}
- if isinstance(out, Mapping):
- out = [out]
- perf.checkpoint("Forward")
-
- optimizer.zero_grad()
- loss_value = loss_mse(out[0]['color'], gt)
- for i in range(1, len(out)):
- loss_value += loss_mse(out[i]['color'], gt)
- if config.depth_ref:
- disp_loss_value = loss_mse(torch.reciprocal(out[0]['depth'] + math.tiny), gt_disp)
- for i in range(1, len(out)):
- disp_loss_value += loss_mse(torch.reciprocal(
- out[i]['depth'] + math.tiny), gt_disp)
- disp_loss_value = disp_loss_value / math.pow(
- 1 / dataset.depth_range[0] - 1 / dataset.depth_range[1], 2)
- else:
- disp_loss_value = 0
- loss_value += disp_loss_value
- perf.checkpoint("Compute loss")
-
- loss_value.backward()
- perf.checkpoint("Backward")
-
- optimizer.step()
- perf.checkpoint("Update")
-
- loss_value = loss_value.item()
- loss_min = min(loss_min, loss_value)
- loss_max = max(loss_max, loss_value)
- loss_avg = (loss_avg * sub_iters + loss_value) / (sub_iters + 1)
- if not args.log_redirect:
- progress_bar(sub_iters, iters_in_epoch,
- f"Loss: {loss_value:.2e} ({loss_min:.2e}/{loss_avg:.2e}/{loss_max:.2e})",
- f"Epoch {epoch:<3d}")
- current_time = time.time()
- if last_toggle_time == 0:
- last_toggle_time = current_time
- if current_time - last_toggle_time > 3:
- toggle_show_dir = not toggle_show_dir
- last_toggle_time = current_time
- if toggle_show_dir:
- sys.stdout.write(f'Epoch {epoch:<3d} [ {run_dir} ]\r')
-
- # Write tensorboard logs.
- writer.add_scalar("loss mse", loss_value, iters)
- # if patch and iters % 100 == 0:
- # output_vs_gt = torch.cat([out[0:4], gt[0:4]], 0).detach()
- # writer.add_image("Output_vs_gt", torchvision.utils.make_grid(
- # output_vs_gt, nrow=4).cpu().numpy(), iters)
-
- iters += 1
- sub_iters += 1
- if args.log_redirect:
- perf1.checkpoint('Epoch %d (%.2e/%.2e/%.2e)' %
- (epoch, loss_min, loss_avg, loss_max), True)
- return iters
-
-
-def save_checkpoint(epoch, iters):
- for i in range(1, epoch):
- if (i < epoch // 50 * 50 and i % 50 != 0 or i % 10 != 0) and \
- os.path.exists(f'{run_dir}model-epoch_{i}.pth'):
- os.remove(f'{run_dir}model-epoch_{i}.pth')
- netio.save(f'{run_dir}model-epoch_{epoch}.pth', model, iters, print_log=False)
-
-
-def train():
- # 1. Initialize data loader
- print("Load dataset: " + data_desc_path)
- dataset = DatasetFactory.load(data_desc_path, c=config.c, load_depths=config.depth_ref,
- load_bins=config.depth_ref)
- data_loader = DataLoader(dataset, BATCH_SIZE, chunk_max_items=MAX_CHUNK_ITEMS, shuffle=True)
-
- if is_cnerf:
- model.set_depth_maps(dataset.rays_o, dataset.rays_d, dataset.view_depths)
-
- # 2. Initialize components
- optimizer = torch.optim.Adam(model.parameters(), lr=5e-4)
-
- if epochRange.start > 1:
- iters = netio.load(f'{run_dir}model-epoch_{epochRange.start - 1}.pth', model)
- else:
- os.makedirs(run_dir, exist_ok=True)
- os.makedirs(log_dir, exist_ok=True)
- iters = 0
-
- # 3. Train
- model.train()
-
- perf = Perf(EVAL_TIME_PERFORMANCE, start=True)
- writer = SummaryWriter(log_dir)
-
- print("Begin training...")
- for epoch in epochRange:
- iters = train_loop(data_loader, optimizer, perf, writer, epoch, iters)
- save_checkpoint(epoch, iters)
- print("Train finished")
-
-
-def test():
- with torch.no_grad():
- # 1. Load dataset
- print("Load dataset: " + data_desc_path)
- dataset = DatasetFactory.load(data_desc_path, res=args.output_res,
- load_images=args.output_flags['perf'])
- data_loader = DataLoader(dataset, TEST_BATCH_SIZE, chunk_max_items=TEST_MAX_CHUNK_ITEMS,
- shuffle=False)
-
- # 2. Load trained model
- netio.load(test_model_path, model)
- model.eval()
-
- # 3. Test on dataset
- print("Begin test, batch size is %d" % TEST_BATCH_SIZE)
-
- i = 0
- offset = 0
- chns = color.chns(config.c)
- n = dataset.n_views
- total_pixels = n * dataset.res[0] * dataset.res[1]
-
- out = {}
- if args.output_flags['layers']:
- out['layers'] = torch.empty(total_pixels, config.sa['n_samples'], chns + 1,
- device=device.default())
- if args.output_flags['perf'] or args.output_flags['color']:
- out['color'] = torch.empty(total_pixels, chns, device=device.default())
- if args.output_flags['depth']:
- out['depth'] = torch.empty(total_pixels, device=device.default())
- out['bins'] = torch.zeros(total_pixels, 3, device=device.default())
-
- if args.output_flags['perf']:
- perf = Perf(True, start=True)
- for _, rays_o, rays_d, _ in data_loader:
- n_rays = rays_o.size(0)
- ret = model(rays_o, rays_d,
- ret_depth=args.output_flags['depth'],
- debug=args.output_flags['layers'])
- if 'bins' in out:
- ret['weight'] = ret['weight'].view(-1, ret['weight'].size(-1) // 2, 2).sum(-1)
- is_local_max = torch.ones_like(ret['weight'], dtype=torch.bool)
- for delta in range(-3, 0):
- is_local_max[..., -delta:].logical_and_(
- ret['weight'][..., -delta:] > ret['weight'][..., :delta])
- for delta in range(1, 4):
- is_local_max[..., :-delta].logical_and_(
- ret['weight'][..., :-delta] > ret['weight'][..., delta:])
- ret['weight'][is_local_max.logical_not()] = 0
- vals, idxs = torch.topk(ret['weight'], 3) # (B, 3)
- vals = vals / vals.sum(-1, keepdim=True)
- ret['bins'] = (idxs.to(torch.float) / (ret['weight'].size(-1) - 1)
- * 0.5 + 0.5) * (vals > 0.1)
- idx = slice(offset, offset + n_rays)
- for key in out:
- print("key ", key, ", idx ", idx, ", out is ",
- out[key].shape, ", ret is ", ret[key].shape, ", rays is ", n_rays)
- out[key][idx] = ret[key]
- if not args.log_redirect:
- progress_bar(i, math.ceil(total_pixels / n_rays), 'Inferring...')
- i += 1
- offset += n_rays
- if args.output_flags['perf']:
- tot_time = perf.checkpoint()
-
- # 4. Save results
- print('Saving results...')
- os.makedirs(output_dir, exist_ok=True)
-
- for key in out:
- shape = [n] + list(dataset.res) + list(out[key].size()[1:])
- out[key] = out[key].view(shape)
- if 'color' in out:
- out['color'] = out['color'].permute(0, 3, 1, 2)
- if 'layers' in out:
- # n, y, x, samples, chns -> samples, n, chns, y, x
- out['layers'] = out['layers'].permute(3, 0, 4, 1, 2)
- if 'bins' in out:
- out['bins'] = out['bins'].permute(0, 3, 1, 2)
-
- if args.output_flags['perf']:
- perf_errors = torch.ones(n) * NaN
- perf_ssims = torch.ones(n) * NaN
- if dataset.images != None:
- for i in range(n):
- perf_errors[i] = loss_mse(dataset.images[i], out['color'][i]).item()
- perf_ssims[i] = ssim(dataset.images[i:i + 1],
- out['color'][i:i + 1]).item() * 100
- perf_mean_time = tot_time / n
- perf_mean_error = torch.mean(perf_errors).item()
- perf_name = 'perf_%s_%.1fms_%.2e.csv' % (
- output_dataset_id, perf_mean_time, perf_mean_error)
-
- # Remove old performance reports
- for file in os.listdir(output_dir):
- if file.startswith(f'perf_{output_dataset_id}'):
- os.remove(f"{output_dir}/{file}")
-
- # Save new performance reports
- with open(f"{output_dir}/{perf_name}", 'w') as fp:
- fp.write('View, PSNR, SSIM\n')
- fp.writelines([
- f'{dataset.indices[i]}, '
- f'{img.mse2psnr(perf_errors[i].item()):.2f}, {perf_ssims[i].item():.2f}\n'
- for i in range(n)
- ])
-
- if args.output_flags['color']:
- if args.output_type == 'video':
- output_file = f"{output_dir}/{output_dataset_id}_color.mp4"
- img.save_video(out['color'], output_file, 30)
- else:
- output_subdir = f"{output_dir}/{output_dataset_id}_color"
- os.makedirs(output_subdir, exist_ok=True)
- img.save(out['color'], [f'{output_subdir}/{i:0>4d}.png' for i in dataset.indices])
-
- if args.output_flags['depth']:
- colorized_depths = img.colorize_depthmap(
- out['depth'], config.sa['sample_range'])
- if args.output_type == 'video':
- output_file = f"{output_dir}/{output_dataset_id}_depth.mp4"
- img.save_video(colorized_depths, output_file, 30)
- else:
- output_subdir = f"{output_dir}/{output_dataset_id}_depth"
- os.makedirs(output_subdir, exist_ok=True)
- img.save(colorized_depths, [
- f'{output_subdir}/{i:0>4d}.png'
- for i in dataset.indices
- ])
- output_subdir = f"{output_dir}/{output_dataset_id}_bins"
- os.makedirs(output_subdir, exist_ok=True)
- img.save(out['bins'], [f'{output_subdir}/{i:0>4d}.png' for i in dataset.indices])
-
- if args.output_flags['layers']:
- if args.output_type == 'video':
- for j in range(config.sa['n_samples']):
- output_file = f"{output_dir}/{output_dataset_id}_layers[{j:0>3d}].mp4"
- img.save_video(out['layers'][j], output_file, 30)
- else:
- output_subdir = f"{output_dir}/{output_dataset_id}_layers"
- os.makedirs(output_subdir, exist_ok=True)
- for j in range(config.sa['n_samples']):
- img.save(out['layers'][j], [
- f'{output_subdir}/{i:0>4d}[{j:0>3d}].png'
- for i in dataset.indices
- ])
-
-
-def test1():
- with torch.no_grad():
- # 1. Load dataset
- print("Load dataset: " + data_desc_path)
- dataset = DatasetFactory.load(data_desc_path, res=args.output_res,
- load_images=args.output_flags['perf'],
- load_depths=True, load_bins=True)
- data_loader = DataLoader(dataset, 1, chunk_max_items=TEST_MAX_CHUNK_ITEMS, shuffle=False)
-
- # 2. Load trained model
- netio.load(test_model_path, model)
- model.eval()
-
- # 3. Test on dataset
- print("Begin test, batch size is %d" % TEST_BATCH_SIZE)
-
- i = 0
- global_offset = 0
- chns = color.chns(config.c)
- n = dataset.n_views
- total_pixels = n * dataset.res[0] * dataset.res[1]
-
- out = {}
- if args.output_flags['perf']:
- perf_times = torch.empty(n)
- perf = Perf(True, start=True)
- if args.output_flags['layers']:
- out['layers'] = torch.empty(total_pixels, config.sa['n_samples'], chns + 1,
- device=device.default())
- if args.output_flags['perf'] or args.output_flags['color']:
- out['color'] = torch.empty(total_pixels, chns, device=device.default())
- if args.output_flags['depth']:
- out['depth'] = torch.empty(total_pixels, device=device.default())
-
- for vi, _, rays_o, rays_d in data_loader:
- rays_o = rays_o.view(-1, 3)
- rays_d = rays_d.view(-1, 3)
- rays_depth = dataset.patched_depths[vi].flatten() if dataset.load_depths else None
- rays_bins = dataset.patched_bins[vi].flatten(0, 2) if dataset.load_bins else None
- n_rays = rays_o.size(0)
- for offset in range(0, n_rays, TEST_MAX_RAYS):
- idx = slice(offset, min(offset + TEST_MAX_RAYS, n_rays))
- global_idx = slice(idx.start + global_offset, idx.stop + global_offset)
- ret = model(rays_o[idx], rays_d[idx],
- rays_depth[idx] if rays_depth is not None else None,
- rays_bins[idx] if rays_bins is not None else None,
- ret_depth=args.output_flags['depth'],
- debug=args.output_flags['layers'])
- if isinstance(ret, torch.Tensor):
- ret = {'color': ret}
- if isinstance(ret, list):
- ret = ret[-1]
- for key in out:
- out[key][global_idx] = ret[key]
- if args.output_flags['perf']:
- perf_times[i] = perf.checkpoint()
- progress_bar(i, n, 'Inferring...')
- i += 1
- global_offset += n_rays
-
- # 4. Save results
- print('Saving results...')
- os.makedirs(output_dir, exist_ok=True)
-
- for key in out:
- shape = [n] + list(dataset.res) + list(out[key].size()[1:])
- out[key] = out[key].view(shape)
- if 'color' in out:
- out['color'] = out['color'].permute(0, 3, 1, 2)
- if 'layers' in out:
- # n, y, x, samples, chns -> samples, n, chns, y, x
- out['layers'] = out['layers'].permute(3, 0, 4, 1, 2)
-
- if args.output_flags['perf']:
- perf_errors = torch.ones(n) * NaN
- perf_ssims = torch.ones(n) * NaN
- if dataset.view_images != None:
- for i in range(n):
- perf_errors[i] = loss_mse(dataset.view_images[i], out['color'][i]).item()
- perf_ssims[i] = ssim(dataset.view_images[i:i + 1],
- out['color'][i:i + 1]).item() * 100
- perf_mean_time = torch.mean(perf_times).item()
- perf_mean_error = torch.mean(perf_errors).item()
- perf_name = 'perf_%s_%.1fms_%.2e.csv' % (
- output_dataset_id, perf_mean_time, perf_mean_error)
-
- # Remove old performance reports
- for file in os.listdir(output_dir):
- if file.startswith(f'perf_{output_dataset_id}'):
- os.remove(f"{output_dir}/{file}")
-
- # Save new performance reports
- with open(f"{output_dir}/{perf_name}", 'w') as fp:
- fp.write('View, Time, PSNR, SSIM\n')
- fp.writelines([
- f'{dataset.indices[i]}, {perf_times[i].item():.2f}, '
- f'{img.mse2psnr(perf_errors[i].item()):.2f}, {perf_ssims[i].item():.2f}\n'
- for i in range(n)
- ])
-
- if args.output_flags['color']:
- if args.output_type == 'video':
- output_file = f"{output_dir}/{output_dataset_id}_color.mp4"
- img.save_video(out['color'], output_file, 30)
- else:
- output_subdir = f"{output_dir}/{output_dataset_id}_color"
- os.makedirs(output_subdir, exist_ok=True)
- img.save(out['color'], [f'{output_subdir}/{i:0>4d}.png' for i in dataset.indices])
-
- if args.output_flags['depth']:
- colorized_depths = img.colorize_depthmap(
- out['depth'], config.sa['sample_range'])
- if args.output_type == 'video':
- output_file = f"{output_dir}/{output_dataset_id}_depth.mp4"
- img.save_video(colorized_depths, output_file, 30)
- else:
- output_subdir = f"{output_dir}/{output_dataset_id}_depth"
- os.makedirs(output_subdir, exist_ok=True)
- img.save(colorized_depths, [
- f'{output_subdir}/{i:0>4d}.png'
- for i in dataset.indices
- ])
-
- if args.output_flags['layers']:
- if args.output_type == 'video':
- for j in range(config.sa['n_samples']):
- output_file = f"{output_dir}/{output_dataset_id}_layers[{j:0>3d}].mp4"
- img.save_video(out['layers'][j], output_file, 30)
- else:
- output_subdir = f"{output_dir}/{output_dataset_id}_layers"
- os.makedirs(output_subdir, exist_ok=True)
- for j in range(config.sa['n_samples']):
- img.save(out['layers'][j], [
- f'{output_subdir}/{i:0>4d}[{j:0>3d}].png'
- for i in dataset.indices
- ])
-
-
-def test2():
- with torch.no_grad():
- # 1. Load dataset
- print("Load dataset: " + data_desc_path)
- dataset = DatasetFactory.load(data_desc_path, res=args.output_res,
- load_images=args.output_flags['perf'],
- load_depths=True)
- data_loader = DataLoader(dataset, 1, chunk_max_items=TEST_MAX_CHUNK_ITEMS, shuffle=False)
-
- # 2. Load trained model
- netio.load(test_model_path, model)
- model.set_depth_maps(dataset.rays_o, dataset.rays_d, dataset.view_depths)
- model.eval()
-
- # 3. Test on dataset
- print("Begin test, batch size is %d" % TEST_BATCH_SIZE)
-
- i = 0
- global_offset = 0
- chns = color.chns(config.c)
- n = dataset.n_views
- total_pixels = n * dataset.res[0] * dataset.res[1]
-
- out = {}
- if args.output_flags['perf']:
- perf_times = torch.empty(n)
- perf = Perf(True, start=True)
- if args.output_flags['layers']:
- out['layers'] = torch.empty(total_pixels, config.sa['n_samples'], chns + 1,
- device=device.default())
- if args.output_flags['perf'] or args.output_flags['color']:
- out['color'] = torch.empty(total_pixels, chns, device=device.default())
- if args.output_flags['depth']:
- out['depth'] = torch.empty(total_pixels, device=device.default())
-
- for vi, _, rays_o, rays_d in data_loader:
- rays_o = rays_o.view(-1, 3)
- rays_d = rays_d.view(-1, 3)
- rays_weights = model.bin_weights[vi].flatten(0, 2)
- n_rays = rays_o.size(0)
- for offset in range(0, n_rays, TEST_MAX_RAYS):
- idx = slice(offset, min(offset + TEST_MAX_RAYS, n_rays))
- global_idx = slice(idx.start + global_offset, idx.stop + global_offset)
- ret = model(rays_o[idx], rays_d[idx], rays_weights[idx],
- ret_depth=args.output_flags['depth'],
- debug=args.output_flags['layers'])
- if isinstance(ret, torch.Tensor):
- ret = {'color': ret}
- if isinstance(ret, list):
- ret = ret[-1]
- for key in out:
- out[key][global_idx] = ret[key]
- if args.output_flags['perf']:
- perf_times[i] = perf.checkpoint()
- progress_bar(i, n, 'Inferring...')
- i += 1
- global_offset += n_rays
-
- # 4. Save results
- print('Saving results...')
- os.makedirs(output_dir, exist_ok=True)
-
- for key in out:
- shape = [n] + list(dataset.res) + list(out[key].size()[1:])
- out[key] = out[key].view(shape)
- if 'color' in out:
- out['color'] = out['color'].permute(0, 3, 1, 2)
- if 'layers' in out:
- # n, y, x, samples, chns -> samples, n, chns, y, x
- out['layers'] = out['layers'].permute(3, 0, 4, 1, 2)
-
- if args.output_flags['perf']:
- perf_errors = torch.ones(n) * NaN
- perf_ssims = torch.ones(n) * NaN
- if dataset.view_images != None:
- for i in range(n):
- perf_errors[i] = loss_mse(dataset.view_images[i], out['color'][i]).item()
- perf_ssims[i] = ssim(dataset.view_images[i:i + 1],
- out['color'][i:i + 1]).item() * 100
- perf_mean_time = torch.mean(perf_times).item()
- perf_mean_error = torch.mean(perf_errors).item()
- perf_name = 'perf_%s_%.1fms_%.2e.csv' % (
- output_dataset_id, perf_mean_time, perf_mean_error)
-
- # Remove old performance reports
- for file in os.listdir(output_dir):
- if file.startswith(f'perf_{output_dataset_id}'):
- os.remove(f"{output_dir}/{file}")
-
- # Save new performance reports
- with open(f"{output_dir}/{perf_name}", 'w') as fp:
- fp.write('View, Time, PSNR, SSIM\n')
- fp.writelines([
- f'{dataset.indices[i]}, {perf_times[i].item():.2f}, '
- f'{img.mse2psnr(perf_errors[i].item()):.2f}, {perf_ssims[i].item():.2f}\n'
- for i in range(n)
- ])
-
- if args.output_flags['color']:
- if args.output_type == 'video':
- output_file = f"{output_dir}/{output_dataset_id}_color.mp4"
- img.save_video(out['color'], output_file, 30)
- else:
- output_subdir = f"{output_dir}/{output_dataset_id}_color"
- os.makedirs(output_subdir, exist_ok=True)
- img.save(out['color'], [f'{output_subdir}/{i:0>4d}.png' for i in dataset.indices])
-
- if args.output_flags['depth']:
- colorized_depths = img.colorize_depthmap(
- out['depth'], config.sa['sample_range'])
- if args.output_type == 'video':
- output_file = f"{output_dir}/{output_dataset_id}_depth.mp4"
- img.save_video(colorized_depths, output_file, 30)
- else:
- output_subdir = f"{output_dir}/{output_dataset_id}_depth"
- os.makedirs(output_subdir, exist_ok=True)
- img.save(colorized_depths, [
- f'{output_subdir}/{i:0>4d}.png'
- for i in dataset.indices
- ])
-
- if args.output_flags['layers']:
- if args.output_type == 'video':
- for j in range(config.sa['n_samples']):
- output_file = f"{output_dir}/{output_dataset_id}_layers[{j:0>3d}].mp4"
- img.save_video(out['layers'][j], output_file, 30)
- else:
- output_subdir = f"{output_dir}/{output_dataset_id}_layers"
- os.makedirs(output_subdir, exist_ok=True)
- for j in range(config.sa['n_samples']):
- img.save(out['layers'][j], [
- f'{output_subdir}/{i:0>4d}[{j:0>3d}].png'
- for i in dataset.indices
- ])
-
-
-if __name__ == "__main__":
- if args.test:
- if is_dnerf:
- test1()
- elif is_cnerf:
- test2()
- else:
- test()
- else:
- train()
diff --git a/setup.py b/setup.py
index 26d489e..d350b8b 100644
--- a/setup.py
+++ b/setup.py
@@ -1,7 +1,6 @@
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
import glob
-import os
import sys
# build clib
diff --git a/term_test.py b/term_test.py
deleted file mode 100644
index 249bf9a..0000000
--- a/term_test.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import os
-import shutil
-from sys import stdout
-from time import sleep
-from utils.progress_bar import *
-
-i = 0
-while True:
- rows = shutil.get_terminal_size().lines
- cols = shutil.get_terminal_size().columns
- os.system('cls' if os.name == 'nt' else 'clear')
- stdout.write("\n" * (rows - 1))
- progress_bar(i, 10000, "Test", "XXX")
- i += 1
- sleep(0.02)
diff --git a/test.ipynb b/test.ipynb
deleted file mode 100644
index e5390d2..0000000
--- a/test.ipynb
+++ /dev/null
@@ -1,127 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Mixin.__init__\n",
- "Base.__init__\n",
- "Child.__init__\n",
- "Mixin.fn\n",
- "Mixin.fn\n",
- "(<class '__main__.Child'>, <class '__main__.Base'>, <class '__main__.Mixin'>, <class '__main__.Obj'>, <class 'object'>)\n"
- ]
- }
- ],
- "source": [
- "class Obj:\n",
- " def fn(self):\n",
- " print(\"Obj.fn\")\n",
- "class Base(Obj):\n",
- " def __init__(self) -> None:\n",
- " super().__init__()\n",
- " print(\"Base.__init__\")\n",
- "\n",
- " def fn(self):\n",
- " super().fn()\n",
- " print(\"Base.fn\")\n",
- " \n",
- " def fn1(self):\n",
- " self.fn()\n",
- "\n",
- "class Mixin(Obj):\n",
- " def __init__(self) -> None:\n",
- " print(\"Mixin.__init__\")\n",
- " self.fn = self._fn\n",
- " \n",
- " def _fn(self):\n",
- " print(\"Mixin.fn\")\n",
- "\n",
- "class Child(Base, Mixin):\n",
- " def __init__(self) -> None:\n",
- " super().__init__()\n",
- " print(\"Child.__init__\")\n",
- "\n",
- " \n",
- "\n",
- "a = Child()\n",
- "a.fn()\n",
- "a.fn1()\n",
- "print(Child.__mro__)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Base.__init__\n",
- "Child.fn: <__main__.Child object at 0x7f62583e0640>\n"
- ]
- }
- ],
- "source": [
- "class Base:\n",
- " def __init__(self) -> None:\n",
- " print(\"Base.__init__\")\n",
- " \n",
- " def fn(self):\n",
- " print(\"Base.fn\")\n",
- "\n",
- " def fn1(self):\n",
- " self.fn()\n",
- "\n",
- "def createChildClass(name):\n",
- " def __init__(self):\n",
- " super(self.__class__, self).__init__()\n",
- " \n",
- " def fn(self):\n",
- " print(f\"{name}.fn: {self}\")\n",
- " \n",
- " return type(name, (Base, ), {\n",
- " \"__init__\": __init__,\n",
- " \"fn\": fn\n",
- " })\n",
- "\n",
- "Child = createChildClass(\"Child\")\n",
- "\n",
- "a = Child()\n",
- "a.fn()"
- ]
- }
- ],
- "metadata": {
- "interpreter": {
- "hash": "65406b00395a48e1d89cf658ae895e7869e05878f5469716b06a752a3915211c"
- },
- "kernelspec": {
- "display_name": "Python 3.8.5 64-bit ('base': conda)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.5"
- },
- "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/test.py b/test.py
index da05da0..3fb949c 100644
--- a/test.py
+++ b/test.py
@@ -1,242 +1,186 @@
-import os
import argparse
+import json
import torch
-import torch.nn.functional as nn_f
-import cv2
-import numpy as np
-from pathlib import Path
+from concurrent.futures import ThreadPoolExecutor
+from matplotlib import pyplot as plt
+from collections import defaultdict
+from tqdm import tqdm, trange
+
+from model import Model
+from utils import device, img, netio, math
+from utils.loss import ssim, mse_loss, LpipsLoss
+from utils.types import *
+from utils.view import Trans
+from utils.profile import Profiler, enable_profile
+from data import *
+from components.render import render
+
parser = argparse.ArgumentParser()
-parser.add_argument('-m', '--model', type=str,
- help='The model file to load for testing')
-parser.add_argument('-r', '--output-res', type=str,
+parser.add_argument('-r', '--output-res', type=Resolution.from_str,
help='Output resolution')
-parser.add_argument('-o', '--output', nargs='*', type=str, default=['perf', 'color'],
+parser.add_argument('-o', '--output', nargs='+', type=str, default=['perf', 'color'],
help='Specify what to output (perf, color, depth, all)')
-parser.add_argument('--output-type', type=str, default='image',
- help='Specify the output type (image, video, debug)')
-parser.add_argument('--views', type=str,
+parser.add_argument('--media', type=str, default='image',
+ help='Specify the media of output (image, video)')
+parser.add_argument('--views', type=lambda s: range(*[int(val) for val in s.split('-')]),
help='Specify the range of views to test')
-parser.add_argument('-s', '--samples', type=int)
-parser.add_argument('-p', '--prompt', action='store_true',
- help='Interactive prompt mode')
-parser.add_argument('--time', action='store_true',
+parser.add_argument('--batch', type=int,
+ help="Batch size (to avoid out-of-memory")
+parser.add_argument('--profile', action='store_true',
help='Enable time measurement')
-parser.add_argument('dataset', type=str,
- help='Dataset description file')
+parser.add_argument("--warm-up", type=int, default=10)
+parser.add_argument('--stereo', type=float, default=0,
+ help='Specify the stereo disparity. If greater than 0, stereo images will be generated')
+parser.add_argument('ckpt_path', type=str,
+ help='Path to the ckpt file')
+parser.add_argument('dataset_path', type=str,
+ help='Path to the dataset')
args = parser.parse_args()
-import model as mdl
-from loss.ssim import ssim
-from utils import color
-from utils import interact
-from utils import device
-from utils import img
-from utils import netio
-from utils import math
-from utils.perf import Perf, enable_perf, get_perf_result
-from utils.progress_bar import progress_bar
-from data import *
+torch.set_grad_enabled(False)
+lpips_loss = LpipsLoss().to(device.default())
+
+
+output_types = list({
+ "color" if item in ["color", "perf"] else item
+ for item in args.output
+})
+
+# Load model
+ckpt_path = netio.find_checkpoint(Path(args.ckpt_path))
+ckpt = torch.load(ckpt_path)
+print(f"Load checkpoint: {ckpt_path}")
+print("Model arguments:", json.dumps(ckpt["args"]["model_args"]))
+model = Model.create(ckpt["args"]["model"], ckpt["args"]["model_args"]
+ # raymarching_early_stop_tolerance=0.01,
+ # raymarching_chunk_size_or_sections=None,
+ # perturb_sample=False
+ )
+model.load_state_dict(ckpt["states"]["model"])
+model.to(device.default()).eval()
+
+# Debug: print model structure
+print(model)
+
+# Load dataset
+dataset = Dataset(args.dataset_path, res=args.output_res,
+ views_to_load=args.views, color_mode=model.color,
+ coord_sys=model.args.coord, device=device.default())
+print(f"Load dataset: {dataset.root}/{dataset.name} ({dataset.color_mode}, {dataset.coord_sys})")
+
+run_dir = ckpt_path.parent
+out_dir = run_dir / f"output_{ckpt_path.stem.split('_')[-1]}"
+out_id = f'{dataset.name}_{args.output_res.w}x{args.output_res.h}' if args.output_res\
+ else dataset.name
+batch_size = args.batch or dataset.pixels_per_view
+n = len(dataset)
+executor = ThreadPoolExecutor(8)
+
+
+if args.media == "video":
+ video_frames = defaultdict(list)
+
+
+def save_image(out: torch.Tensor, out_type: str, view_idx: int):
+ out = out.detach().cpu()
+ if args.media == 'video':
+ video_frames[out_type].append(out)
+ else:
+ output_subdir = out_dir / f"{out_id}_{out_type}{'_stereo' if args.stereo > 0 else ''}"
+ output_subdir.mkdir(parents=True, exist_ok=True)
+ executor.submit(img.save, out, f'{output_subdir}/{view_idx:04d}.png')
-DATA_LOADER_CHUNK_SIZE = 1e8
-torch.set_grad_enabled(False)
+def save_error_image(gt: torch.Tensor, out: torch.Tensor, view_idx: int):
+ error_image = (mse_loss(out, gt, reduction='none').mean(-3, True) / 1e-2).clamp(0, 1)
+ error_image = img.torch2np(error_image)[..., 0]
+ output_subdir = out_dir / f"{out_id}_error"
+ output_subdir.mkdir(exist_ok=True)
+ def save_fn(error_image, view_idx):
+ img.save(plt.get_cmap("jet")(error_image), f'{output_subdir}/{view_idx:04d}.png')
+ executor.submit(save_fn, error_image, view_idx)
+
+
+if args.profile:
+ def handle_profile_result(result: Profiler.ProfileResult):
+ print(result.get_report())
+ enable_profile(0, len(dataset), handle_profile_result)
+
+perf = "perf" in args.output and args.stereo == 0 and defaultdict(list, dummy=[])
+out_dir.mkdir(parents=True, exist_ok=True)
+
+if perf: # Warm-up first for accurate time measurement
+ rays_d = Trans(dataset.centers[0], dataset.rots[0]).trans_vector(
+ dataset.cam.local_rays[:batch_size])
+ rays_o = dataset.centers[:1, None, :].expand_as(rays_d)
+ rays = Rays(rays_o=rays_o, rays_d=rays_d).flatten()
+ print(rays_o.shape, rays_d.shape)
+ for i in trange(args.warm_up, desc="Warm up"):
+ model(rays, *output_types)
+
+for i in trange(n, desc="Test"):
+ view_idx = dataset.indices[i].item()
+ if perf:
+ test_perf = Profiler.Node("Test")
+
+ view = Trans(dataset.centers[i], dataset.rots[i])
+ if args.stereo > 0:
+ left_view = Trans(
+ view.trans_point(torch.tensor([-args.stereo / 2, 0, 0], device=view.device)),
+ view.r)
+ right_view = Trans(
+ view.trans_point(torch.tensor([args.stereo / 2, 0, 0], device=view.device)),
+ view.r)
+ out_left = render(model, dataset.cam, left_view, *output_types, batch_size=batch_size)
+ out_right = render(model, dataset.cam, right_view, *output_types, batch_size=batch_size)
+ out = ReturnData({
+ key: torch.cat([out_left[key], out_right[key]], dim=2)
+ for key in out_left if isinstance(out_left[key], torch.Tensor)
+ })
+ else:
+ out = render(model, dataset.cam, view, *output_types, batch_size=batch_size)
-data_desc_path = get_dataset_desc_path(args.dataset)
-os.chdir(data_desc_path.parent)
-nets_dir = Path("_nets")
-data_desc_path = data_desc_path.name
-
-
-def set_outputs(args, outputs_str: str):
- args.output = [s.strip() for s in outputs_str.split(',')]
-
-
-if args.prompt: # Prompt test model, output resolution, output mode
- model_files = [str(path.relative_to(nets_dir)) for path in nets_dir.rglob("*.tar")] \
- + [str(path.relative_to(nets_dir)) for path in nets_dir.rglob("*.pth")]
- args.model = interact.input_enum('Specify test model:', model_files,
- err_msg='No such model file')
- args.output_res = interact.input_ex('Specify output resolution:',
- default='')
- set_outputs(args, interact.input_ex('Specify the outputs | [perf,color,depth,layers,diffuse,specular]/all:',
- default='perf,color'))
- args.output_type = interact.input_enum('Specify the output type | image/video:',
- ['image', 'video'],
- err_msg='Wrong output type',
- default='image')
-args.output_res = tuple(int(s) for s in reversed(args.output_res.split('x'))) if args.output_res \
- else None
-args.output_flags = {
- item: item in args.output or 'all' in args.output
- for item in ['perf', 'color', 'depth', 'layers', 'diffuse', 'specular']
-}
-args.views = range(*[int(val) for val in args.views.split('-')]) if args.views else None
-
-if args.time:
- enable_perf()
-
-dataset = DatasetFactory.load(data_desc_path, res=args.output_res,
- load_images=args.output_flags['perf'],
- views_to_load=args.views)
-print(f"Dataset loaded: {dataset.root}/{dataset.name}")
-
-RAYS_PER_BATCH = dataset.res[0] * dataset.res[1] // 4
-
-
-model_path: Path = nets_dir / args.model
-model_name = model_path.parent.name
-states, _ = netio.load_checkpoint(model_path)
-if args.samples:
- states['args']['n_samples'] = args.samples
-model = mdl.deserialize(states,
- raymarching_early_stop_tolerance=0.01,
- raymarching_chunk_size_or_sections=None,
- perturb_sample=False).to(device.default()).eval()
-print(f"model: {model_name} ({model._get_name()})")
-print("args:", json.dumps(model.args))
-
-run_dir = model_path.parent
-output_dir = run_dir / f"output_{int(model_path.stem.split('_')[-1])}"
-output_dataset_id = '%s%s' % (
- dataset.name,
- f'_{args.output_res[1]}x{args.output_res[0]}' if args.output_res else ''
-)
-
-
-# 1. Initialize data loader
-data_loader = DataLoader(dataset, RAYS_PER_BATCH, chunk_max_items=DATA_LOADER_CHUNK_SIZE,
- shuffle=False, enable_preload=not args.time,
- color=color.from_str(model.args['color']))
-
-# 3. Test on dataset
-print("Begin test, batch size is %d" % RAYS_PER_BATCH)
-
-i = 0
-offset = 0
-chns = model.chns('color')
-n = dataset.n_views
-total_pixels = math.prod([n, *dataset.res])
-
-out = {}
-if args.output_flags['perf'] or args.output_flags['color']:
- out['color'] = torch.zeros(total_pixels, chns, device=device.default())
-if args.output_flags['diffuse']:
- out['diffuse'] = torch.zeros(total_pixels, chns, device=device.default())
-if args.output_flags['specular']:
- out['specular'] = torch.zeros(total_pixels, chns, device=device.default())
-if args.output_flags['depth']:
- out['depth'] = torch.full([total_pixels, 1], math.huge, device=device.default())
-gt_images = torch.empty_like(out['color']) if dataset.image_path else None
-
-tot_time = 0
-tot_iters = len(data_loader)
-progress_bar(i, tot_iters, 'Inferring...')
-for data in data_loader:
- if args.output_flags['perf']:
- test_perf = Perf.Node("Test")
- n_rays = data['rays_o'].size(0)
- idx = slice(offset, offset + n_rays)
- ret = model(data, *out.keys())
-
- if ret is not None:
- for key in out:
- if key not in ret:
- out[key] = None
- else:
- if 'rays_filter' in ret:
- out[key][idx][ret['rays_filter']] = ret[key]
- else:
- out[key][idx] = ret[key]
- if args.output_flags['perf']:
+ if perf:
test_perf.close()
torch.cuda.synchronize()
- tot_time += test_perf.duration()
- if gt_images is not None:
- gt_images[idx] = data['color']
- i += 1
- progress_bar(i, tot_iters, 'Inferring...')
- offset += n_rays
-
-# 4. Save results
-print('Saving results...')
