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Ruilong Li
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‎.gitignore

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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# lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# pyenv
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.python-version
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# celery beat schedule file
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celerybeat-schedule
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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data/
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.DS_Store

‎README.md

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‎RTL/main.py

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import sys
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import os
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import argparse
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import glob
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import torch
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from monoport.lib.common.config import get_cfg_defaults
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from monoport.lib.modeling.MonoPortNet import MonoPortNet
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from monoport.lib.modeling.MonoPortNet import PIFuNetG, PIFuNetC
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import streamer_pytorch as streamer
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import human_inst_seg
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########################################
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## load configs
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########################################
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'-cfg', '--config_file', default=None, type=str,
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help='path of the yaml config file')
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parser.add_argument(
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'--camera', action="store_true")
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parser.add_argument(
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'--images', default="", nargs="*")
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parser.add_argument(
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'--image_folder', default=None)
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parser.add_argument(
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'--videos', default="", nargs="*")
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parser.add_argument(
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'--loop', action="store_true")
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parser.add_argument(
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'--vis', action="store_true")
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parser.add_argument(
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'--use_VRweb', action="store_true")
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argv = sys.argv[1:sys.argv.index('--')]
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args = parser.parse_args(argv)
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opts = sys.argv[sys.argv.index('--') + 1:]
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cfg = get_cfg_defaults()
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if args.config_file is not None:
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cfg.merge_from_file(args.config_file)
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cfg.merge_from_list(opts)
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cfg.freeze()
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########################################
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## access avaiable GPUs
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########################################
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device_count = torch.cuda.device_count()
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if device_count == 1:
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cuda_backbone_G='cuda:0'
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cuda_backbone_C='cuda:0'
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cuda_recon='cuda:0'
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cuda_color='cuda:0'
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elif device_count == 2:
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cuda_backbone_G='cuda:1'
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cuda_backbone_C='cuda:1'
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cuda_recon='cuda:0'
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cuda_color='cuda:1'
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else:
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raise NotImplementedError
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########################################
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## load networks
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########################################
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print (f'loading networkG from {cfg.netG.ckpt_path} ...')
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netG = MonoPortNet(cfg.netG)
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assert os.path.exists(cfg.netG.ckpt_path), 'we need a ckpt to run RTL demo.'
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netG.load_legacy_pifu(cfg.netG.ckpt_path)
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netG.image_filter = netG.image_filter.to(cuda_backbone_G)
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netG.surface_classifier = netG.surface_classifier.to(cuda_recon)
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print (f'loading networkC from {cfg.netC.ckpt_path} ...')
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netC = MonoPortNet(cfg.netC)
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assert os.path.exists(cfg.netC.ckpt_path), 'we need a ckpt to run RTL demo.'
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netC.load_legacy_pifu(cfg.netC.ckpt_path)
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netC.image_filter = netC.image_filter.to(cuda_backbone_C)
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netC.surface_classifier = netC.surface_classifier.to(cuda_color)
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########################################
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## initialize data streamer
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########################################
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print (f'initialize data streamer ...')
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if args.camera:
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data_stream = streamer.CaptureStreamer()
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elif len(args.videos) > 0:
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data_stream = streamer.VideoListStreamer(
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args.videos * (10 if args.loop else 1))
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elif len(args.images) > 0:
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data_stream = streamer.ImageListStreamer(
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args.images * (10000 if args.loop else 1))
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elif args.image_folder is not None:
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images = sorted(glob.glob(args.image_folder+'/*.jpg'))
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images += sorted(glob.glob(args.image_folder+'/*.png'))
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data_stream = streamer.ImageListStreamer(
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images * (10 if args.loop else 1))
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‎RTL/run_pifu.sh