-output_dir.mkdir(parents=True, exist_ok=True)
-
-out = {key: value for key, value in out.items() if value is not None}
-for key in out:
- out[key] = out[key].reshape([n, *dataset.res, *out[key].shape[1:]])
- if key == 'color' or key == 'diffuse' or key == 'specular':
- out[key] = out[key].permute(0, 3, 1, 2)
-
-if args.output_flags['perf']:
- perf_errors = torch.full([n], math.nan)
- perf_ssims = torch.full([n], math.nan)
- if gt_images is not None:
- gt_images = gt_images.reshape(n, *dataset.res, chns).permute(0, 3, 1, 2)
- for i in range(n):
- perf_errors[i] = nn_f.mse_loss(gt_images[i], out['color'][i]).item()
- perf_ssims[i] = ssim(gt_images[i:i + 1], out['color'][i:i + 1]).item() * 100
- perf_mean_time = tot_time / n
- perf_mean_error = torch.mean(perf_errors).item()
- perf_name = f'perf_{output_dataset_id}_{perf_mean_time:.1f}ms_{perf_mean_error:.2e}.csv'
+ perf["view"].append(view_idx)
+ perf["time"].append(test_perf.device_duration)
+ gt_image = dataset.load_images("color", view_idx)
+ out_image = out.color.movedim(-1, -3)
+ if gt_image is not None:
+ perf["mse"].append(mse_loss(out_image, gt_image).item())
+ perf["ssim"].append(ssim(out_image, gt_image).item() * 100)
+ perf["lpips"].append(lpips_loss(out_image, gt_image).item())
+ save_error_image(gt_image, out_image, view_idx)
+ else:
+ perf["mse"].append(math.nan)
+ perf["ssim"].append(math.nan)
+ perf["lpips"].append(math.nan)
+
+ for key, value in out.items():
+ save_image(value, key, view_idx)
+
+if perf:
+ perf_mean_time = sum(perf['time']) / n
+ perf_mean_error = sum(perf['mse']) / n
+ perf_name = f'perf_{out_id}_{perf_mean_time:.1f}ms_{perf_mean_error:.2e}.csv'
# Remove old performance reports
- for file in output_dir.glob(f'perf_{output_dataset_id}*'):
+ for file in out_dir.glob(f'perf_{out_id}*'):
file.unlink()
# Save new performance reports
- with (output_dir / perf_name).open('w') as fp:
- fp.write('View, PSNR, SSIM\n')
+ with (out_dir / perf_name).open('w') as fp:
+ fp.write('PSNR, SSIM, LPIPS\n')
fp.writelines([
- f'{dataset.indices[i]}, '
- f'{img.mse2psnr(perf_errors[i].item()):.2f}, {perf_ssims[i].item():.2f}\n'
+ f'{img.mse2psnr(perf["mse"][i]):.2f}, {perf["ssim"][i]:.2f}, {perf["lpips"][i]:.2e}\n'
for i in range(n)
])
- error_images = ((gt_images - out['color'])**2).sum(1, True) / chns
- error_images = (error_images / 1e-2).clamp(0, 1) * 255
- error_images = img.torch2np(error_images)
- error_images = np.asarray(error_images, dtype=np.uint8)
- output_subdir = output_dir / f"{output_dataset_id}_error"
- output_subdir.mkdir(exist_ok=True)
- for i in range(n):
- heat_img = cv2.applyColorMap(error_images[i], cv2.COLORMAP_JET) # 注æ„æ¤å¤„的三通é“çƒåŠ›å›¾æ˜¯cv2专有的GBR排列
- cv2.imwrite(f'{output_subdir}/{dataset.indices[i]:0>4d}.png', heat_img)
-
-for output_type in ['color', 'diffuse', 'specular']:
- if output_type not in out:
- continue
- if args.output_type == 'video':
- output_file = output_dir / f"{output_dataset_id}_{output_type}.mp4"
- img.save_video(out[output_type], output_file, 30)
- else:
- output_subdir = output_dir / f"{output_dataset_id}_{output_type}"
- output_subdir.mkdir(exist_ok=True)
- img.save(out[output_type],
- [f'{output_subdir}/{i:0>4d}.png' for i in dataset.indices])
-
-if 'depth' in out:
- colored_depths = img.colorize_depthmap(out['depth'][..., 0], model.args['sample_range'])
- if args.output_type == 'video':
- output_file = output_dir / f"{output_dataset_id}_depth.mp4"
- img.save_video(colored_depths, output_file, 30)
- else:
- output_subdir = output_dir / f"{output_dataset_id}_depth"
- output_subdir.mkdir(exist_ok=True)
- img.save(colored_depths, [f'{output_subdir}/{i:0>4d}.png' for i in dataset.indices])
- #output_subdir = output_dir / f"{output_dataset_id}_bins"
- # output_dir.mkdir(exist_ok=True)
- #img.save(out['bins'], [f'{output_subdir}/{i:0>4d}.png' for i in dataset.indices])
-
-if args.time:
- s = "Performance Report ==>\n"
- res = get_perf_result()
- if res is None:
- s += "No available data.\n"
- else:
- for key, val in res.items():
- path_segs = key.split("/")
- s += " " * (len(path_segs) - 1) + f"{path_segs[-1]}: {val:.1f}ms\n"
- print(s)
+if args.media == "video":
+ for key, frames in video_frames.items():
+ img.save_video(torch.cat(frames, 0),
+ out_dir / f"{out_id}_{key}{'_stereo' if args.stereo > 0 else ''}.mp4", 30)
diff --git a/test.txt b/test.txt
deleted file mode 100644
index c58c664..0000000
--- a/test.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-data/barbershop/_nets/lr_pano_t0.8/_lr_snerfadv_voxels+ls_128x4+128x4/output_50/perf_lr_view_t0.8_r360x60_25.9ms_1.06e-03.csv
-data/barbershop/_nets/lr_pano_t0.8/_lr_snerfadv_voxels+ls/output_50/perf_lr_view_t0.8_r360x60_21.2ms_1.28e-03.csv
-data/barbershop/_nets/lr_pano_t0.8/_lr_snerf_voxels+ls_128x8/output_50/perf_lr_view_t0.8_r360x60_34.1ms_1.11e-03.csv
-data/barbershop/_nets/lr_pano_t0.8/_lr_snerf_voxels+ls_512x1/output_50/perf_lr_view_t0.8_r360x60_51.2ms_1.31e-03.csv
-data/barbershop/_nets/lr_pano_t0.8/_lr_snerf_voxels+ls_512x2/output_50/perf_lr_view_t0.8_r360x60_56.7ms_9.24e-04.csv
-data/barbershop/_nets/lr_pano_t0.8/_lr_snerf_voxels+ls_e12/output_50/perf_lr_view_t0.8_r360x60_28.5ms_1.16e-03.csv
-data/barbershop/_nets/lr_pano_t0.8/_lr_snerf_voxels+ls_e40/output_50/perf_lr_view_t0.8_r360x60_37.2ms_1.28e-03.csv
-data/barbershop/_nets/lr_pano_t0.8/_lr_snerf_voxels+ls_e8/output_50/perf_lr_view_t0.8_r360x60_27.2ms_1.19e-03.csv
-data/barbershop/_nets/lr_pano_t0.8/_lr_snerf_voxels+ls/output_50/perf_lr_view_t0.8_r360x60_27.3ms_1.17e-03.csv
-data/barbershop/_nets/lr_pano_t0.8/_lr_snerfx_voxels+ls/output_50/perf_lr_view_t0.8_r360x60_29.0ms_1.15e-03.csv
diff --git a/test/utils.py b/test/utils.py
new file mode 100644
index 0000000..19f3ce2
--- /dev/null
+++ b/test/utils.py
@@ -0,0 +1 @@
+def prepare_dataset
\ No newline at end of file
diff --git a/test1.txt b/test1.txt
deleted file mode 100644
index 4526b89..0000000
--- a/test1.txt
+++ /dev/null
@@ -1,3455 +0,0 @@
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diff --git a/test_perf.py b/test_perf.py
new file mode 100644
index 0000000..f7b5de1
--- /dev/null
+++ b/test_perf.py
@@ -0,0 +1,70 @@
+import argparse
+import torch
+from matplotlib import pyplot as plt
+from collections import defaultdict
+from tqdm import tqdm
+from pathlib import Path
+
+from utils import img, device
+from utils.loss import ssim, mse_loss, LpipsLoss
+
+
+torch.set_grad_enabled(False)
+lpips_loss = LpipsLoss().to(device.default())
+
+
+def save_error_image(gt: torch.Tensor, out: torch.Tensor, filename: str):
+ error_image = (mse_loss(out, gt, reduction='none').mean(-3, True) / 1e-2).clamp(0, 1)
+ error_image = img.torch2np(error_image)[..., 0]
+ output_subdir = test_dir.parent / f"{dataset_name}_error"
+ output_subdir.mkdir(exist_ok=True)
+ img.save(plt.get_cmap("jet")(error_image), f'{output_subdir}/{filename}')
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('test_dir', type=str,
+ help='Path to the test output dir')
+args = parser.parse_args()
+
+
+test_dir = Path(args.test_dir)
+dataset_name = test_dir.parts[-1].split("_")[0]
+gt_dir = Path(*test_dir.parts[:-5]) / dataset_name
+
+test_image_paths = list(test_dir.iterdir())
+test_image_paths.sort(key=lambda path: path.stem)
+
+gt_image_paths = list(gt_dir.iterdir())
+gt_image_paths.sort(key=lambda path: path.stem)
+
+
+perf = defaultdict(list, dummy=[])
+n = len(test_image_paths)
+for test_image_path, gt_image_path in tqdm(zip(test_image_paths, gt_image_paths), total=n):
+ out_image = img.load(test_image_path).to(device.default())
+ gt_image = img.load(gt_image_path).to(device.default())
+ perf["mse"].append(mse_loss(out_image, gt_image).item())
+ perf["ssim"].append(ssim(out_image, gt_image).item() * 100)
+ perf["lpips"].append(lpips_loss(out_image, gt_image).item())
+ save_error_image(gt_image, out_image, test_image_path.name)
+
+perf_mean_error = sum(perf['mse']) / n
+perf_time = ""
+
+# Remove old performance reports
+for file in test_dir.parent.glob(f'perf_{dataset_name}*'):
+ parts = file.name.split("_")
+ if len(parts) == 4:
+ perf_time = "_" + parts[2]
+ file.unlink()
+
+perf_name = f'perf_{dataset_name}{perf_time}_{perf_mean_error:.2e}.csv'
+
+
+# Save new performance reports
+with (test_dir.parent / perf_name).open('w') as fp:
+ fp.write('PSNR, SSIM, LPIPS\n')
+ fp.writelines([
+ f'{img.mse2psnr(perf["mse"][i]):.2f}, {perf["ssim"][i]:.2f}, {perf["lpips"][i]:.2e}\n'
+ for i in range(n)
+ ])
diff --git a/test_todo.sh b/test_todo.sh
new file mode 100644
index 0000000..d992a9d
--- /dev/null
+++ b/test_todo.sh
@@ -0,0 +1,24 @@
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --n_fields 1 --width 128 --depth 4 --expname eval@fsnerf1_128x4 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --n_fields 1 --width 128 --depth 8 --expname eval@fsnerf1_128x8 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --n_fields 1 --width 256 --depth 4 --expname eval@fsnerf1_256x4 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --n_fields 1 --width 256 --depth 8 --expname eval@fsnerf1_256x8 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --n_fields 1 --width 512 --depth 4 --expname eval@fsnerf1_512x4 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --n_fields 1 --width 512 --depth 8 --expname eval@fsnerf1_512x8 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --n_fields 2 --width 128 --depth 4 --expname eval@fsnerf2_128x4 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --n_fields 2 --width 128 --depth 8 --expname eval@fsnerf2_128x8 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --n_fields 2 --width 256 --depth 4 --expname eval@fsnerf2_256x4 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --n_fields 2 --width 256 --depth 8 --expname eval@fsnerf2_256x8 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --n_fields 2 --width 512 --depth 4 --expname eval@fsnerf2_512x4 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --n_fields 2 --width 512 --depth 8 --expname eval@fsnerf2_512x8 > /dev/null 2>&1 &
+#0 nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --width 128 --depth 4 --expname eval@fsnerf4_128x4 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --width 128 --depth 8 --expname eval@fsnerf4_128x8 > /dev/null 2>&1 &
+#1 nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --width 256 --depth 4 --expname eval@fsnerf4_256x4 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --width 256 --depth 8 --expname eval@fsnerf4_256x8 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --width 512 --depth 4 --expname eval@fsnerf4_512x4 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --width 512 --depth 8 --expname eval@fsnerf4_512x8 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --n_fields 8 --width 128 --depth 4 --expname eval@fsnerf8_128x4 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --n_fields 8 --width 128 --depth 8 --expname eval@fsnerf8_128x8 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --n_fields 8 --width 256 --depth 4 --expname eval@fsnerf8_256x4 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --n_fields 8 --width 256 --depth 8 --expname eval@fsnerf8_256x8 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --n_fields 8 --width 512 --depth 4 --expname eval@fsnerf8_512x4 > /dev/null 2>&1 &
+# nohup python train.py /home/dengnc/Work/fov_nerf/data/__new/classroom_fovea_r360x80_t0.6/r120x80.json -c fsnerf_eval --n_fields 8 --width 512 --depth 8 --expname eval@fsnerf8_512x8 > /dev/null 2>&1 &
diff --git a/tools/convert_data_desc.py b/tools/convert_data_desc.py
index 8545851..f526ce2 100644
--- a/tools/convert_data_desc.py
+++ b/tools/convert_data_desc.py
@@ -21,7 +21,7 @@ data_dir = os.path.dirname(data_desc_path) + '/'
with open(data_desc_path, 'r') as fp:
dataset_desc: Mapping = json.load(fp)
-dataset_desc['cam_params'] = view.CameraParam.convert_camera_params(
+dataset_desc['cam_params'] = view.Camera.convert_camera_params(
dataset_desc['cam_params'],
(dataset_desc['view_res']['y'], dataset_desc['view_res']['x']))
diff --git a/tools/convert_nerf_checkpoint.py b/tools/convert_nerf_checkpoint.py
new file mode 100644
index 0000000..3025807
--- /dev/null
+++ b/tools/convert_nerf_checkpoint.py
@@ -0,0 +1,106 @@
+import sys
+import os
+import configargparse
+import argparse
+import torch
+from pathlib import Path
+from typing import Any
+
+
+sys.path.append(os.path.abspath(sys.path[0] + '/../'))
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('input', type=str)
+args = parser.parse_args()
+
+
+def get_sampler_mode(nerf_config: dict[str, str]) -> str:
+ lindisp = nerf_config["lindisp"] == "True"
+ spherical = nerf_config.get("spherical") == "True"
+ if lindisp:
+ return "spherical" if spherical else "xyz_disp"
+ return "spherical_radius" if spherical else "xyz"
+
+
+def convert_network_state(input_network_state: dict[str, torch.Tensor], output_prefix: str) -> dict[str, torch.Tensor]:
+ output_model_state: dict[str, torch.Tensor] = {}
+ for key, value in input_network_state.items():
+ key_parts = key.split(".")
+ module = key_parts[0]
+ suffix = key_parts[-1]
+ match module:
+ case "pts_linears":
+ output_model_state[f"{output_prefix}core.field.net.layers.{key_parts[1]}.net.0.{suffix}"] = value
+ case "alpha_linear":
+ output_model_state[f"{output_prefix}core.density_decoder.net.net.0.{suffix}"] = value
+ case "feature_linear":
+ output_model_state[f"{output_prefix}core.color_decoder.feature_layer.net.0.{suffix}"] = value
+ case "views_linears" if nerf_config["use_viewdirs"] == "True":
+ output_model_state[f"{output_prefix}core.color_decoder.net.layers.0.net.0.{suffix}"] = value
+ case "rgb_linear":
+ output_model_state[f"{output_prefix}core.color_decoder.net.layers.1.net.0.{suffix}"] = value
+ case "output_linear":
+ output_model_state[f"{output_prefix}core.density_decoder.net.net.0.{suffix}"] = value[3:]
+ output_model_state[f"{output_prefix}core.color_decoder.net.net.0.{suffix}"] = value[:3]
+
+ return output_model_state
+
+
+# Load nerf's args file and checkpoint file
+# checkpoint file is at [nerf_repo_path]/logs/[expname]/xxxx.tar
+ckpt_path = Path(args.input)
+expdir = ckpt_path.parent
+nerf_repo_path = expdir.parent.parent
+args_path = expdir / "args.txt"
+expname = expdir.stem
+ckpt_iters = int(ckpt_path.stem)
+
+config_parser = configargparse.DefaultConfigFileParser()
+with open(args_path) as fp:
+ nerf_config: dict[str, str] = config_parser.parse(fp)
+input_checkpoint: dict[str, Any] = torch.load(ckpt_path)
+
+output_model_state = convert_network_state(input_checkpoint["network_fn_state_dict"], "")
+if "network_fine_state_dict" in input_checkpoint:
+ output_model_state.update(convert_network_state(
+ input_checkpoint["network_fine_state_dict"], "fine_"))
+
+output_args = {
+ "model": "NeRF",
+ "model_args":{
+ "color": "rgb",
+ "n_samples": int(nerf_config["N_samples"]),
+ "sample_mode": get_sampler_mode(nerf_config),
+ "perturb_sampling": nerf_config["perturb"] == "1.0",
+ "depth": int(nerf_config["netdepth"]),
+ "width": int(nerf_config["netwidth"]),
+ "skips": [4],
+ "act": "relu",
+ "ln": False,
+ "color_decoder": "NeRF" if nerf_config["use_viewdirs"] == "True" else "Basic",
+ "n_importance": int(nerf_config["N_importance"]),
+ "fine_depth": int(nerf_config["netdepth_fine"]),
+ "fine_width": int(nerf_config["netwidth_fine"]),
+ "xfreqs": int(nerf_config["multires"]),
+ "dfreqs": int(nerf_config["multires_views"]),
+ "raw_noise_std": float(nerf_config["raw_noise_std"]),
+ "near": float(nerf_config.get("near", "1")),
+ "far": float(nerf_config.get("far", "10")),
+ "white_bg": nerf_config["white_bkgd"] == "True",
+ },
+ "trainer": None
+}
+
+output_path = nerf_repo_path.joinpath(nerf_config["datadir"]) / "_nets" / \
+ Path(nerf_config.get("train_file", "train.json")).stem / expname / \
+ f"checkpoint_{ckpt_iters}.tar"
+
+output_path.parent.mkdir(parents=True, exist_ok=True)
+torch.save({
+ "args": output_args,
+ "states": {
+ "model": output_model_state
+ }
+}, output_path)
+print(f"Checkpoint is saved to {output_path}")
diff --git a/tools/convert_old_snerffast_checkpoint.py b/tools/convert_old_snerffast_checkpoint.py
new file mode 100644
index 0000000..d7c3517
--- /dev/null
+++ b/tools/convert_old_snerffast_checkpoint.py
@@ -0,0 +1,198 @@
+import sys
+import os
+import configargparse
+import argparse
+import torch
+import re
+from pathlib import Path
+from typing import Any
+
+sys.path.append(os.path.abspath(sys.path[0] + '/../'))
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('input', type=str)
+args = parser.parse_args()
+
+
+class SphericalViewSynConfig(object):
+
+ def __init__(self):
+ self.name = 'default'
+ self.COLOR = "rgb"
+
+ # Net parameters
+ self.NET_TYPE = 'msl'
+ self.N_ENCODE_DIM = 10
+ self.N_DIR_ENCODE = None
+ self.NORMALIZE = False
+ self.DEPTH_REF = False
+ self.FC_PARAMS = {
+ 'nf': 256,
+ 'n_layers': 8,
+ 'skips': [],
+ 'activation': 'relu'
+ }
+ self.SAMPLE_PARAMS = {
+ 'spherical': True,
+ 'depth_range': (1, 50),
+ 'n_samples': 32,
+ 'perturb_sample': True,
+ 'lindisp': True,
+ 'inverse_r': True,
+ }
+ self.NERF_FINE_NET_PARAMS = {
+ 'enable': False,
+ 'nf': 256,
+ 'n_layers': 8,
+ 'additional_samples': 64
+ }
+
+ def from_id(self, id: str):
+ id_splited = id.split('@')
+ if len(id_splited) == 2:
+ self.name = id_splited[0]
+ segs = id_splited[-1].split('_')
+ for i, seg in enumerate(segs):
+ if seg.startswith('ffc'): # Full-connected network parameters
+ self.NERF_FINE_NET_PARAMS['nf'], self.NERF_FINE_NET_PARAMS['n_layers'] = (
+ int(str) for str in seg[3:].split('x'))
+ self.NERF_FINE_NET_PARAMS['enable'] = True
+ continue
+ if seg.startswith('fs'): # Number of samples
+ try:
+ self.NERF_FINE_NET_PARAMS['additional_samples'] = int(seg[2:])
+ self.NERF_FINE_NET_PARAMS['enable'] = True
+ continue
+ except ValueError:
+ pass
+ if seg.startswith('fc'): # Full-connected network parameters
+ self.FC_PARAMS['nf'], self.FC_PARAMS['n_layers'] = (
+ int(str) for str in seg[2:].split('x'))
+ continue
+ if seg.startswith('skip'): # Skip connection
+ self.FC_PARAMS['skips'] = [int(str)
+ for str in seg[4:].split(',')]
+ continue
+ if seg.startswith('*'): # Activation
+ self.FC_PARAMS['activation'] = seg[1:]
+ continue
+ if seg.startswith('ed'): # Encode direction
+ self.N_DIR_ENCODE = int(seg[2:])
+ if self.N_DIR_ENCODE == 0:
+ self.N_DIR_ENCODE = None
+ continue
+ if seg.startswith('e'): # Encode
+ self.N_ENCODE_DIM = int(seg[1:])
+ continue
+ if seg.startswith('d'): # Depth range
+ try:
+ self.SAMPLE_PARAMS['depth_range'] = tuple(
+ float(str) for str in seg[1:].split('-'))
+ continue
+ except ValueError:
+ pass
+ if seg.startswith('s'): # Number of samples
+ try:
+ self.SAMPLE_PARAMS['n_samples'] = int(seg[1:])
+ continue
+ except ValueError:
+ pass
+ if seg.startswith('~'): # Negative flags
+ if seg.find('p') >= 0:
+ self.SAMPLE_PARAMS['perturb_sample'] = False
+ if seg.find('l') >= 0:
+ self.SAMPLE_PARAMS['lindisp'] = False
+ if seg.find('i') >= 0:
+ self.SAMPLE_PARAMS['inverse_r'] = False
+ if seg.find('n') >= 0:
+ self.NORMALIZE = False
+ if seg.find('d') >= 0:
+ self.DEPTH_REF = False
+ continue
+ if seg.startswith('+'): # Positive flags
+ if seg.find('p') >= 0:
+ self.SAMPLE_PARAMS['perturb_sample'] = True
+ if seg.find('l') >= 0:
+ self.SAMPLE_PARAMS['lindisp'] = True
+ if seg.find('i') >= 0:
+ self.SAMPLE_PARAMS['inverse_r'] = True
+ if seg.find('n') >= 0:
+ self.NORMALIZE = True
+ if seg.find('d') >= 0:
+ self.DEPTH_REF = True
+ continue
+ if i == 0: # NetType
+ self.NET_TYPE, color_str = seg.split('-')
+ self.COLOR = color_str
+
+ def print(self):
+ print('==== Config %s ====' % self.name)
+ print('Net type: ', self.NET_TYPE)
+ print('Encode dim: ', self.N_ENCODE_DIM)
+ print('Normalize: ', self.NORMALIZE)
+ print('Train with depth: ', self.DEPTH_REF)
+ print('Support direction: ', False if self.N_DIR_ENCODE is None
+ else f'encode to {self.N_DIR_ENCODE}')
+ print('Full-connected network parameters:', self.FC_PARAMS)
+ print('Sample parameters', self.SAMPLE_PARAMS)
+ if self.NERF_FINE_NET_PARAMS['enable']:
+ print('NeRF fine network parameters', self.NERF_FINE_NET_PARAMS)
+ print('==========================')
+
+
+def convert_snerffast(input_network_state: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
+ output_model_state: dict[str, torch.Tensor] = {}
+ for key, value in input_network_state.items():
+ key = re.sub("mlp_(\d+)\.core\.layer(\d+)\.net\.0\.(\w+)",
+ "core.subnets.\\1.field.net.layers.\\2.net.0.\\3", key)
+ key = re.sub("mlp_(\d+)\.density_out\.net\.(\w+)",
+ "core.subnets.\\1.density_decoder.net.net.0.\\2", key)
+ key = re.sub("mlp_(\d+)\.color_out\.net\.(\w+)",
+ "core.subnets.\\1.color_decoder.net.net.0.\\2", key)
+ output_model_state[key] = value
+ return output_model_state
+
+
+ckpt_path = Path(args.input)
+expdir = ckpt_path.parent
+config_id = expdir.name
+ckpt_epochs = int(re.match("model-epoch_(\d+)", ckpt_path.stem).group(1))
+
+config = SphericalViewSynConfig()
+config.from_id(config_id)
+input_checkpoint: dict[str, Any] = torch.load(ckpt_path)
+
+output_model_state = convert_snerffast(input_checkpoint["model"])
+config.print()
+print(f"Epochs: {ckpt_epochs}")
+
+output_args = {
+ "model": "FsNeRF",
+ "model_args": {
+ "color": "rgb",
+ "n_samples": config.SAMPLE_PARAMS["n_samples"],
+ "n_fields": int(config.NET_TYPE[9:]),
+ "depth": config.FC_PARAMS["n_layers"],
+ "width": config.FC_PARAMS["nf"],
+ "skips": config.FC_PARAMS["skips"],
+ "act": config.FC_PARAMS["activation"],
+ "ln": False,
+ "xfreqs": config.N_ENCODE_DIM,
+ "raw_noise_std": 0.,
+ "near": config.SAMPLE_PARAMS["depth_range"][0],
+ "far": config.SAMPLE_PARAMS["depth_range"][1],
+ "white_bg": False,
+ "coord": "dx"
+ },
+ "trainer": None
+}
+
+output_path = expdir / f"checkpoint_{ckpt_epochs}.tar"
+torch.save({
+ "args": output_args,
+ "states": {
+ "model": output_model_state
+ }
+}, output_path)
+print(f"Checkpoint is saved to {output_path}")
diff --git a/assets/dash_test.css b/tools/dash/assets/dash_test.css
similarity index 100%
rename from assets/dash_test.css
rename to tools/dash/assets/dash_test.css
diff --git a/dash_test.py b/tools/dash/dash_test.py
similarity index 96%
rename from dash_test.py
rename to tools/dash/dash_test.py
index 9761996..e4aa246 100644
--- a/dash_test.py
+++ b/tools/dash/dash_test.py
@@ -12,19 +12,18 @@ from dash.dependencies import Input, Output
from dash.exceptions import PreventUpdate
-torch.autograd.set_grad_enabled(False)
+torch.set_grad_enabled(False)
from utils import device
from utils import view
from utils import img
from utils import misc
-import model as mdl
-from modules import AlphaComposition, Sampler
+import model
-datadir = Path('data/__new/classroom_fovea_r360x80_t0.6')
-data_desc_file = 'r120x80.json'
+datadir = Path('data/__object/christmas')
+data_desc_file = 'test.json'
net_config = 'fovea@snerffast4-rgb_e6_fc512x4_d2.00-50.00_s64_~p'
model_path = datadir / 'snerf_voxels/checkpoint_50.tar'
fov = 40
@@ -50,7 +49,7 @@ def load_data_desc(data_desc_file) -> view.Trans:
return view_range, view.Trans(view_centers, view_rots)
-cam = view.CameraParam({
+cam = view.Camera({
'fov': fov,
'cx': 0.5,
'cy': 0.5,
@@ -171,7 +170,7 @@ def plot_pixel_image(ray_o, ray_d, r=1):
with torch.no_grad():
pixel_point = ray_o + ray_d * r
rays_o = torch.cat([
- misc.meshgrid(*pix_img_res, normalize=True) * view_range_size[:2] + view_range[0, :2],
+ misc.grid2d(*pix_img_res, normalize=True) * view_range_size[:2] + view_range[0, :2],
torch.zeros(*pix_img_res, 1)
], dim=-1).to(device.default())
rays_d = pixel_point - rays_o
diff --git a/tools/data/blender_gen/gen_fovea.py b/tools/data/blender_gen/gen_fovea.py
index fab5f91..4cde5c7 100644
--- a/tools/data/blender_gen/gen_fovea.py
+++ b/tools/data/blender_gen/gen_fovea.py
@@ -16,7 +16,7 @@ tbox = [0.6, 0.6, 0.6]
rbox = [320, 40]
dataset_desc = {
- 'view_file_pattern': '%s/view_%%04d.png' % dataset_name,
+ 'color_file': '%s/view_%%04d.png' % dataset_name,
"gl_coord": True,
'view_res': {
'x': 512,
@@ -61,7 +61,7 @@ cam.dof.use_dof = False
def add_sample(i, x, y, z, rx, ry, render_only=False):
cam_obj.location = [x, y, z]
cam_obj.rotation_euler = [math.radians(ry), math.radians(rx), 0]
- scene.render.filepath = 'output/' + dataset_desc['view_file_pattern'] % i
+ scene.render.filepath = 'output/' + dataset_desc['color_file'] % i
bpy.ops.render.render(write_still=True)
if not render_only:
dataset_desc['view_centers'].append(list(cam_obj.location))
@@ -70,7 +70,7 @@ def add_sample(i, x, y, z, rx, ry, render_only=False):
json.dump(dataset_desc, fp, indent=4)
for i in range(len(dataset_desc['view_centers'])):
- if not os.path.exists('output/' + dataset_desc['view_file_pattern'] % i):
+ if not os.path.exists('output/' + dataset_desc['color_file'] % i):
add_sample(i, *dataset_desc['view_centers'][i], *dataset_desc['view_rots'][i], render_only=True)
start_view = len(dataset_desc['view_centers'])
diff --git a/tools/data/blender_gen/gen_periph.py b/tools/data/blender_gen/gen_periph.py
index 4644ac1..7b6d25f 100644
--- a/tools/data/blender_gen/gen_periph.py
+++ b/tools/data/blender_gen/gen_periph.py
@@ -16,7 +16,7 @@ tbox = [0.7, 0.7, 0.7]
rbox = [300, 120]
dataset_desc = {
- 'view_file_pattern': '%s/view_%%04d.png' % dataset_name,
+ 'color_file': '%s/view_%%04d.png' % dataset_name,
"gl_coord": True,
'view_res': {
'x': 512,
@@ -61,7 +61,7 @@ cam.dof.use_dof = False
def add_sample(i, x, y, z, rx, ry, render_only=False):
cam_obj.location = [x, y, z]
cam_obj.rotation_euler = [math.radians(ry), math.radians(rx), 0]
- scene.render.filepath = 'output/' + dataset_desc['view_file_pattern'] % i
+ scene.render.filepath = 'output/' + dataset_desc['color_file'] % i
bpy.ops.render.render(write_still=True)
if not render_only:
dataset_desc['view_centers'].append(list(cam_obj.location))
@@ -70,7 +70,7 @@ def add_sample(i, x, y, z, rx, ry, render_only=False):
json.dump(dataset_desc, fp, indent=4)
for i in range(len(dataset_desc['view_centers'])):
- if not os.path.exists('output/' + dataset_desc['view_file_pattern'] % i):
+ if not os.path.exists('output/' + dataset_desc['color_file'] % i):
add_sample(i, *dataset_desc['view_centers'][i], *dataset_desc['view_rots'][i], render_only=True)
start_view = len(dataset_desc['view_centers'])
diff --git a/tools/data/colmap2dataset.py b/tools/data/colmap2dataset.py
new file mode 100644
index 0000000..e8156b3
--- /dev/null
+++ b/tools/data/colmap2dataset.py
@@ -0,0 +1,178 @@
+import shutil
+import sys
+import os
+import argparse
+import json
+import re
+import numpy as np
+from typing import Any
+from pathlib import Path
+
+sys.path.append(os.path.abspath(sys.path[0] + '/../../'))
+
+from utils.colmap_read_model import read_model, Image
+
+
+def check_model_path(path: Path) -> bool:
+ """
+ Check whether the specified path contains colmap model files.
+
+ :param path `Path`: path to check
+ :return `bool`: whether the specified path contains colmap model files
+ """
+ return all([
+ (path / f"{f}.bin").exists()
+ for f in ['cameras', 'images', 'points3D']
+ ])
+
+
+def get_image_id(im: Image):
+ """
+ Extract image id from image filename like xxxx001.png
+
+ :param im `Image`: colmap's image info
+ :return `int`: image id
+ """
+ return int(re.match(r"\D+(\d+)\.\w+", os.path.split(im.name)[1]).group(1))
+
+
+def normalize(x: np.ndarray) -> np.ndarray: return x / np.linalg.norm(x)
+
+
+def view_matrix(z: np.ndarray, up: np.ndarray, pos: np.ndarray) -> np.ndarray:
+ """
+ Construct view matrix from z, up and position.
+
+ :param z `ndarray(3)`: z axis
+ :param up `ndarray(3): up direction
+ :param pos `ndarray(3)`: center position
+ :return `ndarray(3, 4): view matrix
+ """
+ vec2 = normalize(z)
+ vec0 = normalize(np.cross(up, vec2))
+ vec1 = normalize(np.cross(vec2, vec0))
+ return np.stack([vec0, vec1, vec2, pos], 1)
+
+
+def poses_avg(poses: np.ndarray) -> np.ndarray:
+ """
+ Calculate the average of the given poses
+
+ :param poses `ndarray(B, 3, 4)`: poses
+ :return `ndarray(3, 4)`: average pose
+ """
+ center = np.mean(poses[..., 3])
+ vec2 = normalize(np.sum(poses[..., 2], 0))
+ up = np.sum(poses[..., 1], 0)
+ return view_matrix(vec2, up, center)
+
+
+def recenter(poses: np.ndarray, pts: np.ndarray):
+ center = poses[..., 3:].mean(0) # (1, 3, 1)
+ return np.concatenate([poses[..., :3], poses[..., 3:] - center], -1), pts - center[..., 0]
+ poses_ = poses + 0
+ bottom = np.reshape([0, 0, 0, 1.], [1, 4])
+ c2w = poses_avg(poses)
+ c2w = np.concatenate([c2w[:3, :4], bottom], -2)
+ bottom = np.tile(np.reshape(bottom, [1, 1, 4]), [poses.shape[0], 1, 1])
+ poses = np.concatenate([poses[:, :3, :4], bottom], -2)
+
+ poses = np.linalg.inv(c2w) @ poses
+ poses_[:, :3, :4] = poses[:, :3, :4]
+ poses = poses_
+ return poses
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('dataset', type=str)
+parser.add_argument('--scale-down', type=int, default=1)
+args = parser.parse_args()
+data_dir = Path(args.dataset)
+scale_down = args.scale_down
+
+if check_model_path(data_dir / "input"):
+ model_path = data_dir / "input"
+else:
+ raise RuntimeError("No colmap model found.")
+
+cameras, images, points3D = read_model(model_path, '.bin')
+print("Colmap model loaded.")
+print("num_cameras:", len(cameras))
+print("num_images:", len(images))
+print("num_points3D:", len(points3D))
+
+cam = cameras[1]
+images = [im for im in images.values()]
+
+w2c_mats = np.stack([np.concatenate([np.concatenate([im.qvec2rotmat(), im.tvec.reshape([3, 1])], 1),
+ np.array([[0, 0, 0, 1.]])], 0)
+ for im in images], 0) # (B, 4, 4)
+c2w_mats = np.linalg.inv(w2c_mats)
+poses = c2w_mats[:, :3, :]
+poses[..., 1:3] *= -1 # colmap: [x,-y,-z] -> conventional: [x,y,z]
+pts = np.array([p.xyz for p in points3D.values()])
+poses, pts = recenter(poses, pts)
+norms = np.linalg.norm(pts, axis=1)
+near, far = np.percentile(norms, 1), np.percentile(norms, 99)
+trans_range = np.max(np.linalg.norm(poses[..., 3], axis=1))
+print(f"Near: {near}, far: {far}, trans range: {trans_range}")
+
+if scale_down > 1:
+ print("Scale images...")
+ from tools import image_scale
+ image_scale.run(data_dir / "input/images", data_dir / f"input/images{scale_down}",
+ data_dir / "input/images", 1. / scale_down)
+
+general_desc = {
+ 'color_file': f"view%04d{os.path.splitext(images[0].name)[1]}",
+ 'gl_coord': True,
+ 'view_res': {
+ 'x': cam.width // scale_down,
+ 'y': cam.height // scale_down
+ },
+ 'cam_params': {
+ 'f': cam.params[0] / scale_down,
+ 'cx': cam.params[1] / scale_down,
+ 'cy': cam.params[2] / scale_down
+ },
+ 'depth_range': {
+ 'min': max(near, trans_range * 1.1),
+ 'max': far
+ },
+ # 'samples': [poses.shape[0]],
+ # 'view_centers': poses[..., 3].tolist(),
+ # 'view_rots': poses[:, :3, :3].reshape([-1, 9]).tolist(),
+ # 'views': views
+}
+
+with open(data_dir / "input/dataset.json") as fp:
+ datasets: dict[str, Any] = json.load(fp)
+
+for dataset, image_dirs in datasets.items():
+ if scale_down > 1:
+ dataset = f"{dataset}{scale_down}"
+ view_centers = []
+ view_rots = []
+ im_names = []
+ for image_dir in image_dirs:
+ for i, im in enumerate(images):
+ if im.name.startswith(image_dir):
+ view_centers.append(poses[i, :, 3].tolist())
+ view_rots.append(poses[i, :3, :3].flatten().tolist())
+ im_names.append(im.name)
+
+ # Create symbol links to input images
+ shutil.rmtree(data_dir / dataset, ignore_errors=True)
+ (data_dir / dataset).mkdir()
+ for i, im_name in enumerate(im_names):
+ (data_dir / dataset / (general_desc["color_file"] %
+ i)).symlink_to(f"../input/images{scale_down if scale_down > 1 else ''}/{im_name}")
+
+ dataset_desc = {
+ **general_desc,
+ "samples": [len(view_centers)],
+ "view_centers": view_centers,
+ "view_rots": view_rots
+ }
+ with open(data_dir / f"{dataset}.json", 'w') as fp:
+ json.dump(dataset_desc, fp, indent=4)
diff --git a/tools/data/extract.py b/tools/data/extract.py
index c66914e..eb16cc3 100644
--- a/tools/data/extract.py
+++ b/tools/data/extract.py
@@ -37,20 +37,20 @@ for i in range(3):
for j in range(2):
out_desc_name = f'part{idx:d}'
out_desc = dataset_desc.copy()
- out_desc['view_file_pattern'] = f'{out_desc_name}/view_%04d.png'
+ out_desc['color_file'] = f'{out_desc_name}/view_%04d.png'
n_x = out_desc['samples'][3] // 3
n_y = out_desc['samples'][4] // 2
views = indices[..., i * n_x:(i + 1) * n_x, j * n_y:(j + 1) * n_y].flatten().tolist()
out_desc['samples'] = [len(views)]
out_desc['views'] = views
- out_desc['view_centers'] = np.array(dataset_desc['view_centers'])[views].tolist()
- out_desc['view_rots'] = np.array(dataset_desc['view_rots'])[views].tolist()
+ out_desc['centers'] = np.array(dataset_desc['centers'])[views].tolist()
+ out_desc['rots'] = np.array(dataset_desc['rots'])[views].tolist()
with open(os.path.join(data_dir, f'{out_desc_name}.json'), 'w') as fp:
json.dump(out_desc, fp, indent=4)
os.makedirs(os.path.join(data_dir, out_desc_name), exist_ok=True)
for k in range(len(views)):
- os.symlink(os.path.join('..', dataset_desc['view_file_pattern'] % views[k]),
- os.path.join(data_dir, out_desc['view_file_pattern'] % views[k]))
+ os.symlink(os.path.join('..', dataset_desc['color_file'] % views[k]),
+ os.path.join(data_dir, out_desc['color_file'] % views[k]))
idx += 1
'''
@@ -60,24 +60,24 @@ for xi in range(0, 4, 2):
for zi in range(0, 4, 2):
out_desc_name = f'part{idx:d}'
out_desc = dataset_desc.copy()
- out_desc['view_file_pattern'] = f'{out_desc_name}/view_%04d.png'
+ out_desc['color_file'] = f'{out_desc_name}/view_%04d.png'
views = indices[xi:xi + 2, yi:yi + 2, zi:zi + 2].flatten().tolist()
out_desc['samples'] = [len(views)]
out_desc['views'] = views
- out_desc['view_centers'] = np.array(dataset_desc['view_centers'])[views].tolist()
- out_desc['view_rots'] = np.array(dataset_desc['view_rots'])[views].tolist()
+ out_desc['centers'] = np.array(dataset_desc['centers'])[views].tolist()
+ out_desc['rots'] = np.array(dataset_desc['rots'])[views].tolist()
with open(os.path.join(data_dir, f'{out_desc_name}.json'), 'w') as fp:
json.dump(out_desc, fp, indent=4)
os.makedirs(os.path.join(data_dir, out_desc_name), exist_ok=True)
for k in range(len(views)):
- os.symlink(os.path.join('..', dataset_desc['view_file_pattern'] % views[k]),
- os.path.join(data_dir, out_desc['view_file_pattern'] % views[k]))
+ os.symlink(os.path.join('..', dataset_desc['color_file'] % views[k]),
+ os.path.join(data_dir, out_desc['color_file'] % views[k]))
idx += 1
'''
def extract_by_grid(*grid_indices):
- indices = torch.arange(len(dataset_desc['view_centers'])).view(dataset_desc['samples'])
+ indices = torch.arange(len(dataset_desc['centers'])).view(dataset_desc['samples'])
views = []
for idx in product(*grid_indices):
views += indices[idx].flatten().tolist()
@@ -86,11 +86,11 @@ def extract_by_grid(*grid_indices):
def extract_by_trans(max_trans, max_views):
if max_trans is not None:
- centers = np.array(dataset_desc['view_centers'])
+ centers = np.array(dataset_desc['centers'])
trans = np.linalg.norm(centers, axis=-1)
indices = np.nonzero(trans <= max_trans)[0]
else:
- indices = np.arange(len(dataset_desc['view_centers']))
+ indices = np.arange(len(dataset_desc['centers']))
if max_views is not None:
indices = np.sort(indices[np.random.permutation(indices.shape[0])[:max_views]])
return indices.tolist()
@@ -101,18 +101,18 @@ if args.grids:
else:
views = extract_by_trans(args.trans, args.views)
-image_path = dataset_desc['view_file_pattern']
+image_path = dataset_desc['color_file']
if "/" not in image_path:
image_path = in_name + "/" + image_path
# Save new dataset
out_desc = dataset_desc.copy()
-out_desc['view_file_pattern'] = image_path.split('/')[-1]
+out_desc['color_file'] = image_path.split('/')[-1]
out_desc['samples'] = [len(views)]
out_desc['views'] = views
-out_desc['view_centers'] = np.array(dataset_desc['view_centers'])[views].tolist()
-if 'view_rots' in dataset_desc:
- out_desc['view_rots'] = np.array(dataset_desc['view_rots'])[views].tolist()
+out_desc['centers'] = np.array(dataset_desc['centers'])[views].tolist()
+if 'rots' in dataset_desc:
+ out_desc['rots'] = np.array(dataset_desc['rots'])[views].tolist()
# Write new data desc
with open(out_desc_path, 'w') as fp:
@@ -123,7 +123,7 @@ out_dir.mkdir()
for k in range(len(views)):
if out_dir.parent.absolute() == root_dir.absolute():
os.symlink(Path("..") / (image_path % views[k]),
- out_dir / (out_desc['view_file_pattern'] % views[k]))
+ out_dir / (out_desc['color_file'] % views[k]))
else:
os.symlink(root_dir.absolute() / (image_path % views[k]),
- out_dir / (out_desc['view_file_pattern'] % views[k]))
+ out_dir / (out_desc['color_file'] % views[k]))
diff --git a/tools/data/extract360.py b/tools/data/extract360.py
new file mode 100644
index 0000000..b9195ad
--- /dev/null
+++ b/tools/data/extract360.py
@@ -0,0 +1,62 @@
+import sys
+import os
+import argparse
+import numpy as np
+import cv2
+from tqdm import tqdm
+
+sys.path.append(os.path.abspath(sys.path[0] + '/../../'))
+
+parser = argparse.ArgumentParser()
+parser.add_argument('-s', '--start', type=int)
+parser.add_argument('-t', '--duration', type=int)
+parser.add_argument('--fps', type=str, required=True)
+parser.add_argument('datadir', type=str)
+args = parser.parse_args()
+
+os.chdir(args.datadir)
+
+rawK = np.array([
+ [1369.757446, 0., 1838.643555, 0., 1369.757446, 1524.068604, 0., 0., 1.],
+ [1367.517944, 0., 1840.157837, 0., 1367.517944, 1536.036133, 0., 0., 1.],
+ [1369.830322, 0., 1827.990723, 0., 1369.830322, 1514.463135, 0., 0., 1.],
+ [1368.966187, 0., 1829.976196, 0., 1368.966187, 1512.734375, 0., 0., 1.],
+ [1373.654297, 0., 1838.130859, 0., 1373.654297, 1534.985840, 0., 0., 1.],
+ [1365.853027, 0., 1835.100830, 0., 1365.853027, 1533.032959, 0., 0., 1.]
+]).reshape(-1, 3, 3)
+D = np.array([[-0.044752], [-0.006285], [0.000000], [0.000000]])
+
+mean_focal = np.mean(rawK[:, 0, 0])
+K = None # 1369.2632038333334, 1500.0, 1900.0
+
+for i in range(6):
+ # Extract frames from video
+ os.makedirs(f"raw_images/{i + 1}", exist_ok=True)
+ extra_args = []
+ if args.start is not None:
+ extra_args.append(f"-ss {args.start}")
+ if args.duration is not None:
+ extra_args.append(f"-t {args.duration}")
+ extra_args = ' '.join(extra_args)
+ os.system(f"ffmpeg -i raw_video/{i + 1:02d}.mov {extra_args} -f image2 -q:v 2 -vf fps={args.fps} "
+ f"raw_images/{i + 1}/image%03d.png")
+
+ # Undistort frames and collect
+ os.makedirs(f"images", exist_ok=True)
+ raw_image_files = os.listdir(f"raw_images/{i + 1}")
+ map1, map2 = None, None
+ for raw_file in tqdm(raw_image_files):
+ raw_im = cv2.imread(f"raw_images/{i + 1}/{raw_file}")
+ if K is None:
+ K = np.array([[mean_focal, 0., raw_im.shape[1] / 2],
+ [0., mean_focal, raw_im.shape[0] / 2],
+ [0., 0., 1.]])
+ tqdm.write(
+ f"Intrinsic parameters: {mean_focal}, {raw_im.shape[0] / 2}, {raw_im.shape[1] / 2}")
+ if map1 is None:
+ map1, map2 = cv2.fisheye.initUndistortRectifyMap(
+ rawK[i], D, None, K, (raw_im.shape[1], raw_im.shape[0]), cv2.CV_16SC2)
+ im = cv2.remap(raw_im, map1, map2, interpolation=cv2.INTER_LINEAR,
+ borderMode=cv2.BORDER_CONSTANT)
+ im = cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE)
+ cv2.imwrite(f"images/image{i}{raw_file[5:]}", im)
diff --git a/tools/data/gen_colmap.py b/tools/data/gen_colmap.py
deleted file mode 100644
index b969c89..0000000
--- a/tools/data/gen_colmap.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import sys
-import os
-import argparse
-import json
-import numpy as np
-
-sys.path.append(os.path.abspath(sys.path[0] + '/../'))
-
-from utils import misc
-from utils.colmap_read_model import read_model
-
-parser = argparse.ArgumentParser()
-parser.add_argument('dataset', type=str)
-args = parser.parse_args()
-
-data_dir = args.dataset
-os.makedirs(data_dir, exist_ok=True)
-out_desc_path = os.path.join(data_dir, "train.json")
-
-
-cameras, images, points3D = read_model(os.path.join(data_dir, 'sparse/0'), '.bin')
-print("Model loaded.")
-print("num_cameras:", len(cameras))
-print("num_images:", len(images))
-print("num_points3D:", len(points3D))
-
-cam = cameras[list(cameras.keys())[0]]
-
-views = np.array([int(images[img_id].name[5:9]) for img_id in images])
-view_centers = np.array([images[img_id].tvec for img_id in images])
-view_rots = []
-for img_id in images:
- im = images[img_id]
- R = im.qvec2rotmat()
- view_rots.append(R.reshape([9]).tolist())
-view_rots = np.array(view_rots)
-
-indices = np.argsort(views)
-views = views[indices]
-view_centers = view_centers[indices]
-view_rots = view_rots[indices]
-
-pts = np.array([points3D[pt_id].xyz for pt_id in points3D])
-zvals = np.sqrt(np.sum(pts * pts, 1))
-dataset_desc = {
- 'view_file_pattern': f"images/image%04d.jpg",
- 'gl_coord': True,
- 'view_res': {
- 'x': cam.width,
- 'y': cam.height
- },
- 'cam_params': {
- 'fx': cam.params[0],
- 'fy': cam.params[0],
- 'cx': cam.params[1],
- 'cy': cam.params[2]
- },
- 'range': {
- 'min': np.min(view_centers, 0).tolist() + [0, 0],
- 'max': np.max(view_centers, 0).tolist() + [0, 0]
- },
- 'depth_range': {
- 'min': np.min(zvals),
- 'max': np.max(zvals)
- },
- 'samples': [len(view_centers)],
- 'view_centers': view_centers.tolist(),
- 'view_rots': view_rots.tolist(),
- 'views': views.tolist()
-}
-
-with open(out_desc_path, 'w') as fp:
- json.dump(dataset_desc, fp, indent=4)
diff --git a/tools/data/gen_seq.py b/tools/data/gen_seq.py
index 6318d70..3250350 100644
--- a/tools/data/gen_seq.py
+++ b/tools/data/gen_seq.py
@@ -6,8 +6,9 @@ import numpy as np
sys.path.append(os.path.abspath(sys.path[0] + '/../../'))
-from utils import seqs
-from utils import math
+from utils import seqs, math
+from utils.types import Resolution
+
parser = argparse.ArgumentParser()
parser.add_argument('-r', '--rot-range', nargs='+', type=int)
@@ -25,7 +26,7 @@ args = parser.parse_args()
data_dir = args.dataset
os.makedirs(data_dir, exist_ok=True)
-out_desc_path = os.path.join(data_dir, (args.out_desc if args.out_desc else f"{args.seq}.json"))
+out_desc_path = os.path.join(data_dir, args.out_desc or f"{args.seq}.json")
if args.ref:
with open(os.path.join(data_dir, args.ref), 'r') as fp:
@@ -37,34 +38,17 @@ else:
ref_desc = None
if args.trans_range:
- trans_range = np.array(list(args.trans_range) * 3 if len(args.trans_range) == 1
- else args.trans_range)
+ trans_range = np.array(args.trans_range * 3 if len(args.trans_range) == 1 else args.trans_range)
else:
- trans_range = np.array(ref_desc['range']['max'][0:3]) - \
- np.array(ref_desc['range']['min'][0:3])
+ trans_range = np.array(ref_desc["trs_range"])
if args.rot_range:
- rot_range = np.array(list(args.rot_range) * 2 if len(args.rot_range) == 1
- else args.rot_range)
+ rot_range = np.array(args.rot_range * 2 if len(args.rot_range) == 1 else args.rot_range)
else:
- rot_range = np.array(ref_desc['range']['max'][3:5]) - \
- np.array(ref_desc['range']['min'][3:5])
+ rot_range = np.array(ref_desc["rot_range"])
filter_range = np.concatenate([trans_range, rot_range])
-if args.fov:
- cam_params = {
- 'fov': args.fov,
- 'cx': 0.5,
- 'cy': 0.5,
- 'normalized': True
- }
-else:
- cam_params = ref_desc['cam_params']
-
-if args.res:
- res = tuple(int(s) for s in args.res.split('x'))
- res = {'x': res[0], 'y': res[1]}
-else:
- res = ref_desc['view_res']
+cam_params = { "fov": args.fov } if args.fov else ref_desc["cam"]
+res = Resolution.from_str(args.res or ref_desc["res"])
if args.seq == 'helix':
centers, rots = seqs.helix(trans_range, 4, args.views)
@@ -73,7 +57,7 @@ elif args.seq == 'scan_around':
elif args.seq == 'look_around':
centers, rots = seqs.look_around(trans_range, args.views)
-rots *= 180 / math.pi
+rots = np.degrees(rots)
gl = args.gl or ref_desc and ref_desc.get('gl_coord')
if gl:
centers[:, 2] *= -1
@@ -81,15 +65,13 @@ if gl:
dataset_desc = {
'gl_coord': gl,
- 'view_res': res,
- 'cam_params': cam_params,
- 'range': {
- 'min': (-0.5 * filter_range).tolist(),
- 'max': (0.5 * filter_range).tolist()
- },
+ 'res': f"{res.w}x{res.h}",
+ 'cam': cam_params,
+ "trs_range": trans_range.tolist(),
+ "rot_range": rot_range.tolist(),
'samples': [args.views],
- 'view_centers': centers.tolist(),
- 'view_rots': rots.tolist()
+ 'centers': centers.tolist(),
+ 'rots': rots.tolist()
}
with open(out_desc_path, 'w') as fp:
diff --git a/tools/data/gen_subset.py b/tools/data/gen_subset.py
index cef95a8..a90ef02 100644
--- a/tools/data/gen_subset.py
+++ b/tools/data/gen_subset.py
@@ -76,8 +76,8 @@ print('Test set views: ', len(test_views))
def create_subset(views, out_desc_name):
views = views.tolist()
subset_desc = dataset_desc.copy()
- subset_desc['view_file_pattern'] = \
- f"{out_desc_name}/{dataset_desc['view_file_pattern'].split('/')[-1]}"
+ subset_desc['color_file'] = \
+ f"{out_desc_name}/{dataset_desc['color_file'].split('/')[-1]}"
subset_desc['range'] = {
'min': list(-filter_range / 2),
'max': list(filter_range / 2)
@@ -91,8 +91,8 @@ def create_subset(views, out_desc_name):
json.dump(subset_desc, fp, indent=4)
os.makedirs(os.path.join(out_data_dir, out_desc_name), exist_ok=True)
for i in range(len(views)):
- os.symlink(os.path.join('../../', dataset_desc['view_file_pattern'] % views[i]),
- os.path.join(out_data_dir, subset_desc['view_file_pattern'] % views[i]))
+ os.symlink(os.path.join('../../', dataset_desc['color_file'] % views[i]),
+ os.path.join(out_data_dir, subset_desc['color_file'] % views[i]))
os.makedirs(out_data_dir, exist_ok=True)
diff --git a/tools/data/merge.py b/tools/data/merge.py
index 03e25cd..6a394d0 100644
--- a/tools/data/merge.py
+++ b/tools/data/merge.py
@@ -36,8 +36,8 @@ for i in range(len(input)):
input_desc: Mapping = json.load(fp)
dataset_desc['view_centers'] += input_desc['view_centers']
dataset_desc['view_rots'] += input_desc['view_rots']
- copy_images(get_data_path(input[i], input_desc['view_file_pattern']),
- get_data_path(output, dataset_desc['view_file_pattern']),
+ copy_images(get_data_path(input[i], input_desc['color_file']),
+ get_data_path(output, dataset_desc['color_file']),
len(input_desc['view_centers']), n_views)
n_views += len(input_desc['view_centers'])
diff --git a/tools/data/split.py b/tools/data/split.py
index 574dbf8..d486bce 100644
--- a/tools/data/split.py
+++ b/tools/data/split.py
@@ -2,59 +2,95 @@ import json
import sys
import os
import argparse
+import torch
from pathlib import Path
sys.path.append(os.path.abspath(sys.path[0] + '/../../'))
-from data import get_dataset_desc_path
+from data import DataDesc
+from utils.misc import calculate_autosize
parser = argparse.ArgumentParser()
-parser.add_argument('-o', '--output', type=str, nargs="+", required=True)
-parser.add_argument("-v", "--views", type=int, nargs="+", required=True)
+parser.add_argument('-o', '--outputs', type=str, nargs="+", required=True,
+ help="names of output datasets, leading with ~ to prepend the name of input dataset")
+parser.add_argument("-v", "--views", type=str, nargs="+", required=True,
+ help="views of output datasets, could be -1, a positive number or a colon splited slice")
+parser.add_argument("--random", action="store_true")
parser.add_argument('dataset', type=str)
+parser.usage = """
+Split a dataset into one or more datasets.
+
+Examples:
+ > python split.py path_to_dataset.json -o train test -v 20 -1
+ This will create two datasets "train" and "test" in the folder where input dataset locates,
+ with the first 20 views in "train" and other views in "test".
+
+ > python split.py path_to_dataset.json -o ~_train ~_test -v -1 ::8
+ This will create two datasets "train" and "test" in the folder where input dataset locates,
+ with every 8 views in "test" and other views in "train".
+"""
args = parser.parse_args()
-input = get_dataset_desc_path(args.dataset)
+input = DataDesc.get_json_path(args.dataset)
outputs = [
- get_dataset_desc_path(input.with_name(f"{input.stem}_{appendix}"))
- for appendix in args.output
+ DataDesc.get_json_path(input.with_name(
+ f"{input.stem}{appendix[1:]}" if appendix.startswith("~") else appendix))
+ for appendix in args.outputs
]
with open(input, 'r') as fp:
input_desc: dict = json.load(fp)
-n_views = len(input_desc['view_centers'])
+n_views = len(input_desc['centers'])
assert(len(args.views) == len(outputs))
-sum_views = sum(args.views)
-for i in range(len(args.views)):
- if args.views[i] == -1:
- args.views[i] = n_views - sum_views - 1
- sum_views = n_views
- break
-assert(sum_views <= n_views)
-for i in range(len(args.views)):
- assert(args.views[i] > 0)
+indices = torch.arange(n_views)
+indices_assigned = torch.zeros(n_views, dtype=torch.bool)
+output_dataset_indices: list[torch.Tensor] = [None] * len(outputs)
+output_dataset_views = {}
+for i, output_views in enumerate(args.views):
+ arr = output_views.split(":")
+ if len(arr) > 1:
+ view_slice = slice(*[int(value) if value != "" else None for value in arr])
+ output_dataset_indices[i] = indices[view_slice]
+ indices_assigned[view_slice] = True
+ else:
+ output_dataset_views[i] = int(arr[0])
+indices_remain = indices[indices_assigned.logical_not()]
+n_views_remain = len(indices_remain)
+
+output_dataset_views = {
+ key: value
+ for key, value in zip(output_dataset_views,
+ calculate_autosize(n_views_remain, *output_dataset_views.values())[0])
+}
+
+if args.random:
+ indices_remain = indices_remain[torch.randperm(n_views_remain)]
offset = 0
+for key, value in output_dataset_views.items():
+ output_dataset_indices[key] = indices_remain[offset:offset + value]
+ offset += value
+
+in_views = torch.tensor(input_desc["views"]) if "views" in input_desc else torch.arange(n_views)
+in_centers = torch.tensor(input_desc["centers"])
+in_rots = torch.tensor(input_desc["rots"]) if "rots" in input_desc else None
+
for i in range(len(outputs)):
- n = args.views[i]
- end = offset + n
+ sub_indices = output_dataset_indices[i].sort()[0]
output_desc = input_desc.copy()
- output_desc['samples'] = args.views[i]
- if 'views' in output_desc:
- output_desc['views'] = output_desc['views'][offset:end]
- else:
- output_desc['views'] = list(range(offset, end))
- output_desc['view_centers'] = output_desc['view_centers'][offset:end]
- if 'view_rots' in output_desc:
- output_desc['view_rots'] = output_desc['view_rots'][offset:end]
+ output_desc['samples'] = [len(sub_indices)]
+ output_desc['views'] = in_views[sub_indices].tolist()
+ output_desc['centers'] = in_centers[sub_indices].tolist()
+ if in_rots is not None:
+ output_desc['rots'] = in_rots[sub_indices].tolist()
with open(outputs[i], 'w') as fp:
json.dump(output_desc, fp, indent=4)
# Create symbol links of images
out_dir = outputs[i].with_suffix('')
+
out_dir.mkdir(exist_ok=True)
- for k in range(n):
- os.symlink(Path("..") / input.stem / (output_desc['view_file_pattern'] % output_desc['views'][k]),
- out_dir / (input_desc['view_file_pattern'] % output_desc['views'][k]))
- offset += args.views[i]
+ for k in range(len(sub_indices)):
+ os.symlink(Path("..") / input.stem / (output_desc['color_file'] % output_desc['views'][k]),
+ out_dir / (input_desc['color_file'] % output_desc['views'][k]))
diff --git a/tools/data/split360.py b/tools/data/split360.py
new file mode 100644
index 0000000..bfb87d1
--- /dev/null
+++ b/tools/data/split360.py
@@ -0,0 +1,73 @@
+import json
+import sys
+import os
+import argparse
+import numpy as np
+import shutil
+from typing import List
+from pathlib import Path
+
+sys.path.append(os.path.abspath(sys.path[0] + '/../../'))
+
+from data import DataDesc
+from utils.misc import calculate_autosize
+
+def run(dataset: str, outputs: list[str], views: list[int], random: bool = False):
+ if len(views) != len(outputs):
+ raise ValueError("")
+ input = DataDesc.get_json_path(dataset)
+ outputs = [
+ DataDesc.get_json_path(input.with_name(f"{input.stem}_{appendix}"))
+ for appendix in outputs
+ ]
+
+ with open(input, 'r') as fp:
+ input_desc: dict = json.load(fp)
+ n_views = len(input_desc['view_centers']) // 6
+
+ assert(len(views) == len(outputs))
+ views, sum_views = calculate_autosize(n_views, *views)
+
+ if random:
+ indices = np.random.permutation(n_views)
+ else:
+ indices = np.arange(n_views)
+ in_views = np.array(input_desc["views"]) if "views" in input_desc else np.arange(n_views)
+ in_centers = np.array(input_desc["view_centers"])
+ in_rots = np.array(input_desc["view_rots"]) if "view_rots" in input_desc else None
+
+ offset = 0
+ for i in range(len(outputs)):
+ n = views[i]
+ end = offset + n
+ sub_indices = np.sort(indices[offset:end])
+ sub_indices = np.concatenate([sub_indices + j * n_views for j in range(6)], axis=0)
+ output_desc = input_desc.copy()
+ output_desc['samples'] = [views[i] * 6]
+ output_desc['views'] = in_views[sub_indices].tolist()
+ output_desc['view_centers'] = in_centers[sub_indices].tolist()
+ if in_rots is not None:
+ output_desc['view_rots'] = in_rots[sub_indices].tolist()
+ with open(outputs[i], 'w') as fp:
+ json.dump(output_desc, fp, indent=4)
+
+ # Create symbol links of images
+ out_dir: Path = outputs[i].with_suffix('')
+
+ if out_dir.exists():
+ shutil.rmtree(out_dir)
+ out_dir.mkdir()
+ for view in output_desc['views']:
+ filename = output_desc['color_file'] % view
+ os.symlink(Path("..") / input.stem / filename, out_dir / filename)
+ offset += views[i]
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument('-o', '--outputs', type=str, nargs="+", required=True)
+ parser.add_argument("-v", "--views", type=int, nargs="+", required=True)
+ parser.add_argument("--random", action="store_true")
+ parser.add_argument('dataset', type=str)
+ args = parser.parse_args()
+ run(args.dataset, args.outputs, args.views, args.random)
diff --git a/tools/data/video2images.py b/tools/data/video2images.py
new file mode 100644
index 0000000..3645b9e
--- /dev/null
+++ b/tools/data/video2images.py
@@ -0,0 +1,35 @@
+import sys
+import os
+import argparse
+from pathlib import Path
+
+sys.path.append(os.path.abspath(sys.path[0] + '/../../'))
+
+parser = argparse.ArgumentParser()
+parser.add_argument('-s', '--start', type=int)
+parser.add_argument('-t', '--duration', type=int)
+parser.add_argument('--fps', type=str)
+parser.add_argument('--subset', type=str)
+parser.add_argument('datadir', type=str)
+args = parser.parse_args()
+
+os.chdir(args.datadir)
+
+if args.subset is not None:
+ video_dir = Path(f"videos/{args.subset}")
+else:
+ video_dir = Path(f"video")
+for video_path in video_dir.glob("*.*"):
+ # Extract frames from video
+ image_dir = "images" if args.subset is None else f"images/{args.subset}"
+ os.makedirs(f"{image_dir}/{video_path.stem}", exist_ok=True)
+ extra_args = []
+ if args.start is not None:
+ extra_args.append(f"-ss {args.start}")
+ if args.duration is not None:
+ extra_args.append(f"-t {args.duration}")
+ if args.fps is not None:
+ extra_args.append(f"-vf fps={args.fps}")
+ extra_args = ' '.join(extra_args)
+ os.system(f"ffmpeg -i {video_path} {extra_args} -f image2 -q:v 2 "
+ f"{image_dir}/{video_path.stem}/image%03d.png")
\ No newline at end of file
diff --git a/debug/voxel_sampler_export3d.py b/tools/debug/voxel_sampler_export3d.py
similarity index 100%
rename from debug/voxel_sampler_export3d.py
rename to tools/debug/voxel_sampler_export3d.py
diff --git a/tools/dump_checkpoint.py b/tools/dump_checkpoint.py
new file mode 100644
index 0000000..d3146db
--- /dev/null
+++ b/tools/dump_checkpoint.py
@@ -0,0 +1,24 @@
+import torch
+import argparse
+from operator import itemgetter
+
+parser = argparse.ArgumentParser()
+parser.add_argument("ckpt_path", type=str)
+cli_args = parser.parse_args()
+
+args, states = itemgetter("args", "states")(torch.load(cli_args.ckpt_path))
+
+print(f"Model: {args['model']} >>>>")
+for key, value in args["model_args"].items():
+ print(f"{key}: {value}")
+print("\n")
+
+if args["trainer"]:
+ print(f"Trainer: {args['trainer']} >>>>")
+ for key, value in args["trainer_args"].items():
+ print(f"{key}={value}")
+ print("\n")
+
+print("Model states >>>>")
+for key, value in states["model"].items():
+ print(f"{key}: Tensor{list(value.shape)}")
diff --git a/tools/export_msl.py b/tools/export_msl.py
index e006e56..14daeb7 100644
--- a/tools/export_msl.py
+++ b/tools/export_msl.py
@@ -49,7 +49,7 @@ def load_net(path):
def export_net(net: torch.nn.Module, name: str,
- input: Mapping[str, List[int]], output_names: List[str]):
+ input: Mapping[str, list[int]], output_names: list[str]):
outpath = os.path.join(opt.outdir, config.to_id(), name + ".onnx")
input_tensors = tuple([
torch.empty(size, device=device.default())
diff --git a/tools/export_nmsl.py b/tools/export_nmsl.py
index 1c72a77..40b1bd6 100644
--- a/tools/export_nmsl.py
+++ b/tools/export_nmsl.py
@@ -49,7 +49,7 @@ def load_net(path):
def export_net(net: torch.nn.Module, name: str,
- input: Mapping[str, List[int]], output_names: List[str]):
+ input: Mapping[str, list[int]], output_names: list[str]):
outpath = os.path.join(opt.outdir, config.to_id(), name + ".onnx")
input_tensors = tuple([
torch.empty(size, device=device.default())
diff --git a/tools/export_onnx.py b/tools/export_onnx.py
index f8a247c..fcc5798 100644
--- a/tools/export_onnx.py
+++ b/tools/export_onnx.py
@@ -1,72 +1,29 @@
import sys
-import os
import argparse
import torch
import torch.optim
-from torch import onnx
+from pathlib import Path
-sys.path.append(os.path.abspath(sys.path[0] + '/../'))
+sys.path.append(str(Path(__file__).absolute().parent.parent))
+
+from utils import netio
+import model
parser = argparse.ArgumentParser()
-parser.add_argument('--device', type=int, default=0,
- help='Which CUDA device to use.')
parser.add_argument('--batch-size', type=str,
help='Resolution')
-parser.add_argument('--outdir', type=str, default='./',
+parser.add_argument('--outdir', type=str, default='onnx',
help='Output directory')
parser.add_argument('model', type=str,
help='Path of model to export')
opt = parser.parse_args()
-# Select device
-torch.cuda.set_device(opt.device)
-print("Set CUDA:%d as current device." % torch.cuda.current_device())
-
-from configs.spherical_view_syn import SphericalViewSynConfig
-from utils import device
-from utils import netio
-from utils import misc
-
-dir_path, model_file = os.path.split(opt.model)
-batch_size = eval(opt.batch_size)
-os.chdir(dir_path)
-
-config = SphericalViewSynConfig()
-
-def load_net(path):
- name = os.path.splitext(os.path.basename(path))[0]
- config.from_id(name)
- config.sa['spherical'] = True
- config.sa['perturb_sample'] = False
- config.sa['n_samples'] = 4
- config.print()
- net = config.create_net().to(device.default())
- netio.load(path, net)
- return net, name
-
-
-if __name__ == "__main__":
- with torch.no_grad():
- # Load model
- net, name = load_net(model_file)
- # Input to the model
- rays_o = torch.empty(batch_size, 3, device=device.default())
- rays_d = torch.empty(batch_size, 3, device=device.default())
+with torch.inference_mode():
+ states, model_path = netio.load_checkpoint(opt.model)
+ batch_size = opt.batch_size and eval(opt.batch_size)
+ out_dir = model_path.parent / opt.outdir
- os.makedirs(opt.outdir, exist_ok=True)
+ model.deserialize(states).eval().export_onnx(out_dir, batch_size)
- # Export the model
- outpath = os.path.join(opt.outdir, config.to_id() + ".onnx")
- onnx.export(
- net, # model being run
- (rays_o, rays_d), # model input (or a tuple for multiple inputs)
- outpath,
- export_params=True, # store the trained parameter weights inside the model file
- verbose=True,
- opset_version=9, # the ONNX version to export the model to
- do_constant_folding=True, # whether to execute constant folding
- input_names=['Rays_o', 'Rays_d'], # the model's input names
- output_names=['Colors'] # the model's output names
- )
- print ('Model exported to ' + outpath)
+ print(f'Model exported to {out_dir}')
diff --git a/tools/export_script_model.py b/tools/export_script_model.py
new file mode 100644
index 0000000..6ce7c94
--- /dev/null
+++ b/tools/export_script_model.py
@@ -0,0 +1,39 @@
+from pathlib import Path
+import sys
+import torch
+import torch.optim
+
+sys.path.append(str(Path(__file__).absolute().parent.parent))
+
+import model
+
+torch.set_grad_enabled(False)
+
+m = model.load(
+ "/home/dengnc/dvs/data/classroom/_nets/train_hr_pano_t0.8/_hr_snerf/checkpoint_50.tar").eval().to("cuda")
+print(m.cores[0])
+inputs = (
+ torch.rand(10, 63, device="cuda"),
+ torch.rand(10, 24, device="cuda")
+)
+def fn(*args, **kwargs):
+ return m.cores[0].infer(*args, **kwargs)
+sm = torch.jit.trace(fn, inputs)
+torch.nn.Module.__call__
+print(sm.infer(torch.rand(5, 63, device="cuda"), torch.rand(5, 24, device="cuda")))
+sm.save("test.pt")
+
+torch.onnx.export(sm.infer, # model being run
+ inputs, # model input (or a tuple for multiple inputs)
+ "core_0.onnx", # where to save the model
+ export_params=True, # store the trained parameter weights inside the model file
+ opset_version=10, # the ONNX version to export the model to
+ do_constant_folding=True, # whether to execute constant folding for optimization
+ input_names=["x", "d"], # the model's input names
+ output_names=["densities", "colors"], # the model's output names
+ dynamic_axes={
+ "x": [0],
+ "d": [0],
+ "densities": [0],
+ "colors": [0]
+ }) # variable length axes
diff --git a/tools/export_snerf_fast.py b/tools/export_snerf_fast.py
index a2d036c..72879ab 100644
--- a/tools/export_snerf_fast.py
+++ b/tools/export_snerf_fast.py
@@ -61,8 +61,8 @@ def load_net():
return net
-def export_net(net: torch.nn.Module, path: str, input: Mapping[str, List[int]],
- output_names: List[str]):
+def export_net(net: torch.nn.Module, path: str, input: Mapping[str, list[int]],
+ output_names: list[str]):
input_tensors = tuple([
torch.empty(size, device=device.default())
for size in input.values()
diff --git a/tools/gen_video.py b/tools/gen_video.py
index 2c2e73a..ccac8f4 100644
--- a/tools/gen_video.py
+++ b/tools/gen_video.py
@@ -1,66 +1,54 @@
import json
import sys
import os
+import csv
import argparse
-import torch
import shutil
+import torch
import torch.nn as nn
import torch.nn.functional as nn_f
+from tqdm import trange
sys.path.append(os.path.abspath(sys.path[0] + '/../'))
parser = argparse.ArgumentParser()
-parser.add_argument('-s', '--stereo', action='store_true')
-parser.add_argument('-R', '--replace', action='store_true')
-parser.add_argument('--noCE', action='store_true')
+parser.add_argument('-s', '--stereo', action='store_true',
+ help="Render stereo video")
+parser.add_argument('-d', '--disparity', type=float, default=0.06,
+ help="The stereo disparity")
+parser.add_argument('-R', '--replace', action='store_true',
+ help="Replace the existed frames in the intermediate output directory")
+parser.add_argument('--noCE', action='store_true',
+ help="Disable constrast enhancement")
parser.add_argument('-i', '--input', type=str)
-parser.add_argument('-r', '--range', type=str)
-parser.add_argument('-f', '--fps', type=int)
-parser.add_argument('--device', type=int, default=0,
- help='Which CUDA device to use.')
-parser.add_argument('scene', type=str)
-parser.add_argument('view_file', type=str)
+parser.add_argument('-r', '--range', type=str,
+ help="The range of frames to render, specified as format: start,end")
+parser.add_argument('-f', '--fps', type=int,
+ help="The FPS of output video. if not specified, a sequence of images will be saved instead")
+parser.add_argument('-m', '--model', type=str,
+ help="The directory containing fovea* and periph* model file")
+parser.add_argument('view_file', type=str,
+ help="The path to .csv or .json file which contains a sequence of poses and gazes")
opt = parser.parse_args()
-# Select device
-torch.cuda.set_device(opt.device)
-print("Set CUDA:%d as current device." % torch.cuda.current_device())
-torch.autograd.set_grad_enabled(False)
-from configs.spherical_view_syn import SphericalViewSynConfig
-from utils import netio
-from utils import misc
-from utils import img
-from utils import device
+from utils import netio, img, device
from utils.view import *
-from utils import sphere
+from utils.types import *
from components.fnr import FoveatedNeuralRenderer
-from utils.progress_bar import progress_bar
-
-
-def load_net(path):
- config = SphericalViewSynConfig()
- config.from_id(path[:-4])
- config.sa['perturb_sample'] = False
- # config.print()
- net = config.create_net().to(device.default())
- netio.load(path, net)
- return net
+from model import Model
-def find_file(prefix):
- for path in os.listdir():
- if path.startswith(prefix):
- return path
- return None
+def load_model(path: Path) -> Model:
+ checkpoint, _ = netio.load_checkpoint(path)
+ checkpoint["model"][1]["sampler"]["perturb"] = False
+ model = Model.create(*checkpoint["model"])
+ model.load_state_dict(checkpoint["states"]["model"])
+ model.to(device.default()).eval()
+ return model
-def clamp_gaze(gaze):
- return gaze
- scoord = sphere.cartesian2spherical(gaze)
-
-
-def load_csv(data_desc_file) -> Tuple[Trans, torch.Tensor]:
+def load_csv(data_desc_file: Path) -> tuple[Trans, torch.Tensor]:
def to_tensor(line_content):
return torch.tensor([float(str) for str in line_content.split(',')])
@@ -84,9 +72,9 @@ def load_csv(data_desc_file) -> Tuple[Trans, torch.Tensor]:
if lines[i + 1].startswith('0,0,0') or lines[i + 2].startswith('0,0,0'):
continue
j = i + 1
- gaze_dirs[view_idx, 0] = clamp_gaze(to_tensor(lines[j]))
+ gaze_dirs[view_idx, 0] = to_tensor(lines[j])
j += 1
- gaze_dirs[view_idx, 1] = clamp_gaze(to_tensor(lines[j]))
+ gaze_dirs[view_idx, 1] = to_tensor(lines[j])
j += 1
if not old_fmt:
gazes[view_idx, 0] = to_tensor(lines[j])
@@ -113,11 +101,16 @@ def load_csv(data_desc_file) -> Tuple[Trans, torch.Tensor]:
return Trans(view_t, view_mats[:, 0, :3, :3]), gazes
-def load_json(data_desc_file) -> Tuple[Trans, torch.Tensor]:
+
+def load_json(data_desc_file: Path) -> tuple[Trans, torch.Tensor]:
with open(data_desc_file, 'r', encoding='utf-8') as file:
data = json.load(file)
view_t = torch.tensor(data['view_centers'])
view_r = torch.tensor(data['view_rots']).view(-1, 3, 3)
+ if data.get("gl_coord"):
+ view_t[:, 2] *= -1
+ view_r[:, 2] *= -1
+ view_r[..., 2] *= -1
if data.get('gazes'):
if len(data['gazes'][0]) == 2:
gazes = torch.tensor(data['gazes']).view(-1, 1, 2).expand(-1, 2, -1)
@@ -127,68 +120,61 @@ def load_json(data_desc_file) -> Tuple[Trans, torch.Tensor]:
gazes = torch.zeros(view_t.size(0), 2, 2)
return Trans(view_t, view_r), gazes
-def load_views(data_desc_file: str) -> Tuple[Trans, torch.Tensor]:
- if data_desc_file.endswith('.csv'):
- return load_csv(data_desc_file)
- return load_json(data_desc_file)
-
-rot_range = {
- 'classroom': [120, 80],
- 'barbershop': [360, 80],
- 'lobby': [360, 80],
- 'stones': [360, 80]
-}
-trans_range = {
- 'classroom': 0.6,
- 'barbershop': 0.3,
- 'lobby': 1.0,
- 'stones': 1.0
-}
-fov_list = [20, 45, 110]
-res_list = [(256, 256), (256, 256), (400, 360)]
+
+def load_views_and_gazes(data_desc_file: Path) -> tuple[Trans, torch.Tensor]:
+ if data_desc_file.suffix == '.csv':
+ views, gazes = load_csv(data_desc_file)
+ else:
+ views, gazes = load_json(data_desc_file)
+ gazes[:, :, 1] = (gazes[:, :1, 1] + gazes[:, 1:, 1]) * 0.5
+ return views, gazes
+
+
+torch.set_grad_enabled(False)
+view_file = Path(opt.view_file)
+stereo_disparity = opt.disparity
res_full = (1600, 1440)
-stereo_disparity = 0.06
-cwd = os.getcwd()
-os.chdir(f"{sys.path[0]}/../data/__new/{opt.scene}_all")
-fovea_net = load_net(find_file('fovea'))
-periph_net = load_net(find_file('periph'))
-renderer = FoveatedNeuralRenderer(fov_list, res_list,
- nn.ModuleList([fovea_net, periph_net, periph_net]),
- res_full, device=device.default())
-os.chdir(cwd)
+if opt.model:
+ model_dir = Path(opt.model)
+ fov_list = [20.0, 45.0, 110.0]
+ res_list = [(256, 256), (256, 256), (256, 230)]
+ fovea_net = load_model(next(model_dir.glob("fovea*.tar")))
+ periph_net = load_model(next(model_dir.glob("periph*.tar")))
+ renderer = FoveatedNeuralRenderer(fov_list, res_list,
+ nn.ModuleList([fovea_net, periph_net, periph_net]),
+ res_full, device=device.default())
+else:
+ renderer = None
# Load Dataset
-views, gazes = load_views(opt.view_file)
+views, gazes = load_views_and_gazes(Path(opt.view_file))
if opt.range:
opt.range = [int(val) for val in opt.range.split(",")]
if len(opt.range) == 1:
opt.range = [0, opt.range[0]]
- views = views.get(range(opt.range[0], opt.range[1]))
+ views = views[opt.range[0]:opt.range[1]]
gazes = gazes[opt.range[0]:opt.range[1]]
views = views.to(device.default())
-n_views = views.size()[0]
+n_views = views.shape[0]
print('Dataset loaded. Views:', n_views)
-
-videodir = os.path.dirname(os.path.abspath(opt.view_file))
-tempdir = '/dev/shm/dvs_tmp/video'
-videoname = f"{os.path.splitext(os.path.split(opt.view_file)[-1])[0]}_{'stereo' if opt.stereo else 'mono'}"
-gazeout = f"{videodir}/{videoname}_gaze.csv"
-if opt.noCE:
- videoname += "_noCE"
+videodir = view_file.absolute().parent
+tempdir = Path('/dev/shm/dvs_tmp/video')
+if opt.input:
+ videoname = Path(opt.input).parent.stem
+else:
+ videoname = f"{view_file.stem}_{('stereo' if opt.stereo else 'mono')}"
+ if opt.noCE:
+ videoname += "_noCE"
+gazeout = videodir / f"{videoname}_gaze.csv"
if opt.fps:
- if opt.input:
- videoname = os.path.split(opt.input)[0]
inferout = f"{videodir}/{opt.input}" if opt.input else f"{tempdir}/{videoname}/%04d.bmp"
hintout = f"{tempdir}/{videoname}_hint/%04d.bmp"
else:
inferout = f"{videodir}/{opt.input}" if opt.input else f"{videodir}/{videoname}/%04d.png"
hintout = f"{videodir}/{videoname}_hint/%04d.png"
-if opt.input:
- scale = img.load(inferout % 0).shape[-1] / res_full[1]
-else:
- scale = 1
+scale = img.load(inferout % 0).shape[-1] / res_full[1] if opt.input else 1
hint = img.load(f"{sys.path[0]}/fovea_hint.png", with_alpha=True).to(device=device.default())
hint = nn_f.interpolate(hint, mode='bilinear', scale_factor=scale, align_corners=False)
@@ -233,66 +219,44 @@ if not opt.replace:
hint_offset = max(0, hint_offset - 1)
infer_offset = n_views if opt.input else max(0, infer_offset - 1)
-if opt.stereo:
- gazes_out = torch.empty(n_views, 4)
- for view_idx in range(n_views):
- shift = gazes[view_idx, 0, 0] - gazes[view_idx, 1, 0]
- # print(shift.item())
- gazel = ((gazes[view_idx, 1, 0] + 0.4 * shift).item(),
- 0.5 * (gazes[view_idx, 0, 1] + gazes[view_idx, 1, 1]).item())
- gazer = ((gazes[view_idx, 0, 0] - 0.4 * shift).item(), gazel[1])
- # gazel = ((gazes[view_idx, 0, 0]).item(),
- # 0.5 * (gazes[view_idx, 0, 1] + gazes[view_idx, 1, 1]).item())
- #gazer = ((gazes[view_idx, 1, 0]).item(), gazel[1])
- gazes_out[view_idx] = torch.tensor([gazel[0], gazel[1], gazer[0], gazer[1]])
- if view_idx < hint_offset:
- continue
- if view_idx < infer_offset:
- frame = img.load(inferout % view_idx).to(device=device.default())
- else:
- view_trans = views.get(view_idx)
- left_images, right_images = renderer(view_trans, gazel, gazer,
- stereo_disparity=stereo_disparity,
- mono_periph_mode=3, ret_raw=True)
- frame = torch.cat([
- left_images['blended_raw'] if opt.noCE else left_images['blended'],
- right_images['blended_raw'] if opt.noCE else right_images['blended']], -1)
- frame = img.translate(frame, (0.5, 0.5))
- img.save(frame, inferout % view_idx)
- add_hint(frame, gazel, gazer)
- img.save(frame, hintout % view_idx)
- progress_bar(view_idx, n_views, 'Frame %4d inferred' % view_idx)
-else:
- gazes_out = torch.empty(n_views, 2)
- for view_idx in range(n_views):
- gaze = 0.5 * (gazes[view_idx, 0] + gazes[view_idx, 1])
- gaze = (gaze[0].item(), gaze[1].item())
- gazes_out[view_idx] = torch.tensor([gaze[0], gaze[1]])
- if view_idx < hint_offset:
- continue
- if view_idx < infer_offset:
- frame = img.load(inferout % view_idx).to(device=device.default())
+for view_idx in trange(n_views):
+ if view_idx < hint_offset:
+ continue
+ gaze = gazes[view_idx]
+ if not opt.stereo:
+ gaze = gaze.sum(0, True) * 0.5
+ gaze = gaze.tolist()
+ if view_idx < infer_offset:
+ frame = img.load(inferout % view_idx).to(device=device.default())
+ else:
+ if renderer is None:
+ raise Exception
+ key = 'blended_raw' if opt.noCE else 'blended'
+ view_trans = views[view_idx]
+ if opt.stereo:
+ left_images, right_images = renderer(view_trans, *gaze,
+ stereo_disparity=stereo_disparity,
+ mono_periph_mode=3, ret_raw=True)
+ frame = torch.cat([left_images[key], right_images[key]], -1)
else:
- view_trans = views.get(view_idx)
- frame = renderer(view_trans, gaze,
- ret_raw=True)['blended_raw' if opt.noCE else 'blended']
- frame = img.translate(frame, (0.5, 0.5))
- img.save(frame, inferout % view_idx)
- add_hint(frame, gaze)
- img.save(frame, hintout % view_idx)
- progress_bar(view_idx, n_views, 'Frame %4d inferred' % view_idx)
+ frame = renderer(view_trans, *gaze, ret_raw=True)[key]
+ img.save(frame, inferout % view_idx)
+ add_hint(frame, *gaze)
+ img.save(frame, hintout % view_idx)
+gazes_out = gazes.reshape(-1, 4) if opt.stereo else gazes.sum(1) * 0.5
with open(gazeout, 'w') as fp:
- for i in range(n_views):
- fp.write(','.join([f'{val.item()}' for val in gazes_out[i]]))
- fp.write('\n')
+ csv_writer = csv.writer(fp)
+ csv_writer.writerows(gazes_out.tolist())
if opt.fps:
# Generate video without hint
os.system(f'ffmpeg -y -r {opt.fps:d} -i {inferout} -c:v libx264 {videodir}/{videoname}.mp4')
- if not opt.input:
- shutil.rmtree(os.path.dirname(inferout))
# Generate video with hint
os.system(f'ffmpeg -y -r {opt.fps:d} -i {hintout} -c:v libx264 {videodir}/{videoname}_hint.mp4')
+
+ # Clean temp images
+ if not opt.input:
+ shutil.rmtree(os.path.dirname(inferout))
shutil.rmtree(os.path.dirname(hintout))
diff --git a/tools/image_scale.py b/tools/image_scale.py
index f3e8812..9f3ba82 100644
--- a/tools/image_scale.py
+++ b/tools/image_scale.py
@@ -1,20 +1,28 @@
-import sys
import os
-sys.path.append(os.path.abspath(sys.path[0] + '/../'))
-
import argparse
from PIL import Image
-from utils import misc
+from tqdm import tqdm
+from pathlib import Path
-def batch_scale(src, target, size):
- os.makedirs(target, exist_ok=True)
- for file_name in os.listdir(src):
- postfix = os.path.splitext(file_name)[1]
- if postfix == '.jpg' or postfix == '.png':
- im = Image.open(os.path.join(src, file_name))
- im = im.resize(size)
- im.save(os.path.join(target, file_name))
+def run(src: Path, target: Path, root: Path, scale_factor: float = 1., width: int = -1, height: int = -1):
+ target.mkdir(exist_ok=True)
+ for file_name in tqdm(os.listdir(src), leave=False, desc=src.relative_to(root).__str__()):
+ if (src / file_name).is_dir():
+ run(src / file_name, target / file_name, root, scale_factor, width, height)
+ elif not (target / file_name).exists():
+ postfix = os.path.splitext(file_name)[1]
+ if postfix == '.jpg' or postfix == '.png':
+ im = Image.open(src / file_name)
+ if width == -1 and height == -1:
+ width = round(im.width * scale_factor)
+ height = round(im.height * scale_factor)
+ elif width == -1:
+ width = round(im.width / im.height * height)
+ elif height == -1:
+ height = round(im.height / im.width * width)
+ im = im.resize((width, height))
+ im.save(target / file_name)
if __name__ == '__main__':
@@ -23,9 +31,11 @@ if __name__ == '__main__':
help='Source directory.')
parser.add_argument('target', type=str,
help='Target directory.')