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python RTL/main.py \
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--image_folder /home/rui/local/projects/PIFu-RealTime/zenTelePort/data/recording/test \
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-- \
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netG.projection orthogonal \
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netG.backbone.IMF PIFuHGFilters \
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netG.normalizer.IMF PIFuNomalizer \
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netG.head.IMF PIFuNetGMLP \
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netG.ckpt_path ./data/PIFu/net_G \
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netC.projection orthogonal \
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netC.backbone.IMF PIFuResBlkFilters \
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netC.normalizer.IMF PIFuNomalizer \
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netC.head.IMF PIFuNetCMLP \
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netC.ckpt_path ./data/PIFu/net_C

‎monoport/__init__.py

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‎monoport/lib/common/config.py

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from yacs.config import CfgNode as CN
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_C = CN()
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# needed by trainer
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_C.name = 'default'
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_C.checkpoints_path = '../data/checkpoints/'
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_C.results_path = '../data/results/'
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_C.learning_rate = 1e-3
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_C.weight_decay = 0.0
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_C.momentum = 0.0
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_C.optim = 'RMSprop'
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_C.schedule = [40, 60]
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_C.gamma = 0.1
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_C.resume = False
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# needed by train()
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_C.batch_size = 4
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_C.num_threads = 4
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_C.num_epoch = 100
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_C.freq_plot = 10
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_C.freq_save = 100
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_C.freq_eval = 100
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_C.freq_vis = 100
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# --- netG options ---
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_C.netG = CN()
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_C.netG.ckpt_path = ''
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_C.netG.projection = 'orthogonal'
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# --- netG:backbone options ---
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_C.netG.backbone = CN()
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_C.netG.backbone.IMF = 'PIFuHGFilters'
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# --- netG:normalizer options ---
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_C.netG.normalizer = CN()
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_C.netG.normalizer.IMF = 'PIFuNomalizer'
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_C.netG.normalizer.soft_onehot = False
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_C.netG.normalizer.soft_dim = 64
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# --- netG:head options ---
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_C.netG.head = CN()
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_C.netG.head.IMF = 'PIFuNetGMLP'
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# --- netG:loss options ---
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_C.netG.loss = CN()
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_C.netG.loss.IMF = 'MSE'
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# --- netC options ---
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_C.netC = CN()
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_C.netC.ckpt_path = ''
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_C.netC.projection = 'orthogonal'
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# --- netC:backbone options ---
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_C.netC.backbone = CN()
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_C.netC.backbone.IMF = 'PIFuResBlkFilters'
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# --- netC:normalizer options ---
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_C.netC.normalizer = CN()
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_C.netC.normalizer.IMF = 'PIFuNomalizer'
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_C.netC.normalizer.soft_onehot = False
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_C.netC.normalizer.soft_dim = 64
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# --- netC:head options ---
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_C.netC.head = CN()
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_C.netC.head.IMF = 'PIFuNetCMLP'
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# --- netC:loss options ---
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_C.netC.loss = CN()
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_C.netC.loss.IMF = 'L1'
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# --- dataset options ---
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_C.dataset = CN()
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_C.dataset.aug_bri = 0.0
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_C.dataset.aug_con = 0.0
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_C.dataset.aug_sat = 0.0
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_C.dataset.aug_hue = 0.0
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_C.dataset.blur = 0
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_C.dataset.num_sample_geo = 5000
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_C.dataset.num_sample_color = 0
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_C.dataset.sigma_geo = 0.05
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_C.dataset.sigma_color = 0.001
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_C.dataset.pre_load = False
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def get_cfg_defaults():
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"""Get a yacs CfgNode object with default values for my_project."""
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# Return a clone so that the defaults will not be altered
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# This is for the "local variable" use pattern
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return _C.clone()
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# Alternatively, provide a way to import the defaults as
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# a global singleton:
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# cfg = _C # users can `from config import cfg`
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# cfg = get_cfg_defaults()
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# cfg.merge_from_file('../configs/example.yaml')
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# # Now override from a list (opts could come from the command line)
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# opts = ['dataset.root', '../data/XXXX', 'learning_rate', '1e-2']
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# cfg.merge_from_list(opts)
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