- parser.add_argument('--width', type=int,
+ parser.add_argument('-x', '--scale-factor', type=float, default=1,
+ help='Target directory.')
+ parser.add_argument('--width', type=int, default=-1,
help='Width of output images (pixel)')
- parser.add_argument('--height', type=int,
+ parser.add_argument('--height', type=int, default=-1,
help='Height of output images (pixel)')
opt = parser.parse_args()
- batch_scale(opt.src, opt.target, (opt.width, opt.height))
+ run(Path(opt.src), Path(opt.target), Path(opt.src), opt.scale_factor, opt.width, opt.height)
diff --git a/tools/process_nerf.py b/tools/process_nerf.py
index a445a56..d0421e9 100644
--- a/tools/process_nerf.py
+++ b/tools/process_nerf.py
@@ -40,7 +40,7 @@ for subdir in ['images', 'images_4', 'images_8']:
print(f"Rename {src} to {tgt}")
os.rename(src, tgt)
out_desc = dataset_desc.copy()
- out_desc['view_file_pattern'] = f"{subdir}/view_%04d.jpg"
+ out_desc['color_file'] = f"{subdir}/view_%04d.jpg"
k = res[0] / dataset_desc['view_res']['y']
out_desc['view_res'] = {
'x': res[1],
diff --git a/train.py b/train.py
index b5a8782..8b75a1e 100644
--- a/train.py
+++ b/train.py
@@ -1,142 +1,94 @@
-import argparse
-import logging
-import os
-import sys
-from pathlib import Path
-from typing import List
+from operator import itemgetter
+from configargparse import ArgumentParser, SUPPRESS
-import model as mdl
-import train
-from utils import device
-from utils import netio
-from data import *
-from utils.misc import print_and_log
+from model import Model
+from train import Trainer
+from utils import device, netio
+from utils.types import *
+from data import Dataset
-RAYS_PER_BATCH = 2 ** 12
-DATA_LOADER_CHUNK_SIZE = 1e8
-root_dir = Path(__file__).absolute().parent
-
-
-parser = argparse.ArgumentParser()
-parser.add_argument('-c', '--config', type=str,
- help='Net config files')
-parser.add_argument('-e', '--epochs', type=int,
- help='Max epochs for train')
-parser.add_argument('--perf', type=int,
- help='Performance measurement frames (0 for disabling performance measurement)')
-parser.add_argument('--prune', type=int, nargs='+',
- help='Prune voxels on every # epochs')
-parser.add_argument('--split', type=int, nargs='+',
- help='Split voxels on every # epochs')
-parser.add_argument('--freeze', type=int, nargs='+',
- help='freeze levels on epochs')
-parser.add_argument('--checkpoint-interval', type=int)
-parser.add_argument('--views', type=str,
- help='Specify the range of views to train')
-parser.add_argument('path', type=str,
- help='Dataset description file')
-args = parser.parse_args()
-
-views_to_load = range(*[int(val) for val in args.views.split('-')]) if args.views else None
-argpath = Path(args.path)
-# argpath: May be model path or data path
-# 1) model path: continue training on the specified model
-# 2) data path: train a new model using specified dataset
-
-
-def load_dataset(data_path: Path):
- print(f"Loading dataset {data_path}")
- try:
- dataset = DatasetFactory.load(data_path, views_to_load=views_to_load)
- print(f"Dataset loaded: {dataset.root}/{dataset.name}")
- os.chdir(dataset.root)
- return dataset, dataset.name
- except FileNotFoundError:
- return load_multiscale_dataset(data_path)
-
-
-def load_multiscale_dataset(data_path: Path):
- if not data_path.is_dir():
- raise ValueError(
- f"Path {data_path} is not a directory")
- dataset: List[Union[PanoDataset, ViewDataset]] = []
- for sub_data_desc_path in data_path.glob("*.json"):
- sub_dataset = DatasetFactory.load(sub_data_desc_path, views_to_load=views_to_load)
- print(f"Sub-dataset loaded: {sub_dataset.root}/{sub_dataset.name}")
- dataset.append(sub_dataset)
- if len(dataset) == 0:
- raise ValueError(f"Path {data_path} does not contain sub-datasets")
- os.chdir(data_path.parent)
- return dataset, data_path.name
-
-
-try:
- states, checkpoint_path = netio.load_checkpoint(argpath)
- # Infer dataset path from model path
- # The model path follows such rule: <dataset_dir>/_nets/<dataset_name>/<model_name>/checkpoint_*.tar
- model_name = checkpoint_path.parts[-2]
- dataset, dataset_name = load_dataset(
- Path(*checkpoint_path.parts[:-4]) / checkpoint_path.parts[-3])
-except Exception:
- model_name = args.config
- dataset, dataset_name = load_dataset(argpath)
-
- # Load state 0 from specified configuration
- with Path(f'{root_dir}/configs/{args.config}.json').open() as fp:
- states = json.load(fp)
- states['args']['bbox'] = dataset[0].bbox if isinstance(dataset, list) else dataset.bbox
- states['args']['depth_range'] = dataset[0].depth_range if isinstance(dataset, list)\
- else dataset.depth_range
+def load_dataset(data_path: Path, color: str, coord: str):
+ dataset = Dataset(data_path, color_mode=Color[color], coord_sys=coord)
+ print(f"Load dataset: {dataset.root}/{dataset.name}")
+ return dataset
-if 'train' not in states:
- states['train'] = {}
-if args.prune is not None:
- states['train']['prune_epochs'] = args.prune
-if args.split is not None:
- states['train']['split_epochs'] = args.split
-if args.freeze is not None:
- states['train']['freeze_epochs'] = args.freeze
-if args.perf is not None:
- states['train']['perf_frames'] = args.perf
-if args.checkpoint_interval is not None:
- states['train']['checkpoint_interval'] = args.checkpoint_interval
-if args.epochs is not None:
- states['train']['max_epochs'] = args.epochs
-model = mdl.deserialize(states).to(device.default())
+initial_parser = ArgumentParser()
+initial_parser.add_argument('-c', '--config', type=str, default=SUPPRESS,
+ help='Config name, ignored if path is a checkpoint path')
+initial_parser.add_argument('--expname', type=str, default=SUPPRESS,
+ help='Experiment name, defaults to config name, ignored if path is a checkpoint path')
+initial_parser.add_argument('path', type=str,
+ help='Path to dataset description file or checkpoint file')
+initial_args = vars(initial_parser.parse_known_args()[0])
-# Initialize run directory
-run_dir = Path(f"_nets/{dataset_name}/{model_name}")
-run_dir.mkdir(parents=True, exist_ok=True)
-
-# Initialize logging
-log_file = run_dir / "train.log"
-logging.basicConfig(format='%(asctime)s[%(levelname)s] %(message)s', level=logging.INFO,
- filename=log_file, filemode='a' if log_file.exists() else 'w')
-
-
-def log_exception(exc_type, exc_value, exc_traceback):
- if not issubclass(exc_type, KeyboardInterrupt):
- logging.exception(exc_value, exc_info=(exc_type, exc_value, exc_traceback))
- sys.__excepthook__(exc_type, exc_value, exc_traceback)
-
-
-sys.excepthook = log_exception
-
-print_and_log(f"model: {model_name} ({model.cls})")
-print_and_log(f"args:")
-model.print_config()
-print(model)
-
-
-if __name__ == "__main__":
- # 1. Initialize data loader
- data_loader = get_loader(dataset, RAYS_PER_BATCH, chunk_max_items=DATA_LOADER_CHUNK_SIZE,
- shuffle=True, enable_preload=False, color=model.color)
-
- # 2. Initialize model and trainer
- trainer = train.get_trainer(model, run_dir, states)
-
- # 3. Train
- trainer.train(data_loader)
+root_dir = Path(__file__).absolute().parent
+argpath = Path(initial_args["path"]) # May be checkpoint path or dataset path
+# 1) checkpoint path: continue training a model
+# 2) dataset path: train a new model using specified dataset
+
+ckpt_path = netio.find_checkpoint(argpath)
+if ckpt_path:
+ # Continue training from a checkpoint
+ print(f"Load checkpoint {ckpt_path}")
+ args, states = itemgetter("args", "states")(torch.load(ckpt_path))
+ # args: "model", "model_args", "trainer", "trainer_args"
+ ModelCls = Model.get_class(args["model"])
+ TrainerCls = Trainer.get_class(args["trainer"])
+ model_args = ModelCls.Args(**args["model_args"])
+ trainer_args = TrainerCls.Args(**args["trainer_args"]).parse()
+ trainset = load_dataset(trainer_args.trainset, model_args.color, model_args.coord)
+ run_dir = ckpt_path.parent
+else:
+ # Start a new train
+ expname = initial_args.get("expname", initial_args.get("config", "unnamed"))
+ if "config" in initial_args:
+ config_path = root_dir / "configs" / f"{initial_args['config']}.ini"
+ if not config_path.exists():
+ raise ValueError(f"Config {initial_args['config']} is not found in "
+ f"{root_dir / 'configs'}.")
+ print(f"Load config {config_path}")
+ else:
+ config_path = None
+
+ # First parse model class and trainer class from config file or command-line arguments
+ parser = ArgumentParser(default_config_files=[f"{config_path}"] if config_path else [])
+ parser.add_argument('--color', type=str, default="rgb",
+ help='The color mode')
+ parser.add_argument('--model', type=str, required=True,
+ help='The model to train')
+ parser.add_argument('--trainer', type=str, default="Trainer",
+ help='The trainer to use for training')
+ args = parser.parse_known_args()[0]
+ ModelCls = Model.get_class(args.model)
+ TrainerCls = Trainer.get_class(args.trainer)
+ trainset_path = argpath
+ trainset = load_dataset(trainset_path, args.color, "gl")
+
+ # Then parse model's and trainer's args
+
+ if trainset.depth_range:
+ model_args = ModelCls.Args( # Some model's args are inferred from training dataset
+ color=trainset.color_mode.name,
+ near=trainset.depth_range[0],
+ far=trainset.depth_range[1],
+ white_bg=trainset.white_bg,
+ coord=trainset.coord_sys
+ )
+ else:
+ model_args = ModelCls.Args(white_bg=trainset.white_bg)
+ model_args.parse(config_path)
+ trainer_args = TrainerCls.Args(trainset=f"{trainset_path}").parse(config_path)
+ states = None
+
+ run_dir = trainset.root / "_nets" / trainset.name / expname
+ run_dir.mkdir(parents=True, exist_ok=True)
+
+m = ModelCls(model_args).to(device.default())
+trainer = TrainerCls(m, run_dir, trainer_args)
+if states:
+ trainer.load_state_dict(states)
+
+# Start train
+trainer.train(trainset)
diff --git a/train/__init__.py b/train/__init__.py
index 560bdd2..2597ecc 100644
--- a/train/__init__.py
+++ b/train/__init__.py
@@ -1,27 +1,15 @@
-import importlib
-import os
-from pathlib import Path
+import sys
+from inspect import isclass
-from model.base import BaseModel
-from .train import train_classes, Train
+from .trainer import Trainer, trainer_classes
+from .train_with_space import TrainWithSpace
+#from .train_multi_scale import TrainMultiScale
+__all__ = ["Trainer", "TrainWithSpace"]
-# Automatically import any python files this directory
-package_dir = os.path.dirname(__file__)
-package = os.path.basename(package_dir)
-for file in os.listdir(package_dir):
- path = os.path.join(package_dir, file)
- if file.startswith('_') or file.startswith('.'):
- continue
- if file.endswith('.py') or os.path.isdir(path):
- model_name = file[:-3] if file.endswith('.py') else file
- importlib.import_module(f'{package}.{model_name}')
-
-def get_class(class_name: str) -> type:
- return train_classes[class_name]
-
-
-def get_trainer(model: BaseModel, run_dir: Path, states: dict) -> Train:
- train_class = get_class(model.TrainerClass)
- return train_class(model, run_dir, states)
+# Register all trainer classes
+for item in __all__:
+ var = getattr(sys.modules[__name__], item)
+ if isclass(var) and issubclass(var, Trainer):
+ trainer_classes[item] = var
\ No newline at end of file
diff --git a/train/train.py b/train/train.py
deleted file mode 100644
index 6c001f3..0000000
--- a/train/train.py
+++ /dev/null
@@ -1,271 +0,0 @@
-import csv
-import json
-import logging
-import torch
-import torch.nn.functional as nn_f
-from typing import Any, Dict, Union
-from pathlib import Path
-
-import loss
-from utils import netio, math
-from utils.misc import format_time, print_and_log
-from utils.progress_bar import progress_bar
-from utils.perf import Perf, enable_perf, perf, get_perf_result
-from utils.env import set_env
-from utils.type import InputData, ReturnData
-from data.loader import DataLoader
-from model import serialize
-from model.base import BaseModel
-
-
-train_classes = {}
-
-
-class BaseTrainMeta(type):
-
- def __new__(cls, name, bases, attrs):
- new_cls = type.__new__(cls, name, bases, attrs)
- train_classes[name] = new_cls
- return new_cls
-
-
-class Train(object, metaclass=BaseTrainMeta):
-
- @property
- def perf_mode(self):
- return self.perf_frames > 0
-
- def _arg(self, name: str, default=None):
- return self.states.get("train", {}).get(name, default)
-
- def __init__(self, model: BaseModel, run_dir: Path, states: dict) -> None:
- super().__init__()
- print_and_log(
- f"Create trainer {__class__} with args: {json.dumps(states.get('train', {}))}")
- self.model = model
- self.run_dir = run_dir
- self.states = states
- self.epoch = states.get("epoch", 0)
- self.iters = states.get("iters", 0)
- self.max_epochs = self._arg("max_epochs", 50)
- self.checkpoint_interval = self._arg("checkpoint_interval", 10)
- self.perf_frames = self._arg("perf_frames", 0)
-
- self.model.train()
-
- self.reset_optimizer()
- if 'opti' in states:
- self.optimizer.load_state_dict(states['opti'])
-
- # For performance measurement
- if self.perf_mode:
- enable_perf()
-
- self.env = {
- "trainer": self
- }
-
- def reset_optimizer(self):
- self.optimizer = torch.optim.Adam(self.model.parameters(), lr=5e-4)
-
- def train(self, data_loader: DataLoader):
- set_env(self.env)
- self.data_loader = data_loader
- self.iters_per_epoch = self.perf_frames or len(data_loader)
-
- print(f"Begin training... Max epochs: {self.max_epochs}")
- while self.epoch < self.max_epochs:
- self._train_epoch()
- self._save_checkpoint()
- print("Train finished")
-
- def _save_checkpoint(self):
- (self.run_dir / '_misc').mkdir(exist_ok=True)
- # Clean checkpoints
- for i in range(1, self.epoch):
- if i % self.checkpoint_interval != 0:
- checkpoint_path = self.run_dir / f'checkpoint_{i}.tar'
- if checkpoint_path.exists():
- checkpoint_path.rename(self.run_dir / f'_misc/checkpoint_{i}.tar')
-
- # Save checkpoint
- self.states.update({
- **serialize(self.model),
- "epoch": self.epoch,
- "iters": self.iters,
- "opti": self.optimizer.state_dict()
- })
- netio.save_checkpoint(self.states, self.run_dir, self.epoch)
-
- def _show_progress(self, iters_in_epoch: int, avg_loss: float = 0, recent_loss: float = 0):
- iters_per_epoch = self.perf_frames or len(self.data_loader)
- progress_bar(iters_in_epoch, iters_per_epoch,
- f"Loss: {recent_loss:.2e} ({avg_loss:.2e})",
- f"Epoch {self.epoch + 1:<3d}",
- f" {self.run_dir.absolute()}")
-
- def _show_perf(self):
- s = "Performance Report ==>\n"
- res = get_perf_result()
- if res is None:
- s += "No available data.\n"
- else:
- for key, val in res.items():
- path_segs = key.split("/")
- s += " " * (len(path_segs) - 1) + f"{path_segs[-1]}: {val:.1f}ms\n"
- print(s)
-
- def _forward(self, data: InputData) -> ReturnData:
- return self.model(data, 'color', 'energies', 'speculars')
-
- @perf
- def _train_iter(self, data: Dict[str, Union[torch.Tensor, Any]]) -> float:
- def filtered_data(data, filter):
- if filter is not None:
- return data[filter]
- return data
-
- with perf("Forward"):
- if isinstance(data, list):
- out_colors = []
- out_energies = []
- out_speculars = []
- gt_colors = []
- for datum in data:
- partial_out = self._forward(datum)
- out_colors.append(partial_out['color'])
- out_energies.append(partial_out['energies'].flatten())
- if 'speculars' in partial_out:
- out_speculars.append(partial_out['speculars'].flatten())
- gt_colors.append(filtered_data(datum["color"], partial_out.get("rays_filter")))
- out_colors = torch.cat(out_colors)
- out_energies = torch.cat(out_energies)
- out_speculars = torch.cat(out_speculars) if len(out_speculars) > 0 else None
- gt_colors = torch.cat(gt_colors)
- else:
- out = self._forward(data)
- out_colors = out['color']
- out_energies = out['energies']
- out_speculars = out.get('speculars')
- gt_colors = filtered_data(data['color'], out.get("rays_filter"))
-
- with perf("Compute loss"):
- loss_val = loss.mse_loss(out_colors, gt_colors)
- if self._arg("density_regularization_weight"):
- loss_val += loss.cauchy_loss(out_energies, s=self._arg("density_regularization_scale"))\
- * self._arg("density_regularization_weight")
- if self._arg("specular_regularization_weight") and out_speculars is not None:
- loss_val += loss.cauchy_loss(out_speculars, s=self._arg("specular_regularization_scale")) \
- * self._arg("specular_regularization_weight")
-
- #return loss_val.item() # TODO remove this line
-
- with perf("Backward"):
- self.optimizer.zero_grad(True)
- loss_val.backward()
-
- with perf("Update"):
- self.optimizer.step()
-
- return loss_val.item()
-
- def _train_epoch(self):
- iters_in_epoch = 0
- recent_loss = []
- tot_loss = 0
-
- train_epoch_node = Perf.Node("Train Epoch")
-
- self._show_progress(iters_in_epoch)
- for data in self.data_loader:
- loss_val = self._train_iter(data)
- self.iters += 1
- iters_in_epoch += 1
-
- recent_loss = (recent_loss + [loss_val])[-50:]
- recent_avg_loss = sum(recent_loss) / len(recent_loss)
- tot_loss += loss_val
- avg_loss = tot_loss / iters_in_epoch
-
- #loss_min = min(loss_min, loss_val)
- #loss_max = max(loss_max, loss_val)
- #loss_avg = (loss_avg * iters_in_epoch + loss_val) / (iters_in_epoch + 1)
-
- self._show_progress(iters_in_epoch, avg_loss=avg_loss, recent_loss=recent_avg_loss)
-
- if self.perf_mode and iters_in_epoch >= self.perf_frames:
- self._show_perf()
- exit()
- train_epoch_node.close()
- torch.cuda.synchronize()
- self.epoch += 1
- epoch_dur = train_epoch_node.duration() / 1000
- logging.info(f"Epoch {self.epoch} spent {format_time(epoch_dur)} "
- f"(Avg. {format_time(epoch_dur / self.iters_per_epoch)}/iter). "
- f"Loss is {avg_loss:.2e}")
- #print(list(self.model.model(0).named_parameters())[2])
- #print(list(self.model.model(1).named_parameters())[2])
-
- def _train_epoch_debug(self): # TBR
- iters_in_epoch = 0
- loss_min = math.huge
- loss_max = 0
- loss_avg = 0
-
- self._show_progress(iters_in_epoch, loss={'val': 0, 'min': 0, 'max': 0, 'avg': 0})
- indices = []
- debug_data = []
- for idx, rays_o, rays_d, extra in self.data_loader:
- out = self.model(rays_o, rays_d, extra_outputs=['layers', 'weights'])
- loss_val = nn_f.mse_loss(out['color'], extra['color']).item()
-
- loss_min = min(loss_min, loss_val)
- loss_max = max(loss_max, loss_val)
- loss_avg = (loss_avg * iters_in_epoch + loss_val) / (iters_in_epoch + 1)
-
- self.iters += 1
- iters_in_epoch += 1
- self._show_progress(iters_in_epoch, loss={
- 'val': loss_val,
- 'min': loss_min,
- 'max': loss_max,
- 'avg': loss_avg
- })
-
- indices.append(idx)
- debug_data.append(torch.cat([
- extra['view_idx'][..., None],
- extra['pix_idx'][..., None],
- rays_d,
- #out['samples'].pts[:, 215:225].reshape(idx.size(0), -1),
- #out['samples'].dirs[:, :3].reshape(idx.size(0), -1),
- #out['samples'].voxel_indices[:, 215:225],
- out['states'].densities[:, 210:230].detach().reshape(idx.size(0), -1),
- out['states'].energies[:, 210:230].detach().reshape(idx.size(0), -1)
- # out['color'].detach()
- ], dim=-1))
- # states: VolumnRenderer.States = out['states'] # TBR
-
- indices = torch.cat(indices, dim=0)
- debug_data = torch.cat(debug_data, dim=0)
- indices, sort = indices.sort()
- debug_data = debug_data[sort]
- name = "rand.csv" if self.data_loader.shuffle else "seq.csv"
- with (self.run_dir / name).open("w") as fp:
- csv_writer = csv.writer(fp)
- csv_writer.writerows(torch.cat([indices[:20, None], debug_data[:20]], dim=-1).tolist())
- return
- with (self.run_dir / 'states.csv').open("w") as fp:
- csv_writer = csv.writer(fp)
- for chunk_info in states.chunk_infos:
- csv_writer.writerow(
- [*chunk_info['range'], chunk_info['hits'], chunk_info['core_i']])
- if chunk_info['hits'] > 0:
- csv_writer.writerows(torch.cat([
- chunk_info['samples'].pts,
- chunk_info['samples'].dirs,
- chunk_info['samples'].voxel_indices[:, None],
- chunk_info['colors'],
- chunk_info['energies']
- ], dim=-1).tolist())
- csv_writer.writerow([])
diff --git a/train/train_multi_scale.py b/train/train_multi_scale.py
index d14fe5c..b7e78c7 100644
--- a/train/train_multi_scale.py
+++ b/train/train_multi_scale.py
@@ -1,25 +1,25 @@
-from typing import Union
-from .train_with_space import TrainWithSpace
import torch
from pathlib import Path
-from model.cnerf import CNeRF
-from data.loader import DataLoader, MultiScaleDataLoader
+from .train_with_space import TrainWithSpace
+from .trainer import Trainer
+from model import CNeRF
+from data import RaysLoader, MultiScaleDataLoader
from modules import Voxels
-from utils.misc import print_and_log
+from utils.logging import print_and_log
class TrainMultiScale(TrainWithSpace):
model: CNeRF
- data_loader: Union[DataLoader, MultiScaleDataLoader]
+ data_loader: RaysLoader | MultiScaleDataLoader
def __init__(self, model: CNeRF, run_dir: Path, states: dict) -> None:
super().__init__(model, run_dir, states)
self.freeze_epochs = self._arg("freeze_epochs", [])
self.level_by_level = True#self._arg("level_by_level", False)
- def _train_epoch(self):
+ def _train_epoch(self, profiler: torch.profiler.profile = None):
l = self._check_epoch_matches(self.freeze_epochs, self.epoch)
if l >= 0:
self.model.trigger_stage(l + 1)
@@ -35,7 +35,7 @@ class TrainMultiScale(TrainWithSpace):
space: Voxels = self.model.model(self.model.stage).space
if self._check_epoch_matches(self.prune_epochs, self.epoch + 1) >= 0:
self.voxel_access = torch.zeros(space.n_voxels, dtype=torch.long, device=space.device)
- super(TrainWithSpace, self)._train_epoch()
+ super(Trainer, self)._train_epoch(profiler)
if self.voxel_access is not None:
before, after = space.prune(self.voxel_access > 0)
print_and_log(f"Prune by weights: {before} -> {after}")
diff --git a/train/train_with_space.py b/train/train_with_space.py
index 9da783e..c33b9aa 100644
--- a/train/train_with_space.py
+++ b/train/train_with_space.py
@@ -1,43 +1,39 @@
-from .train import Train
import sys
-import torch
-from pathlib import Path
-from typing import List
+from .trainer import Trainer
from modules import Voxels
-from model.base import BaseModel
-from data.loader import DataLoader
-from utils.samples import Samples
+from model import Model
+from data import RaysLoader
+from utils.types import *
from utils.mem_profiler import MemProfiler
-from utils.misc import print_and_log
-from utils.type import InputData, ReturnData
+from utils.logging import print_and_log
-class TrainWithSpace(Train):
+class TrainWithSpace(Trainer):
- def __init__(self, model: BaseModel, run_dir: Path, states: dict) -> None:
+ def __init__(self, model: Model, run_dir: Path, states: dict) -> None:
super().__init__(model, run_dir, states)
self.prune_epochs = [] if self.perf_mode else self._arg("prune_epochs", [])
self.split_epochs = [] if self.perf_mode else self._arg("split_epochs", [])
self.voxel_access = None
#MemProfiler.enable = True
- def _train_epoch(self):
+ def _train_epoch(self, profiler: torch.profiler.profile = None):
self._split()
space: Voxels = self.model.space
if self._check_epoch_matches(self.prune_epochs, self.epoch + 1) >= 0:
self.voxel_access = torch.zeros(space.n_voxels, dtype=torch.long, device=space.device)
- super()._train_epoch()
+ super()._train_epoch(profiler)
if self.voxel_access is not None:
before, after = space.prune(self.voxel_access > 0)
print_and_log(f"Prune by weights: {before} -> {after}")
self.voxel_access = None
# self._prune()
- def _forward(self, data: InputData) -> ReturnData:
+ def _forward(self, rays: Rays) -> ReturnData:
if self.voxel_access is None:
- return super()._forward(data)
- out = self.model(data, 'color', 'energies', 'speculars', 'weights', "samples")
+ return super()._forward(rays)
+ out = self.model(rays, 'color', 'energies', 'speculars', 'weights', "samples")
with torch.no_grad():
access_voxels = out['samples'].voxel_indices[out['weights'][..., 0] > 0.01]
self.voxel_access.index_add_(0, access_voxels, torch.ones_like(access_voxels))
@@ -63,7 +59,7 @@ class TrainWithSpace(Train):
except NotImplementedError:
print_and_log("The space does not support pruning operation. Just skip it.")
- def _check_epoch_matches(self, key_epochs: List[int], epoch: int = None):
+ def _check_epoch_matches(self, key_epochs: list[int], epoch: int = None):
epoch = epoch if epoch is not None else self.epoch
if epoch == 0 or len(key_epochs) == 0:
return -1
@@ -95,7 +91,7 @@ class TrainWithSpace(Train):
], 0) # (M[, ...])
return space.prune(scores > threshold)
- def _prune_voxels_by_weights(self, data_loader: DataLoader = None):
+ def _prune_voxels_by_weights(self, data_loader: RaysLoader = None):
space: Voxels = self.model.space
data_loader = data_loader or self.data_loader
batch_size = data_loader.batch_size
diff --git a/train/trainer.py b/train/trainer.py
new file mode 100644
index 0000000..d71d024
--- /dev/null
+++ b/train/trainer.py
@@ -0,0 +1,294 @@
+from collections import defaultdict
+from statistics import mean
+from tqdm import tqdm
+from tensorboardX import SummaryWriter
+from torch.optim import Optimizer, Adam
+from torch.optim.lr_scheduler import _LRScheduler, ExponentialLR
+
+from utils import netio, logging, loss, misc
+from utils.args import BaseArgs
+from utils.profile import Profiler, enable_profile, profile
+from utils.types import *
+from data import Dataset, RaysLoader
+from model import Model
+
+
+trainer_classes: dict[str, "Trainer"] = {}
+
+
+class Trainer:
+ class Args(BaseArgs):
+ max_iters: int | None
+ max_epochs: int = 20
+ checkpoint_interval: int | None
+ batch_size: int = 4096
+ loss: list[str] = ["Color_L2", "CoarseColor_L2"]
+ lr: float = 5e-4
+ lr_decay: float | None
+ profile_iters: int | None
+ trainset: str
+
+ args: Args
+ states: dict[str, Any]
+ optimizer: Optimizer
+ scheduler: _LRScheduler | None
+ loss_defs = {
+ "Color_L2": {
+ "fn": lambda out, gt: loss.mse_loss(out["color"], gt["color"]),
+ "required_outputs": ["color"]
+ },
+ "CoarseColor_L2": {
+ "fn": lambda out, gt: loss.mse_loss(out["coarse_color"], gt["color"]),
+ "required_outputs": ["coarse_color"]
+ },
+ "Density_Reg": {
+ "fn": lambda out, gt: loss.cauchy_loss(out["densities"], 1e4) * 1e-4,
+ "required_outputs": ["densities"]
+ }
+ }
+
+ @property
+ def profile_mode(self) -> bool:
+ return self.profile_iters is not None
+
+ @staticmethod
+ def get_class(typename: str) -> Type["Trainer"] | None:
+ return trainer_classes.get(typename)
+
+ def __init__(self, model: Model, run_dir: Path, args: Args = None) -> None:
+ self.model = model.train()
+ self.run_dir = run_dir
+ self.args = args or self.__class__.Args()
+ self.epoch = 0
+ self.iters = 0
+
+ self.profile_warmup_iters = 10
+ self.profile_iters = self.args.profile_iters
+ if self.profile_mode: # profile mode
+ self.max_iters = self.profile_warmup_iters + self.profile_iters
+ self.max_epochs = None
+ self.checkpoint_interval = None
+ elif self.args.max_iters: # iters mode
+ self.max_iters = self.args.max_iters
+ self.max_epochs = None
+ self.checkpoint_interval = self.args.checkpoint_interval or 10000
+ else: # epochs mode
+ self.max_iters = None
+ self.max_epochs = self.args.max_epochs
+ self.checkpoint_interval = self.args.checkpoint_interval or 10
+
+ self._init_optimizer()
+ self._init_scheduler()
+
+ self.required_outputs = []
+ for key in self.args.loss:
+ self.required_outputs += self.loss_defs[key]["required_outputs"]
+ self.required_outputs = list(set(self.required_outputs))
+
+ if self.profile_mode: # Enable performance measurement in profile mode
+ def handle_profile_result(result: Profiler.ProfileResult):
+ print(result.get_report())
+ exit()
+ enable_profile(self.profile_warmup_iters, self.profile_iters, handle_profile_result)
+ else: # Enable logging (Tensorboard & txt) in normal mode
+ tb_log_dir = self.run_dir / "_log"
+ tb_log_dir.mkdir(exist_ok=True)
+ self.tb_writer = SummaryWriter(tb_log_dir, purge_step=0)
+ logging.initialize(self.run_dir / "train.log")
+
+ logging.print_and_log(f"Model arguments: {self.model.args}")
+ logging.print_and_log(f"Trainer arguments: {self.args}")
+
+ # Debug: print model structure
+ print(model)
+
+ def state_dict(self) -> dict[str, Any]:
+ return {
+ "model": self.model.state_dict(),
+ "epoch": self.epoch,
+ "iters": self.iters,
+ "optimizer": self.optimizer.state_dict(),
+ "scheduler": self.scheduler.state_dict() if self.scheduler else None
+ }
+
+ def load_state_dict(self, state_dict: dict[str, Any]):
+ self.epoch = state_dict.get("epoch", self.epoch)
+ self.iters = state_dict.get("iters", self.iters)
+ if "model" in state_dict:
+ self.model.load_state_dict(state_dict["model"])
+ if "optimizer" in state_dict:
+ self.optimizer.load_state_dict(state_dict["optimizer"])
+ if self.scheduler and "scheduler" in state_dict:
+ self.scheduler.load_state_dict(state_dict["scheduler"])
+
+ def reset_optimizer(self):
+ self._init_optimizer()
+ if self.scheduler is not None:
+ scheduler_state = self.scheduler.state_dict()
+ self._init_scheduler()
+ self.scheduler.load_state_dict(scheduler_state)
+
+ def train(self, dataset: Dataset):
+ self.rays_loader = RaysLoader(dataset, self.args.batch_size, shuffle=True,
+ device=self.model.device)
+ self.forward_chunk_size = self.args.batch_size
+
+ if self.max_iters:
+ print(f"Begin training... Max iters: {self.max_iters}")
+ self.progress = tqdm(total=self.max_iters, dynamic_ncols=True)
+ self.rays_iter = self.rays_loader.__iter__()
+ while self.iters < self.max_iters:
+ self._train_iters(min(self.checkpoint_interval, self.max_iters - self.iters))
+ self._save_checkpoint()
+ else:
+ print(f"Begin training... Max epochs: {self.max_epochs}")
+ while self.epoch < self.max_epochs:
+ self._train_epoch()
+ self._save_checkpoint()
+
+ print("Train finished")
+
+ @staticmethod
+ def create(model: Model, run_dir: PathLike, typename: str, args: dict[str, Any] = None) -> "Trainer":
+ if typename not in trainer_classes:
+ raise ValueError(f"Class {typename} is not found")
+ return trainer_classes.get(typename)(model, run_dir, args)
+
+ def _init_scheduler(self):
+ self.scheduler = self.args.lr_decay and ExponentialLR(self.optimizer, self.args.lr_decay)
+
+ def _init_optimizer(self):
+ self.optimizer = Adam(self.model.parameters(), lr=self.args.lr)
+
+ def _save_checkpoint(self):
+ if self.checkpoint_interval is None:
+ return
+
+ ckpt = {
+ "args": {
+ "model": self.model.__class__.__name__,
+ "model_args": vars(self.model.args),
+ "trainer": self.__class__.__name__,
+ "trainer_args": vars(self.args)
+ },
+ "states": self.state_dict()
+ }
+
+ if self.max_iters:
+ # For iters mode, a checkpoint will be saved every `checkpoint_interval` iterations
+ netio.save_checkpoint(ckpt, self.run_dir, self.iters)
+ else:
+ # For epochs mode, a checkpoint will be saved every epoch.
+ # Checkpoints which don't match `checkpoint_interval` will be cleaned later
+ netio.clean_checkpoint(self.run_dir, self.checkpoint_interval)
+ netio.save_checkpoint(ckpt, self.run_dir, self.epoch)
+
+ def _update_progress(self, loss: float = 0):
+ self.progress.set_postfix_str(f"Loss: {loss:.2e}" if loss > 0 else "")
+ self.progress.update()
+
+ @profile("Forward")
+ def _forward(self, rays: Rays) -> ReturnData:
+ return self.model(rays, *self.required_outputs)
+
+ @profile("Compute Loss")
+ def _compute_loss(self, rays: Rays, out: ReturnData) -> dict[str, torch.Tensor]:
+ torch.isnan
+ gt = rays.select(out["rays_filter"]) if "rays_filter" in out else rays
+ loss_terms: dict[str, torch.Tensor] = {}
+ for key in self.args.loss:
+ try:
+ loss_terms[key] = self.loss_defs[key]["fn"](out, gt)
+ except KeyError:
+ pass
+ # Debug: print loss terms
+ #self.progress.write(",".join([f"{key}: {value.item():.2e}" for key, value in loss_terms.items()]))
+ return loss_terms
+
+ @profile("Train iteration")
+ def _train_iter(self, rays: Rays) -> float:
+ try:
+ self.optimizer.zero_grad(True)
+ loss_terms = defaultdict(list)
+ for offset in range(0, rays.shape[0], self.forward_chunk_size):
+ rays_chunk = rays.select(slice(offset, offset + self.forward_chunk_size))
+ out_chunk = self._forward(rays_chunk)
+ loss_chunk = self._compute_loss(rays_chunk, out_chunk)
+ loss_value = sum(loss_chunk.values())
+ with profile("Backward"):
+ loss_value.backward()
+ loss_terms["Overall_Loss"].append(loss_value.item())
+ for key, value in loss_chunk.items():
+ loss_terms[key].append(value.item())
+ loss_terms = {key: mean(value) for key, value in loss_terms.items()}
+
+ with profile("Update"):
+ self.optimizer.step()
+ if self.scheduler:
+ self.scheduler.step()
+ # Debug: print lr
+ #self.progress.write(f"Learning rate: {self.optimizer.param_groups[0]['lr']}")
+ self.iters += 1
+
+ if hasattr(self, "tb_writer"):
+ for key, value in loss_terms.items():
+ self.tb_writer.add_scalar(f"Loss/{key}", value, self.iters)
+
+ return loss_terms["Overall_Loss"]
+ except RuntimeError as e:
+ if not e.__str__().startswith("CUDA out of memory"):
+ raise e
+ self.progress.write("CUDA out of memory, half forward batch and retry.")
+ logging.warning("CUDA out of memory, half forward batch and retry.")
+ self.forward_chunk_size //= 2
+ torch.cuda.empty_cache()
+ return self._train_iter(rays)
+
+ def _train_iters(self, iters: int):
+ recent_loss_list = []
+ tot_loss = 0
+ train_iters_node = Profiler.Node("Train Iterations")
+ for _ in range(iters):
+ try:
+ rays = self.rays_iter.__next__()
+ except StopIteration:
+ self.rays_iter = self.rays_loader.__iter__() # A new epoch
+ rays = self.rays_iter.__next__()
+ loss_val = self._train_iter(rays)
+ recent_loss_list = (recent_loss_list + [loss_val])[-50:] # Keep recent 50 iterations
+ recent_avg_loss = sum(recent_loss_list) / len(recent_loss_list)
+ tot_loss += loss_val
+ self._update_progress(recent_avg_loss)
+ train_iters_node.close()
+ torch.cuda.synchronize()
+ avg_time = train_iters_node.device_duration / 1000 / iters
+ avg_loss = tot_loss / iters
+ state_str = f"Iter {self.iters}: Avg. {misc.format_time(avg_time)}/iter; Loss: {avg_loss:.2e}"
+ self.progress.write(state_str)
+ logging.info(state_str)
+
+ def _train_epoch(self):
+ iters_per_epoch = len(self.rays_loader)
+ recent_loss_list = []
+ tot_loss = 0
+
+ self.progress = tqdm(total=iters_per_epoch, desc=f"Epoch {self.epoch + 1:<3d}", leave=False,
+ dynamic_ncols=True)
+ train_epoch_node = Profiler.Node("Train Epoch")
+ for rays in self.rays_loader:
+ with profile("Train iteration"):
+ loss_val = self._train_iter(rays)
+ recent_loss_list = (recent_loss_list + [loss_val])[-50:]
+ recent_avg_loss = sum(recent_loss_list) / len(recent_loss_list)
+ tot_loss += loss_val
+ self._update_progress(recent_avg_loss)
+ self.progress.close()
+ train_epoch_node.close()
+ torch.cuda.synchronize()
+ self.epoch += 1
+ epoch_time = train_epoch_node.device_duration / 1000
+ avg_time = epoch_time / iters_per_epoch
+ avg_loss = tot_loss / iters_per_epoch
+ state_str = f"Epoch {self.epoch} spent {misc.format_time(epoch_time)} "\
+ f"(Avg. {misc.format_time(avg_time)}/iter). Loss is {avg_loss:.2e}."
+ logging.print_and_log(state_str)
diff --git a/train_oracle.py b/train_oracle.py
deleted file mode 100644
index 3eebb3c..0000000
--- a/train_oracle.py
+++ /dev/null
@@ -1,374 +0,0 @@
-import os
-import sys
-import argparse
-import torch
-import torch.optim
-import time
-from tensorboardX import SummaryWriter
-from torch import nn
-
-parser = argparse.ArgumentParser()
-# Arguments for train >>>
-parser.add_argument('-c', '--config', type=str,
- help='Net config files')
-parser.add_argument('-i', '--config-id', type=str,
- help='Net config id')
-parser.add_argument('-e', '--epochs', type=int, default=200,
- help='Max epochs for train')
-parser.add_argument('-n', '--prev-net', type=str)
-# Arguments for test >>>
-parser.add_argument('-r', '--output-res', type=str,
- help='Output resolution')
-parser.add_argument('-o', '--output', nargs='+', type=str, default=['perf', 'color'],
- help='Specify what to output (perf, color, depth, all)')
-parser.add_argument('--output-type', type=str, default='image',
- help='Specify the output type (image, video, debug)')
-# Other arguments >>>
-parser.add_argument('-t', '--test', action='store_true',
- help='Start in test mode')
-parser.add_argument('-m', '--model', type=str,
- help='The model file to load for continue train or test')
-parser.add_argument('-d', '--device', type=int, default=0,
- help='Which CUDA device to use.')
-parser.add_argument('-l', '--log-redirect', action='store_true',
- help='Is log redirected to file?')
-parser.add_argument('-p', '--prompt', action='store_true',
- help='Interactive prompt mode')
-parser.add_argument('dataset', type=str,
- help='Dataset description file')
-args = parser.parse_args()
-
-
-torch.cuda.set_device(args.device)
-print("Set CUDA:%d as current device." % torch.cuda.current_device())
-
-
-from utils import netio
-from utils import math
-from utils import device
-from utils import img
-from utils import interact
-from utils import color
-from utils.progress_bar import progress_bar
-from utils.perf import Perf
-from data.spherical_view_syn import *
-from data.loader import FastDataLoader
-from configs.spherical_view_syn import SphericalViewSynConfig
-from loss.ssim import ssim
-
-
-data_desc_path = args.dataset if args.dataset.endswith('.json') \
- else os.path.join(args.dataset, 'train.json')
-data_desc_name = os.path.splitext(os.path.basename(data_desc_path))[0]
-data_dir = os.path.dirname(data_desc_path) + '/'
-config = SphericalViewSynConfig()
-BATCH_SIZE = 4096
-SAVE_INTERVAL = 10
-TEST_BATCH_SIZE = 1
-TEST_MAX_RAYS = 32768 // 2
-
-# Toggles
-ROT_ONLY = False
-EVAL_TIME_PERFORMANCE = False
-# ========
-#ROT_ONLY = True
-#EVAL_TIME_PERFORMANCE = True
-
-
-def get_model_files(datadir):
- model_files = []
- for root, _, files in os.walk(datadir):
- model_files += [
- os.path.join(root, file).replace(datadir, '')
- for file in files if file.endswith('.pth')
- ]
- return model_files
-
-
-def set_outputs(args, outputs_str: str):
- args.output = [s.strip() for s in outputs_str.split(',')]
-
-
-if not args.test:
- print('Start in train mode.')
- if args.prompt: # 2.1 Prompt max epochs
- args.epochs = interact.input_ex('Max epochs:', interact.input_to_int(min=1),
- default=200)
- epochRange = range(1, args.epochs + 1)
- if args.prompt: # 2.2 Prompt continue train
- model_files = get_model_files(data_dir)
- args.model = interact.input_enum('Continue train on model:', model_files,
- err_msg='No such model file', default='')
- if args.model:
- cont_model = os.path.join(data_dir, args.model)
- model_name = os.path.splitext(os.path.basename(cont_model))[0]
- epochRange = range(int(model_name[12:]) + 1, epochRange.stop)
- run_dir = os.path.dirname(cont_model) + '/'
- run_id = os.path.basename(run_dir[:-1])
- config.from_id(run_id)
- else:
- if args.prompt: # 2.3 Prompt config file and additional config items
- config_files = [
- f[:-3] for f in os.listdir('configs')
- if f.endswith('.py') and f != 'spherical_view_syn.py'
- ]
- args.config = interact.input_enum('Specify config file:', config_files,
- err_msg='No such config file', default='')
- args.config_id = interact.input_ex('Specify custom config items:',
- default='')
- if args.config:
- config.load(os.path.join('configs', args.config + '.py'))
- if args.config_id:
- config.from_id(args.config_id)
- run_id = config.to_id()
- run_dir = data_dir + run_id + '/'
- log_dir = run_dir + 'log/'
-else: # Test mode
- print('Start in test mode.')
- if args.prompt: # 3. Prompt test model, output resolution, output mode
- model_files = get_model_files(data_dir)
- args.model = interact.input_enum('Specify test model:', model_files,
- err_msg='No such model file')
- args.output_res = interact.input_ex('Specify output resolution:',
- default='')
- set_outputs(args, 'depth')
- test_model_path = os.path.join(data_dir, args.model)
- test_model_name = os.path.splitext(os.path.basename(test_model_path))[0]
- run_dir = os.path.dirname(test_model_path) + '/'
- run_id = os.path.basename(run_dir[:-1])
- config.from_id(run_id)
- config.sa['perturb_sample'] = False
- args.output_res = tuple(int(s) for s in args.output_res.split('x')) \
- if args.output_res else None
- output_dir = f"{run_dir}output_{int(test_model_name.split('_')[-1])}"
- output_dataset_id = '%s%s' % (
- data_desc_name,
- '_%dx%d' % (args.output_res[0], args.output_res[1]) if args.output_res else '')
- args.output_flags = {
- item: item in args.output or 'all' in args.output
- for item in ['perf', 'color', 'depth', 'layers']
- }
-
-
-config.print()
-print("run dir: ", run_dir)
-
-# Initialize model
-model = config.create_net().to(device.default())
-loss_func = nn.MSELoss().to(device.default())
-
-
-if args.prev_net:
- prev_net_config_id = os.path.split(args.prev_net)[-2]
- prev_net_config = SphericalViewSynConfig()
- prev_net_config.from_id(prev_net_config_id)
- prev_net = prev_net_config.create_net().to(device.default())
- netio.load(args.prev_net, prev_net)
- model.prev_net = prev_net
-
-
-toggle_show_dir = False
-last_toggle_time = 0
-
-
-def train_loop(data_loader, optimizer, perf, writer, epoch, iters):
- global toggle_show_dir
- global last_toggle_time
- dataset: SphericalViewSynDataset = data_loader.dataset
- sub_iters = 0
- iters_in_epoch = len(data_loader)
- loss_min = 1e5
- loss_max = 0
- loss_avg = 0
- perf1 = Perf(args.log_redirect, True)
- for idx, _, rays_o, rays_d in data_loader:
- rays_bins = dataset.patched_bins[idx] if dataset.load_bins else None
- perf.checkpoint("Load")
-
- out = model(rays_o, rays_d)
- perf.checkpoint("Forward")
-
- optimizer.zero_grad()
- rays_bins = ((rays_bins[..., 0:1] - 0.5) * 2 * (out.size(-1) - 1)).to(torch.long)
- gt = torch.zeros_like(out)
- gt.scatter_(-1, rays_bins, 1)
- loss_value = loss_func(out, gt)
- #loss_value = loss_func(out, rays_bins[..., 0])
- perf.checkpoint("Compute loss")
-
- loss_value.backward()
- perf.checkpoint("Backward")
-
- optimizer.step()
- perf.checkpoint("Update")
-
- loss_value = loss_value.item()
- loss_min = min(loss_min, loss_value)
- loss_max = max(loss_max, loss_value)
- loss_avg = (loss_avg * sub_iters + loss_value) / (sub_iters + 1)
- if not args.log_redirect:
- progress_bar(sub_iters, iters_in_epoch,
- f"Loss: {loss_value:.2e} ({loss_min:.2e}/{loss_avg:.2e}/{loss_max:.2e})",
- f"Epoch {epoch:<3d}")
- current_time = time.time()
- if last_toggle_time == 0:
- last_toggle_time = current_time
- if current_time - last_toggle_time > 3:
- toggle_show_dir = not toggle_show_dir
- last_toggle_time = current_time
- if toggle_show_dir:
- sys.stdout.write(f'Epoch {epoch:<3d} [ {run_dir} ]\r')
-
- # Write tensorboard logs.
- writer.add_scalar("loss mse", loss_value, iters)
- # if patch and iters % 100 == 0:
- # output_vs_gt = torch.cat([out[0:4], gt[0:4]], 0).detach()
- # writer.add_image("Output_vs_gt", torchvision.utils.make_grid(
- # output_vs_gt, nrow=4).cpu().numpy(), iters)
-
- iters += 1
- sub_iters += 1
- if args.log_redirect:
- perf1.checkpoint('Epoch %d (%.2e/%.2e/%.2e)' %
- (epoch, loss_min, loss_avg, loss_max), True)
- return iters
-
-
-def save_checkpoint(epoch, iters):
- for i in range(1, epoch):
- if (i < epoch // 50 * 50 and i % 50 != 0 or i % 10 != 0) and \
- os.path.exists(f'{run_dir}model-epoch_{i}.pth'):
- os.remove(f'{run_dir}model-epoch_{i}.pth')
- netio.save(f'{run_dir}model-epoch_{epoch}.pth', model, iters, print_log=False)
-
-
-def train():
- # 1. Initialize data loader
- print("Load dataset: " + data_desc_path)
- dataset = SphericalViewSynDataset(data_desc_path, c=config.c, load_images=False,
- load_bins=True)
- dataset.set_patch_size(1)
- data_loader = FastDataLoader(dataset, BATCH_SIZE, shuffle=True, pin_memory=True)
-
- # 2. Initialize components
- optimizer = torch.optim.Adam(model.parameters(), lr=5e-4)
-
- if epochRange.start > 1:
- iters = netio.load(f'{run_dir}model-epoch_{epochRange.start - 1}.pth', model)
- else:
- os.makedirs(run_dir, exist_ok=True)
- os.makedirs(log_dir, exist_ok=True)
- iters = 0
-
- # 3. Train
- model.train()
-
- perf = Perf(EVAL_TIME_PERFORMANCE, start=True)
- writer = SummaryWriter(log_dir)
-
- print("Begin training...")
- for epoch in epochRange:
- iters = train_loop(data_loader, optimizer, perf, writer, epoch, iters)
- save_checkpoint(epoch, iters)
- print("Train finished")
-
-
-def test():
- with torch.no_grad():
- # 1. Load dataset
- print("Load dataset: " + data_desc_path)
- dataset = SphericalViewSynDataset(data_desc_path, res=args.output_res, load_images=False,
- load_bins=args.output_flags['perf'])
- data_loader = FastDataLoader(dataset, 1, shuffle=False, pin_memory=True)
-
- # 2. Load trained model
- netio.load(test_model_path, model)
- model.eval()
-
- # 3. Test on dataset
- print("Begin test, batch size is %d" % TEST_BATCH_SIZE)
-
- i = 0
- global_offset = 0
- chns = color.chns(config.c)
- n = dataset.n_views
- total_pixels = n * dataset.view_res[0] * dataset.view_res[1]
-
- out = {}
- if args.output_flags['perf']:
- perf_times = torch.empty(n)
- perf = Perf(True, start=True)
- out['bins'] = torch.zeros(total_pixels, 3, device=device.default())
-
- for vi, _, rays_o, rays_d in data_loader:
- rays_o = rays_o.view(-1, 3)
- rays_d = rays_d.view(-1, 3)
- #rays_bins = dataset.patched_bins[vi].view(-1, 3)
- n_rays = rays_o.size(0)
- for offset in range(0, n_rays, TEST_MAX_RAYS):
- idx = slice(offset, min(offset + TEST_MAX_RAYS, n_rays))
- global_idx = slice(idx.start + global_offset, idx.stop + global_offset)
- ret = model(rays_o[idx], rays_d[idx])
- is_local_max = torch.ones_like(ret, dtype=torch.bool)
- for delta in range(-3, 0):
- is_local_max[..., -delta:].logical_and_(
- ret[..., -delta:] > ret[..., :delta])
- for delta in range(1, 4):
- is_local_max[..., :-delta].logical_and_(
- ret[..., :-delta] > ret[..., delta:])
- ret[is_local_max.logical_not()] = 0
- vals, idxs = torch.topk(ret, 3) # (B, 3)
- vals = vals / vals.sum(-1, keepdim=True)
- out['bins'][global_idx] = (idxs.to(torch.float) / (ret.size(-1) - 1) * 0.5 + 0.5) * \
- (vals > 0.1)
- if args.output_flags['perf']:
- perf_times[i] = perf.checkpoint()
- progress_bar(i, n, 'Inferring...')
- i += 1
- global_offset += n_rays
-
- # 4. Save results
- print('Saving results...')
- os.makedirs(output_dir, exist_ok=True)
-
- for key in out:
- shape = [n] + list(dataset.view_res) + list(out[key].size()[1:])
- out[key] = out[key].view(shape)
- out['bins'] = out['bins'].permute(0, 3, 1, 2)
-
- if args.output_flags['perf']:
- perf_errors = torch.ones(n) * math.nan
- perf_ssims = torch.ones(n) * math.nan
- if dataset.view_images != None:
- for i in range(n):
- perf_errors[i] = loss_func(dataset.view_images[i], out['color'][i]).item()
- perf_ssims[i] = ssim(dataset.view_images[i:i + 1],
- out['color'][i:i + 1]).item() * 100
- perf_mean_time = torch.mean(perf_times).item()
- perf_mean_error = torch.mean(perf_errors).item()
- perf_name = 'perf_%s_%.1fms_%.2e.csv' % (
- output_dataset_id, perf_mean_time, perf_mean_error)
-
- # Remove old performance reports
- for file in os.listdir(output_dir):
- if file.startswith(f'perf_{output_dataset_id}'):
- os.remove(f"{output_dir}/{file}")
-
- # Save new performance reports
- with open(f"{output_dir}/{perf_name}", 'w') as fp:
- fp.write('View, Time, PSNR, SSIM\n')
- fp.writelines([
- f'{dataset.view_idxs[i]}, {perf_times[i].item():.2f}, '
- f'{img.mse2psnr(perf_errors[i].item()):.2f}, {perf_ssims[i].item():.2f}\n'
- for i in range(n)
- ])
- output_subdir = f"{output_dir}/{output_dataset_id}_bins"
- os.makedirs(output_subdir, exist_ok=True)
- img.save(out['bins'], [f'{output_subdir}/{i:0>4d}.png' for i in dataset.view_idxs])
-
-
-if __name__ == "__main__":
- if args.test:
- test()
- else:
- train()
diff --git a/update_cnerf.py b/update_cnerf.py
deleted file mode 100644
index af34074..0000000
--- a/update_cnerf.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from utils import netio
-from pathlib import Path
-
-dir = "/home/dengnc/dvs/data/classroom/_nets/ms_train_t0.8/_cnerfadv_ioc/"
-
-for epochs in range(1, 151):
- path = f"{dir}checkpoint_{epochs}.tar"
- if not Path(path).exists():
- continue
-
- print(f"Update epoch {epochs}")
- s = netio.load_checkpoint(path)[0]
- args0 = s["args"]
- args0_for_submodel = {
- key: value for key, value in args0.items()
- if key != "sub_models" and key != "interp_on_coarse"
- }
-
- for i in range(len(args0["sub_models"])):
- args0["sub_models"][i] = {**args0_for_submodel, **args0["sub_models"][i]}
- if epochs >= 30:
- args0["sub_models"][0]["n_samples"] = 64
- elif epochs >= 10:
- args0["sub_models"][0]["n_samples"] = 32
- if epochs >= 70:
- args0["sub_models"][1]["n_samples"] = 128
- if epochs >= 120:
- args0["sub_models"][2]["n_samples"] = 256
- netio.save_checkpoint(s, dir, epochs)
\ No newline at end of file
diff --git a/upsampling/FSRCNN/README.md b/upsampling/FSRCNN/README.md
deleted file mode 100644
index 5444f63..0000000
--- a/upsampling/FSRCNN/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-## Fast Super Resolution CNN
-
-
-### note
-this model has high possibility to diverge after 20 epochs.
\ No newline at end of file
diff --git a/upsampling/FSRCNN/model.py b/upsampling/FSRCNN/model.py
deleted file mode 100644
index 0f09cd4..0000000
--- a/upsampling/FSRCNN/model.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import torch
-import torch.nn as nn
-
-
-class Net(torch.nn.Module):
- def __init__(self, num_channels, upscale_factor, d=64, s=12, m=4):
- super(Net, self).__init__()
-
- self.first_part = nn.Sequential(
- nn.Conv2d(in_channels=num_channels, out_channels=d,
- kernel_size=5, stride=1, padding=2),
- nn.PReLU()
- )
-
- self.layers = []
- self.layers += [
- nn.Conv2d(in_channels=d, out_channels=s,
- kernel_size=1, stride=1, padding=0),
- nn.PReLU()
- ]
- for _ in range(m):
- self.layers += [
- nn.Conv2d(in_channels=s, out_channels=s,
- kernel_size=3, stride=1, padding=1),
- nn.PReLU()
- ]
- self.layers += [
- nn.Conv2d(in_channels=s, out_channels=d,
- kernel_size=1, stride=1, padding=0),
- nn.PReLU()
- ]
-
- self.mid_part = nn.Sequential(*self.layers)
-
- # Deconvolution
- if upscale_factor % 2:
- self.last_part = nn.ConvTranspose2d(
- in_channels=d, out_channels=num_channels, kernel_size=9,
- stride=upscale_factor, padding=5 - (upscale_factor + 1) // 2)
- else:
- self.last_part = nn.ConvTranspose2d(
- in_channels=d, out_channels=num_channels, kernel_size=9,
- stride=upscale_factor, padding=5 - upscale_factor // 2,
- output_padding=1)
-
- def forward(self, x):
- out = self.first_part(x)
- out = self.mid_part(out)
- out = self.last_part(out)
- return out
-
- def weight_init(self, mean=0.0, std=0.02):
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- m.weight.data.normal_(mean, std)
- if m.bias is not None:
- m.bias.data.zero_()
- if isinstance(m, nn.ConvTranspose2d):
- m.weight.data.normal_(0.0, 0.0001)
- if m.bias is not None:
- m.bias.data.zero_()
diff --git a/upsampling/FSRCNN/solver.py b/upsampling/FSRCNN/solver.py
deleted file mode 100644
index 2b178cc..0000000
--- a/upsampling/FSRCNN/solver.py
+++ /dev/null
@@ -1,104 +0,0 @@
-from __future__ import print_function
-from math import log10
-import sys
-
-import torch
-import torch.backends.cudnn as cudnn
-import torchvision
-from .model import Net
-from utils.progress_bar import progress_bar
-
-
-class FSRCNNTrainer(object):
- def __init__(self, config, training_loader, testing_loader, writer=None):
- super(FSRCNNTrainer, self).__init__()
- self.CUDA = torch.cuda.is_available()
- self.device = torch.device('cuda' if self.CUDA else 'cpu')
- self.model = None
- self.lr = config.lr
- self.nEpochs = config.nEpochs
- self.criterion = None
- self.optimizer = None
- self.scheduler = None
- self.seed = config.seed
- self.upscale_factor = config.upscale_factor
- self.training_loader = training_loader
- self.testing_loader = testing_loader
- self.writer = writer
-
- def build_model(self):
- self.model = Net(
- num_channels=1, upscale_factor=self.upscale_factor).to(self.device)
- self.model.weight_init(mean=0.0, std=0.2)
- self.criterion = torch.nn.MSELoss()
- torch.manual_seed(self.seed)
-
- if self.CUDA:
- torch.cuda.manual_seed(self.seed)
- cudnn.benchmark = True
- self.criterion.cuda()
-
- self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr)
- self.scheduler = torch.optim.lr_scheduler.MultiStepLR(
- self.optimizer, milestones=[50, 75, 100], gamma=0.5) # lr decay
-
- def save_model(self):
- model_out_path = "model_path.pth"
- torch.save(self.model, model_out_path)
- print("Checkpoint saved to {}".format(model_out_path))
-
- def train(self, epoch, iters):
- self.model.train()
- train_loss = 0
- for batch_num, (_, data, target) in enumerate(self.training_loader):
- data, target = data.to(self.device), target.to(self.device)
- self.optimizer.zero_grad()
- out = self.model(data)
- loss = self.criterion(out, target)
- train_loss += loss.item()
- loss.backward()
- self.optimizer.step()
- sys.stdout.write('Epoch %d: ' % epoch)
- progress_bar(batch_num, len(self.training_loader),
- 'Loss: %.4f' % (train_loss / (batch_num + 1)))
- if self.writer:
- self.writer.add_scalar("loss", loss, iters)
- if iters % 100 == 0:
- output_vs_gt = torch.stack([out, target], 1) \
- .flatten(0, 1).detach()
- self.writer.add_image(
- "Output_vs_gt",
- torchvision.utils.make_grid(output_vs_gt, nrow=2).cpu().numpy(),
- iters)
- iters += 1
-
- print(" Average Loss: {:.4f}".format(
- train_loss / len(self.training_loader)))
- return iters
-
- def test(self):
- self.model.eval()
- avg_psnr = 0
-
- with torch.no_grad():
- for batch_num, (data, target) in enumerate(self.testing_loader):
- data, target = data.to(self.device), target.to(self.device)
- prediction = self.model(data)
- mse = self.criterion(prediction, target)
- psnr = 10 * log10(1 / mse.item())
- avg_psnr += psnr
- progress_bar(batch_num, len(self.testing_loader),
- 'PSNR: %.4f' % (avg_psnr / (batch_num + 1)))
-
- print(" Average PSNR: {:.4f} dB".format(
- avg_psnr / len(self.testing_loader)))
-
- def run(self):
- self.build_model()
- for epoch in range(1, self.nEpochs + 1):
- print("\n===> Epoch {} starts:".format(epoch))
- self.train()
- self.test()
- self.scheduler.step(epoch)
- if epoch == self.nEpochs:
- self.save_model()
diff --git a/upsampling/SRCNN/README.md b/upsampling/SRCNN/README.md
deleted file mode 100644
index 193b135..0000000
--- a/upsampling/SRCNN/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
-## Super Resolution CNN
-The authors of the SRCNN describe their network, pointing out the equivalence of their method to the sparse-coding method, which is a widely used learning method for image SR. This is an important and educational aspect of their work, because it shows how example-based learning methods can be adapted and generalized to CNN models.
-
-The SRCNN consists of the following operations:
-1. **Preprocessing**: Up-scales LR image to desired HR size.
-2. **Feature extraction**: Extracts a set of feature maps from the up-scaled LR image.
-3. **Non-linear mapping**: Maps the feature maps representing LR to HR patches.
-4. **Reconstruction**: Produces the HR image from HR patches.
-
-Operations 2–4 above can be cast as a convolutional layer in a CNN that accepts as input the preprocessed images from step 1 above, and outputs the HR image
diff --git a/upsampling/SRCNN/model.py b/upsampling/SRCNN/model.py
deleted file mode 100644
index c836e07..0000000
--- a/upsampling/SRCNN/model.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import torch
-import torch.nn as nn
-
-
-class Net(torch.nn.Module):
- def __init__(self, num_channels, base_filter, upscale_factor=2):
- super(Net, self).__init__()
-
- self.layers = torch.nn.Sequential(
- nn.Conv2d(in_channels=num_channels, out_channels=base_filter, kernel_size=9, stride=1, padding=4, bias=True),
- nn.ReLU(inplace=True),
- nn.Conv2d(in_channels=base_filter, out_channels=base_filter // 2, kernel_size=1, bias=True),
- nn.ReLU(inplace=True),
- nn.Conv2d(in_channels=base_filter // 2, out_channels=num_channels * (upscale_factor ** 2), kernel_size=5, stride=1, padding=2, bias=True),
- nn.PixelShuffle(upscale_factor)
- )
-
- def forward(self, x):
- out = self.layers(x)
- return out
-
- def weight_init(self, mean, std):
- for m in self._modules:
- normal_init(self._modules[m], mean, std)
-
-
-def normal_init(m, mean, std):
- if isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Conv2d):
- m.weight.data.normal_(mean, std)
- m.bias.data.zero_()
diff --git a/upsampling/SRCNN/solver.py b/upsampling/SRCNN/solver.py
deleted file mode 100644
index 4985528..0000000
--- a/upsampling/SRCNN/solver.py
+++ /dev/null
@@ -1,104 +0,0 @@
-from __future__ import print_function
-
-from math import log10
-import sys
-
-import torch
-import torch.backends.cudnn as cudnn
-import torchvision
-
-from .model import Net
-from utils.progress_bar import progress_bar
-
-
-class SRCNNTrainer(object):
- def __init__(self, config, training_loader, testing_loader, writer=None):
- super(SRCNNTrainer, self).__init__()
- self.CUDA = torch.cuda.is_available()
- self.device = torch.device('cuda' if self.CUDA else 'cpu')
- self.model = None
- self.lr = config.lr
- self.nEpochs = config.nEpochs
- self.criterion = None
- self.optimizer = None
- self.scheduler = None
- self.seed = config.seed
- self.upscale_factor = config.upscale_factor
- self.training_loader = training_loader
- self.testing_loader = testing_loader
- self.writer = writer
-
- def build_model(self, num_channels):
- self.model = Net(num_channels=num_channels, base_filter=64, upscale_factor=self.upscale_factor).to(self.device)
- self.model.weight_init(mean=0.0, std=0.01)
- self.criterion = torch.nn.MSELoss()
- torch.manual_seed(self.seed)
-
- if self.CUDA:
- torch.cuda.manual_seed(self.seed)
- cudnn.benchmark = True
- self.criterion.cuda()
-
- self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr)
- self.scheduler = torch.optim.lr_scheduler.MultiStepLR(self.optimizer, milestones=[50, 75, 100], gamma=0.5)
-
- def save_model(self):
- model_out_path = "model_path.pth"
- torch.save(self.model, model_out_path)
- print("Checkpoint saved to {}".format(model_out_path))
-
- def train(self, epoch, iters, channels = None):
- self.model.train()
- train_loss = 0
- for batch_num, (_, data, target) in enumerate(self.training_loader):
- if channels:
- data = data[..., channels, :, :]
- target = target[..., channels, :, :]
- data =data.to(self.device)
- target = target.to(self.device)
- self.optimizer.zero_grad()
- out = self.model(data)
- loss = self.criterion(out, target)
- train_loss += loss.item()
- loss.backward()
- self.optimizer.step()
- sys.stdout.write('Epoch %d: ' % epoch)
- progress_bar(batch_num, len(self.training_loader), 'Loss: %.4f' % (train_loss / (batch_num + 1)))
- if self.writer:
- self.writer.add_scalar("loss", loss, iters)
- if iters % 100 == 0:
- output_vs_gt = torch.stack([out, target], 1) \
- .flatten(0, 1).detach()
- self.writer.add_image(
- "Output_vs_gt",
- torchvision.utils.make_grid(output_vs_gt, nrow=2).cpu().numpy(),
- iters)
- iters += 1
-
- print(" Average Loss: {:.4f}".format(train_loss / len(self.training_loader)))
- return iters
-
- def test(self):
- self.model.eval()
- avg_psnr = 0
-
- with torch.no_grad():
- for batch_num, (data, target) in enumerate(self.testing_loader):
- data, target = data.to(self.device), target.to(self.device)
- prediction = self.model(data)
- mse = self.criterion(prediction, target)
- psnr = 10 * log10(1 / mse.item())
- avg_psnr += psnr
- progress_bar(batch_num, len(self.testing_loader), 'PSNR: %.4f' % (avg_psnr / (batch_num + 1)))
-
- print(" Average PSNR: {:.4f} dB".format(avg_psnr / len(self.testing_loader)))
-
- def run(self):
- self.build_model()
- for epoch in range(1, self.nEpochs + 1):
- print("\n===> Epoch {} starts:".format(epoch))
- self.train()
- self.test()
- self.scheduler.step(epoch)
- if epoch == self.nEpochs:
- self.save_model()
diff --git a/upsampling/SRGAN/README.md b/upsampling/SRGAN/README.md
deleted file mode 100644
index f34ae71..0000000
--- a/upsampling/SRGAN/README.md
+++ /dev/null
@@ -1,26 +0,0 @@
-# SRGAN: Super-Resolution using GANs
-This is a complete Pytorch implementation of [Christian Ledig et al: "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"](https://arxiv.org/abs/1609.04802),
-reproducing their results.
-This paper's main result is that through using an adversarial and a content loss, a convolutional neural network is able to produce sharp, almost photo-realistic upsamplings of images.
-
-The implementation tries to be as faithful as possible to the original paper.
-See [implementation details](#method-and-implementation-details) for a closer look.
-
-
-## Method and Implementation Details
-Architecture diagram of the super-resolution and discriminator networks by Ledig et al:
-
-<p align='center'>
-<img src='https://github.com/mseitzer/srgan/blob/master/images/architecture.png' width=580>
-</p>
-
-The implementation tries to stay as close as possible to the details given in the paper.
-As such, the pretrained SRGAN is also trained with 1e6 and 1e5 update steps.
-The high amount of update steps proved to be essential for performance, which pretty much monotonically increases with training time.
-
-Some further implementation choices where the paper does not give any details:
-- Initialization: orthogonal for the super-resolution network, randomly from a normal distribution with std=0.02 for the discriminator network
-- Padding: reflection padding (instead of the more commonly used zero padding)
-
-## Batch-size
-batch size of 2 is recommended if GPU has only 8G RAM.
\ No newline at end of file
diff --git a/upsampling/SRGAN/model.py b/upsampling/SRGAN/model.py
deleted file mode 100644
index 168262b..0000000
--- a/upsampling/SRGAN/model.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def swish(x):
- return x * torch.sigmoid(x)
-
-
-class ResidualBlock(nn.Module):
- def __init__(self, in_channels, kernel, out_channels, stride):
- super(ResidualBlock, self).__init__()
-
- self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=kernel, stride=stride, padding=kernel // 2)
- self.bn1 = nn.BatchNorm2d(out_channels)
- self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=kernel, stride=stride, padding=kernel // 2)
- self.bn2 = nn.BatchNorm2d(out_channels)
-
- def forward(self, x):
- y = swish(self.bn1(self.conv1(x)))
- return self.bn2(self.conv2(y)) + x
-
-
-class UpsampleBlock(nn.Module):
- # Implements resize-convolution
- def __init__(self, in_channels):
- super(UpsampleBlock, self).__init__()
- self.conv = nn.Conv2d(in_channels, in_channels * 4, kernel_size=3, stride=1, padding=1)
- self.shuffler = nn.PixelShuffle(2)
-
- def forward(self, x):
- return swish(self.shuffler(self.conv(x)))
-
-
-class Generator(nn.Module):
- def __init__(self, n_residual_blocks, upsample_factor, num_channel=1, base_filter=64):
- super(Generator, self).__init__()
- self.n_residual_blocks = n_residual_blocks
- self.upsample_factor = upsample_factor
-
- self.conv1 = nn.Conv2d(num_channel, base_filter, kernel_size=9, stride=1, padding=4)
-
- for i in range(self.n_residual_blocks):
- self.add_module('residual_block' + str(i + 1), ResidualBlock(in_channels=base_filter, out_channels=base_filter, kernel=3, stride=1))
-
- self.conv2 = nn.Conv2d(base_filter, base_filter, kernel_size=3, stride=1, padding=1)
- self.bn2 = nn.BatchNorm2d(base_filter)
-
- for i in range(self.upsample_factor // 2):
- self.add_module('upsample' + str(i + 1), UpsampleBlock(base_filter))
-
- self.conv3 = nn.Conv2d(base_filter, num_channel, kernel_size=9, stride=1, padding=4)
-
- def forward(self, x):
- x = swish(self.conv1(x))
-
- y = x.clone()
- for i in range(self.n_residual_blocks):
- y = self.__getattr__('residual_block' + str(i + 1))(y)
-
- x = self.bn2(self.conv2(y)) + x
-
- for i in range(self.upsample_factor // 2):
- x = self.__getattr__('upsample' + str(i + 1))(x)
-
- return self.conv3(x)
-
- def weight_init(self, mean=0.0, std=0.02):
- for m in self._modules:
- normal_init(self._modules[m], mean, std)
-
-
-class Discriminator(nn.Module):
- def __init__(self, num_channel=1, base_filter=64):
- super(Discriminator, self).__init__()
- self.conv1 = nn.Conv2d(num_channel, base_filter, kernel_size=3, stride=1, padding=1)
-
- self.conv2 = nn.Conv2d(base_filter, base_filter, kernel_size=3, stride=2, padding=1)
- self.bn2 = nn.BatchNorm2d(base_filter)
- self.conv3 = nn.Conv2d(base_filter, base_filter * 2, kernel_size=3, stride=1, padding=1)
- self.bn3 = nn.BatchNorm2d(base_filter * 2)
- self.conv4 = nn.Conv2d(base_filter * 2, base_filter * 2, kernel_size=3, stride=2, padding=1)
- self.bn4 = nn.BatchNorm2d(base_filter * 2)
- self.conv5 = nn.Conv2d(base_filter * 2, base_filter * 4, kernel_size=3, stride=1, padding=1)
- self.bn5 = nn.BatchNorm2d(base_filter * 4)
- self.conv6 = nn.Conv2d(base_filter * 4, base_filter * 4, kernel_size=3, stride=2, padding=1)
- self.bn6 = nn.BatchNorm2d(base_filter * 4)
- self.conv7 = nn.Conv2d(base_filter * 4, base_filter * 8, kernel_size=3, stride=1, padding=1)
- self.bn7 = nn.BatchNorm2d(base_filter * 8)
- self.conv8 = nn.Conv2d(base_filter * 8, base_filter * 8, kernel_size=3, stride=2, padding=1)
- self.bn8 = nn.BatchNorm2d(base_filter * 8)
-
- # Replaced original paper FC layers with FCN
- self.conv9 = nn.Conv2d(base_filter * 8, num_channel, kernel_size=1, stride=1, padding=0)
-
- def forward(self, x):
- x = swish(self.conv1(x))
-
- x = swish(self.bn2(self.conv2(x)))
- x = swish(self.bn3(self.conv3(x)))
- x = swish(self.bn4(self.conv4(x)))
- x = swish(self.bn5(self.conv5(x)))
- x = swish(self.bn6(self.conv6(x)))
- x = swish(self.bn7(self.conv7(x)))
- x = swish(self.bn8(self.conv8(x)))
-
- x = self.conv9(x)
- return torch.sigmoid(F.avg_pool2d(x, x.size()[2:])).view(x.size()[0], -1)
-
- def weight_init(self, mean=0.0, std=0.02):
- for m in self._modules:
- normal_init(self._modules[m], mean, std)
-
-
-def normal_init(m, mean, std):
- if isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Conv2d):
- m.weight.data.normal_(mean, std)
- m.bias.data.zero_()
diff --git a/upsampling/SRGAN/solver.py b/upsampling/SRGAN/solver.py
deleted file mode 100644
index 78ffae1..0000000
--- a/upsampling/SRGAN/solver.py
+++ /dev/null
@@ -1,163 +0,0 @@
-from __future__ import print_function
-from math import log10
-import sys
-
-import torch
-import torch.nn as nn
-import torch.optim as optim
-import torch.backends.cudnn as cudnn
-import torchvision
-from torchvision.models.vgg import vgg16
-from .model import Generator, Discriminator
-from utils.progress_bar import progress_bar
-
-
-class SRGANTrainer(object):
- def __init__(self, config, training_loader, testing_loader, writer):
- super(SRGANTrainer, self).__init__()
- self.GPU_IN_USE = torch.cuda.is_available()
- self.device = torch.device('cuda' if self.GPU_IN_USE else 'cpu')
- self.netG = None
- self.netD = None
- self.lr = config.lr
- self.nEpochs = config.nEpochs
- self.epoch_pretrain = 10
- self.criterionG = None
- self.criterionD = None
- self.optimizerG = None
- self.optimizerD = None
- self.feature_extractor = None
- self.scheduler = None
- self.seed = config.seed
- self.upscale_factor = config.upscale_factor
- self.num_residuals = 16
- self.training_loader = training_loader
- self.testing_loader = testing_loader
- self.writer = writer
-
- def build_model(self):
- self.netG = Generator(n_residual_blocks=self.num_residuals, upsample_factor=self.upscale_factor, base_filter=64, num_channel=1).to(self.device)
- self.netD = Discriminator(base_filter=64, num_channel=1).to(self.device)
- self.feature_extractor = vgg16(pretrained=True)
- self.netG.weight_init(mean=0.0, std=0.2)
- self.netD.weight_init(mean=0.0, std=0.2)
- self.criterionG = nn.MSELoss()
- self.criterionD = nn.BCELoss()
- torch.manual_seed(self.seed)
-
- if self.GPU_IN_USE:
- torch.cuda.manual_seed(self.seed)
- self.feature_extractor.cuda()
- cudnn.benchmark = True
- self.criterionG.cuda()
- self.criterionD.cuda()
-
- self.optimizerG = optim.Adam(self.netG.parameters(), lr=self.lr, betas=(0.9, 0.999))
- self.optimizerD = optim.SGD(self.netD.parameters(), lr=self.lr / 100, momentum=0.9, nesterov=True)
- self.scheduler = optim.lr_scheduler.MultiStepLR(self.optimizerG, milestones=[50, 75, 100], gamma=0.5) # lr decay
- self.scheduler = optim.lr_scheduler.MultiStepLR(self.optimizerD, milestones=[50, 75, 100], gamma=0.5) # lr decay
-
- @staticmethod
- def to_data(x):
- if torch.cuda.is_available():
- x = x.cpu()
- return x.data
-
- def save(self):
- g_model_out_path = "SRGAN_Generator_model_path.pth"
- d_model_out_path = "SRGAN_Discriminator_model_path.pth"
- torch.save(self.netG, g_model_out_path)
- torch.save(self.netD, d_model_out_path)
- print("Checkpoint saved to {}".format(g_model_out_path))
- print("Checkpoint saved to {}".format(d_model_out_path))
-
- def pretrain(self):
- self.netG.train()
- for batch_num, (_, data, target) in enumerate(self.training_loader):
- data, target = data.to(self.device), target.to(self.device)
- self.netG.zero_grad()
- loss = self.criterionG(self.netG(data), target)
- loss.backward()
- self.optimizerG.step()
-
- def train(self, epoch, iters):
- # models setup
- self.netG.train()
- self.netD.train()
- g_train_loss = 0
- d_train_loss = 0
- for batch_num, (_, data, target) in enumerate(self.training_loader):
- # setup noise
- real_label = torch.ones(data.size(0), data.size(1)).to(self.device)
- fake_label = torch.zeros(data.size(0), data.size(1)).to(self.device)
- data, target = data.to(self.device), target.to(self.device)
-
- # Train Discriminator
- self.optimizerD.zero_grad()
- d_real = self.netD(target)
- d_real_loss = self.criterionD(d_real, real_label)
-
- d_fake = self.netD(self.netG(data))
- d_fake_loss = self.criterionD(d_fake, fake_label)
- d_total = d_real_loss + d_fake_loss
- d_train_loss += d_total.item()
- d_total.backward()
- self.optimizerD.step()
-
- # Train generator
- self.optimizerG.zero_grad()
- g_real = self.netG(data)
- g_fake = self.netD(g_real)
- gan_loss = self.criterionD(g_fake, real_label)
- mse_loss = self.criterionG(g_real, target)
-
- g_total = mse_loss + 1e-3 * gan_loss
- g_train_loss += g_total.item()
- g_total.backward()
- self.optimizerG.step()
-
- sys.stdout.write('Epoch %d: ' % epoch)
- progress_bar(batch_num, len(self.training_loader), 'G_Loss: %.4f | D_Loss: %.4f' % (g_train_loss / (batch_num + 1), d_train_loss / (batch_num + 1)))
- if self.writer:
- self.writer.add_scalar("G_Loss", g_train_loss / (batch_num + 1), iters)
- self.writer.add_scalar("D_Loss", d_train_loss / (batch_num + 1), iters)
- if iters % 100 == 0:
- output_vs_gt = torch.stack([g_real, target], 1) \
- .flatten(0, 1).detach()
- self.writer.add_image(
- "Output_vs_gt",
- torchvision.utils.make_grid(output_vs_gt, nrow=2).cpu().numpy(),
- iters)
- iters += 1
-
- print(" Average G_Loss: {:.4f}".format(g_train_loss / len(self.training_loader)))
- return iters
-
- def test(self):
- self.netG.eval()
- avg_psnr = 0
-
- with torch.no_grad():
- for batch_num, (data, target) in enumerate(self.testing_loader):
- data, target = data.to(self.device), target.to(self.device)
- prediction = self.netG(data)
- mse = self.criterionG(prediction, target)
- psnr = 10 * log10(1 / mse.item())
- avg_psnr += psnr
- progress_bar(batch_num, len(self.testing_loader), 'PSNR: %.4f' % (avg_psnr / (batch_num + 1)))
-
- print(" Average PSNR: {:.4f} dB".format(avg_psnr / len(self.testing_loader)))
-
- def run(self):
- self.build_model()
- for epoch in range(1, self.epoch_pretrain + 1):
- self.pretrain()
- print("{}/{} pretrained".format(epoch, self.epoch_pretrain))
-
- for epoch in range(1, self.nEpochs + 1):
- print("\n===> Epoch {} starts:".format(epoch))
- self.train()
- self.test()
- self.scheduler.step(epoch)
- if epoch == self.nEpochs:
- self.save()
diff --git a/upsampling/SubPixelCNN/model.py b/upsampling/SubPixelCNN/model.py
deleted file mode 100644
index 7e6ae61..0000000
--- a/upsampling/SubPixelCNN/model.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import torch.nn as nn
-import torch.nn.init as init
-
-
-class Net(nn.Module):
- def __init__(self, upscale_factor):
- super(Net, self).__init__()
-
- self.relu = nn.ReLU()
- self.conv1 = nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2)
- self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
- self.conv3 = nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1)
- self.conv4 = nn.Conv2d(32, upscale_factor ** 2, kernel_size=3, stride=1, padding=1)
- self.pixel_shuffle = nn.PixelShuffle(upscale_factor)
-
- self._initialize_weights()
-
- def _initialize_weights(self):
- init.orthogonal_(self.conv1.weight, init.calculate_gain('relu'))
- init.orthogonal_(self.conv2.weight, init.calculate_gain('relu'))
- init.orthogonal_(self.conv3.weight, init.calculate_gain('relu'))
- init.orthogonal_(self.conv4.weight)
-
- def forward(self, x):
- x = self.conv1(x)
- x = self.relu(x)
- x = self.conv2(x)
- x = self.relu(x)
- x = self.conv3(x)
- x = self.relu(x)
- x = self.conv4(x)
- x = self.pixel_shuffle(x)
- return x
diff --git a/upsampling/SubPixelCNN/solver.py b/upsampling/SubPixelCNN/solver.py
deleted file mode 100644
index 7d28e1c..0000000
--- a/upsampling/SubPixelCNN/solver.py
+++ /dev/null
@@ -1,110 +0,0 @@
-from __future__ import print_function
-
-from math import log10
-import sys
-
-import torch
-import torch.backends.cudnn as cudnn
-import torchvision
-
-from .model import Net
-from utils.progress_bar import progress_bar
-
-
-class SubPixelTrainer(object):
- def __init__(self, config, training_loader, testing_loader, writer=None):
- super(SubPixelTrainer, self).__init__()
- self.CUDA = torch.cuda.is_available()
- self.device = torch.device('cuda' if self.CUDA else 'cpu')
- self.model = None
- self.lr = config.lr
- self.nEpochs = config.nEpochs
- self.criterion = None
- self.optimizer = None
- self.scheduler = None
- self.seed = config.seed
- self.upscale_factor = config.upscale_factor
- self.training_loader = training_loader
- self.testing_loader = testing_loader
- self.writer = writer
-
- def build_model(self, num_channels):
- if num_channels != 1:
- raise ValueError('num_channels must be 1')
- self.model = Net(upscale_factor=self.upscale_factor).to(self.device)
- self.criterion = torch.nn.MSELoss()
- torch.manual_seed(self.seed)
-
- if self.CUDA:
- torch.cuda.manual_seed(self.seed)
- cudnn.benchmark = True
- self.criterion.cuda()
-
- self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr)
- self.scheduler = torch.optim.lr_scheduler.MultiStepLR(
- self.optimizer, milestones=[50, 75, 100], gamma=0.5) # lr decay
-
- def save(self):
- model_out_path = "model_path.pth"
- torch.save(self.model, model_out_path)
- print("Checkpoint saved to {}".format(model_out_path))
-
- def train(self, epoch, iters, channels=None):
- self.model.train()
- train_loss = 0
- for batch_num, (_, data, target) in enumerate(self.training_loader):
- if channels:
- data = data[..., channels, :, :]
- target = target[..., channels, :, :]
- data, target = data.to(self.device), target.to(self.device)
- self.optimizer.zero_grad()
- out = self.model(data)
- loss = self.criterion(out, target)
- train_loss += loss.item()
- loss.backward()
- self.optimizer.step()
- sys.stdout.write('Epoch %d: ' % epoch)
- progress_bar(batch_num, len(self.training_loader),
- 'Loss: %.4f' % (train_loss / (batch_num + 1)))
- if self.writer:
- self.writer.add_scalar("loss", loss, iters)
- if iters % 100 == 0:
- output_vs_gt = torch.stack([out, target], 1) \
- .flatten(0, 1).detach()
- self.writer.add_image(
- "Output_vs_gt",
- torchvision.utils.make_grid(
- output_vs_gt, nrow=2).cpu().numpy(),
- iters)
- iters += 1
-
- print(" Average Loss: {:.4f}".format(
- train_loss / len(self.training_loader)))
- return iters
-
- def test(self):
- self.model.eval()
- avg_psnr = 0
-
- with torch.no_grad():
- for batch_num, (data, target) in enumerate(self.testing_loader):
- data, target = data.to(self.device), target.to(self.device)
- prediction = self.model(data)
- mse = self.criterion(prediction, target)
- psnr = 10 * log10(1 / mse.item())
- avg_psnr += psnr
- progress_bar(batch_num, len(self.testing_loader),
- 'PSNR: %.4f' % (avg_psnr / (batch_num + 1)))
-
- print(" Average PSNR: {:.4f} dB".format(
- avg_psnr / len(self.testing_loader)))
-
- def run(self):
- self.build_model()
- for epoch in range(1, self.nEpochs + 1):
- print("\n===> Epoch {} starts:".format(epoch))
- self.train()
- self.test()
- self.scheduler.step(epoch)
- if epoch == self.nEpochs:
- self.save()
diff --git a/upsampling/run_upsampling.py b/upsampling/run_upsampling.py
deleted file mode 100644
index 8b90e2f..0000000
--- a/upsampling/run_upsampling.py
+++ /dev/null
@@ -1,112 +0,0 @@
-from __future__ import print_function
-
-import argparse
-import os
-import sys
-import torch
-import torch.nn.functional as nn_f
-from tensorboardX.writer import SummaryWriter
-
-sys.path.append(os.path.abspath(sys.path[0] + '/../'))
-
-# ===========================================================
-# Training settings
-# ===========================================================
-parser = argparse.ArgumentParser(description='PyTorch Super Res Example')
-# hyper-parameters
-parser.add_argument('--device', type=int, default=3,
- help='Which CUDA device to use.')
-parser.add_argument('--batchSize', type=int, default=1,
- help='training batch size')
-parser.add_argument('--testBatchSize', type=int,
- default=1, help='testing batch size')
-parser.add_argument('--nEpochs', type=int, default=20,
- help='number of epochs to train for')
-parser.add_argument('--lr', type=float, default=0.01,
- help='Learning Rate. Default=0.01')
-parser.add_argument('--seed', type=int, default=123,
- help='random seed to use. Default=123')
-parser.add_argument('--dataset', type=str, required=True,
- help='dataset directory')
-parser.add_argument('--test', type=str, help='path of model to test')
-parser.add_argument('--testOutPatt', type=str, help='test output path pattern')
-parser.add_argument('--color', type=str, default='rgb',
- help='color')
-
-# model configuration
-parser.add_argument('--upscale_factor', '-uf', type=int,
- default=2, help="super resolution upscale factor")
-#parser.add_argument('--model', '-m', type=str, default='srgan', help='choose which model is going to use')
-
-args = parser.parse_args()
-
-# Select device
-torch.cuda.set_device(args.device)
-print("Set CUDA:%d as current device." % torch.cuda.current_device())
-
-from utils import misc
-from utils import netio
-from utils import img
-from utils import color
-#from .upsampling.SubPixelCNN.solver import SubPixelTrainer as Solver
-from upsampling.SRCNN.solver import SRCNNTrainer as Solver
-from upsampling.upsampling_dataset import UpsamplingDataset
-from data.loader import FastDataLoader
-
-os.chdir(args.dataset)
-print('Change working directory to ' + os.getcwd())
-run_dir = 'run/'
-args.color = color.from_str(args.color)
-
-
-def train():
- os.makedirs(run_dir, exist_ok=True)
- train_set = UpsamplingDataset('.', 'input/out_view_%04d.png',
- 'gt/view_%04d.png', color=args.color)
- training_data_loader = FastDataLoader(dataset=train_set,
- batch_size=args.batchSize,
- shuffle=True,
- drop_last=False)
- trainer = Solver(args, training_data_loader, training_data_loader,
- SummaryWriter(run_dir))
- trainer.build_model(3 if args.color == color.RGB else 1)
- iters = 0
- for epoch in range(1, args.nEpochs + 1):
- print("\n===> Epoch {} starts:".format(epoch))
- iters = trainer.train(epoch, iters,
- channels=slice(2, 3) if args.color == color.YCbCr
- else None)
- netio.save(run_dir + 'model-epoch_%d.pth' % args.nEpochs, trainer.model)
-
-
-def test():
- os.makedirs(os.path.dirname(args.testOutPatt), exist_ok=True)
- train_set = UpsamplingDataset(
- '.', 'input/out_view_%04d.png', None, color=args.color)
- training_data_loader = FastDataLoader(dataset=train_set,
- batch_size=args.testBatchSize,
- shuffle=False,
- drop_last=False)
- trainer = Solver(args, training_data_loader, training_data_loader,
- SummaryWriter(run_dir))
- trainer.build_model(3 if args.color == color.RGB else 1)
- netio.load(args.test, trainer.model)
- for idx, input, _ in training_data_loader:
- if args.color == color.YCbCr:
- output_y = trainer.model(input[:, -1:])
- output_cbcr = nn_f.upsample(input[:, 0:2], scale_factor=2)
- output = color.ycbcr2rgb(torch.cat([output_cbcr, output_y], -3))
- else:
- output = trainer.model(input)
- img.save(output, args.testOutPatt % idx)
-
-
-def main():
- if (args.test):
- test()
- else:
- train()
-
-
-if __name__ == '__main__':
- main()
diff --git a/upsampling/upsampling_dataset.py b/upsampling/upsampling_dataset.py
deleted file mode 100644
index f52d643..0000000
--- a/upsampling/upsampling_dataset.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import os
-import torch
-import torchvision.transforms.functional as trans_f
-from utils import device
-from utils import color
-from utils import img
-
-
-class UpsamplingDataset(torch.utils.data.dataset.Dataset):
- """
- Dataset for upsampling task
-
- """
-
- def __init__(self, data_dir: str, input_patt: str, gt_patt: str,
- c: int, load_once: bool = True):
- """
- Initialize dataset for upsampling task
-
- :param data_dir: directory of dataset
- :param input_patt: file pattern for input (low resolution) images
- :param gt_patt: file pattern for ground truth (high resolution) images
- :param load_once: load all samples to current device at once to accelerate
- training, suitable for small dataset
- :param load_gt: whether to load ground truth images
- """
- self.input_patt = os.path.join(data_dir, input_patt)
- self.gt_patt = os.path.join(data_dir, gt_patt) if gt_patt != None else None
- self.n = len(list(filter(
- lambda file_name: os.path.exists(file_name),
- [self.input_patt % i for i in range(
- len(os.listdir(os.path.dirname(self.input_patt))))]
- )))
- self.load_once = load_once
- self.load_gt = self.gt_patt != None
- self.color = c
- self.input = img.load([self.input_patt % i for i in range(self.n)]) \
- .to(device.default()) if self.load_once else None
- self.gt = img.load([self.gt_patt % i for i in range(self.n)]) \
- .to(device.default()) if self.load_once and self.load_gt else None
- if self.color == color.GRAY:
- self.input = trans_f.rgb_to_grayscale(self.input)
- self.gt = trans_f.rgb_to_grayscale(self.gt) \
- if self.gt != None else None
- elif self.color == color.YCbCr:
- self.input = color.rgb2ycbcr(self.input)
- self.gt = color.rgb2ycbcr(self.gt) if self.gt != None else None
-
- def __len__(self):
- return self.n
-
- def __getitem__(self, idx):
- if self.load_once:
- return idx, self.input[idx], self.gt[idx] if self.load_gt else False
- if isinstance(idx, torch.Tensor):
- input = img.load([self.input_patt % i for i in idx])
- gt = img.load([self.gt_patt % i for i in idx]) if self.load_gt else False
- else:
- input = img.load([self.input_patt % idx])
- gt = img.load([self.gt_patt % idx]) if self.load_gt else False
- if self.color == color.GRAY:
- input = trans_f.rgb_to_grayscale(input)
- gt = trans_f.rgb_to_grayscale(gt) if isinstance(gt, torch.Tensor) else False
- return idx, input, gt
- elif self.color == color.YCbCr:
- input = color.rgb2ycbcr(input)
- gt = color.rgb2ycbcr(gt) if isinstance(gt, torch.Tensor) else False
- return idx, input, gt
\ No newline at end of file
diff --git a/utils/args.py b/utils/args.py
new file mode 100644
index 0000000..86f4e71
--- /dev/null
+++ b/utils/args.py
@@ -0,0 +1,80 @@
+from operator import countOf
+from types import UnionType
+from typing_extensions import Self
+from configargparse import ArgumentParser, Namespace
+
+from .types import *
+
+
+class BaseArgs(Namespace):
+
+ @property
+ def defaults(self) -> dict[str, Any]:
+ return {
+ key: getattr(self.__class__, key)
+ for key in self.__annotations__ if hasattr(self.__class__, key)
+ }
+
+ def __init__(self, **kwargs) -> None:
+ super().__init__(**self.defaults | kwargs)
+
+ def merge_with(self, dict: dict[str, Any]) -> Self:
+ return self.__class__(**vars(self) | dict)
+
+ def parse(self, config_path: PathLike = None, debug: bool = False) -> Self:
+ parser = ArgumentParser(default_config_files=[f"{config_path}"] if config_path else [])
+ self.setup_parser(parser, debug)
+ return parser.parse_known_args(namespace=self)[0]
+
+ def setup_parser(self, parser: ArgumentParser, debug: bool = False):
+ def build_debug_str(key: str, params_for_parser: dict[str, Any], prefix="parser") -> str:
+ def to_str(value): return value.__name__ if isinstance(value, Type) else (
+ f"\"{value}\"" if isinstance(value, str) else value.__str__())
+ params_str = ", ".join([
+ f"{name}={to_str(value)}" for name, value in params_for_parser.items()
+ ])
+ return f"{prefix}.add_argument(\"--{key}\", {params_str})"
+
+ def add_argument(parser: ArgumentParser, key: str, type: Type, required: bool, **kwargs):
+ params = {}
+ if type == bool:
+ bool_group = parser.add_mutually_exclusive_group()
+ bool_group.add_argument(f"--{key}", action="store_true")
+ bool_group.add_argument(f"--no-{key}", action="store_false", dest=key)
+ if debug:
+ print("bool_group = parser.add_mutually_exclusive_group()")
+ print(build_debug_str(key, {"action": "store_true"}, "bool_group"))
+ print(build_debug_str(f"no-{key}", {"action": "store_false", "dest": key},
+ "bool_group"))
+ else:
+ params["type"] = type
+ if "nargs" in kwargs:
+ params["nargs"] = kwargs["nargs"]
+ if "default" in kwargs:
+ params["default"] = kwargs["default"]
+ elif required:
+ params["required"] = True
+ parser.add_argument(f"--{key}", **params)
+ if debug:
+ print(build_debug_str(key, params))
+
+ for key, arg_type in self.__annotations__.items():
+ required = True
+ kwargs = {}
+ if isinstance(arg_type, UnionType):
+ if len(arg_type.__args__) != 2 or countOf(arg_type.__args__, type(None)) != 1:
+ raise ValueError(f"{key} cannot be union of two or more different types")
+ arg_type = arg_type.__args__[0] if arg_type.__args__[1] == type(None) \
+ else arg_type.__args__[1]
+ required = False
+ if getattr(arg_type, "__origin__", None) == list:
+ arg_type = arg_type.__args__[0]
+ kwargs["nargs"] = "*"
+ elif getattr(arg_type, "__origin__", None) == tuple:
+ arg_type = arg_type.__args__[0]
+ if any([arg != arg_type for arg in arg_type.__args__]):
+ raise ValueError(f"{key} cannot be tuple of different types")
+ kwargs["nargs"] = len(arg_type.__args__)
+ if hasattr(self, key):
+ kwargs["default"] = getattr(self, key)
+ add_argument(parser, key, arg_type, required, **kwargs)
diff --git a/utils/colmap_read_model.py b/utils/colmap_read_model.py
index 4acaa8c..ad614f5 100644
--- a/utils/colmap_read_model.py
+++ b/utils/colmap_read_model.py
@@ -257,7 +257,7 @@ def read_points3d_binary(path_to_model_file):
return points3D
-def read_model(path, ext):
+def read_model(path, ext) -> tuple[dict[int, Camera], dict[int, Image], dict[int, Point3D]]:
if ext == ".txt":
cameras = read_cameras_text(os.path.join(path, "cameras" + ext))
images = read_images_text(os.path.join(path, "images" + ext))
diff --git a/utils/config.py b/utils/config.py
new file mode 100644
index 0000000..1e98b19
--- /dev/null
+++ b/utils/config.py
@@ -0,0 +1,30 @@
+import json5
+from .types import *
+
+__all__ = ["load_from_json", "get_type_and_args"]
+
+def load_from_json(json_path: PathLike) -> dict[str, Any]:
+ try:
+ with Path(json_path).open() as fp:
+ config: dict[str, Any] = json5.load(fp)
+ except Exception:
+ raise ValueError(f"{json_path} is not a valid json file")
+ if "parent" in config:
+ parent_config = load_from_json((json_path.parent / config.pop("parent")).with_suffix(".json"))
+ config["model"][1] = parent_config["model"][1] | config["model"][1]
+ config["train"][1] = parent_config["train"][1] | config["train"][1]
+ return config
+
+
+def get_type_and_args(config_item: dict[str, Any] | list | str, default_type=None, default_args={}) -> tuple[str, dict[str, Any]]:
+ match config_item:
+ case None:
+ return default_type, default_args
+ case str() as type_name:
+ return type_name, default_args
+ case dict() as args:
+ return default_type, default_args | args
+ case str() as type_name, dict() as args:
+ return type_name, default_args | args
+ case _:
+ raise ValueError("\"config_item\" is invalid")
diff --git a/utils/env.py b/utils/env.py
index 05146f0..b2aac50 100644
--- a/utils/env.py
+++ b/utils/env.py
@@ -1,10 +1,12 @@
-env = None
+env = {}
-def get_env():
- return env
+def get(key: str):
+ return env.get(key)
+def get_all() -> dict:
+ return env
-def set_env(new_env: dict):
+def set(**kwargs):
global env
- env = new_env
+ env |= kwargs
diff --git a/utils/export.py b/utils/export.py
new file mode 100644
index 0000000..d15ecf7
--- /dev/null
+++ b/utils/export.py
@@ -0,0 +1,53 @@
+import torch
+import itertools
+from os import PathLike
+from typing import List, Dict
+
+from .nn import Module
+
+
+class ModelExporter(object):
+ inputs: dict[str, list[int]]
+ output_names: list[str]
+ module: Module
+
+ @property
+ def input_names(self) -> list[str]:
+ return list(self.inputs.keys())
+
+ @property
+ def module(self) -> Module:
+ return self.fn.__self__
+
+ def __init__(self, fn, *outputs: str, **inputs: list[int]) -> None:
+ super().__init__()
+ self.inputs = inputs
+ self.output_names = list(outputs)
+ self.fn = fn
+
+ def prepare_inputs(self, batch_size: int = None):
+ return tuple(
+ torch.rand(batch_size or 1, *size, device=self.module.device)
+ for size in self.inputs.values()
+ )
+
+ def export_onnx(self, path: PathLike, batch_size: int = None, **kwargs):
+ dynamic_axes = {
+ name: {0: "batch_size"}
+ for name in itertools.chain(self.input_names, self.output_names)
+ } if not batch_size else None
+
+ # Replace module's forward method with target method and recover later
+ self.module.forward = self.fn
+ kwargs = {
+ "export_params": True, # store the trained parameter weights inside the model file
+ "opset_version": 10, # the ONNX version to export the model to
+ "do_constant_folding": True, # whether to execute constant folding for optimization
+ **kwargs
+ }
+ torch.onnx.export(self.module, self.prepare_inputs(batch_size), path,
+ input_names=self.input_names, # the model's input names
+ output_names=self.output_names, # the model's output names
+ dynamic_axes=dynamic_axes, # variable length axes
+ **kwargs)
+ self.module.forward = self.module._forward
diff --git a/utils/geometry.py b/utils/geometry.py
index 527ac4a..f9ce34c 100644
--- a/utils/geometry.py
+++ b/utils/geometry.py
@@ -3,7 +3,6 @@
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
-from typing import Union
import numpy as np
import torch
import torch.nn.functional as F
@@ -215,7 +214,7 @@ def pruning_points(feats, points, scores, depth=0, th=0.5):
return feats, points
-def offset_points(point_xyz: torch.Tensor, half_voxel: Union[torch.Tensor, int, float] = 1,
+def offset_points(point_xyz: torch.Tensor, half_voxel: torch.Tensor | int | float = 1,
offset_only: bool = False, bits: int = 2) -> torch.Tensor:
"""
[summary]
diff --git a/utils/img.py b/utils/img.py
index 3a93b12..3786ffd 100644
--- a/utils/img.py
+++ b/utils/img.py
@@ -1,13 +1,13 @@
import os
-from pathlib import Path
import shutil
import torch
+import uuid
import matplotlib.pyplot as plt
import numpy as np
import torch.nn.functional as nn_f
-from typing import List, Tuple, Union
-from . import misc
-from . import math
+from . import misc, math
+from .types import *
+
def is_image_file(filename):
"""
@@ -19,7 +19,7 @@ def is_image_file(filename):
return any(filename.endswith(extension) for extension in [".png", ".jpg", ".jpeg"])
-def np2torch(img, permute=True):
+def np2torch(img: np.ndarray, permute: bool = True) -> torch.Tensor:
"""
Convert numpy-images(s) to torch-image(s), permute channels dim if `permute=True`
@@ -28,8 +28,7 @@ def np2torch(img, permute=True):
"""
batch_input = len(img.shape) == 4
if permute:
- t = torch.from_numpy(np.transpose(
- img, [0, 3, 1, 2] if batch_input else [2, 0, 1]))
+ t = torch.from_numpy(np.transpose(img, [0, 3, 1, 2] if batch_input else [2, 0, 1]))
else:
t = torch.from_numpy(img)
if not batch_input:
@@ -76,49 +75,51 @@ def load_seq(path: str, n: int, permute=True, with_alpha=False) -> torch.Tensor:
return load([path % i for i in range(n)], permute=permute, with_alpha=with_alpha)
-def save(input: torch.Tensor, *paths: Union[str, Path, List[Union[str, Path]]]):
+def save(input: torch.Tensor | np.ndarray, *paths: PathLike | list[PathLike]):
"""
Save one or multiple torch-image(s) to `paths`
- :param input `torch.Tensor`: torch-image(s) to save
+ :param input `Tensor|ndarray`: torch-image(s) to save
:param *paths `str...`: paths to save torch-image(s) to
:raises `ValueError`: if number of paths does not match batches of input image(s)
"""
new_paths = []
for path in paths:
new_paths += [path] if isinstance(path, (str, Path)) else list(path)
- if len(input.size()) < 4:
+ if len(input.shape) < 4:
input = input[None]
- if input.size(0) != len(new_paths):
+ if input.shape[0] != len(new_paths):
raise ValueError
- np_img = torch2np(input)
+ np_img = torch2np(input) if isinstance(input, torch.Tensor) else input
if np_img.dtype.kind == 'f':
np_img = np.clip(np_img, 0, 1)
+ if np_img.shape[-1] == 1:
+ np_img = np.repeat(np_img, 3, axis=-1)
if not np_img.flags['C_CONTIGUOUS']:
np_img = np.ascontiguousarray(np_img)
for i, path in enumerate(new_paths):
plt.imsave(path, np_img[i])
-def save_seq(input: torch.Tensor, path: Union[str, Path]):
+def save_seq(input: torch.Tensor, path: str | Path):
n = 1 if len(input.size()) <= 3 else input.size(0)
return save(input, [str(path) % i for i in range(n)])
-def plot(input: torch.Tensor, *, ax: plt.Axes = None):
+def plot(input: torch.Tensor | np.ndarray, *, ax: plt.Axes = None):
"""
Plot a torch-image using matplotlib
:param input `Tensor(HW|[B]CHW|[B]HWC)`: 2D, 3D or 4D torch-image(s)
:param ax `plt.Axes`: (optional) specify the axes to plot image
"""
- im = torch2np(input)
+ im = torch2np(input) if isinstance(input, torch.Tensor) else input
if len(im.shape) == 4:
im = im[0]
return plt.imshow(im) if ax is None else ax.imshow(im)
-def save_video(frames: torch.Tensor, path: Union[str, Path], fps: int,
+def save_video(frames: torch.Tensor, path: str | Path, fps: int,
repeat: int = 1, pingpong: bool = False):
"""
Encode and save a sequence of frames as video file
@@ -136,14 +137,14 @@ def save_video(frames: torch.Tensor, path: Union[str, Path], fps: int,
frames = frames.expand(repeat, -1, -1, -1, -1).flatten(0, 1)
path = Path(path)
- tempdir = Path('/dev/shm/dvs_tmp/video')
- inferout = tempdir / path.stem / f"%04d.bmp"
- os.makedirs(inferout.parent, exist_ok=True)
- os.makedirs(path.parent, exist_ok=True)
+ tempdir = Path(f'/dev/shm/dvs_tmp/video/{uuid.uuid4().hex}')
+ temp_frame_files = tempdir / f"%04d.bmp"
+ path.parent.mkdir(parents=True, exist_ok=True)
+ tempdir.mkdir(parents=True, exist_ok=True)
- save_seq(frames, inferout)
- os.system(f'ffmpeg -y -r {fps:d} -i {inferout} -c:v libx264 {path}')
- shutil.rmtree(inferout.parent)
+ save_seq(frames, temp_frame_files)
+ os.system(f'ffmpeg -y -r {fps:d} -i {temp_frame_files} -c:v libx264 {path}')
+ shutil.rmtree(tempdir)
def horizontal_shift(input: torch.Tensor, offset: int, dim=-1) -> torch.Tensor:
@@ -165,7 +166,7 @@ def horizontal_shift(input: torch.Tensor, offset: int, dim=-1) -> torch.Tensor:
return shifted
-def translate(input: torch.Tensor, offset: Tuple[float, float]) -> torch.Tensor:
+def translate(input: torch.Tensor, offset: tuple[float, float]) -> torch.Tensor:
theta = torch.tensor([
[1, 0, -offset[0] / input.size(-1) * 2],
[0, 1, -offset[1] / input.size(-2) * 2]
diff --git a/utils/interact.py b/utils/interact.py
index b2a7d3d..7a3b18a 100644
--- a/utils/interact.py
+++ b/utils/interact.py
@@ -62,7 +62,7 @@ def input_ex(prompt, *actions, default=None):
return s
-def input_enum(prompt, complete_list: List[str], *, err_msg: str, default=None):
+def input_enum(prompt, complete_list: list[str], *, err_msg: str, default=None):
readline.set_completer(make_completer(complete_list))
prompt_default = '(Default: %s) ' % default if default != None else ''
while True:
diff --git a/utils/logging.py b/utils/logging.py
new file mode 100644
index 0000000..56c1857
--- /dev/null
+++ b/utils/logging.py
@@ -0,0 +1,25 @@
+import sys
+from logging import *
+from pathlib import Path
+
+
+enable_logging = False
+
+def _log_exception(exc_type, exc_value, exc_traceback):
+ if not issubclass(exc_type, KeyboardInterrupt):
+ exception(exc_value, exc_info=(exc_type, exc_value, exc_traceback))
+ sys.__excepthook__(exc_type, exc_value, exc_traceback)
+
+
+def initialize(path: Path):
+ global enable_logging
+ basicConfig(format='%(asctime)s[%(levelname)s] %(message)s', level=INFO,
+ filename=path, filemode='a' if path.exists() else 'w')
+ sys.excepthook = _log_exception
+ enable_logging = True
+
+
+def print_and_log(msg: str):
+ print(msg)
+ if enable_logging:
+ info(msg)
diff --git a/utils/loss.py b/utils/loss.py
index 3f6009f..5705ad5 100644
--- a/utils/loss.py
+++ b/utils/loss.py
@@ -4,7 +4,7 @@ from torch import nn
class CombinedLoss(nn.Module):
- def __init__(self, loss_modules: List[nn.Module], weights: List[float]):
+ def __init__(self, loss_modules: list[nn.Module], weights: list[float]):
super().__init__()
self.loss_modules = nn.ModuleList(loss_modules)
self.weights = weights
diff --git a/loss/__init__.py b/utils/loss/__init__.py
similarity index 53%
rename from loss/__init__.py
rename to utils/loss/__init__.py
index a4eecbc..4424a74 100644
--- a/loss/__init__.py
+++ b/utils/loss/__init__.py
@@ -1,5 +1,6 @@
from torch.nn import L1Loss, MSELoss
from torch.nn.functional import l1_loss, mse_loss
-from .ssim import SSIM
+from .ssim import ssim, SSIM
from .perc_loss import VGGPerceptualLoss
-from .cauchy import cauchy_loss, CauchyLoss
\ No newline at end of file
+from .cauchy import cauchy_loss, CauchyLoss
+from .lpips import lpips_loss, LpipsLoss
\ No newline at end of file
diff --git a/loss/cauchy.py b/utils/loss/cauchy.py
similarity index 55%
rename from loss/cauchy.py
rename to utils/loss/cauchy.py
index 5dd213e..44e5a05 100644
--- a/loss/cauchy.py
+++ b/utils/loss/cauchy.py
@@ -1,16 +1,18 @@
import torch
-def cauchy_loss(input: torch.Tensor, target: torch.Tensor = None, *, s = 1.0):
+def cauchy_loss(input: torch.Tensor, target: torch.Tensor = None, *, s=1.0, sum=False):
x = input - target if target is not None else input
- return (s * x * x * 0.5 + 1).log().mean()
+ y = (s * x * x * 0.5 + 1).log()
+ return y.sum() if sum else y.mean()
class CauchyLoss(torch.nn.Module):
- def __init__(self, s = 1.0):
+ def __init__(self, s=1.0, sum=False):
super().__init__()
self.s = s
+ self.sum = sum
def forward(self, input: torch.Tensor, target: torch.Tensor = None):
- return cauchy_loss(input, target, s=self.s)
+ return cauchy_loss(input, target, s=self.s, sum=self.sum)
diff --git a/utils/loss/lpips.py b/utils/loss/lpips.py
new file mode 100644
index 0000000..4f0f242
--- /dev/null
+++ b/utils/loss/lpips.py
@@ -0,0 +1,22 @@
+import torch
+from lpips import LPIPS
+
+
+class LpipsLoss(torch.nn.Module):
+
+ def __init__(self, net: str = "alex") -> None:
+ super().__init__()
+ self.fn = LPIPS(net)
+
+ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+ return self.fn(input * 2. - 1., target * 2. - 1.)
+
+
+default_loss_fn = None
+
+
+def lpips_loss(input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+ global default_loss_fn
+ if default_loss_fn is None:
+ default_loss_fn = LpipsLoss().to(input.device)
+ return default_loss_fn(input, target)
diff --git a/loss/perc_loss.py b/utils/loss/perc_loss.py
similarity index 100%
rename from loss/perc_loss.py
rename to utils/loss/perc_loss.py
diff --git a/loss/ssim.py b/utils/loss/ssim.py
similarity index 100%
rename from loss/ssim.py
rename to utils/loss/ssim.py
diff --git a/utils/math.py b/utils/math.py
index 9f69a61..d7f0452 100644
--- a/utils/math.py
+++ b/utils/math.py
@@ -1,8 +1,12 @@
+import numpy as np
import torch
+from typing import TypeVar
from math import *
+TensorType = TypeVar('TensorType', torch.Tensor, np.ndarray)
+
huge = 1e10
-tiny = 1e-6
+tiny = 1e-5
def expected_sin(x: torch.Tensor, x_var: torch.Tensor):
@@ -93,3 +97,12 @@ def cylinder_to_gaussian(d: torch.Tensor, t0: float, t1: float, radius: float, d
r_var = radius**2 / 4
t_var = (t1 - t0)**2 / 12
return lift_gaussian(d, t_mean, t_var, r_var, diag)
+
+def lerp(t, range):
+ return t * (range[1] - range[0]) + range[0]
+
+def normalize(value: TensorType) -> TensorType:
+ if isinstance(value, torch.Tensor):
+ return value / torch.norm(value, dim=-1, keepdim=True)
+ else:
+ return value / np.linalg.norm(value, axis=-1, keepdims=True)
\ No newline at end of file
diff --git a/utils/mem_profiler.py b/utils/mem_profiler.py
index 848d4e7..8942f48 100644
--- a/utils/mem_profiler.py
+++ b/utils/mem_profiler.py
@@ -1,8 +1,13 @@
-from cgitb import enable
import torch
from .device import *
+def simple_memory_state(device: torch.device = None) -> str:
+ return f"PyTorch allocates {torch.cuda.memory_allocated(device)/1024/1024:.2f}MB "\
+ f"(peak is {torch.cuda.max_memory_allocated(device)/1024/1024:.2f}MB) and "\
+ f"reserves {torch.cuda.memory_reserved(device)/1024/1024:.2f}MB memory"
+
+
class MemProfiler:
enable = False
@@ -20,8 +25,7 @@ class MemProfiler:
else:
delta_str = ''
print(f'{prefix}: {delta_str}currently PyTorch allocates {torch.cuda.memory_allocated(device)/1024/1024:.2f}MB and '
- f'reserves {torch.cuda.memory_reserved(device)/1024/1024:.2f}MB memory')
-
+ f'reserves {torch.cuda.memory_reserved(device)/1024/1024:.2f}MB memory')
def __init__(self, name, device=None) -> None:
self.name = name
@@ -33,4 +37,4 @@ class MemProfiler:
return self
def __exit__(self, exc_type, exc_val, exc_traceback):
- MemProfiler.print_memory_stats(self.name, self.alloc0, self.device)
\ No newline at end of file
+ MemProfiler.print_memory_stats(self.name, self.alloc0, self.device)
diff --git a/utils/misc.py b/utils/misc.py
index b520497..ffbe7de 100644
--- a/utils/misc.py
+++ b/utils/misc.py
@@ -1,15 +1,12 @@
-from itertools import repeat
-import logging
-from pathlib import Path
import re
-import shutil
-import torch
import glm
import csv
import numpy as np
-from typing import List, Tuple, Union
-from torch.types import Number
+from typing import SupportsFloat
+from itertools import repeat
+
from . import math
+from .types import *
from .device import *
@@ -46,23 +43,26 @@ def glm2torch(val) -> torch.Tensor:
return torch.from_numpy(np.array(val))
-def meshgrid(*size: int, normalize: bool = False, swap_dim: bool = False, device: torch.device = None) -> torch.Tensor:
+def grid2d(rows: int, cols: int = None, normalize: bool = False, indexing: str = "xy",
+ device: torch.device = None) -> torch.Tensor:
"""
- Generate a mesh grid
-
- :param *size: grid size (rows, columns)
- :param normalize: return coords in normalized space? defaults to False
- :param swap_dim: if True, return coords in (y, x) order, defaults to False
- :return: rows x columns x 2 tensor
+ Generate a 2D grid
+
+ :param rows `int`: number of rows
+ :param cols `int`: number of columns
+ :param normalize `bool`: whether return coords in normalized space, defaults to False
+ :param indexing `str`: specify the order of returned coordinates. Optional values are "xy" and "ij",
+ defaults to "xy"
+ :return `Tensor(R, C, 2)`: the coordinates of the grid
"""
- if len(size) == 1:
- size = (size[0], size[0])
- y, x = torch.meshgrid(torch.arange(size[0], device=device),
- torch.arange(size[1], device=device))
+ if cols is None:
+ cols = rows
+ i, j = torch.meshgrid(torch.arange(rows, device=device),
+ torch.arange(cols, device=device), indexing="ij") # (R, C)
if normalize:
- x.div_(size[1] - 1.)
- y.div_(size[0] - 1.)
- return torch.stack([y, x], 2) if swap_dim else torch.stack([x, y], 2)
+ i.div_(rows - 1)
+ j.div_(cols - 1)
+ return torch.stack([j, i] if indexing == "xy" else [i, j], 2) # (R, C, 2)
def get_angle(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
@@ -70,52 +70,6 @@ def get_angle(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
return angle
-def broadcast_cat(input: torch.Tensor,
- s: Union[Number, List[Number], torch.Tensor],
- dim=-1,
- append: bool = True) -> torch.Tensor:
- """
- Concatenate a tensor with a scalar along last dimension
-
- :param input `Tensor(..., N)`: input tensor
- :param s: scalar
- :param append: append or prepend the scalar to input tensor
- :return: `Tensor(..., N+1)`
- """
- if dim != -1:
- raise NotImplementedError('currently only support the last dimension')
- if isinstance(s, torch.Tensor):
- x = s
- elif isinstance(s, list):
- x = torch.tensor(s, dtype=input.dtype, device=input.device)
- else:
- x = torch.tensor([s], dtype=input.dtype, device=input.device)
- expand_shape = list(input.size())
- expand_shape[dim] = -1
- x = x.expand(expand_shape)
- return torch.cat([input, x] if append else [x, input], dim)
-
-
-def save_2d_tensor(path, x):
- with open(path, 'w', encoding='utf-8', newline='') as f:
- csv_writer = csv.writer(f)
- for i in range(x.shape[0]):
- csv_writer.writerow(x[i])
-
-
-def view_like(input: torch.Tensor, ref: torch.Tensor) -> torch.Tensor:
- """
- Reshape input to be the same size as ref except the last dimension
-
- :param input `Tensor(..., C)`: input tensor
- :param ref `Tensor(B.., *): reference tensor
- :return `Tensor(B.., C)`: reshaped tensor
- """
- out_shape = list(ref.size())
- out_shape[-1] = -1
- return input.view(out_shape)
-
-
def format_time(seconds):
days = int(seconds / 3600 / 24)
seconds = seconds - days * 3600 * 24
@@ -142,24 +96,17 @@ def format_time(seconds):
return output
-def print_and_log(s):
- print(s)
- logging.info(s)
-
-
-def masked_scatter(mask: torch.Tensor, value: torch.Tensor, initial: Union[torch.Tensor, Number] = 0):
+def masked_scatter(mask: torch.Tensor, value: torch.Tensor, initial: torch.Tensor | SupportsFloat = 0):
"""
Extend PyTorch's built-in `masked_scatter` function
:param mask `Tensor(M...)`: the boolean mask
:param value `Tensor(N, D...)`: the value to fill in with, should have at least as many elements
- as the number of ones in `mask`
- :param destination `Tensor(M..., D...)`: (optional) the destination tensor to fill,
- if not specified, a new tensor filled with
- `empty_value` will be created and used as destination
- :param empty_value `Number`: the initial elements in the newly created destination tensor,
- defaults to 0
- :return `Tensor(M..., D...)`: the destination tensor after filled
+ as the number of ones in `mask`
+ :param initial `Tensor(M..., D...)|Number`: the initial values. Could be a tensor or a number.
+ If specified by a number, a new tensor filled with the number will be created as the initial values.
+ Defaults to 0
+ :return `Tensor(M..., D...)`: the result tensor
"""
M_ = mask.size()
D_ = value.size()[1:]
@@ -181,28 +128,40 @@ def rename_seqs_with_offset(dir: Path, file_pattern: str, offset: int):
(dir / (file_pattern % i)).rename(dir / (file_pattern % (i + offset)))
-def merge(*args: dict, **kwargs) -> dict:
- ret_args = {}
- for arg in args:
- ret_args.update(arg)
- ret_args.update(kwargs)
- return ret_args
-
-def union(*tensors: torch.Tensor) -> torch.Tensor:
- return torch.cat(tensors, dim=-1)
-
-def split(tensor: torch.Tensor, *sizes: int) -> Tuple[torch.Tensor, ...]:
+def calculate_autosize(max_size: int, *sizes: int) -> tuple[list[int], int]:
sizes = list(sizes)
- tot_size = sum(sizes)
+ sum_size = sum(sizes)
for i in range(len(sizes)):
if sizes[i] == -1:
- sizes[i] = tensor.shape[-1] - tot_size - 1
- tot_size = tensor.shape[-1]
+ sizes[i] = max_size - sum_size - 1
+ sum_size = max_size
break
- if tot_size > tensor.shape[-1]:
- raise ValueError("The total number of sizes is larger than the last dim of input tensor")
+ if sum_size > max_size:
+ raise ValueError("The sum of 'sizes' exceeds 'max_size'")
if any([size < 0 for size in sizes]):
- raise ValueError("Only one element in sizes could be -1")
+ raise ValueError(
+ "Only one of the 'sizes' could be -1 and all others must be positive or zero")
+ return sizes, sum_size
+
+
+def union(*tensors: torch.Tensor | SupportsFloat) -> torch.Tensor:
+ try:
+ first_tensor = next((item for item in tensors if isinstance(item, torch.Tensor)))
+ except StopIteration:
+ raise ValueError("Arguments should contain at least one tensor")
+ tensors = [
+ item if isinstance(item, torch.Tensor) else first_tensor.new_tensor([item])
+ for item in tensors
+ ]
+ if any(item.device != first_tensor.device or item.dtype != first_tensor.dtype
+ for item in tensors):
+ raise ValueError("All tensors should have same dtype and locate on same device")
+ shape = torch.broadcast_shapes(*(item.shape[:-1] for item in tensors))
+ return torch.cat([item.expand(*shape, -1) for item in tensors], dim=-1)
+
+
+def split(tensor: torch.Tensor, *sizes: int) -> tuple[torch.Tensor, ...]:
+ sizes, tot_size = calculate_autosize(tensor.shape[-1], *sizes)
if tot_size < tensor.shape[-1]:
sizes = [*sizes, tensor.shape[-1] - tot_size]
return torch.split(tensor, sizes, -1)[:-1]
@@ -210,12 +169,28 @@ def split(tensor: torch.Tensor, *sizes: int) -> Tuple[torch.Tensor, ...]:
return torch.split(tensor, sizes, -1)
-def dump_tensors_to_csv(path, *tensors: torch.Tensor, open_mode = "w"):
- for i in range(len(tensors)):
- if len(tensors[i].shape) == 1:
- tensors[i] = tensors[i][:, None]
- elif len(tensors[i].shape) > 2:
- tensors[i] = tensors[i].flatten(1, -1)
+def dump_tensors_to_csv(path, *tensors: torch.Tensor, open_mode="w"):
+ data = []
+ for tensor in tensors:
+ if len(tensor.shape) == 1:
+ tensor = tensor[:, None]
+ elif len(tensor.shape) > 2:
+ tensor = tensor.flatten(1, -1)
+ tensor_data = tensor.tolist()
+ if not data:
+ data = [
+ [
+ f"{value:.6e}" if isinstance(value, float) else f"{value}"
+ for value in tensor_data[i]
+ ] for i in range(len(tensor_data))
+ ]
+ else:
+ for i, row in enumerate(data):
+ row += [
+ f"{value:.6e}" if isinstance(value, float) else f"{value}"
+ for value in tensor_data[i]
+ ]
+
with open(path, open_mode) as fp:
csv_writer = csv.writer(fp)
- csv_writer.writerows(torch.cat(tensors, -1).tolist())
\ No newline at end of file
+ csv_writer.writerows(data)
diff --git a/utils/netio.py b/utils/netio.py
index c6b830b..3ddbcf7 100644
--- a/utils/netio.py
+++ b/utils/netio.py
@@ -1,7 +1,6 @@
-from typing import List, Tuple, Union
-import torch
import numpy as np
-from pathlib import Path
+
+from utils.types import *
checkpoint_file_prefix = "checkpoint_"
@@ -12,8 +11,7 @@ def get_checkpoint_filename(epoch):
return f"{checkpoint_file_prefix}{epoch}{checkpoint_file_suffix}"
-def list_epochs(directory: Union[str, Path]) -> List[int]:
- directory = Path(directory)
+def list_epochs(directory: Path) -> list[int]:
epoch_list = [
int(file_path.stem[len(checkpoint_file_prefix):])
for file_path in directory.glob(get_checkpoint_filename("*"))
@@ -22,20 +20,32 @@ def list_epochs(directory: Union[str, Path]) -> List[int]:
return epoch_list
-def load_checkpoint(path: Union[str, Path]) -> Tuple[dict, Path]:
- path = Path(path)
+def find_checkpoint(path: Path) -> Path | None:
if path.suffix != checkpoint_file_suffix:
existed_epochs = list_epochs(path)
- if len(existed_epochs) == 0:
- raise FileNotFoundError(f"{path} does not contain checkpoint files")
- path = path / get_checkpoint_filename(existed_epochs[-1])
+ return path / get_checkpoint_filename(existed_epochs[-1]) if existed_epochs else None
+ return path if path.exists() else None
+
+
+def load_checkpoint(path: Path) -> tuple[dict, Path]:
+ path = find_checkpoint(path)
+ if path is None:
+ raise FileNotFoundError(f"{path} does not contain checkpoint files")
return torch.load(path), path
-def save_checkpoint(states_dict: dict, directory: Union[str, Path], epoch: int):
+def save_checkpoint(states_dict: dict, directory: Path, epoch: int):
torch.save(states_dict, Path(directory) / get_checkpoint_filename(epoch))
+def clean_checkpoint(directory: Path, keep_interval: int):
+ (directory / '_misc').mkdir(exist_ok=True)
+ for file in directory.glob(f"{checkpoint_file_prefix}*{checkpoint_file_suffix}"):
+ i = int(file.name[len(checkpoint_file_prefix):-len(checkpoint_file_suffix)])
+ if i % keep_interval != 0:
+ file.rename(directory / "_misc" / file.name)
+
+
def log(model):
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
diff --git a/__init__.py b/utils/nn.py
similarity index 100%
rename from __init__.py
rename to utils/nn.py
diff --git a/utils/nn/__init__.py b/utils/nn/__init__.py
new file mode 100644
index 0000000..c749317
--- /dev/null
+++ b/utils/nn/__init__.py
@@ -0,0 +1,3 @@
+from .fn import *
+from .linear import *
+from .module import *
\ No newline at end of file
diff --git a/modules/generic/fn.py b/utils/nn/fn.py
similarity index 92%
rename from modules/generic/fn.py
rename to utils/nn/fn.py
index 2b5be4b..d2cfbc8 100644
--- a/modules/generic/fn.py
+++ b/utils/nn/fn.py
@@ -1,6 +1,8 @@
import torch
import torch.nn.functional as F
+__all__ = ["Sine", "Mise"]
+
class Sine(torch.nn.Module):
def __init__(self):
diff --git a/utils/nn/linear.py b/utils/nn/linear.py
new file mode 100644
index 0000000..021257b
--- /dev/null
+++ b/utils/nn/linear.py
@@ -0,0 +1,118 @@
+from .weight_init import *
+from .fn import *
+from .module import Module
+
+__all__ = ["BatchLinear", "FcLayer", "FcBlock"]
+
+
+class BatchLinear(nn.Linear):
+ '''
+ A linear meta-layer that can deal with batched weight matrices and biases,
+ as for instance output by a hypernetwork.
+ '''
+ __doc__ = nn.Linear.__doc__
+
+ def forward(self, input, params=None):
+ bias = params.get('bias', None)
+ weight = params['weight']
+
+ output = input.matmul(weight.permute(*[i for i in range(len(weight.shape) - 2)], -1, -2))
+ output += bias.unsqueeze(-2)
+ return output
+
+
+class FcLayer(Module):
+
+ def __init__(self, in_chns: int, out_chns: int, act: str = 'linear', with_ln: bool = False):
+ """
+ Initialize a full-connection layer module. This module is a wrap of torch's Linear module,
+ adding support for activation function and layer normalization
+
+ :param in_chns `int`: channels of input
+ :param out_chns `int`: channels of output
+ :param act `str`: the activation function, defaults to `"linear"`
+ :param with_ln `bool`: whether to apply layer normalization to the output, defaults to `False`
+ """
+ super().__init__()
+ nls_and_inits = {
+ 'sine': (Sine, init_weights_sine),
+ 'relu': (nn.ReLU, init_weights_relu),
+ 'leakyrelu': (nn.LeakyReLU, init_weights_leakyrelu),
+ 'sigmoid': (nn.Sigmoid, init_weights_xavier),
+ 'tanh': (nn.Tanh, init_weights_xavier),
+ 'selu': (nn.SELU, init_weights_selu),
+ 'softplus': (nn.Softplus, init_weights_trunc_normal),
+ 'elu': (nn.ELU, init_weights_elu),
+ 'softmax': (nn.Softmax, init_weights_softmax),
+ 'logsoftmax': (nn.LogSoftmax, init_weights_softmax),
+ 'mise': (Mise, init_weights_xavier),
+ 'linear': (nn.Identity, init_weights_xavier)
+ }
+ nl_cls, weight_init_fn = nls_and_inits[act]
+
+ self.net = [nn.Linear(in_chns, out_chns)]
+ if with_ln:
+ self.net.append(nn.LayerNorm([out_chns]))
+ self.net.append(nl_cls())
+ self.net = nn.Sequential(*self.net)
+ self.net.apply(weight_init_fn)
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ return self.net(x)
+
+ def __repr__(self) -> str:
+ s = f"{self.net[0].in_features} -> {self.net[0].out_features}, "\
+ + ", ".join(module.__class__.__name__ for module in self.net[1:])
+ return f"{self._get_name()}({s})"
+
+
+class FcBlock(Module):
+
+ def __init__(self, in_chns: int, out_chns: int, nl: int, nf: int, skips: list[int] = [],
+ act: str = 'relu', out_act: str = 'linear', with_ln: bool = False):
+ """
+ Initialize a full-connection block module
+
+ :param in_chns `int`: channels of input
+ :param out_chns `int`: channels of output, if non-zero, an output layer (`nf` -> `out_chns`)
+ will be appended, so the block will have `n_layers + 1` layers
+ :param nl `int`: number of hidden layers
+ :param nf `int`: number of features in each hidden layer
+ :param skips `[int]`: create skip connections from input to hidden layers in this list
+ :param act `str`: the activation function for hidden layers, defaults to `"relu"`
+ :param out_act `str`: the activation function for the output layer defaults to `"linear"`
+ :param with_ln `bool`: whether to apply the layer normalization to each hidden layer's output,
+ defaults to `False`
+ """
+ super().__init__()
+ self.skips = skips
+ self.layers = nn.ModuleList([
+ FcLayer(in_chns, nf, act, with_ln=with_ln)] + [
+ FcLayer(nf + (i in skips) * in_chns, nf, act, with_ln=with_ln)
+ for i in range(nl - 1)
+ ])
+ if out_chns:
+ self.layers.append(FcLayer(nf, out_chns, out_act, with_ln=False))
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ h = x
+ for i, layer in enumerate(self.layers):
+ h = layer(h)
+ if i in self.skips:
+ h = torch.cat([x, h], -1)
+ return h
+
+ def __repr__(self):
+ lines = []
+ for i, layer in enumerate(self.layers):
+ mod_str = repr(layer)
+ mod_str = nn.modules.module._addindent(mod_str, 2)
+ if i - 1 in self.skips:
+ mod_str += " <-"
+ lines.append(f"({i}): {mod_str}")
+
+ main_str = self._get_name() + '('
+ if lines:
+ main_str += '\n ' + '\n '.join(lines) + '\n'
+ main_str += ')'
+ return main_str
diff --git a/utils/module.py b/utils/nn/module.py
similarity index 66%
rename from utils/module.py
rename to utils/nn/module.py
index 0065081..92262a8 100644
--- a/utils/module.py
+++ b/utils/nn/module.py
@@ -1,25 +1,61 @@
import torch
from collections import OrderedDict
-from typing import Any, Optional, Union
+from typing import Any
+__all__ = ["Parameter", "Module"]
-class Module(torch.nn.Module):
+
+Parameter = torch.nn.parameter.Parameter
+
+
+class ModuleMeta(type):
+
+ def __new__(cls, name, bases, attrs):
+ if "__call__" in attrs:
+ del attrs["__call__"] # remove __call__ stub
+ return type.__new__(cls, name, bases, attrs)
+
+
+class Module(torch.nn.Module, metaclass=ModuleMeta):
@property
def cls(self) -> str:
return self._get_name()
- def __init__(self):
+ @property
+ def in_chns(self) -> int | dict[str, int]:
+ match(len(self._in_chns)):
+ case 0:
+ return 0
+ case 1:
+ return next(self._in_chns.values().__iter__())
+ return self._in_chns
+
+ @property
+ def out_chns(self) -> int | dict[str, int]:
+ match(len(self._out_chns)):
+ case 0:
+ return 0
+ case 1:
+ return next(self._out_chns.values().__iter__())
+ return self._out_chns
+
+ def __init__(self, in_chns: dict[str, int] = None, out_chns: dict[str, int] = None):
super().__init__()
self.device = torch.device("cpu")
+ self._in_chns = in_chns or {}
+ self._out_chns = out_chns or {}
self._temp = OrderedDict()
self._register_load_state_dict_pre_hook(self._before_load_state_dict)
- @staticmethod
- def create_multiple(fn, n: int) -> torch.nn.ModuleList:
- return torch.nn.ModuleList([fn() for _ in range(n)])
-
- def register_temp(self, name: str, value: Union[torch.Tensor, torch.nn.Module]):
+ def chns(self, name: str) -> int:
+ if name.startswith("in"):
+ return self._in_chns.get(name[2:], 0)
+ if name.startswith("out"):
+ return self._out_chns.get(name[3:], 0)
+ return self._in_chns.get(name, self._out_chns.get(name, 0))
+
+ def register_temp(self, name: str, value: torch.Tensor | torch.nn.Module):
temp = self.__dict__.get('_temp')
if temp is None:
raise AttributeError("cannot assign temp before Module.__init__() call")
@@ -41,17 +77,17 @@ class Module(torch.nn.Module):
parameter.requires_grad = False
return self
- def add_module(self, name: str, module: Optional[torch.nn.Module]) -> None:
+ def add_module(self, name: str, module: torch.nn.Module | None) -> None:
if isinstance(module, torch.nn.Module):
module = module.to(self.device)
return super().add_module(name, module)
- def register_parameter(self, name: str, param: Optional[torch.nn.Parameter]) -> None:
+ def register_parameter(self, name: str, param: torch.nn.Parameter | None) -> None:
if isinstance(param, torch.nn.Parameter):
param = param.to(self.device)
return super().register_parameter(name, param)
- def register_buffer(self, name: str, tensor: Optional[torch.Tensor], persistent: bool = True) -> None:
+ def register_buffer(self, name: str, tensor: torch.Tensor | None, persistent: bool = True) -> None:
if isinstance(tensor, torch.Tensor):
tensor = tensor.to(self.device)
return super().register_buffer(name, tensor, persistent=persistent)
@@ -60,7 +96,7 @@ class Module(torch.nn.Module):
super().to(*args, **kwargs)
target_device = None
try:
- target_device = torch.device(kwargs['device'] if 'device' in kwargs else args[0])
+ target_device = torch.device(kwargs['device'] if 'd evice' in kwargs else args[0])
except Exception:
pass
if target_device is not None:
@@ -79,13 +115,13 @@ class Module(torch.nn.Module):
self.apply(fn)
return ret
- def __getattr__(self, name: str) -> Union[torch.Tensor, torch.nn.Module, Any]:
+ def __getattr__(self, name: str) -> torch.Tensor | torch.nn.Module | Any:
temp = self.__dict__.get('_temp')
if temp is not None and name in temp:
return temp[name]
return super().__getattr__(name)
- def __setattr__(self, name: str, value: Union[torch.Tensor, torch.nn.Module, Any]) -> None:
+ def __setattr__(self, name: str, value: torch.Tensor | torch.nn.Module | Any) -> None:
if isinstance(value, (torch.Tensor, torch.nn.Module)):
value = value.to(self.device)
temp = self.__dict__.get('_temp')
diff --git a/modules/generic/weight_init.py b/utils/nn/weight_init.py
similarity index 99%
rename from modules/generic/weight_init.py
rename to utils/nn/weight_init.py
index 919c423..95f21c1 100644
--- a/modules/generic/weight_init.py
+++ b/utils/nn/weight_init.py
@@ -1,7 +1,6 @@
import torch
import torch.nn as nn
-
-from utils import math
+import math
def init_weights_trunc_normal(m):
diff --git a/utils/perf.py b/utils/perf.py
deleted file mode 100644
index 1525e36..0000000
--- a/utils/perf.py
+++ /dev/null
@@ -1,173 +0,0 @@
-import torch
-import torch.cuda
-from numpy import average
-from typing import Any, Callable, Dict, List, OrderedDict, Union
-
-
-class Perf(object):
- frames: List[Dict[str, float]]
-
- class Node:
- def __init__(self, name, parent=None) -> None:
- self.name = name
- self.parent = parent
- self.events = []
- self.event_names = []
- self.child_nodes = []
- self.child_nodes_event_idx = []
- self.add_checkpoint("Start")
-
- def add_checkpoint(self, name):
- event = torch.cuda.Event(enable_timing=True)
- event.record()
- self.events.append(event)
- self.event_names.append(name)
-
- def add_child(self, name):
- child = Perf.Node(name, self)
- self.child_nodes.append(child)
- self.child_nodes_event_idx.append(len(self.events))
- return child
-
- def close(self):
- self.add_checkpoint("End")
- return self.parent
-
- def duration(self, i0=0, i1=-1) -> float:
- return self.events[i0].elapsed_time(self.events[i1])
-
- def result(self, prefix: str = '') -> OrderedDict[str, float]:
- path = f"{prefix}{self.name}"
- res = {path: self.duration()}
- j = 0
- for i in range(1, len(self.events) - 1):
- event_path = f"{path}/{self.event_names[i]}"
- res[event_path] = self.duration(i - 1, i)
- while j < len(self.child_nodes):
- if self.child_nodes_event_idx[j] > i:
- break
- res.update(self.child_nodes[j].result(f"{event_path}/"))
- j += 1
- while j < len(self.child_nodes):
- res.update(self.child_nodes[j].result(f"{path}/"))
- j += 1
- return res
-
- def __init__(self) -> None:
- super().__init__()
- self.root_node = None
- self.current_node = None
- self.frames = []
-
- def start_node(self, name):
- if self.current_node is None:
- self.root_node = self.current_node = Perf.Node(name)
- else:
- self.current_node = self.current_node.add_child(name)
-
- def checkpoint(self, name):
- self.current_node.add_checkpoint(name)
-
- def end_node(self):
- self.current_node = self.current_node.close()
- if self.current_node is None:
- torch.cuda.synchronize()
- self.frames.append(self.root_node.result())
-
- def get_result(self, i=None):
- if i is not None:
- return self.frames[i]
- if len(self.frames) == 0:
- return {}
- res = {key: [val] for key, val in self.frames[0].items()}
- for i in range(1, len(self.frames)):
- for key, val in self.frames[i].items():
- res[key].append(val)
- return {key: average(val) for key, val in res.items()}
-
-
-default_perf_object = None
-
-
-def enable_perf():
- global default_perf_object
- default_perf_object = Perf()
-
-
-class _PerfWrap(object):
- def __init__(self, fn: Callable = None, name: str = None) -> None:
- super().__init__()
- self.fn = fn
- self.name = name
-
- def __call__(self, *args: Any, **kwargs: Any) -> Any:
- if self.fn == None and len(args) == 1 and isinstance(args[0], Callable):
- self.fn = args[0]
- return lambda *args, **kwargs: self(*args, **kwargs)
- self.__enter__()
- ret = self.fn(*args, **kwargs)
- self.__exit__()
- return ret
-
- def __enter__(self):
- #print(f"Start node \"{self.name or self.fn.__qualname__}\"")
- start_node(self.name or self.fn.__qualname__)
- return self
-
- def __exit__(self, *args: Any, **kwargs: Any):
- #print(f"End node \"{self.name or self.fn.__qualname__}\"")
- end_node()
-
-
-def perf(arg: Union[str, Callable]):
- if isinstance(arg, str):
- return _PerfWrap(name=arg)
- else:
- return lambda *args, **kwargs: _PerfWrap(fn=arg)(*args, **kwargs)
-
-
-def debug_perf(fn_or_name):
- if isinstance(fn_or_name, str):
- name = fn_or_name
-
- def perf_with_name(fn):
- def wrap_perf(*args, **kwargs):
- node = Perf.Node(name)
- ret = fn(*args, **kwargs)
- node.close()
- torch.cuda.synchronize()
- print(f"Debug Node {name}: {node.duration():.1f}ms")
- return ret
- return wrap_perf
- return perf_with_name
- fn = fn_or_name
-
- def wrap_perf(*args, **kwargs):
- node = Perf.Node(fn.__qualname__)
- ret = fn(*args, **kwargs)
- node.close()
- torch.cuda.synchronize()
- print(f"Debug Node {fn.__qualname__}: {node.duration():.1f}ms")
- return ret
- return wrap_perf
-
-
-def start_node(name):
- if default_perf_object is not None:
- default_perf_object.start_node(name)
-
-
-def end_node():
- if default_perf_object is not None:
- default_perf_object.end_node()
-
-
-def checkpoint(name):
- if default_perf_object is not None:
- default_perf_object.checkpoint(name)
-
-
-def get_perf_result(i=None):
- if default_perf_object is not None:
- return default_perf_object.get_result(i)
- return None
diff --git a/utils/profile.py b/utils/profile.py
new file mode 100644
index 0000000..62d24ea
--- /dev/null
+++ b/utils/profile.py
@@ -0,0 +1,325 @@
+import os
+import time
+from itertools import groupby
+from typing import TypeVar
+
+from .types import *
+
+
+class Profiler(object):
+ frames: list["Profiler.ResultNode"]
+
+ class Node:
+ @property
+ def host_duration(self) -> float:
+ if hasattr(self, "_host_duration"):
+ return self._host_duration
+ if not self.closed:
+ raise RuntimeError("Cannot get host duration of an unclosed node")
+ self._host_duration = (self.host_end_time - self.host_start_time) * 1000
+ return self._host_duration
+
+ @property
+ def device_duration(self) -> float:
+ if hasattr(self, "_device_duration"):
+ return self._device_duration
+ if not self.closed:
+ raise RuntimeError("Cannot get device duration of an unclosed node")
+ self._device_duration = self.device_start_event.elapsed_time(
+ self.device_end_event)
+ return self._device_duration
+
+ def __init__(self, name, parent: "Profiler.Node" = None) -> None:
+ self.name = name
+ self.parent = parent
+ self.child_nodes: list[Profiler.Node] = []
+ self.device_start_event = torch.cuda.Event(True)
+ self.device_start_event.record()
+ self.host_start_time = time.perf_counter()
+ self.closed = False
+
+ def add_child(self, name):
+ if self.closed:
+ raise RuntimeError("Cannot add child to a closed node")
+ child = Profiler.Node(name, self)
+ self.child_nodes.append(child)
+ return child
+
+ def close(self):
+ if self.closed:
+ raise RuntimeError("The node has been closed")
+ self.closed = True
+ self.host_end_time = time.perf_counter()
+ self.device_end_event = torch.cuda.Event(True)
+ self.device_end_event.record()
+ return self.parent
+
+ def get_result_node(self, parent_path: str = "") -> "Profiler.ResultNode":
+ if not self.closed:
+ raise RuntimeError("Cannot get result of an unclosed node")
+ path = f"{parent_path}/{self.name}"
+ return Profiler.ResultNode(
+ path, [child.get_result_node(parent_path=path) for child in self.child_nodes],
+ host_duration=self.host_duration, device_duration=self.device_duration
+ )
+
+ def __repr__(self) -> str:
+ ret = f"{self.__class__.__name__} \"{self.name}\" "
+ if self.closed:
+ ret += f"[host spent {self.host_duration:2f}ms, device spent {self.device_duration:2f}ms]"
+ else:
+ ret += "[Not closed]"
+
+ class ResultNode(object):
+ path: str
+ data: dict[str, float]
+
+ @property
+ def name(self) -> str:
+ return os.path.split(self.path)[1]
+
+ def __init__(self, path: str, child_nodes: list["Profiler.ResultNode"],
+ **data: float) -> None:
+ self.path = path
+ self.child_nodes = child_nodes
+ self.data = data
+
+ def value(self, key: str) -> float:
+ if key.startswith("self_"):
+ key = key[5:]
+ return self.data[key] - sum([child.value(key) for child in self.child_nodes])
+ return self.data[key]
+
+ def flatten(self) -> list["Profiler.ResultNode"]:
+ result_list = [self]
+ for child in self.child_nodes:
+ result_list += child.flatten()
+ return result_list
+
+ class ResultNodeGroup(object):
+
+ @property
+ def path(self) -> str:
+ return self.nodes[0].path
+
+ @property
+ def name(self) -> str:
+ return self.nodes[0].name
+
+ def __init__(self, nodes: Iterable["Profiler.ResultNode"]) -> None:
+ self.nodes = list(nodes)
+ self.count = len(self.nodes)
+
+ def total(self, key: str):
+ return sum([node.value(key) for node in self.nodes])
+
+ def average(self, key: str):
+ return self.total(key) / self.count
+
+ class ResultNodeGroupList(list["Profiler.ResultNodeGroup"]):
+ def __init__(self, path: str, __iterable: Iterable["Profiler.ResultNodeGroup"]) -> None:
+ super().__init__(__iterable)
+ self.path = path
+
+ def total_in_frame(self, key: str) -> list[float]:
+ return [node_group and node_group.total(key) for node_group in self]
+
+ def average_in_frame(self, key: str) -> list[float]:
+ return [node_group and node_group.average(key) for node_group in self]
+
+ def count_in_frame(self) -> list[int]:
+ return [node_group and node_group.count for node_group in self]
+
+ def total(self, key: str) -> float:
+ return sum(filter(None, self.total_in_frame(key)))
+
+ def count(self) -> int:
+ return sum(filter(None, self.count_in_frame()))
+
+ def average_by_frame(self, key: str) -> float:
+ n_frames = len(list(filter(None, self)))
+ return self.total(key) / n_frames
+
+ def average_by_node(self, key: str) -> float:
+ return self.total(key) / self.count()
+
+ class ProfileResult(list["ResultNodeGroupList"]):
+ def get_index_by_path(self, path: str, start: int = 0, end: int = None) -> int:
+ if end is None:
+ end = len(self)
+ elif end < 0:
+ end = len(self) + end
+ for i in range(start, end):
+ if self[i].path == path:
+ return i
+ return -1
+
+ def get_report(self):
+ s = "Performance Report:\n"
+ if len(self) == 0:
+ s += "No available data.\n"
+ return s
+ for node_group_list in self:
+ parts = node_group_list.path.split("/")
+ s += f"{' ' * (len(parts) - 2)}{parts[-1]}: "\
+ f"{node_group_list.average_by_frame('device_duration'):.2f}ms\n"
+ return s
+
+ def __init__(self, warmup_frames: int = 0, record_frames: int = 0,
+ then: Callable[["Profiler.ProfileResult"], Any] = None) -> None:
+ super().__init__()
+ self.root_node = None
+ self.current_node = None
+ self.frames = []
+ self.warmup_frames = warmup_frames
+ self.record_frames = record_frames
+ self.frame_counter = 0
+ self.enabled = True
+ self.then_fn = then
+
+ def enter_node(self, name):
+ if not self.enabled:
+ return
+ if self.current_node is None:
+ self.root_node = self.current_node = Profiler.Node(name)
+ else:
+ self.current_node = self.current_node.add_child(name)
+
+ def leave_node(self):
+ if not self.enabled:
+ return
+ self.current_node = self.current_node.close()
+ if self.current_node is None:
+ torch.cuda.synchronize()
+ if self.frame_counter >= self.warmup_frames:
+ self.frames.append(self.root_node.get_result_node())
+ self.frame_counter += 1
+ if self.frame_counter >= self.warmup_frames + self.record_frames:
+ self.enabled = False
+ self.then_fn(self.get_result())
+
+ def get_result(self) -> "Profiler.ProfileResult":
+ if len(self.frames) == 0:
+ return Profiler.ProfileResult()
+
+ flat_frames = [frame.flatten() for frame in self.frames]
+ grouped_frames = [
+ [
+ Profiler.ResultNodeGroup(node_iter)
+ for _, node_iter in groupby(frame, lambda item: item.path)
+ ] for frame in flat_frames
+ ]
+ profile_result = Profiler.ProfileResult(
+ Profiler.ResultNodeGroupList(node_group.path, [node_group])
+ for node_group in grouped_frames[0]
+ )
+ for i, frame in enumerate(grouped_frames):
+ if i == 0:
+ continue
+ target_head = 0
+ for node_group in frame:
+ matched_index = profile_result.get_index_by_path(node_group.path, target_head)
+ if matched_index == -1:
+ profile_result.insert(target_head,
+ Profiler.ResultNodeGroupList(node_group.path,
+ [None] * i + [node_group]))
+ target_head += 1
+ else:
+ for j in range(target_head, matched_index):
+ profile_result[j].append(None)
+ profile_result[matched_index].append(node_group)
+ target_head = matched_index + 1
+ return profile_result
+
+
+default_profiler = None
+
+
+def enable_profile(warmup_frames: int = 0, record_frames: int = 0,
+ then: Callable[["Profiler.ProfileResult"], Any] = None):
+ global default_profiler
+ default_profiler = Profiler(warmup_frames, record_frames, then)
+
+
+class _ProfileWrap(object):
+ def __init__(self, fn: Callable = None, name: str = None) -> None:
+ super().__init__()
+ self.fn = fn
+ self.name = name
+
+ def __call__(self, *args: Any, **kwargs: Any) -> Any:
+ if self.fn == None and len(args) == 1 and isinstance(args[0], Callable):
+ self.fn = args[0]
+ return lambda *args, **kwargs: self(*args, **kwargs)
+ self.__enter__()
+ ret = self.fn(*args, **kwargs)
+ self.__exit__()
+ return ret
+
+ def __enter__(self):
+ #print(f"Start node \"{self.name or self.fn.__qualname__}\"")
+ start_profile_node(self.name or self.fn.__qualname__)
+ return self
+
+ def __exit__(self, *args: Any, **kwargs: Any):
+ #print(f"End node \"{self.name or self.fn.__qualname__}\"")
+ end_profile_node()
+
+
+class _DebugProfileWrap(object):
+ def __init__(self, fn: Callable = None, name: str = None) -> None:
+ super().__init__()
+ self.fn = fn
+ self.name = name
+
+ def __call__(self, *args: Any, **kwargs: Any) -> Any:
+ if self.fn == None and len(args) == 1 and isinstance(args[0], Callable):
+ self.fn = args[0]
+ return lambda *args, **kwargs: self(*args, **kwargs)
+ self.__enter__()
+ ret = self.fn(*args, **kwargs)
+ self.__exit__()
+ return ret
+
+ def __enter__(self):
+ #print(f"Start node \"{self.name or self.fn.__qualname__}\"")
+ self.node = Profiler.Node(self.name)
+ return self
+
+ def __exit__(self, *args: Any, **kwargs: Any):
+ #print(f"End node \"{self.name or self.fn.__qualname__}\"")
+ self.node.close()
+ torch.cuda.synchronize()
+ print(f"Node {self.name}: host duration {self.node.host_duration:.1f}ms, "
+ f"device duration {self.node.device_duration:.1f}ms")
+
+
+FnRet = TypeVar("FnRet")
+def profile(arg: str | Callable[..., FnRet]) -> _ProfileWrap | Callable[..., FnRet]:
+ if isinstance(arg, str):
+ return _ProfileWrap(name=arg)
+ else:
+ return lambda *args, **kwargs: _ProfileWrap(fn=arg)(*args, **kwargs)
+
+
+def debug_profile(arg: str | Callable):
+ if isinstance(arg, str):
+ return _DebugProfileWrap(name=arg)
+ else:
+ return lambda *args, **kwargs: _DebugProfileWrap(fn=arg)(*args, **kwargs)
+
+
+def start_profile_node(name):
+ if default_profiler is not None:
+ default_profiler.enter_node(name)
+
+
+def end_profile_node():
+ if default_profiler is not None:
+ default_profiler.leave_node()
+
+
+def get_profile_result():
+ if default_profiler is not None:
+ return default_profiler.get_result_report()
+ return None
diff --git a/utils/samples.py b/utils/samples.py
deleted file mode 100644
index fb6263f..0000000
--- a/utils/samples.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import torch
-from typing import Optional, Union, Any, List, Tuple
-from . import math
-
-
-class Samples(object):
- indices: torch.Tensor
- """ Tensor(N[, P], 2)` The unique indices of samples, e.g. (i-th ray, j-th sample)"""
-
- pts: torch.Tensor
- """`Tensor(N[, P], 3)`"""
-
- dirs: torch.Tensor
- """`Tensor(N[, P], 3)`"""
-
- depths: torch.Tensor
- """`Tensor(N[, P])`"""
-
- dists: torch.Tensor
- """`Tensor(N[, P])`"""
-
- voxel_indices: torch.Tensor
- """`Tensor(N[, P])`"""
-
- size: List[int]
- """Size of the samples"""
-
- device: torch.device
- """Device where tensors of this object locate"""
-
- def __init__(self, **_data: Union[torch.Tensor, float, int]) -> None:
- super().__init__()
- super().__setattr__("_data", _data)
- super().__setattr__("size", self.pts.size()[:-1])
- super().__setattr__("device", self.pts.device)
-
- def __getitem__(self, index: Union[int, slice, list, tuple, torch.Tensor, None]):
- if isinstance(index, torch.Tensor) and index.dtype == torch.bool:
- index = index.nonzero(as_tuple=True)
- return Samples(**{
- key: value[index] if isinstance(value, torch.Tensor) else value
- for key, value in self._data.items()
- })
-
- def __getattr__(self, __name: str) -> Union[torch.Tensor, Any]:
- try:
- return self._data[__name]
- except KeyError:
- return None
-
- def __setattr__(self, __name: str, __value: Any) -> None:
- self._data[__name] = __value
-
- def reshape(self, *shape: int):
- return Samples(**{
- key: value.reshape(*shape, *value.shape[len(self.size):])
- if isinstance(value, torch.Tensor) else value
- for key, value in self._data.items()
- })
-
- def filter_rays(self) -> Optional[torch.Tensor]:
- if isinstance(self.voxel_indices, torch.Tensor):
- valid_rays_mask = self.voxel_indices.ne(-1).any(dim=-1) # (N)
- rays_filter = valid_rays_mask.nonzero(as_tuple=True)[0] # (N) -> (N')
- super().__setattr__("_data", {
- key: value[rays_filter] if isinstance(value, torch.Tensor) else value
- for key, value in self._data.items()
- })
- super().__setattr__("size", self.pts.size()[:-1])
- return rays_filter
- return None
-
- def interpolate(self, fine_samples, *values: torch.Tensor) -> Union[torch.Tensor, Tuple[torch.Tensor, ...]]:
- P1 = self.size[-1]
- P2 = fine_samples.size[-1]
- K = P2 // P1
- if K > 1:
- # Do interpolation
- t1 = self.t # ([N, ]P1)
- t2 = fine_samples.t # ([N, ]P2)
- lo = torch.arange(P1, device=fine_samples.device).repeat_interleave(K)[:P2]
- up = (lo + 1).clamp(max=P1 - 1)
- t1_lo, t1_up = t1[..., lo], t1[..., up]
- k = ((t2 - t1_lo) / (t1_up - t1_lo + math.tiny))[..., None] # ([N, ]P2, 1)
- values = [
- value[..., lo, :] * (1 - k) + value[..., up, :] * k # ([N, ]P2, X)
- for value in values
- ]
- return values[0] if len(values) == 1 else tuple(values)
diff --git a/utils/seqs.py b/utils/seqs.py
index 21142ee..35c8698 100644
--- a/utils/seqs.py
+++ b/utils/seqs.py
@@ -3,7 +3,7 @@ from . import math
def helix(t_range, loops, n):
n_per_loop = n // loops
- angles = np.linspace(0, 2 * math.math.pi, n_per_loop, endpoint=False)[None, :]. \
+ angles = np.linspace(0, 2 * math.pi, n_per_loop, endpoint=False)[None, :]. \
repeat(loops, axis=0).flatten()
centers = np.empty([n, 3])
centers[:, 0] = 0.5 * t_range[0] * np.cos(angles)
diff --git a/utils/sphere.py b/utils/sphere.py
index bb74e7f..b328c24 100644
--- a/utils/sphere.py
+++ b/utils/sphere.py
@@ -1,17 +1,24 @@
-from typing import Union
-import torch
-from . import math
-from . import misc
+from . import math, misc
+from .types import *
def cartesian2spherical(cart: torch.Tensor, inverse_r: bool = False) -> torch.Tensor:
"""
Convert coordinates from Cartesian to Spherical
- :param cart `Tensor(..., 3)`: coordinates in Cartesian
- :param inverse_r: whether to inverse r
- :return `Tensor(..., 3)`: coordinates in Spherical (r, theta, phi)
+ :param cart `Tensor([N...,] 3)`: coordinates in Cartesian
+ :param inverse_r: whether to convert r to reciprocal form, defaults to `False`
+ :return `Tensor([N...,] 3)`: coordinates in Spherical ([r | 1/r], theta, phi)
"""
+ #rho = torch.sqrt(torch.sum(cart * cart, dim=-1))
+ #theta = -torch.atan(cart[..., 0] / cart[..., 2]) + (cart[..., 2] < 0) * math.pi + 0.5 * math.pi
+ #if inverse_r:
+ # rho = rho.reciprocal()
+ # phi = torch.acos(cart[..., 1] * rho)
+ #else:
+ # phi = torch.acos(cart[..., 1] / rho)
+ #return torch.stack([rho, theta, phi], dim=-1)
+
rho = torch.sqrt(torch.sum(cart * cart, dim=-1))
theta = misc.get_angle(cart[..., 2], cart[..., 0])
if inverse_r:
@@ -26,8 +33,9 @@ def spherical2cartesian(spher: torch.Tensor, inverse_r: bool = False) -> torch.T
"""
Convert coordinates from Spherical to Cartesian
- :param spher: ... x 3, coordinates in Spherical
- :return: ... x 3, coordinates in Cartesian (r, theta, phi)
+ :param spher `Tensor([N...,] 3)`: coordinates in Spherical ([r | 1/r], theta, phi)
+ :param inverse_r `bool`: whether r is in reciprocal form, defaults to `False`
+ :return `Tensor([N...,] 3)`:, coordinates in Cartesian
"""
rho = spher[..., 0]
if inverse_r:
@@ -40,34 +48,32 @@ def spherical2cartesian(spher: torch.Tensor, inverse_r: bool = False) -> torch.T
return torch.stack([x, y, z], dim=-1)
-def ray_sphere_intersect(p: torch.Tensor, v: torch.Tensor, r: torch.Tensor) -> torch.Tensor:
+def ray_sphere_intersect(rays: Rays, r: torch.Tensor) -> torch.Tensor:
"""
Calculate intersections of each rays and each spheres
- :param p `Tensor(B, 3)`: positions of rays
- :param v `Tensor(B, 3)`: directions of rays
- :param r `Tensor(N)`: , radius of spheres
- :return `Tensor(B, N, 3)`: points of intersection
- :return `Tensor(B, N)`: depths of intersection along ray
+ :param rays `Rays(B)`: rays
+ :param r `Tensor(P)`: , radius of spheres
+ :return `Tensor(B, P)`: depths of intersections along rays
"""
# p, v: Expand to (B, 1, 3)
- p = p.unsqueeze(1)
- v = v.unsqueeze(1)
+ p = rays.rays_o.unsqueeze(1)
+ v = rays.rays_d.unsqueeze(1)
# pp, vv, pv: (B, 1)
pp = (p * p).sum(dim=2)
vv = (v * v).sum(dim=2)
pv = (p * v).sum(dim=2)
- depths = (((pv * pv - vv * (pp - r * r)).sqrt() - pv) / vv)
- return p + depths[..., None] * v, depths
+ z = (((pv * pv - vv * (pp - r * r)).sqrt() - pv) / vv) # (B, P)
+ return z
-def get_rot_matrix(theta: Union[float, torch.Tensor], phi: Union[float, torch.Tensor]) -> torch.Tensor:
+def get_rot_matrix(theta: float | torch.Tensor, phi: float | torch.Tensor) -> torch.Tensor:
"""
Get rotation matrix from angles in spherical space
- :param theta `Tensor(..., 1) | float`: rotation angles around y axis
- :param phi `Tensor(..., 1) | float`: rotation angles around x axis
- :return: `Tensor(..., 3, 3)` rotation matrices
+ :param theta `Tensor([N...,] 1) | float`: rotation angles around y axis
+ :param phi `Tensor([N...,] 1) | float`: rotation angles around x axis
+ :return: `Tensor([N...,] 3, 3)` rotation matrices
"""
if not isinstance(theta, torch.Tensor):
theta = torch.tensor([theta])
@@ -75,14 +81,14 @@ def get_rot_matrix(theta: Union[float, torch.Tensor], phi: Union[float, torch.Te
phi = torch.tensor([phi])
spher = torch.cat([torch.ones_like(theta), theta, phi], dim=-1)
print(spher)
- forward = spherical2cartesian(spher) # (..., 3)
+ forward = spherical2cartesian(spher) # ([N...,] 3)
up = torch.tensor([0.0, 1.0, 0.0])
forward, up = torch.broadcast_tensors(forward, up)
print(forward, up)
- right = torch.cross(forward, up, dim=-1) # (..., 3)
- up = torch.cross(right, forward, dim=-1) # (..., 3)
+ right = torch.cross(forward, up, dim=-1) # ([N...,] 3)
+ up = torch.cross(right, forward, dim=-1) # ([N...,] 3)
print(right, up, forward)
- return torch.stack([right, up, forward], dim=-2) # (..., 3, 3)
+ return torch.stack([right, up, forward], dim=-2) # ([N...,] 3, 3)
def calc_local_dir(dirs, spherical_coords, pts):
@@ -101,4 +107,4 @@ def calc_local_dir(dirs, spherical_coords, pts):
local_y = torch.cross(local_x, local_z, -1)
local_rot = torch.stack([local_x, local_y, local_z], dim=-2) # (B, N, 3, 3)
return cartesian2spherical(torch.matmul(dirs[:, None, None, :], local_rot)) \
- .squeeze(-2)[..., 1:3]
\ No newline at end of file
+ .squeeze(-2)[..., 1:3]
diff --git a/utils/type.py b/utils/type.py
deleted file mode 100644
index 0819d78..0000000
--- a/utils/type.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from typing import Any, Dict, Union
-import torch
-
-InputData = Dict[str, Union[torch.Tensor, Any]]
-ReturnData = Dict[str, Union[torch.Tensor, Any]]
-NetOutput = Dict[str, torch.Tensor]
-
-
-class NetInput:
- def __init__(self, x: torch.Tensor = None, d: torch.Tensor = None, f: torch.Tensor = None) -> None:
- self.x = x
- self.d = d
- self.f = f
- if x is not None:
- self.shape = x.shape[:-1]
- elif d is not None:
- self.shape = d.shape[:-1]
- else:
- self.shape = [0]
-
- def __getitem__(self, index: Union[int, slice, list, tuple, torch.Tensor, None]) -> 'NetInput':
- if isinstance(index, torch.Tensor) and index.dtype == torch.bool:
- index = index.nonzero(as_tuple=True)
- return NetInput(
- self.x[index] if self.x is not None else None,
- self.d[index] if self.d is not None else None,
- self.f[index] if self.f is not None else None
- )
diff --git a/utils/types/__common__.py b/utils/types/__common__.py
new file mode 100644
index 0000000..f54bc7e
--- /dev/null
+++ b/utils/types/__common__.py
@@ -0,0 +1,6 @@
+import torch
+from pathlib import Path
+from typing import Any, Type, Callable, NamedTuple, Iterable, Iterator, overload
+
+PathLike = str | Path
+IndexSelector = int | slice | list[int] | torch.Tensor
\ No newline at end of file
diff --git a/utils/types/__init__.py b/utils/types/__init__.py
new file mode 100644
index 0000000..ef87dcb
--- /dev/null
+++ b/utils/types/__init__.py
@@ -0,0 +1,24 @@
+from .__common__ import *
+from .data_pack import *
+from .rays import Rays
+from .samples import Samples
+from .color import Color
+
+
+class ReturnData(DataPack):
+ color: Tensor1D | None
+ depth: Tensor1D | None
+ colors: Tensor2D | None
+ densities: Tensor2D | None
+ alphas: Tensor2D | None
+ weights: Tensor2D | None
+
+
+class Resolution(NamedTuple):
+ h: int
+ w: int
+
+ @staticmethod
+ def from_str(res_str: str) -> "Resolution":
+ arr = res_str.split("x")
+ return Resolution(int(arr[1]), int(arr[0]))
\ No newline at end of file
diff --git a/utils/color.py b/utils/types/color.py
similarity index 66%
rename from utils/color.py
rename to utils/types/color.py
index 76ba1e1..e6823ad 100644
--- a/utils/color.py
+++ b/utils/types/color.py
@@ -1,35 +1,40 @@
-from typing import Union
-import torch
import torchvision.transforms.functional as vtf
+from enum import Enum
+from .__common__ import *
-RGB = 0
-GRAY = 1
-YCbCr = 2
+class Color(Enum):
+ rgb = 0
+ gray = 1
+ ybr = 2
-def to_str(color):
- return "gray" if color == GRAY \
- else ("ybr" if color == YCbCr
- else "rgb")
+ @property
+ def chns(self) -> int:
+ match self:
+ case Color.gray:
+ return 1
+ case _:
+ return 3
+
+ @staticmethod
+ def cvt(input: torch.Tensor, from_color: "Color", to_color: "Color") -> torch.Tensor:
+ """
+ Convert input tensor from `from_color` to `to_color`
-
-def from_str(color_str):
- return GRAY if color_str == 'gray' \
- else (YCbCr if color_str == 'ybr'
- else RGB)
-
-
-def chns(color):
- color = from_str(color) if isinstance(color, str) else color
- return 1 if color == GRAY else 3
+ :param input `Tensor(..., C) | Tensor(..., C, H, W)`: The image to convert
+ :param from_color `Color`: The color of input image
+ :param to_color `Color`: The color of output image
+ :return `Tensor(..., C) | Tensor(..., C, H, W)`: converted image
+ """
+ return cvt_funcs[from_color][to_color](input)
def noop(input: torch.Tensor) -> torch.Tensor:
return input
-def rgb2ycbcr(input: torch.Tensor) -> torch.Tensor:
+def rgb2ybr(input: torch.Tensor) -> torch.Tensor:
"""
Convert input tensor from RGB to YCbCr
@@ -52,7 +57,7 @@ def rgb2ycbcr(input: torch.Tensor) -> torch.Tensor:
return torch.cat([y, cb, cr], dim_c)
-def rgb2ycbcr(input: torch.Tensor) -> torch.Tensor:
+def rgb2ybr(input: torch.Tensor) -> torch.Tensor:
"""
Convert input tensor from RGB to YCbCr
@@ -75,7 +80,7 @@ def rgb2ycbcr(input: torch.Tensor) -> torch.Tensor:
return torch.cat([y, cb, cr], dim_c)
-def ycbcr2rgb(input: torch.Tensor) -> torch.Tensor:
+def ybr2rgb(input: torch.Tensor) -> torch.Tensor:
"""
Convert input tensor from YCbCr to RGB
@@ -116,7 +121,7 @@ def gray2rgb(input: torch.Tensor) -> torch.Tensor:
return input.expand(out_size)
-def gray2ycbcr(input: torch.Tensor) -> torch.Tensor:
+def gray2ybr(input: torch.Tensor) -> torch.Tensor:
"""
Convert input tensor from GRAY to YCbCr
@@ -132,34 +137,20 @@ def gray2ycbcr(input: torch.Tensor) -> torch.Tensor:
return torch.cat([y, cb, cr], dim_c)
-def ycbcr2gray(input: torch.Tensor) -> torch.Tensor:
+def ybr2gray(input: torch.Tensor) -> torch.Tensor:
"""
Convert input tensor from YCbCr to GRAY
:param input `Tensor(..., 3) | Tensor(..., 3, H, W)`:
:return `Tensor(..., 1) | Tensor(..., 1, H, W)`:
"""
- return vtf.rgb_to_grayscale(ycbcr2rgb(input))
+ return vtf.rgb_to_grayscale(ybr2rgb(input))
cvt_funcs = [
- [noop, vtf.rgb_to_grayscale, rgb2ycbcr], # RGB->RGB,GRAY,YCbCr
- [gray2rgb, noop, gray2ycbcr], # GRAY->RGB,GRAY,YCbCr
- [ycbcr2rgb, ycbcr2gray, noop] # YCbCr->RGB,GRAY,YCbCr
+ [noop, vtf.rgb_to_grayscale, rgb2ybr], # RGB->RGB,GRAY,YCbCr
+ [gray2rgb, noop, gray2ybr], # GRAY->RGB,GRAY,YCbCr
+ [ybr2rgb, ybr2gray, noop] # YCbCr->RGB,GRAY,YCbCr
]
-def cvt(input: torch.Tensor, from_color: Union[int, str], to_color: Union[int, str]) -> torch.Tensor:
- """
- Convert input tensor from `from_color` to `to_color`
-
- :param input `Tensor(..., 1|3) | Tensor(..., 1|3, H, W)`: The image to convert
- :param from_color `int | str`: The color of input image
- :param to_color `int | str`: The color of output image
- :return `Tensor(..., 1|3) | Tensor(..., 1|3, H, W)`: converted image
- """
- if isinstance(from_color, str):
- from_color = from_str(from_color)
- if isinstance(to_color, str):
- to_color = from_str(to_color)
- return cvt_funcs[from_color][to_color](input)
diff --git a/utils/types/data_pack.py b/utils/types/data_pack.py
new file mode 100644
index 0000000..619ab46
--- /dev/null
+++ b/utils/types/data_pack.py
@@ -0,0 +1,178 @@
+from inspect import isclass
+from operator import countOf
+from types import UnionType
+from .__common__ import *
+
+__all__ = ["DataPack", "Tensor0D", "Tensor1D", "Tensor2D"]
+
+
+class _Tensor(torch.Tensor):
+ data_dim: int
+
+
+class Tensor0D(_Tensor):
+ data_dim: int = 0
+
+
+class Tensor1D(_Tensor):
+ data_dim: int = 1
+
+
+class Tensor2D(_Tensor):
+ data_dim: int = 2
+
+
+class DataPack(dict[str, torch.Tensor | Any]):
+ shape: list[int]
+ """Shape of the data pack"""
+
+ device: torch.device
+ """Device where tensors in the data pack locate"""
+
+ def __init__(self, map: dict[str, torch.Tensor | Any] = None, shape: list[int] = None,
+ **_data: torch.Tensor | Any) -> None:
+ super().__init__((map or {}) | _data)
+ self.update_shape(shape)
+
+ tensors = {key: val for key, val in self.items() if isinstance(val, torch.Tensor)}
+ tensor0 = next(tensors.values().__iter__(), None)
+ device = None if tensor0 is None else tensor0.device
+ self._validate_device(device, tensors)
+ super().__setattr__("device", device)
+
+ def update_shape(self, new_shape: list[int] = None):
+ tensors = {key: val for key, val in self.items() if isinstance(val, torch.Tensor)}
+ if new_shape is None:
+ # Infer shape from tensors in the data pack and type annotations
+ first_key = next((key for key in tensors if self._get_data_dim(key) >= 0), None)
+ if first_key is None:
+ self._infer_shape_from_tensors_only(tensors)
+ return
+ # Infer batch shape from the first tensor with specified data_dim
+ new_shape = self._get_batch_shape(tensors[first_key], self._get_data_dim(first_key))
+ self._validate_shape(new_shape, tensors)
+ super().__setattr__("shape", new_shape)
+
+ def transform(self, fn: Callable[[torch.Tensor], torch.Tensor | Any]):
+ return self.__class__(**{
+ key: fn(val) if isinstance(val, torch.Tensor) else val
+ for key, val in self.items()
+ })
+
+ def select(self, index: IndexSelector):
+ if isinstance(index, torch.Tensor) and index.dtype == torch.bool:
+ index = index.nonzero(as_tuple=True)
+ return self.transform(lambda tensor: tensor[index])
+
+ def reshape(self, *shape: int):
+ return self.transform(lambda tensor: tensor.reshape(*shape, *tensor.shape[len(self.shape):]))
+
+ def flatten(self, start_dim: int = 0, end_dim: int = -1):
+ if start_dim < 0:
+ start_dim += len(self.shape)
+ if end_dim < 0:
+ end_dim += len(self.shape)
+ if start_dim < 0 or start_dim >= len(self.shape) - 1:
+ raise ValueError("\"start_dim\" is out of range")
+ if end_dim < 1 or end_dim >= len(self.shape):
+ raise ValueError("\"end_dim\" is out of range")
+ return self.transform(lambda tensor: tensor.flatten(start_dim, end_dim))
+
+ def to(self, device: torch.device):
+ return self.transform(lambda tensor: tensor.to(device))
+
+ def __getattr__(self, __name: str) -> torch.Tensor | Any:
+ try:
+ return self[__name]
+ except KeyError:
+ return None
+
+ def __setattr__(self, __name: str, __value: torch.Tensor | Any) -> None:
+ self[__name] = __value
+
+ def __setitem__(self, __key: str, __value: torch.Tensor | Any) -> None:
+ if isinstance(__value, torch.Tensor):
+ if self.shape is None:
+ super().__setitem__(__key, __value)
+ self.update_shape()
+ super().__setattr__("device", __value.device)
+ return
+ self._validate_shape(self.shape, {__key: __value})
+ self._validate_device(self.device, {__key: __value})
+ super().__setitem__(__key, __value)
+
+ def _get_data_dim(self, key: str = None, t: Any = None) -> int:
+ if key is not None: # Called from outside with argument `key`
+ if key not in self.__annotations__:
+ return -1 # Auto dims if not specified in annotations
+ t = self.__annotations__[key]
+ if isinstance(t, UnionType):
+ return max([self._get_data_dim(t=t1) for t1 in t.__args__])
+ if isclass(t):
+ if issubclass(t, _Tensor):
+ return t.data_dim
+ elif issubclass(t, torch.Tensor):
+ return -1 # Auto dims no data_dim is specified
+ return -2 # t is not `Tensor` type
+
+ def _get_batch_shape(self, tensor: torch.Tensor, data_dim: int):
+ return list(tensor.shape) if data_dim == 0 else list(tensor.shape[:-data_dim])
+
+ def _infer_shape_from_tensors_only(self, tensors: dict[str, torch.Tensor]):
+ """
+ Infer `shape` from input tensors without relying on type annotations.
+
+ Called when no type annotations are available for tensors.
+
+ :param tensors `{str: Tensor}`: tensors
+ """
+ if len(tensors) == 0: # Input contains no tensors
+ # `shape` is empty
+ super().__setattr__("shape", None)
+ elif len(tensors) == 1: # Input contains only one tensor
+ # `shape` equals to the tensor's shape
+ super().__setattr__("shape", list(next(tensors.values().__iter__()).shape))
+ else: # Input contains more than one tensor
+ # Get same leading shapes in input tensors
+ first_tensor = next(tensors.values().__iter__())
+ shape = []
+ for i, s in enumerate(first_tensor.shape):
+ if countOf([tensor.shape[i] for tensor in tensors.values()
+ if len(tensor.shape) > i], s) == len(tensors):
+ shape.append(s)
+ else:
+ break
+ super().__setattr__("shape", shape)
+
+ def _validate_device(self, device: torch.device, tensors: dict[str, torch.Tensor]):
+ """
+ Validate whether specified tensors match the device.
+
+ :param device `torch.device`: the required device
+ :param tensors `{str: Tensor}`: tensors to validate
+ :raises ValueError: if some tensor doesn't match the device
+ """
+ for key, tensor in tensors.items():
+ if tensor.device != device:
+ raise ValueError(f"Require \"{key}\" on {device} but on {tensor.device}")
+
+ def _validate_shape(self, shape: list[int], tensors: dict[str, torch.Tensor]):
+ """
+ Validate whether specified tensors match the batch shape.
+
+ :param shape `[int]`: the required batch shape
+ :param tensors `{str: Tensor}`: tensors to validate
+ :raises ValueError: if some tensor doesn't match the shape
+ """
+ # Validate whether all tensors in data match the batch shape and device
+ for key, val in tensors.items():
+ data_dim = self._get_data_dim(key)
+ match(data_dim):
+ case -2:
+ raise ValueError(f"\"{key}\" cannot be a Tensor")
+ case -1:
+ if list(val.shape[:len(shape)]) != shape:
+ raise ValueError(f"\"{key}\" does not match the batch shape {shape}")
+ case _:
+ if self._get_batch_shape(val, data_dim) != shape:
+ raise ValueError(f"\"{key}\" does not match the batch shape {shape}")
diff --git a/utils/types/rays.py b/utils/types/rays.py
new file mode 100644
index 0000000..3cdea5e
--- /dev/null
+++ b/utils/types/rays.py
@@ -0,0 +1,24 @@
+from .__common__ import *
+from .data_pack import *
+
+
+class Rays(DataPack):
+ rays_o: Tensor1D
+ """`Tensor(B..., 3)`"""
+ rays_d: Tensor1D
+ """`Tensor(B..., 3)`"""
+ level: Tensor0D | int | None
+ """`Tensor(B...) | int`"""
+ idx: Tensor0D | None
+ """`Tensor(B...)`"""
+ color: Tensor1D | None
+ """`Tensor(B..., C)`"""
+
+ def get_points(self, z: torch.Tensor) -> torch.Tensor:
+ """
+ Get points along rays at distance z
+
+ :param z `Tensor(B..., P)`: distances along rays
+ :return `Tensor(B..., P, 3)`: points along rays
+ """
+ return self.rays_o[..., None, :] + self.rays_d[..., None, :] * z[..., None]
diff --git a/utils/types/samples.py b/utils/types/samples.py
new file mode 100644
index 0000000..f992a9b
--- /dev/null
+++ b/utils/types/samples.py
@@ -0,0 +1,51 @@
+from .__common__ import *
+from .data_pack import *
+from .. import math
+
+
+class Samples(DataPack):
+ indices: Tensor1D
+ """ Tensor(N[, P], 2)` The unique indices of samples, e.g. (i-th ray, j-th sample)"""
+
+ pts: Tensor1D
+ """`Tensor(N[, P], 3)`"""
+
+ dirs: Tensor1D
+ """`Tensor(N[, P], 3)`"""
+
+ depths: Tensor0D
+ """`Tensor(N[, P])`"""
+
+ dists: Tensor0D
+ """`Tensor(N[, P])`"""
+
+ t_vals: Tensor0D
+ """`Tensor(N[, P])`"""
+
+ voxel_indices: Tensor0D | int
+ """`Tensor(N[, P])`"""
+
+ def filter_rays(self) -> tuple["Samples", torch.Tensor | None]:
+ if self.voxel_indices is None:
+ return self, None
+ valid_rays_mask = self.voxel_indices.ne(-1).any(dim=-1) # (N)
+ rays_filter = valid_rays_mask.nonzero(as_tuple=True)[0] # (N) -> (N')
+ return self.select(rays_filter), rays_filter
+
+ def interpolate(self, fine_samples, *values: torch.Tensor) -> torch.Tensor | tuple[torch.Tensor, ...]:
+ P1 = self.size[-1]
+ P2 = fine_samples.size[-1]
+ K = P2 // P1
+ if K > 1:
+ # Do interpolation
+ t1 = self.t # ([N, ]P1)
+ t2 = fine_samples.t # ([N, ]P2)
+ lo = torch.arange(P1, device=fine_samples.device).repeat_interleave(K)[:P2]
+ up = (lo + 1).clamp(max=P1 - 1)
+ t1_lo, t1_up = t1[..., lo], t1[..., up]
+ k = ((t2 - t1_lo) / (t1_up - t1_lo + math.tiny))[..., None] # ([N, ]P2, 1)
+ values = [
+ val[..., lo, :] * (1 - k) + val[..., up, :] * k # ([N, ]P2, X)
+ for val in values
+ ]
+ return values[0] if len(values) == 1 else tuple(values)
diff --git a/utils/view.py b/utils/view.py
index f6b7271..7baaf2a 100644
--- a/utils/view.py
+++ b/utils/view.py
@@ -1,142 +1,215 @@
-from typing import List, Mapping, Tuple, Union
import torch
import glm
-from . import misc
-from . import math
+
+from . import misc, math
+from .types import *
+from .sphere import spherical2cartesian
def fov2length(angle):
return math.tan(math.radians(angle) / 2) * 2
-class CameraParam(object):
+def length2fov(length):
+ return math.degrees(math.atan(length / 2) * 2)
+
+
+class Camera(object):
- def __init__(self, params: Mapping[str, Union[float, bool]],
- res: Tuple[int, int], *, device=None) -> None:
+ _local_rays_cached: torch.Tensor
+
+ @property
+ def local_rays(self) -> torch.Tensor:
+ if self._local_rays_cached is None:
+ self._build_local_rays()
+ return self._local_rays_cached
+
+ @staticmethod
+ def create(params: dict[str, float | bool], res: tuple[int, int],
+ coord_sys: str = "dx", device: torch.device = None, **kwargs):
+ if params.get("type", "perspective") == "pano":
+ return PanoCamera(params, res, coord_sys, device, **kwargs)
+ return PerspectiveCamera(params, res, coord_sys, device, **kwargs)
+
+ def __init__(self, res: tuple[int, int], coord_sys: str = "dx",
+ device: torch.device = None) -> None:
super().__init__()
- params = CameraParam.convert_camera_params(params, res)
- self.res = res
- self.f = torch.tensor([params['fx'], params['fy'], 1], device=device)
- self.c = torch.tensor([params['cx'], params['cy']], device=device)
+ self.res = Resolution(*res)
self.device = device
+ self.coord_sys = coord_sys
+ self.forward = 1. if coord_sys == "dx" else -1.
+ self._local_rays_cached = None
def to(self, device: torch.device):
- self.f = self.f.to(device)
- self.c = self.c.to(device)
self.device = device
+ self._local_rays_cached = None if self._local_rays_cached is None else\
+ self._local_rays_cached.to(self.device)
+
+ def resize(self, res: Resolution | tuple[int, int]):
+ old_res = self.res
+ new_res = Resolution(*res)
+ self._resize(old_res, new_res)
+ self.res = Resolution(*res)
+ self._local_rays_cached = None
+
+ def get_pixels(self, image: torch.Tensor) -> torch.Tensor:
+ return image.movedim(-3, -1).flatten(-3, -2)
+
+ def _resize(self, old_res: Resolution, new_res: Resolution):
+ raise NotImplementedError()
+
+ def _build_local_rays(self):
+ raise NotImplementedError()
+
+
+class PerspectiveCamera(Camera):
+
+ def __init__(self, params: dict[str, float | bool], res: tuple[int, int],
+ coord_sys: str = "dx", device: torch.device = None, **kwargs) -> None:
+ super().__init__(res, coord_sys, device)
+ params = PerspectiveCamera.convert_params(params, self.res)
+ self.f = torch.tensor(params["f"], device=self.device)
+ self.c = torch.tensor(params["c"], device=self.device)
+
+ def to(self, device: torch.device):
+ super().to(device)
+ self.f = self.f.to(self.device)
+ self.c = self.c.to(self.device)
return self
- def resize(self, res: Tuple[int, int]):
- self.f[0] = self.f[0] / self.res[1] * res[1]
- self.f[1] = self.f[1] / self.res[0] * res[0]
- self.c[0] = self.c[0] / self.res[1] * res[1]
- self.c[1] = self.c[1] / self.res[0] * res[0]
- self.res = res
+ def _resize(self, old_res: Resolution, new_res: Resolution):
+ scale = torch.tensor([new_res.w / old_res.w, new_res.h / old_res.h], device=self.device)
+ self.f.mul_(scale)
+ self.c.mul_(scale)
+
+ def _build_local_rays(self):
+ self._local_rays_cached = self.get_local_rays(flatten=True)
def proj(self, p: torch.Tensor, normalize=False, center_as_origin=False) -> torch.Tensor:
"""
- Project positions in local space to image plane
+ Project positions in camera space to image plane
:param p `Tensor(..., 3)`: positions in local space
:param normalize: use normalized coord for image plane
:param center_as_origin: take center as the origin if image plane instead of top-left corner
:return `Tensor(..., 2)`: positions in image plane
"""
- p = p * self.f
- p = p[..., 0:2] / p[..., 2:3]
+ p = p[..., :2] / p[..., 2:] * self.forward * self.f
if not center_as_origin:
p = p + self.c
if normalize:
- p = p / torch.tensor([self.res[1] - 1, self.res[0] - 1], device=self.device)
+ p = p / torch.tensor([self.res.w - 1, self.res.h - 1], device=self.device)
return p
- def unproj(self, p: torch.Tensor, z: torch.Tensor = None, normalize=False, center_as_origin=False) -> torch.Tensor:
+ def unproj(self, p: torch.Tensor) -> torch.Tensor:
"""
- Unproject positions in image plane to local space
+ Unproject positions in image plane to camera space
:param p `Tensor(..., 2)`: positions in image plane
- :param z `Tensor(..., 1)`: depths of positions, None means all depths set to 1
- :param normalize: use normalized coord for image plane
- :param center_as_origin: take center as the origin if image plane instead of top-left corner
:return: positions in local space
"""
- if normalize:
- p = p * torch.tensor([self.res[1] - 1, self.res[0] - 1], device=self.device)
- if not center_as_origin:
- p = p - self.c
- p = torch.cat([p / self.f[:2], torch.ones_like(p[..., :1])], dim=-1)
- if z != None:
- p = p * z
- return p
+ return misc.union((p - self.c) / self.f, self.forward)
- def get_local_rays(self, flatten=False, norm=True) -> torch.Tensor:
+ def get_local_rays(self, normalize: bool = False, flatten: bool = False) -> torch.Tensor:
"""
- Get view rays in local space
+ Get view rays in camera space
- :param flatten: whether flatten the return tensor
- :param norm: whether normalize rays to unit length
+ :param normalize: whether normalize rays to unit length, defaults to False
+ :param flatten: whether flatten the return tensor, defaults to False
:return `Tensor(H, W, 3)|Tensor(HW, 3)`: the shape is determined by parameter 'flatten'
"""
- coords = misc.meshgrid(*self.res).to(self.device)
- rays = self.unproj(coords)
- if norm:
+ pixels = misc.grid2d(*self.res, device=self.device)
+ rays = self.unproj(pixels)
+ if normalize:
rays /= rays.norm(dim=-1, keepdim=True)
if flatten:
rays = rays.flatten(0, 1)
return rays
- def get_global_rays(self, trans, flatten=False, norm=True) -> torch.Tensor:
- """
- [summary]
-
- :param t `Tensor(N.., 3)`: translation vectors
- :param r `Tensor(N.., 3, 3)`: rotation matrices
- :param flatten: [description], defaults to False
- :param norm: [description], defaults to True
- :return: [description]
- """
- rays = self.get_local_rays(flatten, norm) # (M.., 3)
- rays_o, _ = torch.broadcast_tensors(trans.t[..., None, :], rays) if flatten \
- else torch.broadcast_tensors(trans.t[..., None, None, :], rays) # (N.., M.., 3)
- rays_d = trans.trans_vector(rays)
- return rays_o, rays_d
-
@staticmethod
- def convert_camera_params(input_camera_params: Mapping[str, Union[float, bool]],
- view_res: Tuple[int, int]) -> Mapping[str, Union[float, bool]]:
+ def convert_params(input_params: dict[str, Any], res: Resolution) -> dict[str, Any]:
"""
Check and convert camera parameters in config file to pixel-space
- :param cam_params: { ["fx", "fy" | "fov"], "cx", "cy", ["normalized"] },
- the parameters of camera
- :return: camera parameters
+ :param cam_params `{str: any}`: the parameters of camera,
+ { [("f": float | [float, float]) | ("fov": float)], "c": float | [float, float], ["normalized": bool] },
+ :param res `Resolution`: resolution of view
+ :return `{str: any}`: converted camera parameters, {"f": [float, float], "c": [float, float]}
"""
- input_is_normalized = bool(input_camera_params.get('normalized'))
- camera_params = {}
- if 'fov' in input_camera_params:
+ input_is_normalized = input_params.get("normalized", False)
+ params = {}
+ if "fov" in input_params:
+ params["f"] = [res.h / fov2length(input_params["fov"])] * 2
+ params["f"][1] *= -1
+ else:
+ params["f"] = input_params["f"] if isinstance(input_params["f"], list)\
+ else [input_params["f"], -input_params["f"]]
if input_is_normalized:
- camera_params['fy'] = 1 / fov2length(input_camera_params['fov'])
- camera_params['fx'] = camera_params['fy'] / view_res[1] * view_res[0]
- else:
- camera_params['fx'] = camera_params['fy'] = view_res[0] / \
- fov2length(input_camera_params['fov'])
- camera_params['fy'] *= -1
+ params["f"][0] *= res.w
+ params["f"][1] *= res.h
+
+ if "c" not in input_params:
+ params["c"] = [res.w / 2, res.h / 2]
else:
- camera_params['fx'] = input_camera_params['fx']
- camera_params['fy'] = input_camera_params['fy']
- camera_params['cx'] = input_camera_params['cx']
- camera_params['cy'] = input_camera_params['cy']
- if input_is_normalized:
- camera_params['fx'] *= view_res[1]
- camera_params['fy'] *= view_res[0]
- camera_params['cx'] *= view_res[1] - 1
- camera_params['cy'] *= view_res[0] - 1
- return camera_params
+ params["c"] = input_params["c"] if isinstance(input_params["c"], list)\
+ else [input_params["c"]] * 2
+ if input_is_normalized:
+ params["c"][0] *= res.w
+ params["c"][1] *= res.h
+ return params
+
+
+class PanoCamera(Camera):
+ def __init__(self, params: dict[str, float | bool], res: tuple[int, int],
+ coord_sys: str = "dx", device: torch.device = None, **kwargs) -> None:
+ super().__init__(res, coord_sys, device)
+ self.pixs = None
+
+ def to(self, device: torch.device):
+ super().to(device)
+ self.pixs = None if self.pixs is None else self.pixs.to(self.device)
+ return self
+
+ def get_pixels(self, image: torch.Tensor) -> torch.Tensor:
+ if self.pixs is None:
+ self._build_local_rays()
+ return super().get_pixels(image)[..., self.pixs]
+
+ def _resize(self, old_res: Resolution, new_res: Resolution):
+ self.pixs = None
+ pass
+
+ def _build_local_rays(self):
+ self.pixs, self._local_rays_cached = self._get_pano_rays()
+
+ def _get_pano_rays(self) -> tuple[torch.Tensor, torch.Tensor]:
+ """
+ Get unprojected rays of pixels on a panorama
+
+ :return `Tensor(N)`: rays' pixel indices in flattened pano image
+ :return `Tensor(N, 3)`: rays' directions with one unit length
+ """
+ phi = (torch.arange(self.res.h, device=self.device) + 0.5) / self.res.h * math.pi # (H)
+ length = (phi.sin() * self.res.w * 0.5).ceil() * 2
+ cols = torch.arange(self.res.w, device=self.device)[None, :].expand(*self.res) # (H, W)
+ mask = torch.logical_and(cols >= (self.res.w - length[:, None]) / 2,
+ cols < (self.res.w + length[:, None]) / 2) # (H, W)
+ pixs = mask.nonzero(as_tuple=True) # ((N), (N))
+ pixs_phi = (0.5 - (pixs[0] + 0.5) / self.res.h) * math.pi
+ pixs_theta = (pixs[1] * 2 + 1 - self.res.w) / length[pixs[0]] * math.pi
+ spher_coords = torch.stack([torch.ones_like(pixs_phi), pixs_theta, pixs_phi], dim=-1)
+ rays = spherical2cartesian(spher_coords)
+ rays[..., -1] *= self.forward
+ return pixs[0] * self.res.w + pixs[1], rays
class Trans(object):
+ @property
+ def shape(self):
+ return self.t.shape[:-1]
+
def __init__(self, t: torch.Tensor, r: torch.Tensor):
self.t = t
self.r = r
@@ -145,7 +218,7 @@ class Trans(object):
self.r = self.r[None, :, :]
self.to(self.t.device)
- def to(self, device: torch.device):
+ def to(self, device: torch.device) -> "Trans":
self.t = self.t.to(device)
self.r = self.r.to(device)
self.device = device
@@ -156,8 +229,6 @@ class Trans(object):
Transform points by given translation vectors and rotation matrices
:param p `Tensor(N.., 3)`: points to transform
- :param t `Tensor(M.., 3)`: translation vectors
- :param r `Tensor(M.., 3, 3)`: rotation matrices
:param inverse: whether perform inverse transform
:return `Tensor(M.., N.., 3)`: transformed points
"""
@@ -182,19 +253,17 @@ class Trans(object):
Transform vectors by given translation vectors and rotation matrices
:param v `Tensor(N.., 3)`: vectors to transform
- :param r `Tensor(M.., 3, 3)`: rotation matrices
:param inverse: whether perform inverse transform
:return `Tensor(M.., N.., 3)`: transformed vectors
"""
out_size = list(self.r.size())[:-2] + list(v.size())[:-1] + [3]
- r = self.r if inverse else self.r.movedim(-1, -2) # Transpose rotation matrices
- out = torch.matmul(v.view(-1, 3), r).view(out_size)
- return out
+ r = self.r.movedim(-1, -2) if inverse else self.r
+ return (r.unsqueeze(-3) @ v.reshape(-1, 3, 1)).reshape(out_size)
- def size(self) -> List[int]:
- return list(self.t.size()[:-1])
+ def reshape(self, *size) -> "Trans":
+ return Trans(self.t.reshape(*size, 3), self.r.reshape(*size, 3, 3))
- def get(self, *index):
+ def __getitem__(self, index: IndexSelector) -> "Trans":
return Trans(self.t[index], self.r[index])
@@ -240,7 +309,7 @@ def trans_vector(v: torch.Tensor, r: torch.Tensor, inverse=False) -> torch.Tenso
return out
-def euler_to_matrix(euler: Union[Tuple[float, float, float], List[float]]) -> List[float]:
- q = glm.quat(glm.radians(glm.vec3(euler[0], euler[1], euler[2])))
+def euler_to_matrix(euler_x: float, euler_y: float, euler_z: float) -> list[float]:
+ q = glm.quat(glm.radians(glm.vec3(euler_x, euler_y, euler_z)))
vec_list = glm.transpose(glm.mat3_cast(q)).to_list()
return vec_list[0] + vec_list[1] + vec_list[2]
diff --git a/utils/voxels.py b/utils/voxels.py
index 6ddf6a0..af8b29b 100644
--- a/utils/voxels.py
+++ b/utils/voxels.py
@@ -1,10 +1,8 @@
-import torch
-from typing import Tuple, Union
-
from . import math
+from .types import *
-def get_grid_steps(bbox: torch.Tensor, step_size: Union[torch.Tensor, float]) -> torch.Tensor:
+def get_grid_steps(bbox: torch.Tensor, step_size: torch.Tensor | float) -> torch.Tensor:
"""
Get grid steps alone every dim.
@@ -30,7 +28,7 @@ def get_out_of_bound_mask(pts: torch.Tensor, bbox: torch.Tensor) -> torch.Tensor
def to_flat_indices(grid_coords: torch.Tensor, steps: torch.Tensor) -> torch.Tensor:
indices = grid_coords[..., 0]
for i in range(1, grid_coords.shape[-1]):
- indices = indices * steps[i] + grid_coords[..., i]
+ indices = indices * steps[..., i] + grid_coords[..., i]
return indices
@@ -74,7 +72,7 @@ def init_voxels(bbox: torch.Tensor, steps: torch.Tensor):
"""
Initialize voxels.
"""
- x, y, z = torch.meshgrid(*[torch.arange(steps[i]) for i in range(3)])
+ x, y, z = torch.meshgrid(*[torch.arange(steps[i]) for i in range(3)], indexing="ij")
return to_voxel_centers(torch.stack([x, y, z], -1).reshape(-1, 3), bbox, steps)
@@ -91,8 +89,11 @@ def to_voxel_centers(grid_coords: torch.Tensor, bbox: torch.Tensor, steps: torch
return grid_coords / steps * (bbox[1] - bbox[0]) + bbox[0]
-def split_voxels_local(voxel_size: Union[torch.Tensor, float], n: int, align_border: bool = True,
- dims=3, *, dtype: torch.dtype = None, device: torch.device = None,
+split_voxels_local_device_cache: dict[str, torch.Tensor] = {}
+
+
+def split_voxels_local(voxel_size: torch.Tensor | float, n: int, align_border: bool = True,
+ dims: int = 3, device: torch.device = torch.device("cpu"),
like: torch.Tensor = None):
"""
[summary]
@@ -107,15 +108,20 @@ def split_voxels_local(voxel_size: Union[torch.Tensor, float], n: int, align_bor
:return `Tensor(X, D)`: [description]
"""
if like is not None:
- dtype = like.dtype
+ dims = like.shape[-1]
device = like.device
- c = torch.arange(1 - n, n, 2, dtype=dtype, device=device)
- offset = torch.stack(torch.meshgrid([c] * dims), -1).flatten(0, -2)\
- * voxel_size * .5 / (n - 1 if align_border else n)
+ cache_key = f"{n}_{dims}_{device}_{align_border}"
+ offset = split_voxels_local_device_cache.get(cache_key)
+ if offset is None:
+ c = torch.arange(1 - n, n, 2, dtype=torch.float, device=device)
+ offset = torch.stack(torch.meshgrid([c] * dims, indexing="ij"), -1).flatten(0, -2)
+ offset.mul_(.5 / (n - align_border))
+ split_voxels_local_device_cache[cache_key] = offset
+ offset.mul_(voxel_size)
return offset
-def split_voxels(voxel_centers: torch.Tensor, voxel_size: Union[torch.Tensor, float], n: int,
+def split_voxels(voxel_centers: torch.Tensor, voxel_size: torch.Tensor | float, n: int,
align_border: bool = True):
"""
[summary]
@@ -131,7 +137,7 @@ def split_voxels(voxel_centers: torch.Tensor, voxel_size: Union[torch.Tensor, fl
voxel_size, n, align_border, voxel_centers.shape[-1], like=voxel_centers)
-def get_corners(voxel_centers: torch.Tensor, bbox: torch.Tensor, steps: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+def get_corners(voxel_centers: torch.Tensor, bbox: torch.Tensor, steps: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
half_voxel_size = (bbox[1] - bbox[0]) / steps * 0.5
expand_bbox = bbox.clone()
expand_bbox[0] -= 0.5 * half_voxel_size
@@ -148,17 +154,16 @@ def get_corners(voxel_centers: torch.Tensor, bbox: torch.Tensor, steps: torch.Te
return corners, corner_indices.reshape(-1, 8)
-def trilinear_interp(pts: torch.Tensor, corner_values: torch.Tensor) -> torch.Tensor:
+def linear_interp(pts: torch.Tensor, corner_values: torch.Tensor) -> torch.Tensor:
"""
- Perform trilinear interpolation in unit voxel ([0,0,0] ~ [1,1,1]).
+ Perform trilinear interpolation in unit voxel ([0,...] ~ [1,...]).
- :param pts `Tensor(N, 3)`: uniform coordinates in voxels
- :param corner_values `Tensor(N, 8X)|Tensor(N, 8, X)`: values at corners of voxels
- :return `Tensor(N, X)`: interpolated values
+ :param pts `Tensor(N..., D)`: uniform coordinates in voxels
+ :param corner_values `Tensor(N, CX)|Tensor(N..., C, X)`: values at corners of voxels
+ :return `Tensor(N..., X)`: interpolated values
"""
- pts = pts[:, None] # (N, 1, 3)
- corners = split_voxels_local(1, 2, like=pts) + 0.5 # (8, 3)
- corner_values = corner_values.reshape(corner_values.size(0), 8, -1) # (N, 8, X)
-
- weights = (pts * corners * 2 - pts - corners + 1).prod(-1, keepdim=True) # (N, 8, 1)
- return (weights * corner_values).sum(1)
+ pts = pts.unsqueeze(-2) # (N..., 1, D)
+ corners = split_voxels_local(1, 2, like=pts) + 0.5 # (C, D)
+ corner_values = corner_values.reshape(*pts.shape[:-2], corners.shape[0], -1) # (N..., C, X)
+ weights = (pts * corners * 2 - pts - corners + 1).prod(-1, keepdim=True) # (N..., C, 1)
+ return (weights * corner_values).sum(-2)
--
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